# coding=utf-8 # 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 inspect import tempfile import unittest import numpy as np from datasets import load_dataset from packaging import version from parameterized import parameterized from transformers import BeitConfig from transformers.testing_utils import ( require_torch, require_torch_multi_gpu, require_torch_sdpa, require_vision, slow, torch_device, ) from transformers.utils import ( cached_property, is_torch_available, is_torch_bf16_available_on_device, is_torch_fp16_available_on_device, is_vision_available, ) from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, sdpa_kernel 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(): import PIL 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.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_pruning = False test_resize_embeddings = False test_head_masking = False 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 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 architecure 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 architecure 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 def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): # we skip lambda parameters as these require special initial values # determined by config.layer_scale_init_value if "lambda" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) @slow def test_model_from_pretrained(self): model_name = "microsoft/beit-base-patch16-224" model = BeitModel.from_pretrained(model_name) self.assertIsNotNone(model) @parameterized.expand([("float16",), ("bfloat16",), ("float32",)]) @require_torch_sdpa def test_eager_matches_sdpa_inference(self, torch_dtype: str): # The common test modifies the num_hidden_layers to be 1. However, for Beit we want to # avoid that because the num_hidden_layers is generally assumed to be 4. Also, the code # related to attention masks in the original common tests is not required as the Beit # model does not handle attention masks. Furthermore, some extra code like modifying # the norm layers eps values for specialized configs and checking for the 'noise' # has been omitted to simply the test. if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") if not self.all_model_classes[0]._supports_sdpa: self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA") if torch_dtype == "float16" and not is_torch_fp16_available_on_device(torch_device): self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)") if torch_dtype == "bfloat16" and not is_torch_bf16_available_on_device(torch_device): self.skipTest( f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)" ) # Not sure whether it's fine to put torch.XXX in a decorator if torch is not available so hacking it here instead. if torch_dtype == "float16": torch_dtype = torch.float16 elif torch_dtype == "bfloat16": torch_dtype = torch.bfloat16 elif torch_dtype == "float32": torch_dtype = torch.float32 atols = { ("cpu", False, torch.float32): 1e-6, ("cpu", False, torch.float16): 5e-3, ("cpu", False, torch.bfloat16): 1e-2, ("cpu", True, torch.float32): 1e-6, ("cpu", True, torch.float16): 5e-3, ("cpu", True, torch.bfloat16): 1e-2, ("cuda", False, torch.float32): 1e-6, ("cuda", False, torch.bfloat16): 1e-2, ("cuda", False, torch.float16): 5e-3, ("cuda", True, torch.float32): 1e-6, ("cuda", True, torch.bfloat16): 1e-2, ("cuda", True, torch.float16): 5e-3, } rtols = { ("cpu", False, torch.float32): 1e-4, ("cpu", False, torch.float16): 5e-3, ("cpu", False, torch.bfloat16): 1e-2, ("cpu", True, torch.float32): 1e-4, ("cpu", True, torch.float16): 5e-3, ("cpu", True, torch.bfloat16): 1e-2, ("cuda", False, torch.float32): 1e-4, ("cuda", False, torch.bfloat16): 1e-2, ("cuda", False, torch.float16): 5e-3, ("cuda", True, torch.float32): 1e-4, ("cuda", True, torch.bfloat16): 3e-2, ("cuda", True, torch.float16): 5e-3, } def get_mean_reldiff(failcase, x, ref, atol, rtol): return f"{failcase}: mean relative difference: {((x - ref).abs() / (ref.abs() + 1e-12)).mean():.3e}, torch atol = {atol}, torch rtol = {rtol}" for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.rms_norm_eps = 1.0 config.layer_norm_eps = 1.0 config.norm_eps = 1.0 config.norm_epsilon = 1.0 config.layer_norm_epsilon = 1.0 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_sdpa = model_class.from_pretrained(tmpdirname, torch_dtype=torch_dtype, use_mask_token=True) model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype) model_eager = model_class.from_pretrained( tmpdirname, torch_dtype=torch_dtype, attn_implementation="eager", use_mask_token=True, ) model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype) # Another way to make sure norm layers have desired epsilon. (Some models don't set it from its config.) for x in model_eager.modules(): if isinstance(x, (nn.LayerNorm, nn.GroupNorm)): x.eps = 1.0 for x in model_sdpa.modules(): if isinstance(x, (nn.LayerNorm, nn.GroupNorm)): x.eps = 1.0 # We use these for loops instead of parameterized.expand just for the interest of avoiding loading/saving 16 times the model, # but it would be nicer to have an efficient way to use parameterized.expand fail_cases = [] for padding_side in ["left", "right"]: for use_mask in [False, True]: for output_attentions in [True, False]: can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters if not (self.has_attentions and can_output_attn) and output_attentions: continue # TODO: if we can also check with `batch_size=1` without being flaky? for batch_size in [7]: dummy_input = inputs_dict[model.