tests / test_feature_extraction_clip.py

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	# 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 unittest

	import numpy as np

	from transformers.file_utils import is_torch_available, is_vision_available
	from transformers.testing_utils import require_torch, require_vision

	from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


	if is_torch_available():
	import torch

	if is_vision_available():
	from PIL import Image

	from transformers import CLIPFeatureExtractor


	class CLIPFeatureExtractionTester(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=20,
	do_center_crop=True,
	crop_size=18,
	do_normalize=True,
	image_mean=[0.48145466, 0.4578275, 0.40821073],
	image_std=[0.26862954, 0.26130258, 0.27577711],
	):
	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_center_crop = do_center_crop
	self.crop_size = crop_size
	self.do_normalize = do_normalize
	self.image_mean = image_mean
	self.image_std = image_std

	def prepare_feat_extract_dict(self):
	return {
	"do_resize": self.do_resize,
	"size": self.size,
	"do_center_crop": self.do_center_crop,
	"crop_size": self.crop_size,
	"do_normalize": self.do_normalize,
	"image_mean": self.image_mean,
	"image_std": self.image_std,
	}

	def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False):
	"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
	or a list of PyTorch tensors if one specifies torchify=True.
	"""

	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	if equal_resolution:
	image_inputs = []
	for i in range(self.batch_size):
	image_inputs.append(
	np.random.randint(
	255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8
	)
	)
	else:
	image_inputs = []
	for i in range(self.batch_size):
	width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2)
	image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8))

	if not numpify and not torchify:
	# PIL expects the channel dimension as last dimension
	image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

	if torchify:
	image_inputs = [torch.from_numpy(x) for x in image_inputs]

	return image_inputs


	@require_torch
	@require_vision
	class CLIPFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):

	feature_extraction_class = CLIPFeatureExtractor if is_vision_available() else None

	def setUp(self):
	self.feature_extract_tester = CLIPFeatureExtractionTester(self)

	@property
	def feat_extract_dict(self):
	return self.feature_extract_tester.prepare_feat_extract_dict()

	def test_feat_extract_properties(self):
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	self.assertTrue(hasattr(feature_extractor, "do_resize"))
	self.assertTrue(hasattr(feature_extractor, "size"))
	self.assertTrue(hasattr(feature_extractor, "do_center_crop"))
	self.assertTrue(hasattr(feature_extractor, "center_crop"))
	self.assertTrue(hasattr(feature_extractor, "do_normalize"))
	self.assertTrue(hasattr(feature_extractor, "image_mean"))
	self.assertTrue(hasattr(feature_extractor, "image_std"))

	def test_batch_feature(self):
	pass

	def test_call_pil(self):
	# Initialize feature_extractor
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	# create random PIL images
	image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False)
	for image in image_inputs:
	self.assertIsInstance(image, Image.Image)

	# Test not batched input
	encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	1,
	self.feature_extract_tester.num_channels,
	self.feature_extract_tester.crop_size,
	self.feature_extract_tester.crop_size,
	),
	)

	# Test batched
	encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	self.feature_extract_tester.batch_size,
	self.feature_extract_tester.num_channels,
	self.feature_extract_tester.crop_size,
	self.feature_extract_tester.crop_size,
	),
	)

	def test_call_numpy(self):
	# Initialize feature_extractor
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	# create random numpy tensors
	image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False, numpify=True)
	for image in image_inputs:
	self.assertIsInstance(image, np.ndarray)

	# Test not batched input
	encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	1,
	self.feature_extract_tester.num_channels,
	self.feature_extract_tester.crop_size,
	self.feature_extract_tester.crop_size,
	),
	)

	# Test batched
	encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	self.feature_extract_tester.batch_size,
	self.feature_extract_tester.num_channels,
	self.feature_extract_tester.crop_size,
	self.feature_extract_tester.crop_size,
	),
	)

	def test_call_pytorch(self):
	# Initialize feature_extractor
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	# create random PyTorch tensors
	image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False, torchify=True)
	for image in image_inputs:
	self.assertIsInstance(image, torch.Tensor)

	# Test not batched input
	encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	1,
	self.feature_extract_tester.num_channels,
	self.feature_extract_tester.crop_size,
	self.feature_extract_tester.crop_size,
	),
	)

	# Test batched
	encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	self.feature_extract_tester.batch_size,
	self.feature_extract_tester.num_channels,
	self.feature_extract_tester.crop_size,
	self.feature_extract_tester.crop_size,
	),
	)