docs/transformers/tests/models/convnext/test_image_processing_convnext.py

# Copyright 2022s 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

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torchvision_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_vision_available():
    from transformers import ConvNextImageProcessor

    if is_torchvision_available():
        from transformers import ConvNextImageProcessorFast


class ConvNextImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        crop_pct=0.875,
        do_normalize=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 {"shortest_edge": 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_resize = do_resize
        self.size = size
        self.crop_pct = crop_pct
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

    def prepare_image_processor_dict(self):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_pct": self.crop_pct,
        }

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

    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 ConvNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = ConvNextImageProcessor if is_vision_available() else None
    fast_image_processing_class = ConvNextImageProcessorFast if is_torchvision_available() else None

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

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

    def test_image_processor_properties(self):
        for image_processing_class in self.image_processor_list:
            image_processing = 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, "crop_pct"))
            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):
        for image_processing_class in self.image_processor_list:
            image_processor = image_processing_class.from_dict(self.image_processor_dict)
            self.assertEqual(image_processor.size, {"shortest_edge": 20})

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

    @unittest.skip(
        "Skipping as ConvNextImageProcessor uses center_crop and center_crop functions are not equivalent for fast and slow processors"
    )
    def test_slow_fast_equivalence_batched(self):
        pass