371 lines
15 KiB
Python
371 lines
15 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.file_utils import cached_property, is_flax_available, is_vision_available
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from transformers.testing_utils import require_flax, require_vision, slow
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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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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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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, 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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# expected sequence length = num_patches
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, 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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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 in Beit, the seq_len equals the number of patches + 1
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# we compute that here
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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num_patches = (config.image_size // config.patch_size) ** 2
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seq_length = num_patches + 1
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_length, seq_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_length, seq_length],
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)
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# We neeed 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 neeed 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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# We need to override this test because in Beit, the seq_len equals the number of patches + 1
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# we compute that here
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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num_patches = (config.image_size // config.patch_size) ** 2
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seq_length = num_patches + 1 # we add 1 for the [CLS] token
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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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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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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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=np.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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