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16, torch.float16]: dummy_input = dummy_input.to(torch_dtype) dummy_input = dummy_input[:batch_size] for enable_kernels in [False, True]: failcase = f"padding_side={padding_side}, use_mask={use_mask}, enable_kernels={enable_kernels}" processed_inputs = { model.main_input_name: dummy_input, "output_hidden_states": True, } if ( self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters ): processed_inputs["output_attentions"] = output_attentions if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters: dummy_mask = torch.ones((self.model_tester.num_masks,)) mask_length = self.model_tester.seq_length - 1 - dummy_mask.size(0) dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)]) dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool() processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device) with torch.no_grad(): with sdpa_kernel( enable_flash=enable_kernels, enable_math=True, enable_mem_efficient=enable_kernels, ): prepared_inputs = self._prepare_for_class(processed_inputs, model_class) outputs_eager = model_eager(**prepared_inputs) outputs_sdpa = model_sdpa(**prepared_inputs) logits_eager = outputs_eager.hidden_states[-1] logits_sdpa = outputs_sdpa.hidden_states[-1] if torch_device in ["cpu", "cuda"]: atol = atols[torch_device, enable_kernels, torch_dtype] rtol = rtols[torch_device, enable_kernels, torch_dtype] elif torch_device == "xpu": # As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH # which is implemented on PyTorch level using aten operators and is # device agnostic with respect to implementation of each aten operator. atol = atols["cuda", False, torch_dtype] rtol = rtols["cuda", False, torch_dtype] else: atol = 1e-7 rtol = 1e-4 # Masked tokens output slightly deviates - we don't mind that. if use_mask: _logits_sdpa = torch.zeros_like(input=logits_sdpa) _logits_eager = torch.zeros_like(input=logits_eager) _logits_sdpa[:-1] = logits_sdpa[:-1] _logits_eager[:-1] = logits_eager[:-1] if padding_side == "left": _logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:] _logits_eager[-1:, 2:] = logits_eager[-1:, 2:] elif padding_side == "right": _logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2] _logits_eager[-1:, 2:] = logits_eager[-1:, :-2] logits_sdpa = _logits_sdpa logits_eager = _logits_eager results = [ torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol) for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager) ] # If 80% batch elements have matched results, it's fine if np.mean(results) < 0.8: fail_cases.append( get_mean_reldiff(failcase, logits_sdpa, logits_eager, atol, rtol) ) self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases)) # 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) self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3], expected_slice, 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) self.assertTrue(torch.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 = 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) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, 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", trust_remote_code=True) image = Image.open(ds[0]["file"]) 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) is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0") if is_pillow_less_than_9: expected_slice = torch.tensor( [ [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]], [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]], [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]], ], device=torch_device, ) else: expected_slice = torch.tensor( [ [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -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, ) self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, 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", trust_remote_code=True) image = Image.open(ds[0]["file"]) 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 False an exception should be raised with higher resolution # images than what the model supports. self.assertFalse(processor.do_center_crop) with torch.no_grad(): with self.assertRaises(ValueError, msg="doesn't match model"): model(pixel_values, interpolate_pos_encoding=False) # 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) expected_shape = torch.Size((1, 1801, 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)