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# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import PIL.Image
import torch
from diffusers.image_processor import VaeImageProcessor
class ImageProcessorTest(unittest.TestCase):
@property
def dummy_sample(self):
batch_size = 1
num_channels = 3
height = 8
width = 8
sample = torch.rand((batch_size, num_channels, height, width))
return sample
@property
def dummy_mask(self):
batch_size = 1
num_channels = 1
height = 8
width = 8
sample = torch.rand((batch_size, num_channels, height, width))
return sample
def to_np(self, image):
if isinstance(image[0], PIL.Image.Image):
return np.stack([np.array(i) for i in image], axis=0)
elif isinstance(image, torch.Tensor):
return image.cpu().numpy().transpose(0, 2, 3, 1)
return image
def test_vae_image_processor_pt(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
input_pt = self.dummy_sample
input_np = self.to_np(input_pt)
for output_type in ["pt", "np", "pil"]:
out = image_processor.postprocess(
image_processor.preprocess(input_pt),
output_type=output_type,
)
out_np = self.to_np(out)
in_np = (input_np * 255).round() if output_type == "pil" else input_np
assert (
np.abs(in_np - out_np).max() < 1e-6
), f"decoded output does not match input for output_type {output_type}"
def test_vae_image_processor_np(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
for output_type in ["pt", "np", "pil"]:
out = image_processor.postprocess(image_processor.preprocess(input_np), output_type=output_type)
out_np = self.to_np(out)
in_np = (input_np * 255).round() if output_type == "pil" else input_np
assert (
np.abs(in_np - out_np).max() < 1e-6
), f"decoded output does not match input for output_type {output_type}"
def test_vae_image_processor_pil(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
input_pil = image_processor.numpy_to_pil(input_np)
for output_type in ["pt", "np", "pil"]:
out = image_processor.postprocess(image_processor.preprocess(input_pil), output_type=output_type)
for i, o in zip(input_pil, out):
in_np = np.array(i)
out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round()
assert (
np.abs(in_np - out_np).max() < 1e-6
), f"decoded output does not match input for output_type {output_type}"
def test_preprocess_input_3d(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
input_pt_4d = self.dummy_sample
input_pt_3d = input_pt_4d.squeeze(0)
out_pt_4d = image_processor.postprocess(
image_processor.preprocess(input_pt_4d),
output_type="np",
)
out_pt_3d = image_processor.postprocess(
image_processor.preprocess(input_pt_3d),
output_type="np",
)
input_np_4d = self.to_np(self.dummy_sample)
input_np_3d = input_np_4d.squeeze(0)
out_np_4d = image_processor.postprocess(
image_processor.preprocess(input_np_4d),
output_type="np",
)
out_np_3d = image_processor.postprocess(
image_processor.preprocess(input_np_3d),
output_type="np",
)
assert np.abs(out_pt_4d - out_pt_3d).max() < 1e-6
assert np.abs(out_np_4d - out_np_3d).max() < 1e-6
def test_preprocess_input_list(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
input_pt_4d = self.dummy_sample
input_pt_list = list(input_pt_4d)
out_pt_4d = image_processor.postprocess(
image_processor.preprocess(input_pt_4d),
output_type="np",
)
out_pt_list = image_processor.postprocess(
image_processor.preprocess(input_pt_list),
output_type="np",
)
input_np_4d = self.to_np(self.dummy_sample)
input_np_list = list(input_np_4d)
out_np_4d = image_processor.postprocess(
image_processor.preprocess(input_np_4d),
output_type="np",
)
out_np_list = image_processor.postprocess(
image_processor.preprocess(input_np_list),
output_type="np",
)
assert np.abs(out_pt_4d - out_pt_list).max() < 1e-6
assert np.abs(out_np_4d - out_np_list).max() < 1e-6
def test_preprocess_input_mask_3d(self):
image_processor = VaeImageProcessor(
do_resize=False, do_normalize=False, do_binarize=True, do_convert_grayscale=True
)
input_pt_4d = self.dummy_mask
input_pt_3d = input_pt_4d.squeeze(0)
input_pt_2d = input_pt_3d.squeeze(0)
out_pt_4d = image_processor.postprocess(
image_processor.preprocess(input_pt_4d),
output_type="np",
)
