# Copyright 2022 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 Mask2Former model.""" import unittest from functools import cached_property import numpy as np import pytest from tests.test_modeling_common import floats_tensor from transformers import AutoModelForImageClassification, Mask2FormerConfig, is_torch_available, is_vision_available from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4 from transformers.testing_utils import ( Expectations, require_timm, require_torch, require_torch_accelerator, require_torch_fp16, require_torch_multi_gpu, require_vision, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import Mask2FormerForUniversalSegmentation, Mask2FormerModel if is_vision_available(): from transformers import Mask2FormerImageProcessor if is_vision_available(): from PIL import Image class Mask2FormerModelTester: def __init__( self, parent, batch_size=2, is_training=True, use_auxiliary_loss=False, num_queries=10, num_channels=3, min_size=32 * 8, max_size=32 * 8, num_labels=4, hidden_dim=64, num_attention_heads=4, num_hidden_layers=2, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_auxiliary_loss = use_auxiliary_loss self.num_queries = num_queries self.num_channels = num_channels self.min_size = min_size self.max_size = max_size self.num_labels = num_labels self.hidden_dim = hidden_dim self.mask_feature_size = hidden_dim self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_hidden_layers def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to( torch_device ) pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_size], device=torch_device) mask_labels = ( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size], device=torch_device) > 0.5 ).float() class_labels = (torch.rand((self.batch_size, self.num_labels), device=torch_device) > 0.5).long() config = self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def get_config(self): config = Mask2FormerConfig( hidden_size=self.hidden_dim, num_attention_heads=self.num_attention_heads, num_hidden_layers=self.num_hidden_layers, encoder_feedforward_dim=16, dim_feedforward=32, num_queries=self.num_queries, num_labels=self.num_labels, decoder_layers=2, encoder_layers=2, feature_size=16, ) config.num_queries = self.num_queries config.num_labels = self.num_labels config.backbone_config.embed_dim = 16 config.backbone_config.depths = [1, 1, 1, 1] config.backbone_config.hidden_size = 16 config.backbone_config.num_channels = self.num_channels config.backbone_config.num_heads = [1, 1, 2, 2] config.backbone = None config.hidden_dim = self.hidden_dim config.mask_feature_size = self.hidden_dim config.feature_size = self.hidden_dim return config def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, _, _ = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask} return config, inputs_dict def check_output_hidden_state(self, output, config): encoder_hidden_states = output.encoder_hidden_states pixel_decoder_hidden_states = output.pixel_decoder_hidden_states transformer_decoder_hidden_states = output.transformer_decoder_hidden_states self.parent.assertTrue(len(encoder_hidden_states), len(config.backbone_config.depths)) self.parent.assertTrue(len(pixel_decoder_hidden_states), len(config.backbone_config.depths)) self.parent.assertTrue(len(transformer_decoder_hidden_states), config.decoder_layers) def create_and_check_mask2former_model(self, config, pixel_values, pixel_mask, output_hidden_states=False): with torch.no_grad(): model = Mask2FormerModel(config=config) model.to(torch_device) model.eval() output = model(pixel_values=pixel_values, pixel_mask=pixel_mask) output = model(pixel_values, output_hidden_states=True) self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_dim), ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None) self.parent.assertTrue(output.encoder_last_hidden_state is not None) if output_hidden_states: self.check_output_hidden_state(output, config) def create_and_check_mask2former_instance_segmentation_head_model( self, config, pixel_values, pixel_mask, mask_labels, class_labels ): model = Mask2FormerForUniversalSegmentation(config=config) model.to(torch_device) model.eval() def comm_check_on_output(result): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None) self.parent.assertTrue(result.encoder_last_hidden_state is not None) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape, (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4), ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) comm_check_on_output(result) result = model( pixel_values=pixel_values, pixel_mask=pixel_mask, mask_labels=mask_labels, class_labels=class_labels ) comm_check_on_output(result) self.parent.assertTrue(result.loss is not None) self.parent.assertEqual(result.loss.shape, torch.Size([])) @require_torch class Mask2FormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Mask2FormerModel, Mask2FormerForUniversalSegmentation) if is_torch_available() else () pipeline_model_mapping = {"image-feature-extraction": Mask2FormerModel} if is_torch_available() else {} is_encoder_decoder = False test_missing_keys = False test_torch_exportable = True def