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tests/test_backbone_common.py
218 строк
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Raushan Turganbay
🚨 Delete duplicate code in backbone utils (#43323)
04 фев 2026, 13:37
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04 фев 2026, 13:37
0c4fe5c
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# Copyright 2023 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. import copy import inspect import tempfile from transformers.backbone_utils import BackboneType from transformers.testing_utils import require_torch, torch_device @require_torch class BackboneTesterMixin: all_model_classes = () has_attentions = True def test_config(self): config_class = self.config_class # test default config config = config_class() self.assertIsNotNone(config) num_stages = len(config.depths) if hasattr(config, "depths") else config.num_hidden_layers expected_stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, num_stages + 1)] self.assertEqual(config.stage_names, expected_stage_names) self.assertTrue(set(config.out_features).issubset(set(config.stage_names))) # Test out_features and out_indices are correctly set # out_features and out_indices both None config = config_class(out_features=None, out_indices=None) self.assertEqual(config.out_features, [config.stage_names[-1]]) self.assertEqual(config.out_indices, [len(config.stage_names) - 1]) # out_features and out_indices both set config = config_class(out_features=["stem", "stage1"], out_indices=[0, 1]) self.assertEqual(config.out_features, ["stem", "stage1"]) self.assertEqual(config.out_indices, [0, 1]) # Only out_features set config = config_class(out_features=["stage1", "stage3"]) self.assertEqual(config.out_features, ["stage1", "stage3"]) self.assertEqual(config.out_indices, [1, 3]) # Only out_indices set config = config_class(out_indices=[0, 2]) self.assertEqual(config.out_features, [config.stage_names[0], config.stage_names[2]]) self.assertEqual(config.out_indices, [0, 2]) # Error raised when out_indices do not correspond to out_features with self.assertRaises(ValueError): config = config_class(out_features=["stage1", "stage2"], out_indices=[0, 2]) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_config_save_pretrained(self): config_class = self.config_class config_first = config_class(out_indices=[0, 1, 2, 3]) with tempfile.TemporaryDirectory() as tmpdirname: config_first.save_pretrained(tmpdirname) config_second = self.config_class.from_pretrained(tmpdirname) self.assertEqual(config_second.to_dict(), config_first.to_dict()) def test_channels(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertEqual(len(model.channels), len(config.out_features)) num_features = model.num_features out_indices = [config.stage_names.index(feat) for feat in config.out_features] out_channels = [num_features[idx] for idx in out_indices] self.assertListEqual(model.channels, out_channels) new_config = copy.deepcopy(config) new_config.out_features = None model = model_class(new_config) self.assertEqual(len(model.channels), 1) self.assertListEqual(model.channels, [num_features[-1]]) new_config = copy.deepcopy(config) new_config.out_indices = None model = model_class(new_config) self.assertEqual(len(model.channels), 1) self.assertListEqual(model.channels, [num_features[-1]]) def test_create_from_modified_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() result = model(**inputs_dict) self.assertEqual(len(result.feature_maps), len(config.out_features)) self.assertEqual(len(model.channels), len(config.out_features)) self.assertEqual(len(result.feature_maps), len(config.out_indices)) self.assertEqual(len(model.channels), len(config.out_indices)) # Check output of last stage is taken if out_features=None, out_indices=None modified_config = copy.deepcopy(config) modified_config.out_features = None model = model_class(modified_config) model.to(torch_device) model.eval() result = model(**inputs_dict) self.assertEqual(len(result.feature_maps), 1) self.assertEqual(len(model.channels), 1) modified_config = copy.deepcopy(config) modified_config.out_indices = None model = model_class(modified_config) model.to(torch_device) model.eval() result = model(**inputs_dict) self.assertEqual(len(result.feature_maps), 1) self.assertEqual(len(model.channels), 1) def test_backbone_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for backbone_class in self.all_model_classes: backbone = backbone_class(config) self.assertTrue(hasattr(backbone, "backbone_type")) self.assertTrue(hasattr(backbone, "stage_names")) self.assertTrue(hasattr(backbone, "num_features")) self.assertTrue(hasattr(backbone, "out_indices")) self.assertTrue(hasattr(backbone, "out_features")) self.assertTrue(hasattr(backbone, "out_feature_channels")) self.assertTrue(hasattr(backbone, "channels")) self.assertIsInstance(backbone.backbone_type, BackboneType) # Verify num_features has been initialized in the backbone init self.assertIsNotNone(backbone.num_features) self.assertTrue(len(backbone.channels) == len(backbone.out_indices)) self.assertTrue(len(backbone.stage_names) == len(backbone.num_features)) self.assertTrue(len(backbone.channels) <= len(backbone.num_features)) self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names)) def test_backbone_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() batch_size = inputs_dict["pixel_values"].shape[0] for backbone_class in self.all_model_classes: backbone = backbone_class(config) backbone.to(torch_device) backbone.eval() outputs = backbone(**inputs_dict) # Test default outputs and verify feature maps self.assertIsInstance(outputs.feature_maps, tuple) self.assertTrue(len(outputs.feature_maps) == len(backbone.channels)) for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels): self.assertTrue(feature_map.shape[:2], (batch_size, n_channels)) self.assertIsNone(outputs.hidden_states) self.assertIsNone(outputs.attentions) # Test output_hidden_states=True outputs = backbone(**inputs_dict, output_hidden_states=True) self.assertIsNotNone(outputs.hidden_states) self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names)) for hidden_state, n_channels in zip(outputs.hidden_states, backbone.channels): self.assertTrue(hidden_state.shape[:2], (batch_size, n_channels)) # Test output_attentions=True if self.has_attentions: outputs = backbone(**inputs_dict, output_attentions=True) self.assertIsNotNone(outputs.attentions) def test_backbone_stage_selection(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() batch_size = inputs_dict["pixel_values"].shape[0] for backbone_class in self.all_model_classes: config.out_indices = [-2, -1] backbone = backbone_class(config) backbone.to(torch_device) backbone.eval() outputs = backbone(**inputs_dict) # Test number of feature maps returned self.assertIsInstance(outputs.feature_maps, tuple) self.assertTrue(len(outputs.feature_maps) == 2) # Order of channels returned is same as order of channels iterating over stage names channels_from_stage_names = [ backbone.out_feature_channels[name] for name in backbone.stage_names if name in backbone.out_features ] self.assertEqual(backbone.channels, channels_from_stage_names) for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels): self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))