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# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert | isscalar(b) | megengine.core.tensor.utils.isscalar |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@ | trace(symbolic=symbolic, capture_as_const=True) | megengine.jit.trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = | F.topk(x, 3) | megengine.functional.topk |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = | F.warp_perspective(x, M, (2, 2)) | megengine.functional.warp_perspective |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@ | trace(symbolic=symbolic, capture_as_const=True) | megengine.jit.trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = | F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20) | megengine.functional.nn.nms |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = ( | tensor([2]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), | tensor([2]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = | F.broadcast_to(x, shape) | megengine.functional.broadcast_to |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = | F.clip(x, lower, upper) | megengine.functional.clip |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = | Parameter([1.0], dtype=np.float32) | megengine.Parameter |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = | F.exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = | GradManager() | megengine.autodiff.GradManager |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast( | tensor([1, 10], dtype=np.int32) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = | F.broadcast_to(x, (3, 4, 5)) | megengine.functional.broadcast_to |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return | tensor(boxes) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), | tensor(scores) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with | exclude_from_trace() | megengine.jit.exclude_from_trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = | F.nn.nms(boxes, scores=scores, iou_thresh=0.5) | megengine.functional.nn.nms |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, | tensor([0]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), | tensor([1]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), tensor([1]))
# test returning noncontiguous tensor from trace
def test_slice():
@trace
def f(x):
return x[:, 1::2]
x = | F.arange(8) | megengine.functional.arange |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + exp(y))
val = 1.0e4
d = tensor(val)
o = tensor(0.0)
assert not np.isfinite(f(d, o).numpy())
np.testing.assert_almost_equal(g(d, o), val)
@pytest.mark.skip(reason="could not use opt_level=0 with dump")
def test_goptions_log_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
return log(exp(x))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
return log(exp(x))
f(tensor(1.0))
_, out = mkstemp()
f.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_1 = cgtools.get_oprs_seq(outputs)
g(tensor(1.0))
g.dump(out, optimize_for_inference=False)
*_, outputs = G.load_graph(out)
oprs_2 = cgtools.get_oprs_seq(outputs)
assert len(oprs_1) - len(oprs_2) == 2
def test_optimize_for_inference():
@trace(symbolic=True, capture_as_const=True)
def f(x):
return exp(x)
_, out = mkstemp()
f(tensor(5.0))
f.dump(out, enable_io16xc32=True)
res = G.load_graph(out)
computing_input = res.output_vars_list[0].owner.inputs[0]
assert computing_input.dtype == np.float16
def test_optimize_for_inference_broadcast():
a = tensor(np.ones(1, dtype=np.float32))
@trace(capture_as_const=True, symbolic_shape=True)
def f():
return a._broadcast(tensor([1, 10], dtype=np.int32))
f()
f.dump(io.BytesIO())
def test_trace_cvt_bool():
x = tensor([0], dtype=np.int32)
@trace(symbolic=True)
def f(x):
a = x.shape
b = a[0]
assert isscalar(b)
return b == 0
for i in range(3):
np.testing.assert_equal(f(x).numpy(), False)
def test_trace_reshape():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(2, 10, 10))
x2 = tensor(np.random.randn(4, 10, 10))
x3 = tensor(np.random.randn(8, 10, 10))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = x.reshape(x.shape[0], 100)
return y
f(x1)
f(x2)
f(x3)
def test_trace_topk():
x = tensor([5, 2, 7, 1, 0, 3, 2])
@trace(symbolic=True)
def f(x):
y = F.topk(x, 3)
np.testing.assert_equal(y[0].shape.numpy(), np.array([3,]))
return y
for i in range(3):
f(x)
def test_trace_warp_perspective():
inp_shape = (1, 1, 4, 4)
x = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
M_shape = (1, 3, 3)
M = tensor(
np.array(
