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from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
assert input_size in [112, 224], "input_size should be 112 or 224"
assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
blocks = get_blocks(num_layers)
if mode == 'ir':
unit_module = bottleneck_IR
elif mode == 'ir_se':
unit_module = bottleneck_IR_SE
self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
BatchNorm2d(64),
PReLU(64))
if input_size == 112:
self.output_layer = Sequential(BatchNorm2d(512),
Dropout(drop_ratio),
Flatten(),
Linear(512 * 7 * 7, 512),
BatchNorm1d(512, affine=affine))
else:
self.output_layer = Sequential(BatchNorm2d(512),
Dropout(drop_ratio),
Flatten(),
Linear(512 * 14 * 14, 512),
BatchNorm1d(512, affine=affine))
modules = []
for block in blocks:
for bottleneck in block:
modules.append(unit_module(bottleneck.in_channel,
bottleneck.depth,
bottleneck.stride))
self.body = Sequential(*modules)
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
x = self.output_layer(x)
return l2_norm(x)
def IR_50(input_size):
"""Constructs a ir-50 model."""
model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
return model
def IR_101(input_size):
"""Constructs a ir-101 model."""
model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
return model
def IR_152(input_size):
"""Constructs a ir-152 model."""
model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
return model
def IR_SE_50(input_size):
"""Constructs a ir_se-50 model."""
model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
return model
def IR_SE_101(input_size):
"""Constructs a ir_se-101 model."""
model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
return model
def IR_SE_152(input_size):
"""Constructs a ir_se-152 model."""
model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
return model
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