CLIP (#11445)
* begin second draft * fix import, style * add loss * fix embeds, logits_scale, and projection * fix imports * add conversion script * add feature_extractor and processor * style * add tests for tokenizer, extractor and processor * add vision model tests * add weight init * add more tests * fix save_load test * model output, dosstrings, causal mask * config doc * add clip model tests * return dict * bigin integration test * add integration tests * fix-copies * fix init * Clip => CLIP * fix module name * docs * fix doc * output_dim => projection_dim * fix checkpoint names * remoe fast tokenizer file * fix conversion script * fix tests, quality * put causal mask on device * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * fix attribute test * style * address sylvains comments * style * fix docstrings * add qucik_gelu in activations, docstrings * clean-up attention test * fix act fun * fix config * fix torchscript tests * even batch_size * remove comment * fix ouput tu_tuple * fix save load tests * fix add tokens test * add fast tokenizer * update copyright * new processor API * fix docs * docstrings * docs * fix doc * fix doc * fix tokenizer * fix import in doc example * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * check types of config * valhalla => openai * load image using url * fix test * typo Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Suraj Patil
229
tests/test_feature_extraction_clip.py
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229
tests/test_feature_extraction_clip.py
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# coding=utf-8
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# Copyright 2021 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.file_utils import is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision
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from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
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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 CLIPFeatureExtractor
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class CLIPFeatureExtractionTester(unittest.TestCase):
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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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do_resize=True,
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size=20,
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do_center_crop=True,
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crop_size=18,
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do_normalize=True,
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image_mean=[0.48145466, 0.4578275, 0.40821073],
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image_std=[0.26862954, 0.26130258, 0.27577711],
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):
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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.do_resize = do_resize
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self.size = size
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self.do_center_crop = do_center_crop
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self.crop_size = crop_size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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def prepare_feat_extract_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_center_crop": self.do_center_crop,
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"crop_size": self.crop_size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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}
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def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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if equal_resolution:
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image_inputs = []
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for i in range(self.batch_size):
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image_inputs.append(
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np.random.randint(
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255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8
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)
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)
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else:
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image_inputs = []
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for i in range(self.batch_size):
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width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2)
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image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8))
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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if torchify:
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image_inputs = [torch.from_numpy(x) for x in image_inputs]
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return image_inputs
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@require_torch
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@require_vision
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class CLIPFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
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feature_extraction_class = CLIPFeatureExtractor if is_vision_available() else None
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def setUp(self):
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self.feature_extract_tester = CLIPFeatureExtractionTester(self)
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@property
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def feat_extract_dict(self):
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return self.feature_extract_tester.prepare_feat_extract_dict()
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def test_feat_extract_properties(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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self.assertTrue(hasattr(feature_extractor, "do_resize"))
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self.assertTrue(hasattr(feature_extractor, "size"))
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self.assertTrue(hasattr(feature_extractor, "do_center_crop"))
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self.assertTrue(hasattr(feature_extractor, "center_crop"))
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self.assertTrue(hasattr(feature_extractor, "do_normalize"))
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self.assertTrue(hasattr(feature_extractor, "image_mean"))
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self.assertTrue(hasattr(feature_extractor, "image_std"))
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def test_batch_feature(self):
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pass
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def test_call_pil(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PIL images
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image_inputs = self.feature_extract_tester.prepare_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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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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),
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)
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def test_call_numpy(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random numpy tensors
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image_inputs = self.feature_extract_tester.prepare_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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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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),
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)
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def test_call_pytorch(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PyTorch tensors
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image_inputs = self.feature_extract_tester.prepare_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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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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),
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)
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