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# Copyright 2023 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers.image_utils import load_image
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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from ...test_processing_common import url_to_local_path
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import Pix2StructImageProcessor
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class Pix2StructImageProcessingTester:
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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size=None,
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do_normalize=True,
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do_convert_rgb=True,
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patch_size=None,
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):
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size = size if size is not None else {"height": 20, "width": 20}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.size = size
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self.do_normalize = do_normalize
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self.do_convert_rgb = do_convert_rgb
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self.max_patches = [512, 1024, 2048, 4096]
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self.patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16}
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def prepare_image_processor_dict(self):
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return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
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def prepare_dummy_image(self):
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img_url = url_to_local_path(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
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)
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raw_image = load_image(img_url).convert("RGB")
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return raw_image
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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class Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Pix2StructImageProcessor if is_vision_available() else None
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def setUp(self):
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super().setUp()
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self.image_processor_tester = Pix2StructImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processor, "do_normalize"))
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self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
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def test_expected_patches(self):
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dummy_image = self.image_processor_tester.prepare_dummy_image()
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image_processor = self.image_processing_class(**self.image_processor_dict)
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max_patch = 2048
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inputs = image_processor(dummy_image, return_tensors="pt", max_patches=max_patch)
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torch.testing.assert_close(inputs.flattened_patches.mean(), torch.tensor(0.0606), rtol=1e-3, atol=1e-3)
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def test_call_pil(self):
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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expected_hidden_dim = (
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(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
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* self.image_processor_tester.num_channels
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) + 2
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for max_patch in self.image_processor_tester.max_patches:
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(1, max_patch, expected_hidden_dim),
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)
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# Test batched
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encoded_images = image_processor(
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image_inputs, return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
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)
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def test_call_vqa(self):
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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expected_hidden_dim = (
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(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
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* self.image_processor_tester.num_channels
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) + 2
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image_processor.is_vqa = True
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for max_patch in self.image_processor_tester.max_patches:
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# Test not batched input
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with self.assertRaises(ValueError):
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch
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).flattened_patches
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dummy_text = "Hello"
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch, header_text=dummy_text
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(1, max_patch, expected_hidden_dim),
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)
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# Test batched
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encoded_images = image_processor(
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image_inputs, return_tensors="pt", max_patches=max_patch, header_text=dummy_text
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
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)
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def test_call_numpy(self):
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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expected_hidden_dim = (
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(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
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* self.image_processor_tester.num_channels
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) + 2
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for max_patch in self.image_processor_tester.max_patches:
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(1, max_patch, expected_hidden_dim),
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)
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# Test batched
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encoded_images = image_processor(
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image_inputs, return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
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)
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def test_call_numpy_4_channels(self):
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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expected_hidden_dim = (
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(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
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* self.image_processor_tester.num_channels
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) + 2
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for max_patch in self.image_processor_tester.max_patches:
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(1, max_patch, expected_hidden_dim),
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)
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# Test batched
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encoded_images = image_processor(
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image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
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)
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self.image_processor_tester.num_channels = 3
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def test_call_pytorch(self):
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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expected_hidden_dim = (
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(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
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* self.image_processor_tester.num_channels
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) + 2
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for max_patch in self.image_processor_tester.max_patches:
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(1, max_patch, expected_hidden_dim),
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)
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# Test batched
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encoded_images = image_processor(
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image_inputs, return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
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)
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@require_torch
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@require_vision
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class Pix2StructImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Pix2StructImageProcessor if is_vision_available() else None
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def setUp(self):
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super().setUp()
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self.image_processor_tester = Pix2StructImageProcessingTester(self, num_channels=4)
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self.expected_encoded_image_num_channels = 3
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processor, "do_normalize"))
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self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
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def test_call_pil(self):
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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expected_hidden_dim = (
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(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
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* (self.image_processor_tester.num_channels - 1)
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) + 2
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for max_patch in self.image_processor_tester.max_patches:
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0], return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(1, max_patch, expected_hidden_dim),
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)
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# Test batched
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encoded_images = image_processor(
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image_inputs, return_tensors="pt", max_patches=max_patch
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).flattened_patches
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self.assertEqual(
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encoded_images.shape,
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(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
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)
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@unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet") # FIXME Amy
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def test_call_numpy(self):
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return super().test_call_numpy()
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@unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet") # FIXME Amy
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def test_call_pytorch(self):
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return super().test_call_torch()
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@unittest.skip(
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reason="Pix2StructImageProcessor does treat numpy and PIL 4 channel images consistently"
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) # FIXME Amy
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def test_call_numpy_4_channels(self):
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return super().test_call_torch()
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