# Copyright 2021 The HuggingFace Inc. 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. """Testing suite for the PyTorch BEiT model.""" import unittest from functools import cached_property import pytest from datasets import load_dataset from transformers import BeitConfig from transformers.testing_utils import ( require_torch, require_torch_multi_gpu, require_vision, slow, torch_device, ) from transformers.utils import ( is_torch_available, is_vision_available, ) from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( BeitBackbone, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, ) from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image from transformers import BeitImageProcessor class BeitModelTester: 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=4, 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, scope=None, out_indices=[1, 2, 3, 4], out_features=["stage1", "stage2", "stage3", "stage4"], attn_implementation="eager", mask_ratio=0.5, ): 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 self.scope = scope self.out_indices = out_indices self.out_features = out_features self.num_labels = num_labels # 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 self.mask_length = self.seq_length - 1 self.num_masks = int(mask_ratio * self.seq_length) self.attn_implementation = attn_implementation def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None pixel_labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels, pixel_labels def get_config(self): return 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, out_indices=self.out_indices, out_features=self.out_features, attn_implementation=self.attn_implementation, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = BeitModel(config=config) model.to(torch_device) model.eval() 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_backbone(self, config, pixel_values, labels, pixel_labels): model = BeitBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify hidden states self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) expected_height = expected_width = self.image_size // config.patch_size self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width] ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = BeitBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width] ) # verify channels self.parent.assertEqual(len(model.channels), 1) def create_and_check_for_masked_lm(self, config, pixel_values, labels, pixel_labels): model = BeitForMaskedImageModeling(config=config) model.to(torch_device) model.eval() 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, pixel_labels): config.num_labels = self.type_sequence_label_size model = BeitForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) # test greyscale images config.num_channels = 1 model = BeitForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = BeitForSemanticSegmentation(config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) result = model(pixel_values, labels=pixel_labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels, pixel_labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as BEiT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": BeitModel, "image-classification": BeitForImageClassification, "image-segmentation": BeitForSemanticSegmentation, } if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = BeitModelTester(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() @unittest.skip(reason="BEiT does not use inputs_embeds") def test_inputs_embeds(self): pass @require_torch_multi_gpu @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`") def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip(reason="BEiT does not support feedforward chunking yet") def test_feed_forward_chunking(self): pass @unittest.skip(reason="BEiT can't compile dynamic") @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): pass def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) 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_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*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) def test_for_semantic_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs) def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: # we don't test BeitForMaskedImageModeling if model_class.__name__ in [ *MODEL_MAPPING_NAMES.values(), *MODEL_FOR_BACKBONE_MAPPING_NAMES.values(), "BeitForMaskedImageModeling", ]: continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") config.use_cache = False config.return_dict = True for model_class in self.all_model_classes: # we don't test BeitForMaskedImageModeling if ( model_class.__name__ in [ *MODEL_MAPPING_NAMES.values(), *MODEL_FOR_BACKBONE_MAPPING_NAMES.values(), "BeitForMaskedImageModeling", ] or not model_class.supports_gradient_checkpointing ): continue model = model_class(config) model.gradient_checkpointing_enable() model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @slow def test_model_from_pretrained(self): model_name = "microsoft/beit-base-patch16-224" model = BeitModel.from_pretrained(model_name) self.assertIsNotNone(model) # 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_torch @require_vision class BeitModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None @slow def test_inference_masked_image_modeling_head(self): model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device) image_processor = self.default_image_processor image = prepare_img() pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device) # prepare bool_masked_pos bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device) # forward pass with torch.no_grad(): outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 196, 8192)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(torch_device) torch.testing.assert_close(logits[bool_masked_pos][:3, :3], expected_slice, rtol=1e-2, atol=1e-2) @slow def test_inference_image_classification_head_imagenet_1k(self): model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor([-1.2385, -1.0987, -1.0108]).to(torch_device) torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, 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 = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k").to( torch_device ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 21841)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor([1.6881, -0.2787, 0.5901]).to(torch_device) torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4) expected_class_idx = 2396 self.assertEqual(logits.argmax(-1).item(), expected_class_idx) @slow def test_inference_semantic_segmentation(self): model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640") model = model.to(torch_device) image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False) ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image = ds[0]["image"].convert("RGB") inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 150, 160, 160)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [ [[-4.8960, -2.3688, -3.0355], [-2.8479, -0.9836, -1.7418], [-2.9449, -1.3333, -2.1456]], [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]], [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]], ], device=torch_device, ) torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_post_processing_semantic_segmentation(self): model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640") model = model.to(torch_device) image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False) ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image = ds[0]["image"].convert("RGB") inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) outputs.logits = outputs.logits.detach().cpu() segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)]) expected_shape = torch.Size((500, 300)) self.assertEqual(segmentation[0].shape, expected_shape) segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs) expected_shape = torch.Size((160, 160)) self.assertEqual(segmentation[0].shape, expected_shape) @slow def test_inference_interpolate_pos_encoding(self): model_name = "microsoft/beit-base-patch16-224-pt22k" model = BeitModel.from_pretrained(model_name, **{"use_absolute_position_embeddings": True}).to(torch_device) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") processor = BeitImageProcessor.from_pretrained(model_name) inputs = processor(images=image, return_tensors="pt", size={"height": 480, "width": 480}) pixel_values = inputs.pixel_values.to(torch_device) # with interpolate_pos_encoding being True the model should process the higher resolution image # successfully and produce the expected output. with torch.no_grad(): outputs = model(pixel_values, interpolate_pos_encoding=True) # num_cls_tokens + (height / patch_size) * (width / patch_size) # 1 + (480 / 16) * (480 / 16) = 1 + 30 * 30 = 901 expected_shape = torch.Size((1, 901, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) @require_torch class BeitBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (BeitBackbone,) if is_torch_available() else () config_class = BeitConfig def setUp(self): self.model_tester = BeitModelTester(self)