setUp(self): self.model_tester = Mask2FormerModelTester(self) self.config_tester = ConfigTester(self, config_class=Mask2FormerConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() def test_mask2former_model(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_mask2former_model(config, **inputs, output_hidden_states=False) def test_mask2former_instance_segmentation_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mask2former_instance_segmentation_head_model(*config_and_inputs) @unittest.skip(reason="Mask2Former does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Mask2Former is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="Mask2Former does not use token embeddings") def test_resize_tokens_embeddings(self): pass @require_torch_multi_gpu @unittest.skip( reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`" ) def test_multi_gpu_data_parallel_forward(self): pass @slow def test_model_from_pretrained(self): for model_name in ["facebook/mask2former-swin-small-coco-instance"]: model = Mask2FormerModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_model_with_labels(self): size = (self.model_tester.min_size,) * 2 inputs = { "pixel_values": torch.randn((2, 3, *size), device=torch_device), "mask_labels": torch.randn((2, 10, *size), device=torch_device), "class_labels": torch.zeros(2, 10, device=torch_device).long(), } config = self.model_tester.get_config() model = Mask2FormerForUniversalSegmentation(config).to(torch_device) outputs = model(**inputs) self.assertTrue(outputs.loss is not None) def test_hidden_states_output(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_mask2former_model(config, **inputs, output_hidden_states=True) def test_attention_outputs(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config).to(torch_device) outputs = model(**inputs, output_attentions=True) self.assertTrue(outputs.attentions is not None) def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") model_class = self.all_model_classes[1] config, pixel_values, pixel_mask, mask_labels, class_labels = self.model_tester.prepare_config_and_inputs() model = model_class(config) model.to(torch_device) model.train() loss = model(pixel_values, mask_labels=mask_labels, class_labels=class_labels).loss loss.backward() def test_retain_grad_hidden_states_attentions(self): model_class = self.all_model_classes[1] config, pixel_values, pixel_mask, mask_labels, class_labels = self.model_tester.prepare_config_and_inputs() config.output_hidden_states = True config.output_attentions = True model = model_class(config).to(torch_device) model.train() outputs = model(pixel_values, mask_labels=mask_labels, class_labels=class_labels) encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() attentions = outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(pixel_decoder_hidden_states.grad) self.assertIsNotNone(transformer_decoder_hidden_states.grad) self.assertIsNotNone(attentions.grad) @require_timm def test_backbone_selection(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() config.backbone_config = None config.backbone_kwargs = {"out_indices": [1, 2, 3]} config.use_pretrained_backbone = True # Load a timm backbone # We can't load transformer checkpoint with timm backbone, as we can't specify features_only and out_indices config.backbone = "resnet18" config.use_timm_backbone = True for model_class in self.all_model_classes: model = model_class(config).to(torch_device).eval() if model.__class__.__name__ == "Mask2FormerModel": self.assertEqual(model.pixel_level_module.encoder.out_indices, [1, 2, 3]) elif model.__class__.__name__ == "Mask2FormerForUniversalSegmentation": self.assertEqual(model.model.pixel_level_module.encoder.out_indices, [1, 2, 3]) # Load a HF backbone config.backbone = "microsoft/resnet-18" config.use_timm_backbone = False for model_class in self.all_model_classes: model = model_class(config).to(torch_device).eval() if model.__class__.__name__ == "Mask2FormerModel": self.assertEqual(model.pixel_level_module.encoder.out_indices, [1, 2, 3]) elif model.__class__.__name__ == "Mask2FormerForUniversalSegmentation": self.assertEqual(model.model.pixel_level_module.encoder.out_indices, [1, 2, 3]) def test_initialization_pretrained_backbone(self): backbone_name = "microsoft/resnet-18" # load Mask2Former config with a pretrained backbone config = Mask2FormerConfig( backbone=backbone_name, use_pretrained_backbone=True, ) # load pretrained backbone backbone_model = AutoModelForImageClassification.from_pretrained(backbone_name, device_map=torch_device) def params_match(params1, params2): return all((p1 == p2).all() for p1, p2 in zip(params1, params2)) for model_class in self.all_model_classes: model = model_class(config).to(torch_device).eval() if model.__class__.__name__ == "Mask2FormerModel": self.assertTrue( params_match( backbone_model.base_model.encoder.parameters(), model.pixel_level_module.encoder.encoder.parameters(), ) ) elif model.__class__.