out_pt_3d = image_processor.postprocess(
image_processor.preprocess(input_pt_3d),
output_type="np",
)
out_pt_2d = image_processor.postprocess(
image_processor.preprocess(input_pt_2d),
output_type="np",
)
input_np_4d = self.to_np(self.dummy_mask)
input_np_3d = input_np_4d.squeeze(0)
input_np_3d_1 = input_np_4d.squeeze(-1)
input_np_2d = input_np_3d.squeeze(-1)
out_np_4d = image_processor.postprocess(
image_processor.preprocess(input_np_4d),
output_type="np",
)
out_np_3d = image_processor.postprocess(
image_processor.preprocess(input_np_3d),
output_type="np",
)
out_np_3d_1 = image_processor.postprocess(
image_processor.preprocess(input_np_3d_1),
output_type="np",
)
out_np_2d = image_processor.postprocess(
image_processor.preprocess(input_np_2d),
output_type="np",
)
assert np.abs(out_pt_4d - out_pt_3d).max() == 0
assert np.abs(out_pt_4d - out_pt_2d).max() == 0
assert np.abs(out_np_4d - out_np_3d).max() == 0
assert np.abs(out_np_4d - out_np_3d_1).max() == 0
assert np.abs(out_np_4d - out_np_2d).max() == 0
def test_preprocess_input_mask_list(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False, do_convert_grayscale=True)
input_pt_4d = self.dummy_mask
input_pt_3d = input_pt_4d.squeeze(0)
input_pt_2d = input_pt_3d.squeeze(0)
inputs_pt = [input_pt_4d, input_pt_3d, input_pt_2d]
inputs_pt_list = [[input_pt] for input_pt in inputs_pt]
for input_pt, input_pt_list in zip(inputs_pt, inputs_pt_list):
out_pt = image_processor.postprocess(
image_processor.preprocess(input_pt),
output_type="np",
)
out_pt_list = image_processor.postprocess(
image_processor.preprocess(input_pt_list),
output_type="np",
)
assert np.abs(out_pt - out_pt_list).max() < 1e-6
input_np_4d = self.to_np(self.dummy_mask)
input_np_3d = input_np_4d.squeeze(0)
input_np_2d = input_np_3d.squeeze(-1)
inputs_np = [input_np_4d, input_np_3d, input_np_2d]
inputs_np_list = [[input_np] for input_np in inputs_np]
for input_np, input_np_list in zip(inputs_np, inputs_np_list):
out_np = image_processor.postprocess(
image_processor.preprocess(input_np),
output_type="np",
)
out_np_list = image_processor.postprocess(
image_processor.preprocess(input_np_list),
output_type="np",
)
assert np.abs(out_np - out_np_list).max() < 1e-6
def test_preprocess_input_mask_3d_batch(self):
image_processor = VaeImageProcessor(do_resize=False, do_normalize=False, do_convert_grayscale=True)
# create a dummy mask input with batch_size 2
dummy_mask_batch = torch.cat([self.dummy_mask] * 2, axis=0)
# squeeze out the channel dimension
input_pt_3d = dummy_mask_batch.squeeze(1)
input_np_3d = self.to_np(dummy_mask_batch).squeeze(-1)
input_pt_3d_list = list(input_pt_3d)
input_np_3d_list = list(input_np_3d)
out_pt_3d = image_processor.postprocess(
image_processor.preprocess(input_pt_3d),
output_type="np",
)
out_pt_3d_list = image_processor.postprocess(
image_processor.preprocess(input_pt_3d_list),
output_type="np",
)
assert np.abs(out_pt_3d - out_pt_3d_list).max() < 1e-6
out_np_3d = image_processor.postprocess(
image_processor.preprocess(input_np_3d),
output_type="np",
)
out_np_3d_list = image_processor.postprocess(
image_processor.preprocess(input_np_3d_list),
output_type="np",
)
assert np.abs(out_np_3d - out_np_3d_list).max() < 1e-6
def test_vae_image_processor_resize_pt(self):
image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)
input_pt = self.dummy_sample
b, c, h, w = input_pt.shape
scale = 2
out_pt = image_processor.resize(image=input_pt, height=h // scale, width=w // scale)
exp_pt_shape = (b, c, h // scale, w // scale)
assert (
out_pt.shape == exp_pt_shape
), f"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'."
def test_vae_image_processor_resize_np(self):
image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)
input_pt = self.dummy_sample
b, c, h, w = input_pt.shape
scale = 2
input_np = self.to_np(input_pt)
out_np = image_processor.resize(image=input_np, height=h // scale, width=w // scale)
exp_np_shape = (b, h // scale, w // scale, c)
assert (
out_np.shape == exp_np_shape
), f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."
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