[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [0.0, 0.0, 1.0]], dtype=np.float32
).reshape(M_shape)
)
@trace(symbolic=True)
def f(x, M):
out = F.warp_perspective(x, M, (2, 2))
np.testing.assert_equal(out.shape.numpy(), np.array([1, 1, 2, 2]))
return out
for i in range(1):
f(x, M)
def test_raise_on_trace():
step_count = 0
catch_count = 0
bad_step = 10
class CatchMe(Exception):
pass
a = tensor([1, 2, 3, 4])
b = tensor([5, 6, 7, 8])
c = tensor([9, 0, 1, 2])
@trace
def add_abc(a, b, c):
ps = a + b
result = ps + c
if step_count == bad_step:
raise CatchMe("catch me")
return result
for i in range(100):
try:
d = add_abc(a, b, c)
except CatchMe as e:
catch_count += 1
else:
np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
step_count += 1
assert catch_count == 1
def test_trace_broadcast():
for symbolic in [False, True]:
x1 = tensor(np.random.randn(3, 1, 1))
x2 = tensor(np.random.randn(1, 4, 1))
x3 = tensor(np.random.randn(1, 1, 5))
@trace(symbolic=symbolic, capture_as_const=True)
def f(x):
y = F.broadcast_to(x, (3, 4, 5))
return y
f(x1)
f(x2)
f(x3)
def test_trace_nms():
def make_inputs(n):
boxes = np.zeros((n, 4))
boxes[:, :2] = np.random.rand(n, 2) * 100
boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
scores = np.random.rand(n)
return tensor(boxes), tensor(scores)
@trace(symbolic=False)
def f(boxes, scores):
# with tracing, max_output must be specified
results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
# without tracing, max output can be inferred inside nms
with exclude_from_trace():
_ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
return results
f(*make_inputs(10))
f(*make_inputs(20))
f(*make_inputs(30))
def test_trace_valid_broadcast():
x1 = tensor(np.random.randn(1, 1))
x2 = tensor(np.random.randn(1, 2))
shape = (tensor([2]), tensor([2]))
@trace(symbolic=False)
def f(x, shape):
y = F.broadcast_to(x, shape)
return y
f(x1, shape)
f(x2, shape)
def test_clip():
x = tensor(np.random.randn(10, 10))
@trace(symbolic=True)
def f(x, lower, upper):
y = F.clip(x, lower, upper)
return y
for i in range(3):
f(x, tensor([0]), tensor([1]))
# test returning noncontiguous tensor from trace
def test_slice():
@trace
def f(x):
return x[:, 1::2]
x = F.arange(8).reshape(2, 4)
f(x)
y = f(x)
np.testing.assert_array_equal(y.numpy(), x.numpy()[:, 1::2])
y + y
def test_random():
def run_test(op):
for symbolic_shape in [True, False]:
@ | trace(symbolic=True, symbolic_shape=symbolic_shape) | megengine.jit.trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with | exclude_from_trace() | megengine.jit.exclude_from_trace |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type( | cgtools.get_owner_opr_inputs(out) | megengine.utils.comp_graph_tools.get_owner_opr_inputs |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + | exp(y) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log( | exp(x) | megengine.functional.exp |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp
import numpy as np
import pytest
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.optimizer as optim
import megengine.utils.comp_graph_tools as cgtools
from megengine import Parameter, tensor
from megengine.autodiff import GradManager
from megengine.core._trace_option import set_symbolic_shape
from megengine.core.ops import builtin as ops
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor.utils import isscalar
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace
from megengine.module import Module
from megengine.random import normal, uniform
def test_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=False)
def train_f1(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
@trace(symbolic=True)
def train_f2(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(2):
y1 = train_f1(data).numpy()
y2 = train_f2(data).numpy()
np.testing.assert_equal(y1, y2)
def test_exclude_from_trace():
for symbolic in [False, True]:
@trace(symbolic=symbolic)
def f(x):
x = -x
with exclude_from_trace():
if i % 2:
x = -x
x = -x
return x
x = tensor([1])
for i in range(3):
y = f(x).numpy()
np.testing.assert_equal(f(x).numpy(), y)
def test_print_in_trace():
for symbolic in [False]: # cannot read value in symbolic mode
@trace(symbolic=symbolic)
def f(x):
nonlocal buf
x = -x
buf = x.numpy()
x = -x
return x
buf = None
x = tensor([1])
for i in range(3):
y = f(x).numpy()
z = buf
buf = None
np.testing.assert_equal(f(x).numpy(), y)
np.testing.assert_equal(z, buf)
def test_dump():
@trace(symbolic=True, capture_as_const=True)
def f(a, b):
return a + b
