File size: 5,324 Bytes
fd43906
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
# coding=utf-8
# Copyright 2023 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
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

    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=False)

        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=False)
        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=False)

        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)
        list(input_np_4d)

        out_np_4d = image_processor.postprocess(
            image_processor.preprocess(input_pt_4d),
            output_type="np",
        )

        out_np_list = image_processor.postprocess(
            image_processor.preprocess(input_pt_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