# coding=utf-8 # Copyright 2021 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 json import os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers.testing_utils import require_torch, require_vision, slow 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 ImageGPTImageProcessor class ImageGPTImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, ): size = size if size is not None else {"height": 18, "width": 18} 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.do_normalize = do_normalize def prepare_image_processor_dict(self): return { # here we create 2 clusters for the sake of simplicity "clusters": np.asarray( [ [0.8866443634033203, 0.6618829369544983, 0.3891746401786804], [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296], ] ), "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, } def expected_output_image_shape(self, images): return (self.size["height"] * self.size["width"],) 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 ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = ImageGPTImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = ImageGPTImageProcessingTester(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, "clusters")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_normalize")) 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": 18, "width": 18}) 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_image_processor_to_json_string(self): image_processor = self.image_processing_class(**self.image_processor_dict) obj = json.loads(image_processor.to_json_string()) for key, value in self.image_processor_dict.items(): if key == "clusters": self.assertTrue(np.array_equal(value, obj[key])) else: self.assertEqual(obj[key], value) def test_image_processor_to_json_file(self): image_processor_first = self.image_processing_class(**self.image_processor_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "image_processor.json") image_processor_first.to_json_file(json_file_path) image_processor_second = self.image_processing_class.from_json_file(json_file_path).to_dict() image_processor_first = image_processor_first.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, image_processor_second[key])) else: self.assertEqual(image_processor_first[key], value) def test_image_processor_from_and_save_pretrained(self): image_processor_first = self.image_processing_class(**self.image_processor_dict) with tempfile.TemporaryDirectory() as tmpdirname: image_processor_first.save_pretrained(tmpdirname) image_processor_second = self.image_processing_class.from_pretrained(tmpdirname).to_dict() image_processor_first = image_processor_first.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, image_processor_second[key])) else: self.assertEqual(image_processor_first[key], value) @unittest.skip("ImageGPT requires clusters at initialization") def test_init_without_params(self): pass # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input def test_call_pil(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input def test_call_numpy(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) @unittest.skip("ImageGPT assumes clusters for 3 channels") def test_call_numpy_4_channels(self): pass # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input def test_call_pytorch(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape), ) def prepare_images(): dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test") image1 = Image.open(dataset[4]["file"]) image2 = Image.open(dataset[5]["file"]) images = [image1, image2] return images @require_vision @require_torch class ImageGPTImageProcessorIntegrationTest(unittest.TestCase): @slow def test_image(self): image_processing = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small") images = prepare_images() # test non-batched encoding = image_processing(images[0], return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (1, 1024)) expected_slice = [306, 191, 191] self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice) # test batched encoding = image_processing(images, return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (2, 1024)) expected_slice = [303, 13, 13] self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)