a = tensor([2])
b = tensor([4])
y = f(a, b).numpy()
for i in range(3):
np.testing.assert_equal(f(a, b).numpy(), y)
file = io.BytesIO()
dump_info = f.dump(file)
assert dump_info.nr_opr == 3
np.testing.assert_equal(dump_info.inputs, ["arg_0", "arg_1"])
np.testing.assert_equal(dump_info.outputs, ["ADD(arg_0,arg_1)[4]"])
file.seek(0)
result = cgtools.load_and_inference(file, [a, b])
np.testing.assert_equal(result[0], y)
def test_capture_dump():
a = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * a
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file)
file.seek(0)
result = cgtools.load_and_inference(file, [x])
np.testing.assert_equal(result[0], y)
def test_dump_volatile():
p = tensor([2])
@trace(symbolic=True, capture_as_const=True)
def f(x):
return x * p
x = tensor([3])
y = f(x).numpy()
for i in range(3):
np.testing.assert_equal(f(x).numpy(), y)
file = io.BytesIO()
f.dump(file, optimize_for_inference=False)
file.seek(0)
cg, _, outputs = G.load_graph(file)
(out,) = outputs
assert (
cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
== "ImmutableTensor"
)
def test_trace_profiler():
for symbolic in [False, True]:
@trace(symbolic=symbolic, profiling=True)
def f(x):
return -x
x = tensor([1])
y = f(x).numpy()
f(x)
f(x) # XXX: has to run twice
out = f.get_profile()
assert out.get("profiler")
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x):
# directly return x / x will not trigger gopt
# since there's no way to tell the two x are the same
y = 2.0 * x
return y / y
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x):
y = 2.0 * x
return y / y
d = tensor(0.0)
assert not np.isfinite(f(d).numpy())
np.testing.assert_equal(g(d).numpy().item(), 1.0)
@pytest.mark.skip(reason="force opt_level=0 when building graph")
def test_goptions_log_sum_exp():
@trace(symbolic=True, opt_level=0, capture_as_const=True)
def f(x, y):
return log(exp(x) + exp(y))
@trace(symbolic=True, opt_level=1, capture_as_const=True)
def g(x, y):
return log(exp(x) + | exp(y) | megengine.functional.exp |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
)
def effnetv2_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
)
def effnetv2_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl"
)
def effnetv2_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b2/effnetv2_b2.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
)
def effnetv2_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl"
)
def effnetv2_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b2/effnetv2_b2.pkl"
)
def effnetv2_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b3/effnetv2_b3.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
)
def effnetv2_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl"
)
def effnetv2_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b2/effnetv2_b2.pkl"
)
def effnetv2_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b3/effnetv2_b3.pkl"
)
def effnetv2_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_s/effnetv2_s.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
)
def effnetv2_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl"
)
def effnetv2_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b2/effnetv2_b2.pkl"
)
def effnetv2_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b3/effnetv2_b3.pkl"
)
def effnetv2_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_s/effnetv2_s.pkl"
)
def effnetv2_s(**kwargs):
model_args = dict(stem_w=24, depths=[2, 4, 4, 6, 9, 15], widths=[24, 48, 64, 128, 160, 256])
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_m/effnetv2_m.pkl"
) | megengine.hub.pretrained |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 2020 <NAME>
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""EfficientNet Series
EfficientNet: `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
<https://arxiv.org/abs/1905.11946>`_
References:
https://github.com/facebookresearch/pycls/blob/main/pycls/models/effnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py
"""
import math
from numbers import Real
from typing import Any, Callable, Mapping, Sequence, Union
import megengine.hub as hub
import megengine.module as M
from basecls.layers import (
SE,
DropPath,
activation,
build_head,
conv2d,
init_weights,
make_divisible,
norm2d,
)
from basecls.utils import recursive_update, registers
from .mbnet import MBConv
from .resnet import AnyStage, SimpleStem
__all__ = ["FuseMBConv", "EffNet"]
class FuseMBConv(M.Module):
"""Fusing the proj conv1x1 and depthwise conv into a conv2d.
