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