# 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 SegFormer model.""" import unittest from transformers import SegformerConfig, is_torch_available, is_vision_available from transformers.testing_utils import Expectations, require_torch, slow, torch_device 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 transformers import ( SegformerForImageClassification, SegformerForSemanticSegmentation, SegformerModel, ) from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image from transformers import SegformerImageProcessor class SegformerConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_attention_heads")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class SegformerModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[1, 1, 1, 1], sr_ratios=[8, 4, 2, 1], hidden_sizes=[8, 8, 16, 16], downsampling_rates=[1, 4, 8, 16], num_attention_heads=[1, 1, 2, 2], is_training=True, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.num_attention_heads = num_attention_heads self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope 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.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return SegformerConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = SegformerModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def create_and_check_for_image_segmentation(self, config, pixel_values, labels): config.num_labels = self.num_labels model = SegformerForSemanticSegmentation(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 // 4, self.image_size // 4) ) result = model(pixel_values, labels=labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) self.parent.assertGreater(result.loss, 0.0) def create_and_check_for_binary_image_segmentation(self, config, pixel_values, labels): config.num_labels = 1 model = SegformerForSemanticSegmentation(config=config) model.to(torch_device) model.eval() labels = torch.randint(0, 1, (self.batch_size, self.image_size, self.image_size)).to(torch_device) result = model(pixel_values, labels=labels) self.parent.assertGreater(result.loss, 0.0) 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_torch class SegformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( SegformerModel, SegformerForSemanticSegmentation, SegformerForImageClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": SegformerModel, "image-classification": SegformerForImageClassification, "image-segmentation": SegformerForSemanticSegmentation, } if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = SegformerModelTester(self) self.config_tester = SegformerConfigTester(self, config_class=SegformerConfig) def test_config(self): self.config_tester.run_common_tests() 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_binary_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_binary_image_segmentation(*config_and_inputs) def test_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) def test_batching_equivalence(self, atol=2e-4, rtol=2e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) @unittest.skip(reason="SegFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="SegFormer does not have get_input_embeddings method and get_output_embeddings methods") def test_model_get_set_embeddings(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = sum(self.model_tester.depths) self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) # verify the last attentions (last block, last layer) expected_seq_len = (self.model_tester.image_size // 32) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2 self.assertListEqual( list(attentions[-1].shape[-3:]), [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_encoder_blocks self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.hidden_sizes[0], self.model_tester.image_size // 4, self.model_tester.image_size // 4, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) 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: if model_class.__name__ in MODEL_MAPPING_NAMES.values(): 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() @slow def test_model_from_pretrained(self): model_name = "nvidia/segformer-b0-finetuned-ade-512-512" model = SegformerModel.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 class SegformerModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation_ade(self): # only resize + normalize image_processor = SegformerImageProcessor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to( torch_device ) image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="pt") pixel_values = encoded_inputs.pixel_values.to(torch_device) with torch.no_grad(): outputs = model(pixel_values) expected_shape = torch.Size((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expectations = Expectations( { (None, None): [ [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]], [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]], [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]], ], ("cuda", 8): [ [[-4.6310, -5.5232, -6.2361], [-5.1918, -6.1445, -6.5996], [-5.4427, -6.2792, -6.7580]], [[-12.1397, -13.3124, -13.9551], [-12.8736, -13.9347, -14.3569], [-12.9440, -13.8222, -14.2514]], [[-12.5135, -13.4682, -14.4913], [-12.8670, -14.4339, -14.7766], [-13.2519, -14.5800, -15.0685]], ], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3, :3], expected_slice, rtol=2e-4, atol=2e-4) @slow def test_inference_image_segmentation_city(self): # only resize + normalize image_processor = SegformerImageProcessor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = SegformerForSemanticSegmentation.from_pretrained( "nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ).to(torch_device) image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="pt") pixel_values = encoded_inputs.pixel_values.to(torch_device) with torch.no_grad(): outputs = model(pixel_values) expected_shape = torch.Size((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([]).to(torch_device) expectations = Expectations( { (None, None): [ [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]], [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]], [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]], ], ("cuda", 8): [ [[-13.5728, -13.9089, -12.6492], [-14.3478, -15.3656, -14.2309], [-14.7512, -16.0394, -15.6065]], [[-17.1642, -15.8704, -12.9641], [-17.2572, -17.3701, -14.8214], [-16.6043, -16.8761, -16.7425]], [[-3.6444, -3.0189, -1.4195], [-3.0787, -3.1953, -1.9993], [-1.8755, -1.9219, -1.7002]], ], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3, :3], expected_slice, rtol=1e-1, atol=1e-1) @slow def test_post_processing_semantic_segmentation(self): # only resize + normalize image_processor = SegformerImageProcessor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to( torch_device ) image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="pt") pixel_values = encoded_inputs.pixel_values.to(torch_device) with torch.no_grad(): outputs = model(pixel_values) 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((128, 128)) self.assertEqual(segmentation[0].shape, expected_shape)