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https://github.com/huggingface/transformers.git
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a23819ed6a
* Clean up deprecation warnings Notes: Changed some strings in tests to raw strings, which will change the literal content of the strings as they are fed into whatever machine handles them. Test cases for past in the past/past_key_values switch changed/removed due to warning of impending removal * Add PILImageResampling abstraction for PIL.Image.Resampling
297 lines
11 KiB
Python
297 lines
11 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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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 inspect
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import unittest
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import numpy as np
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from transformers import BeitConfig
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from transformers.testing_utils import require_flax, require_vision, slow
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from transformers.utils import cached_property, is_flax_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
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if is_flax_available():
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import jax
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from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
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if is_vision_available():
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from PIL import Image
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from transformers import BeitFeatureExtractor
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class FlaxBeitModelTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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vocab_size=100,
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batch_size=13,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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use_labels=True,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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type_sequence_label_size=10,
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initializer_range=0.02,
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num_labels=3,
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):
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self.parent = parent
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self.vocab_size = vocab_size
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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config = BeitConfig(
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vocab_size=self.vocab_size,
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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return config, pixel_values, labels
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def create_and_check_model(self, config, pixel_values, labels):
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model = FlaxBeitModel(config=config)
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_masked_lm(self, config, pixel_values, labels):
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model = FlaxBeitForMaskedImageModeling(config=config)
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result = model(pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size))
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def create_and_check_for_image_classification(self, config, pixel_values, labels):
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config.num_labels = self.type_sequence_label_size
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model = FlaxBeitForImageClassification(config=config)
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result = model(pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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# test greyscale images
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config.num_channels = 1
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model = FlaxBeitForImageClassification(config)
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pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
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result = model(pixel_values)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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pixel_values,
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labels,
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) = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_flax
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class FlaxBeitModelTest(FlaxModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
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)
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def setUp(self) -> None:
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self.model_tester = FlaxBeitModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BeitConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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# We need to override this test because Beit's forward signature is different than text models.
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.__call__)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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# We need to override this test because Beit expects pixel_values instead of input_ids
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def test_jit_compilation(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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@jax.jit
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def model_jitted(pixel_values, **kwargs):
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return model(pixel_values=pixel_values, **kwargs)
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with self.subTest("JIT Enabled"):
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jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs, outputs):
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self.assertEqual(jitted_output.shape, output.shape)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_image_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_class_name in self.all_model_classes:
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model = model_class_name.from_pretrained("microsoft/beit-base-patch16-224")
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outputs = model(np.ones((1, 3, 224, 224)))
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self.assertIsNotNone(outputs)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_vision
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@require_flax
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class FlaxBeitModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_feature_extractor(self):
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return (
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BeitFeatureExtractor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
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)
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@slow
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def test_inference_masked_image_modeling_head(self):
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model = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
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feature_extractor = self.default_feature_extractor
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image = prepare_img()
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pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values
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# prepare bool_masked_pos
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bool_masked_pos = np.ones((1, 196), dtype=bool)
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# forward pass
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outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
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logits = outputs.logits
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# verify the logits
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expected_shape = (1, 196, 8192)
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = np.array(
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[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]
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)
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self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2))
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@slow
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def test_inference_image_classification_head_imagenet_1k(self):
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model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224")
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feature_extractor = self.default_feature_extractor
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image = prepare_img()
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inputs = feature_extractor(images=image, return_tensors="np")
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# forward pass
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outputs = model(**inputs)
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logits = outputs.logits
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# verify the logits
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expected_shape = (1, 1000)
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = np.array([-1.2385, -1.0987, -1.0108])
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self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4))
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expected_class_idx = 281
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self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
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@slow
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def test_inference_image_classification_head_imagenet_22k(self):
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model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k")
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feature_extractor = self.default_feature_extractor
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image = prepare_img()
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inputs = feature_extractor(images=image, return_tensors="np")
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# forward pass
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outputs = model(**inputs)
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logits = outputs.logits
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# verify the logits
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expected_shape = (1, 21841)
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = np.array([1.6881, -0.2787, 0.5901])
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self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4))
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expected_class_idx = 2396
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self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
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