Args:
w_in: input width.
w_out: output width.
stride: stride of conv.
kernel: kernel of conv.
exp_r: expansion ratio.
se_r: SE ratio.
has_skip: whether apply skip connection.
drop_path_prob: drop path probability.
norm_name: normalization function.
act_name: activation function.
"""
def __init__(
self,
w_in: int,
w_out: int,
stride: int,
kernel: int,
exp_r: float,
se_r: float,
has_skip: bool,
drop_path_prob: float,
norm_name: str,
act_name: str,
**kwargs,
):
super().__init__()
# Expansion
w_mid = w_in
w_exp = int(w_in * exp_r)
if exp_r != 1.0:
self.exp = conv2d(w_in, w_exp, kernel, stride=stride)
self.exp_bn = norm2d(norm_name, w_exp)
self.exp_act = activation(act_name)
w_mid = w_exp
# SE
if se_r > 0.0:
w_se = int(w_in * se_r)
self.se = SE(w_mid, w_se, act_name)
# PWConv
self.proj = conv2d(
w_mid, w_out, 1 if exp_r != 1.0 else kernel, stride=1 if exp_r != 1.0 else stride
)
self.proj_bn = norm2d(norm_name, w_out)
self.has_proj_act = exp_r == 1.0
if self.has_proj_act:
self.proj_act = activation(act_name)
# Skip
self.has_skip = has_skip and w_in == w_out and stride == 1
if self.has_skip:
self.drop_path = DropPath(drop_path_prob)
def forward(self, x):
x_p = x
if getattr(self, "exp", None) is not None:
x = self.exp(x)
x = self.exp_bn(x)
x = self.exp_act(x)
if getattr(self, "se", None) is not None:
x = self.se(x)
x = self.proj(x)
x = self.proj_bn(x)
if self.has_proj_act:
x = self.proj_act(x)
if self.has_skip:
x = self.drop_path(x)
x = x + x_p
return x
@registers.models.register()
class EffNet(M.Module):
"""EfficientNet model.
Args:
stem_w: stem width.
block_name: block name.
depths: depth for each stage (number of blocks in the stage).
widths: width for each stage (width of each block in the stage).
strides: strides for each stage (applies to the first block of each stage).
kernels: kernel sizes for each stage.
exp_rs: expansion ratios for MBConv blocks in each stage.
se_r: Squeeze-and-Excitation (SE) ratio. Default: ``0.25``
drop_path_prob: drop path probability. Default: ``0.0``
depth_mult: depth multiplier. Default: ``1.0``
width_mult: width multiplier. Default: ``1.0``
omit_mult: omit multiplier for stem width, head width, the first stage depth and
the last stage depth, enabled in EfficientNet-Lite. Default: ``False``
norm_name: normalization function. Default: ``"BN"``
act_name: activation function. Default: ``"silu"``
head: head args. Default: ``None``
"""
def __init__(
self,
stem_w: int,
block_name: Union[Union[str, Callable], Sequence[Union[str, Callable]]],
depths: Sequence[int],
widths: Sequence[int],
strides: Sequence[int],
kernels: Sequence[int],
exp_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 1.0,
se_rs: Union[float, Sequence[Union[float, Sequence[float]]]] = 0.0,
drop_path_prob: float = 0.0,
depth_mult: float = 1.0,
width_mult: float = 1.0,
omit_mult: bool = False,
norm_name: str = "BN",
act_name: str = "silu",
head: Mapping[str, Any] = None,
):
super().__init__()
depths = [
d if omit_mult and i in (0, len(depths) - 1) else math.ceil(d * depth_mult)
for i, d in enumerate(depths)
]
self.depths = depths
stem_w = stem_w if omit_mult else make_divisible(stem_w * width_mult, round_limit=0.9)
self.stem = SimpleStem(3, stem_w, norm_name, act_name)
if isinstance(block_name, (str, Callable)):
block_name = [block_name] * len(depths)
block_func = [self.get_block_func(bn) for bn in block_name]
widths = [make_divisible(w * width_mult, round_limit=0.9) for w in widths]
if isinstance(exp_rs, Real):
exp_rs = [exp_rs] * len(depths)
if isinstance(se_rs, Real):
se_rs = [se_rs] * len(depths)
drop_path_prob_iter = (i / sum(depths) * drop_path_prob for i in range(sum(depths)))
drop_path_probs = [[next(drop_path_prob_iter) for _ in range(d)] for d in depths]
model_args = [depths, widths, strides, block_func, kernels, exp_rs, se_rs, drop_path_probs]
prev_w = stem_w
for i, (d, w, s, bf, k, exp_r, se_r, dp_p) in enumerate(zip(*model_args)):
stage = AnyStage(
prev_w,
w,
s,
d,
bf,
kernel=k,
exp_r=exp_r,
se_r=se_r,
se_from_exp=False,
se_act_name=act_name,
se_approx=False,
se_rd_fn=int,
has_proj_act=False,
has_skip=True,
