test_image_processing_nougat.py

# 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
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import NougatImageProcessor


class NougatImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_crop_margin=True,
        do_resize=True,
        size=None,
        do_thumbnail=True,
        do_align_long_axis: bool = False,
        do_pad=True,
        do_normalize: bool = True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
    ):
        size = size if size is not None else {"height": 20, "width": 20}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_crop_margin = do_crop_margin
        self.do_resize = do_resize
        self.size = size
        self.do_thumbnail = do_thumbnail
        self.do_align_long_axis = do_align_long_axis
        self.do_pad = do_pad
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

    def prepare_image_processor_dict(self):
        return {
            "do_crop_margin": self.do_crop_margin,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_long_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }

    def expected_output_image_shape(self, images):
        return self.num_channels, self.size["height"], self.size["width"]

    def prepare_dummy_image(self):
        filepath = hf_hub_download(
            repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
        )
        image = Image.open(filepath).convert("RGB")
        return image

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )


@require_torch
@require_vision
class NougatImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = NougatImageProcessor if is_vision_available() else None

    def setUp(self):
        super().setUp()
        self.image_processor_tester = NougatImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    @cached_property
    def image_processor(self):
        return self.image_processing_class(**self.image_processor_dict)

    def test_image_processor_properties(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processing, "do_resize"))
        self.assertTrue(hasattr(image_processing, "size"))
        self.assertTrue(hasattr(image_processing, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))

    def test_image_processor_from_dict_with_kwargs(self):
        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size, {"height": 20, "width": 20})

        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
        self.assertEqual(image_processor.size, {"height": 42, "width": 42})

    def test_expected_output(self):
        dummy_image = self.image_processor_tester.prepare_dummy_image()
        image_processor = self.image_processor
        inputs = image_processor(dummy_image, return_tensors="pt")
        self.assertTrue(torch.allclose(inputs["pixel_values"].mean(), torch.tensor(0.4906), atol=1e-3, rtol=1e-3))

    def test_crop_margin_all_white(self):
        image = np.uint8(np.ones((100, 100, 3)) * 255)
        image_processor = self.image_processor
        cropped_image = image_processor.crop_margin(image)
        self.assertTrue(np.array_equal(image, cropped_image))

    def test_crop_margin_centered_black_square(self):
        image = np.ones((100, 100, 3), dtype=np.uint8) * 255
        image[45:55, 45:55, :] = 0
        image_processor = self.image_processor
        cropped_image = image_processor.crop_margin(image)
        expected_cropped = image[45:55, 45:55, :]
        self.assertTrue(np.array_equal(expected_cropped, cropped_image))

    def test_align_long_axis_no_rotation(self):
        image = np.uint8(np.ones((100, 200, 3)) * 255)
        image_processor = self.image_processor
        size = {"height": 200, "width": 300}
        aligned_image = image_processor.align_long_axis(image, size)
        self.assertEqual(image.shape, aligned_image.shape)

    def test_align_long_axis_with_rotation(self):
        image = np.uint8(np.ones((200, 100, 3)) * 255)
        image_processor = self.image_processor
        size = {"height": 300, "width": 200}
        aligned_image = image_processor.align_long_axis(image, size)
        self.assertEqual((200, 100, 3), aligned_image.shape)

    def test_align_long_axis_data_format(self):
        image = np.uint8(np.ones((100, 200, 3)) * 255)
        data_format = "channels_first"
        size = {"height": 200, "width": 300}
        image_processor = self.image_processor
        aligned_image = image_processor.align_long_axis(image, size, data_format=data_format)
        self.assertEqual((3, 100, 200), aligned_image.shape)

    def prepare_dummy_np_image(self):
        filepath = hf_hub_download(
            repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
        )
        image = Image.open(filepath).convert("RGB")
        return np.array(image)

    def test_crop_margin_equality_cv2_python(self):
        image = self.prepare_dummy_np_image()
        image_processor = self.image_processor
        image_cropped_python = image_processor.crop_margin(image)

        self.assertEqual(image_cropped_python.shape, (850, 685, 3))
        self.assertEqual(image_cropped_python.mean(), 237.43881150708458)