__name__ == "Mask2FormerForUniversalSegmentation": self.assertTrue( params_match( backbone_model.base_model.encoder.parameters(), model.model.pixel_level_module.encoder.encoder.parameters(), ) ) TOLERANCE = 2e-4 # 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 @slow class Mask2FormerModelIntegrationTest(unittest.TestCase): @cached_property def model_checkpoints(self): return "facebook/mask2former-swin-small-coco-instance" @cached_property def default_image_processor(self): return Mask2FormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None def test_inference_no_head(self): model = Mask2FormerModel.from_pretrained(self.model_checkpoints).to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) inputs_shape = inputs["pixel_values"].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0) # check size self.assertEqual(inputs_shape, (1, 3, 384, 384)) with torch.no_grad(): outputs = model(**inputs) expected_slice_hidden_state = torch.tensor( [ [-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197], ] ).to(torch_device) torch.testing.assert_close( outputs.encoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE, rtol=TOLERANCE, ) expectations = Expectations( { (None, None): [ [0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951], ], ("cuda", 8): [ [0.8974, 1.1848, 1.1777], [1.1933, 1.5041, 1.5128], [1.1154, 1.4487, 1.4950], ], } ) expected_slice_hidden_state = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE,rtol=TOLERANCE) # fmt: skip expectations = Expectations( { (None, None): [ [2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999], ], ("cuda", 8): [ [2.1153, 1.7004, -0.8604], [1.5807, 1.8007, -0.9354], [1.6040, 1.7498, -0.6001], ], } ) expected_slice_hidden_state = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.transformer_decoder_last_hidden_state[0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE, rtol=TOLERANCE) # fmt: skip def test_inference_universal_segmentation_head(self): model = Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval() image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) inputs_shape = inputs["pixel_values"].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0) # check size self.assertEqual(inputs_shape, (1, 3, 384, 384)) with torch.no_grad(): outputs = model(**inputs) # masks_queries_logits masks_queries_logits = outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape, (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) expectations = Expectations( { (None, None): [ [-8.7839, -9.0056, -8.8121], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3427, -6.4675], ], ("cuda", 8): [ [-8.7839, -9.0056, -8.8122], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3428, -6.4675], ], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(masks_queries_logits[0, 0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) # class_queries_logits class_queries_logits = outputs.class_queries_logits self.assertEqual(class_queries_logits.shape, (1, model.config.num_queries, model.config.num_labels + 1)) expectations = Expectations( { (None, None): [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ], ("cuda", 8): [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close( outputs.class_queries_logits[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE ) @require_torch_accelerator @require_torch_fp16 def test_inference_fp16(self): model = ( Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints) .to(torch_device, dtype=torch.float16) .eval() ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device, dtype=torch.float16) with torch.no_grad(): _ = model(**inputs) def test_with_segmentation_maps_and_loss(self): model = Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval() image_processor = self.default_image_processor inputs = image_processor( [np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))], segmentation_maps=[np.zeros((384, 384)).astype(np.float32), np.zeros((384, 384)).astype(np.float32)], return_tensors="pt", ) inputs["pixel_values"] = inputs["pixel_values"].to(torch_device) inputs["mask_labels"] = [el.to(torch_device) for el in inputs["mask_labels"]] inputs["class_labels"] = [el.to(torch_device) for el in inputs["class_labels"]] with torch.no_grad(): outputs = model(**inputs) self.assertTrue(outputs.loss is not None) @pytest.mark.torch_export_test def test_export(self): if not is_torch_greater_or_equal_than_2_4: self.skipTest(reason="This test requires torch >= 2.4 to run.") model = Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval() image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) exported_program = torch.export.export( model, args=(inputs["pixel_values"], inputs["pixel_mask"]), strict=True, ) with torch.no_grad(): eager_outputs = model(**inputs) exported_outputs = exported_program.module().forward(inputs["pixel_values"], inputs["pixel_mask"]) self.assertEqual(eager_outputs.masks_queries_logits.shape, exported_outputs.masks_queries_logits.shape) torch.testing.assert_close( eager_outputs.masks_queries_logits, exported_outputs.masks_queries_logits, rtol=TOLERANCE, atol=TOLERANCE ) self.assertEqual(eager_outputs.class_queries_logits.shape, exported_outputs.class_queries_logits.shape) torch.testing.assert_close( eager_outputs.class_queries_logits, exported_outputs.class_queries_logits, rtol=TOLERANCE, atol=TOLERANCE )