drop_path_prob=dp_p,
norm_name=norm_name,
act_name=act_name,
)
setattr(self, f"s{i + 1}", stage)
prev_w = w
if head:
if head.get("width", 0) > 0 and not omit_mult:
head["width"] = make_divisible(head["width"] * width_mult, round_limit=0.9)
self.head = build_head(prev_w, head, norm_name, act_name)
self.apply(init_weights)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.depths)):
stage = getattr(self, f"s{i + 1}")
x = stage(x)
if getattr(self, "head", None) is not None:
x = self.head(x)
return x
@staticmethod
def get_block_func(name: Union[str, Callable]):
"""Retrieves the block function by name."""
if callable(name):
return name
if isinstance(name, str):
block_funcs = {
"FuseMBConv": FuseMBConv,
"MBConv": MBConv,
}
if name in block_funcs.keys():
return block_funcs[name]
raise ValueError(f"Block '{name}' not supported")
def _build_effnet(**kwargs):
model_args = dict(
stem_w=32,
block_name=MBConv,
depths=[1, 2, 2, 3, 3, 4, 1],
widths=[16, 24, 40, 80, 112, 192, 320],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 5, 3, 5, 5, 3],
exp_rs=[1, 6, 6, 6, 6, 6, 6],
se_rs=0.25,
drop_path_prob=0.2,
head=dict(name="ClsHead", width=1280, dropout_prob=0.2),
)
recursive_update(model_args, kwargs)
return EffNet(**model_args)
def _build_effnet_lite(**kwargs):
model_args = dict(se_rs=0.0, omit_mult=True, act_name="relu6")
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
def _build_effnetv2(**kwargs):
model_args = dict(
stem_w=32,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv],
depths=[1, 2, 2, 3, 5, 8],
widths=[16, 32, 48, 96, 112, 192],
strides=[1, 2, 2, 2, 1, 2],
kernels=[3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25],
)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl"
)
def effnet_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl"
)
def effnet_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl"
)
def effnet_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl"
)
def effnet_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl"
)
def effnet_b4(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl"
)
def effnet_b5(**kwargs):
model_args = dict(depth_mult=2.2, width_mult=1.6, head=dict(dropout_prob=0.4))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl"
)
def effnet_b6(**kwargs):
model_args = dict(depth_mult=2.6, width_mult=1.8, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl"
)
def effnet_b7(**kwargs):
model_args = dict(depth_mult=3.1, width_mult=2.0, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl"
)
def effnet_b8(**kwargs):
model_args = dict(depth_mult=3.6, width_mult=2.2, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl"
)
def effnet_l2(**kwargs):
model_args = dict(depth_mult=5.3, width_mult=4.3, head=dict(dropout_prob=0.5))
recursive_update(model_args, kwargs)
return _build_effnet(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl"
)
def effnet_b0_lite(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl"
)
def effnet_b1_lite(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl"
)
def effnet_b2_lite(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl"
)
def effnet_b3_lite(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl"
)
def effnet_b4_lite(**kwargs):
model_args = dict(depth_mult=1.8, width_mult=1.4, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnet_lite(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl"
)
def effnetv2_b0(**kwargs):
model_args = dict(depth_mult=1.0, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl"
)
def effnetv2_b1(**kwargs):
model_args = dict(depth_mult=1.1, width_mult=1.0)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b2/effnetv2_b2.pkl"
)
def effnetv2_b2(**kwargs):
model_args = dict(depth_mult=1.2, width_mult=1.1, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b3/effnetv2_b3.pkl"
)
def effnetv2_b3(**kwargs):
model_args = dict(depth_mult=1.4, width_mult=1.2, head=dict(dropout_prob=0.3))
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_s/effnetv2_s.pkl"
)
def effnetv2_s(**kwargs):
model_args = dict(stem_w=24, depths=[2, 4, 4, 6, 9, 15], widths=[24, 48, 64, 128, 160, 256])
