# 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 UperNet.""" import unittest from datasets import load_dataset from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import ( require_timm, require_torch, require_torch_multi_gpu, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_vision_available from transformers.utils.import_utils import get_torch_major_and_minor_version 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 UperNetForSemanticSegmentation if is_vision_available(): from transformers import AutoImageProcessor class UperNetModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, num_stages=4, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 1, 1], is_training=True, use_labels=True, intermediate_size=37, hidden_act="gelu", type_sequence_label_size=10, initializer_range=0.02, out_features=["stage2", "stage3", "stage4"], 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_stages = num_stages self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.out_features = out_features 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.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_backbone_config(self): return ConvNextConfig( num_channels=self.num_channels, num_stages=self.num_stages, hidden_sizes=self.hidden_sizes, depths=self.depths, is_training=self.is_training, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, out_features=self.out_features, ) def get_config(self): return UperNetConfig( backbone_config=self.get_backbone_config(), backbone=None, hidden_size=64, pool_scales=[1, 2, 3, 6], use_auxiliary_head=True, auxiliary_loss_weight=0.4, auxiliary_in_channels=40, auxiliary_channels=32, auxiliary_num_convs=1, auxiliary_concat_input=False, loss_ignore_index=255, num_labels=self.num_labels, ) def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels): model = UperNetForSemanticSegmentation(config=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, self.image_size) ) 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 UperNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as UperNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (UperNetForSemanticSegmentation,) if is_torch_available() else () pipeline_model_mapping = {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {} test_resize_embeddings = False has_attentions = False test_torch_exportable = True test_torch_exportable_strictly = get_torch_major_and_minor_version() != "2.7" def setUp(self): self.model_tester = UperNetModelTester(self) self.config_tester = ConfigTester( self, config_class=UperNetConfig, has_text_modality=False, hidden_size=37, common_properties=["hidden_size"], ) def test_config(self): self.config_tester.run_common_tests() 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) @unittest.skip(reason="UperNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="UperNet does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @require_torch_multi_gpu @unittest.skip(reason="UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`") def test_multi_gpu_data_parallel_forward(self): pass 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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [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) @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__ == "UperNetForUniversalSegmentation": self.assertEqual(model.backbone.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__ == "UperNetForUniversalSegmentation": self.assertEqual(model.backbone.out_indices, [1, 2, 3]) @unittest.skip(reason="UperNet does not have tied weights") def test_tied_model_weights_key_ignore(self): pass @slow def test_model_from_pretrained(self): model_name = "openmmlab/upernet-convnext-tiny" model = UperNetForSemanticSegmentation.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of ADE20k def prepare_img(): ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") return ds[0]["image"].convert("RGB") @require_torch @require_vision @slow class UperNetModelIntegrationTest(unittest.TestCase): def test_inference_swin_backbone(self): processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny").to(torch_device) image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) expected_shape = torch.Size((1, model.config.num_labels, 512, 512)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(torch_device) torch.testing.assert_close(outputs.logits[0, 0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_inference_convnext_backbone(self): processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny").to(torch_device) image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) expected_shape = torch.Size((1, model.config.num_labels, 512, 512)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(torch_device) torch.testing.assert_close(outputs.logits[0, 0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)