transformers/tests/models/pixtral/test_image_processing_pixtral.py

# 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 random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import 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 PixtralImageProcessor


class PixtralImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        max_num_images_per_sample=3,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        patch_size=None,
        do_normalize=True,
        image_mean=[0.48145466, 0.4578275, 0.40821073],
        image_std=[0.26862954, 0.26130258, 0.27577711],
        do_convert_rgb=True,
    ):
        size = size if size is not None else {"longest_edge": 24}
        patch_size = patch_size if patch_size is not None else {"height": 8, "width": 8}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.max_num_images_per_sample = max_num_images_per_sample
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.patch_size = patch_size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_convert_rgb = do_convert_rgb

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

    def expected_output_image_shape(self, image):
        if isinstance(image, Image.Image):
            width, height = image.size
        elif isinstance(image, np.ndarray):
            height, width = image.shape[:2]
        elif isinstance(image, torch.Tensor):
            height, width = image.shape[-2:]

        max_height = max_width = self.size.get("longest_edge")

        ratio = max(height / max_height, width / max_width)
        if ratio > 1:
            height = int(np.ceil(height / ratio))
            width = int(np.ceil(width / ratio))

        patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
        num_height_tokens = (height - 1) // patch_height + 1
        num_width_tokens = (width - 1) // patch_width + 1

        height = num_height_tokens * patch_height
        width = num_width_tokens * patch_width

        return self.num_channels, height, width

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        # Use prepare_image_inputs to make a list of list of single images

        images_list = []
        for _ in range(self.batch_size):
            images = []
            for _ in range(random.randint(1, self.max_num_images_per_sample)):
                img = prepare_image_inputs(
                    batch_size=1,
                    num_channels=self.num_channels,
                    min_resolution=self.min_resolution,
                    max_resolution=self.max_resolution,
                    equal_resolution=equal_resolution,
                    numpify=numpify,
                    torchify=torchify,
                )[0]
                images.append(img)
            images_list.append(images)
        return images_list


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

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

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_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, "patch_size"))
        self.assertTrue(hasattr(image_processing, "do_rescale"))
        self.assertTrue(hasattr(image_processing, "rescale_factor"))
        self.assertTrue(hasattr(image_processing, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))
        self.assertTrue(hasattr(image_processing, "do_convert_rgb"))

    def test_call_pil(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        image_inputs_list = self.image_processor_tester.prepare_image_inputs()
        for image_inputs in image_inputs_list:
            for image in image_inputs:
                self.assertIsInstance(image, Image.Image)

        # Test not batched input
        encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0][0])
        self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)

        # Test batched
        batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
        for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
            for encoded_image, image in zip(encoded_images, images):
                expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
                self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)

    def test_call_numpy(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        image_inputs_list = self.image_processor_tester.prepare_image_inputs(numpify=True)
        for image_inputs in image_inputs_list:
            for image in image_inputs:
                self.assertIsInstance(image, np.ndarray)

        # Test not batched input
        encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0][0])
        self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)

        # Test batched
        batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
        for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
            for encoded_image, image in zip(encoded_images, images):
                expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
                self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)

    def test_call_pytorch(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        image_inputs_list = self.image_processor_tester.prepare_image_inputs(torchify=True)
        for image_inputs in image_inputs_list:
            for image in image_inputs:
                self.assertIsInstance(image, torch.Tensor)

        # Test not batched input
        encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0][0])
        self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)

        # Test batched
        batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
        for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
            for encoded_image, image in zip(encoded_images, images):
                expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
                self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)

    @unittest.skip(reason="PixtralImageProcessor doesn't treat 4 channel PIL and numpy consistently yet")  # FIXME Amy
    def test_call_numpy_4_channels(self):
        pass