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_m/effnetv2_m.pkl"
)
def effnetv2_m(**kwargs):
model_args = dict(
stem_w=24,
block_name=[FuseMBConv, FuseMBConv, FuseMBConv, MBConv, MBConv, MBConv, MBConv],
depths=[3, 5, 5, 7, 14, 18, 5],
widths=[24, 48, 80, 160, 176, 304, 512],
strides=[1, 2, 2, 2, 1, 2, 1],
kernels=[3, 3, 3, 3, 3, 3, 3],
exp_rs=[1, 4, 4, 4, 6, 6, 6],
se_rs=[0, 0, 0, 0.25, 0.25, 0.25, 0.25],
head=dict(dropout_prob=0.3),
)
recursive_update(model_args, kwargs)
return _build_effnetv2(**model_args)
@registers.models.register()
@ | hub.pretrained(
"https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_l/effnetv2_l.pkl"
) | megengine.hub.pretrained |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
| fill_(x, 5.0) | megengine.module.init.fill_ |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = | Linear(in_features=3, out_features=8) | megengine.module.Linear |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = | calculate_fan_in_and_fan_out(l.weight) | megengine.module.init.calculate_fan_in_and_fan_out |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = | Conv1d(in_channels=2, out_channels=3, kernel_size=5) | megengine.module.Conv1d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = | calculate_fan_in_and_fan_out(l.weight) | megengine.module.init.calculate_fan_in_and_fan_out |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5
assert fanout == 3 * 5
# FIXME: will be wrong for group conv1d
# l = Conv1d(in_channels=2, out_channels=4, kernel_size=5, groups=2)
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 // 2 * 5
# assert fanout == 4 // 2 * 5
l = | Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7)) | megengine.module.Conv2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5
assert fanout == 3 * 5
# FIXME: will be wrong for group conv1d
# l = Conv1d(in_channels=2, out_channels=4, kernel_size=5, groups=2)
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 // 2 * 5
# assert fanout == 4 // 2 * 5
l = Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7))
fanin, fanout = | calculate_fan_in_and_fan_out(l.weight) | megengine.module.init.calculate_fan_in_and_fan_out |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5
assert fanout == 3 * 5
# FIXME: will be wrong for group conv1d
# l = Conv1d(in_channels=2, out_channels=4, kernel_size=5, groups=2)
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 // 2 * 5
# assert fanout == 4 // 2 * 5
l = Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7))
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5 * 7
assert fanout == 3 * 5 * 7
l = | Conv2d(in_channels=2, out_channels=4, kernel_size=(5, 7), groups=2) | megengine.module.Conv2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5
assert fanout == 3 * 5
# FIXME: will be wrong for group conv1d
# l = Conv1d(in_channels=2, out_channels=4, kernel_size=5, groups=2)
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 // 2 * 5
# assert fanout == 4 // 2 * 5
l = Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7))
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5 * 7
assert fanout == 3 * 5 * 7
l = Conv2d(in_channels=2, out_channels=4, kernel_size=(5, 7), groups=2)
fanin, fanout = | calculate_fan_in_and_fan_out(l.weight) | megengine.module.init.calculate_fan_in_and_fan_out |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5
assert fanout == 3 * 5
# FIXME: will be wrong for group conv1d
# l = Conv1d(in_channels=2, out_channels=4, kernel_size=5, groups=2)
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 // 2 * 5
# assert fanout == 4 // 2 * 5
l = Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7))
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5 * 7
assert fanout == 3 * 5 * 7
l = Conv2d(in_channels=2, out_channels=4, kernel_size=(5, 7), groups=2)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 // 2 * 5 * 7
assert fanout == 4 // 2 * 5 * 7
# FIXME: will be wrong for conv3d
# l = Conv3d(in_channels=2, out_channels=3, kernel_size=(5, 7, 9))
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 * 5 * 7 * 9
# assert fanout == 3 * 5 * 7 * 9
l = | Conv3d(in_channels=2, out_channels=4, kernel_size=(5, 7, 9), groups=2) | megengine.module.Conv3d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
calculate_fan_in_and_fan_out(l.bias)
l = Conv1d(in_channels=2, out_channels=3, kernel_size=5)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5
assert fanout == 3 * 5
# FIXME: will be wrong for group conv1d
# l = Conv1d(in_channels=2, out_channels=4, kernel_size=5, groups=2)
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 // 2 * 5
# assert fanout == 4 // 2 * 5
l = Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7))
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 * 5 * 7
assert fanout == 3 * 5 * 7
l = Conv2d(in_channels=2, out_channels=4, kernel_size=(5, 7), groups=2)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 2 // 2 * 5 * 7
assert fanout == 4 // 2 * 5 * 7
# FIXME: will be wrong for conv3d
# l = Conv3d(in_channels=2, out_channels=3, kernel_size=(5, 7, 9))
# fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
# assert fanin == 2 * 5 * 7 * 9
# assert fanout == 3 * 5 * 7 * 9
l = Conv3d(in_channels=2, out_channels=4, kernel_size=(5, 7, 9), groups=2)
fanin, fanout = | calculate_fan_in_and_fan_out(l.weight) | megengine.module.init.calculate_fan_in_and_fan_out |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
from megengine import tensor
from megengine.module import Conv1d, Conv2d, Conv3d, Linear
from megengine.module.init import calculate_fan_in_and_fan_out, fill_
def test_fill_():
x = tensor(np.zeros((2, 3, 4)), dtype=np.float32)
fill_(x, 5.0)
np.testing.assert_array_equal(
x.numpy(), np.full(shape=(2, 3, 4), fill_value=5.0, dtype=np.float32)
)
def test_calculate_fan_in_and_fan_out():
l = Linear(in_features=3, out_features=8)
fanin, fanout = calculate_fan_in_and_fan_out(l.weight)
assert fanin == 3
assert fanout == 8
with pytest.raises(ValueError):
| calculate_fan_in_and_fan_out(l.bias) | megengine.module.init.calculate_fan_in_and_fan_out |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = | Tensor(0) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = | Tensor([1, 2]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = | Tensor([3, 4]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = | F.mul(vara, varb) | megengine.functional.mul |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = | F.relu(out) | megengine.functional.relu |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = | GraphInference(modified_model) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = | Tensor([1, 2]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = | Tensor([3, 4]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = | F.sub(vara, varb) | megengine.functional.sub |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = | F.relu(out1) | megengine.functional.relu |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = | GraphInference(modified_model1) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = | F.ones((2,)) | megengine.functional.ones |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = | F.ones((2,)) | megengine.functional.ones |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = | Net.load(model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = | Net.load(model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = | Net.load(model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = | Tensor([1, 2]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = | Tensor([3, 4]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
param_const = graph.params_filter.as_unique()
param_const.set_value(3)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = | GraphInference(modified_model) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
param_const = graph.params_filter.as_unique()
param_const.set_value(3)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [12, 18])
def test_make_const():
a = | Tensor([1, 2]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
param_const = graph.params_filter.as_unique()
param_const.set_value(3)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [12, 18])
def test_make_const():
a = Tensor([1, 2])
b = | Tensor([3, 4]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
param_const = graph.params_filter.as_unique()
param_const.set_value(3)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [12, 18])
def test_make_const():
a = Tensor([1, 2])
b = Tensor([3, 4])
@ | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
from megengine.utils.network import Network as Net
from megengine.utils.network import as_oprnode, set_symbolic_shape
from megengine.utils.network_node import Host2DeviceCopy, VarNode
def test_metadata():
x = Tensor(0)
@trace(symbolic=True, capture_as_const=True)
def fwd(x):
return x * 2
fwd(x)
orig_model = io.BytesIO()
fwd.dump(orig_model, user_info="test", optimize_for_inference=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": "test",
"graph_modified": False, # False: tracing.dump
"optimized_for_inference": False,
}
orig_model.seek(0)
graph.dump(
orig_model,
user_info={"str": "x", "tensor": x, "module": M.Module, "none": None},
optimize_for_inference=True,
enable_nchw4=True,
enable_ioc16=True,
)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata == {
"user_info": {"str": "x", "tensor": x, "module": M.Module, "none": None},
"graph_modified": True, # True: Network.dump
"optimized_for_inference": True,
"enable_nchw4": True,
"enable_ioc16": True,
}
orig_model.seek(0)
fwd.dump(orig_model, enable_metadata=False)
orig_model.seek(0)
graph = Net.load(orig_model)
assert graph.metadata is None
def test_replace_var():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out = F.mul(vara, varb)
out = F.relu(out)
opnode = list(graph.opr_filter.has_input(vara))
repl_dict = {opnode[0].outputs[0]: out}
graph.replace_vars(repl_dict)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [6, 16])
def test_replace_opr():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
vara = graph.var_filter.name("a").as_unique()
varb = graph.var_filter.name("b").as_unique()
out1 = F.sub(vara, varb)
out1 = F.relu(out1)
out1 = graph.add_dep_oprs(out1)
orig_opr = graph.opr_filter.has_input(vara).as_unique()
repl_dict = {orig_opr: out1[0].owner}
graph.replace_oprs(repl_dict)
modified_model1 = io.BytesIO()
graph.dump(modified_model1)
modified_model1.seek(0)
load_graph = GraphInference(modified_model1)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [0, 0])
def test_splice_network():
x = F.ones((2,))
y = F.ones((2,))
@trace(symbolic=True, capture_as_const=True)
def fun1(a, b):
return (a + b) * 2
@trace(symbolic=True, capture_as_const=True)
def fun2(a):
return a * 2 - 1
model = io.BytesIO()
fun1(x, y)
fun2(x)
fun1.dump(
model,
arg_names=["net1_i0", "net1_i1"],
output_names=["net1_o0"],
optimize_for_inference=False,
)
model.seek(0)
net1 = Net.load(model)
model.seek(0)
fun2.dump(
model,
arg_names=["net2_i0"],
output_names=["net2_o0"],
optimize_for_inference=False,
)
model.seek(0)
net2 = Net.load(model)
net1.add_output(*net2.output_vars)
var = net1.var_filter.name("net1_i0").as_unique()
repl_var = net2.var_filter.name("net2_o0").as_unique()
net1.replace_vars({var: repl_var})
assert "net1_i0" not in [var.name for var in net1.all_vars]
assert "net2_i0" in [var.name for var in net1.all_vars]
model.seek(0)
net1.dump(model, keep_var_name=2, optimize_for_inference=False)
model.seek(0)
net = Net.load(model)
assert "net1_i0" not in [var.name for var in net.all_vars]
assert "net2_i0" in [var.name for var in net.all_vars]
def test_modify_params():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = Net.load(orig_model)
param_const = graph.params_filter.as_unique()
param_const.set_value(3)
modified_model = io.BytesIO()
graph.dump(modified_model)
modified_model.seek(0)
load_graph = GraphInference(modified_model)
out = load_graph.run(a, b)
np.testing.assert_equal(out["o"], [12, 18])
def test_make_const():
a = Tensor([1, 2])
b = Tensor([3, 4])
@trace(symbolic=True, capture_as_const=True)
def fwd(a, b):
return (a + b) * 2
fwd(a, b)
orig_model = io.BytesIO()
fwd.dump(
orig_model, arg_names=["a", "b"], output_names="o", optimize_for_inference=False
)
orig_model.seek(0)
graph = | Net.load(orig_model) | megengine.utils.network.Network.load |