228 lines
10 KiB
Python
228 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import inspect
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import tempfile
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from transformers.testing_utils import require_torch, torch_device
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from transformers.utils.backbone_utils import BackboneType
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@require_torch
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class BackboneTesterMixin:
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all_model_classes = ()
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has_attentions = True
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def test_config(self):
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config_class = self.config_class
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# test default config
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config = config_class()
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self.assertIsNotNone(config)
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num_stages = len(config.depths) if hasattr(config, "depths") else config.num_hidden_layers
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expected_stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, num_stages + 1)]
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self.assertEqual(config.stage_names, expected_stage_names)
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self.assertTrue(set(config.out_features).issubset(set(config.stage_names)))
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# Test out_features and out_indices are correctly set
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# out_features and out_indices both None
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config = config_class(out_features=None, out_indices=None)
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self.assertEqual(config.out_features, [config.stage_names[-1]])
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self.assertEqual(config.out_indices, [len(config.stage_names) - 1])
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# out_features and out_indices both set
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config = config_class(out_features=["stem", "stage1"], out_indices=[0, 1])
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self.assertEqual(config.out_features, ["stem", "stage1"])
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self.assertEqual(config.out_indices, [0, 1])
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# Only out_features set
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config = config_class(out_features=["stage1", "stage3"])
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self.assertEqual(config.out_features, ["stage1", "stage3"])
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self.assertEqual(config.out_indices, [1, 3])
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# Only out_indices set
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config = config_class(out_indices=[0, 2])
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self.assertEqual(config.out_features, [config.stage_names[0], config.stage_names[2]])
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self.assertEqual(config.out_indices, [0, 2])
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# Error raised when out_indices do not correspond to out_features
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with self.assertRaises(ValueError):
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config = config_class(out_features=["stage1", "stage2"], out_indices=[0, 2])
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_config_save_pretrained(self):
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config_class = self.config_class
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config_first = config_class(out_indices=[0, 1, 2, 3])
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with tempfile.TemporaryDirectory() as tmpdirname:
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config_first.save_pretrained(tmpdirname)
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config_second = self.config_class.from_pretrained(tmpdirname)
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self.assertEqual(config_second.to_dict(), config_first.to_dict())
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def test_channels(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertEqual(len(model.channels), len(config.out_features))
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num_features = model.num_features
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out_indices = [config.stage_names.index(feat) for feat in config.out_features]
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out_channels = [num_features[idx] for idx in out_indices]
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self.assertListEqual(model.channels, out_channels)
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new_config = copy.deepcopy(config)
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new_config.out_features = None
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model = model_class(new_config)
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self.assertEqual(len(model.channels), 1)
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self.assertListEqual(model.channels, [num_features[-1]])
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new_config = copy.deepcopy(config)
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new_config.out_indices = None
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model = model_class(new_config)
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self.assertEqual(len(model.channels), 1)
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self.assertListEqual(model.channels, [num_features[-1]])
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def test_create_from_modified_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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result = model(**inputs_dict)
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self.assertEqual(len(result.feature_maps), len(config.out_features))
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self.assertEqual(len(model.channels), len(config.out_features))
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self.assertEqual(len(result.feature_maps), len(config.out_indices))
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self.assertEqual(len(model.channels), len(config.out_indices))
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# Check output of last stage is taken if out_features=None, out_indices=None
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modified_config = copy.deepcopy(config)
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modified_config.out_features = None
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model = model_class(modified_config)
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model.to(torch_device)
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model.eval()
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result = model(**inputs_dict)
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self.assertEqual(len(result.feature_maps), 1)
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self.assertEqual(len(model.channels), 1)
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modified_config = copy.deepcopy(config)
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modified_config.out_indices = None
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model = model_class(modified_config)
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model.to(torch_device)
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model.eval()
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result = model(**inputs_dict)
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self.assertEqual(len(result.feature_maps), 1)
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self.assertEqual(len(model.channels), 1)
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# Check backbone can be initialized with fresh weights
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modified_config = copy.deepcopy(config)
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modified_config.use_pretrained_backbone = False
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model = model_class(modified_config)
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model.to(torch_device)
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model.eval()
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result = model(**inputs_dict)
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def test_backbone_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for backbone_class in self.all_model_classes:
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backbone = backbone_class(config)
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self.assertTrue(hasattr(backbone, "backbone_type"))
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self.assertTrue(hasattr(backbone, "stage_names"))
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self.assertTrue(hasattr(backbone, "num_features"))
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self.assertTrue(hasattr(backbone, "out_indices"))
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self.assertTrue(hasattr(backbone, "out_features"))
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self.assertTrue(hasattr(backbone, "out_feature_channels"))
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self.assertTrue(hasattr(backbone, "channels"))
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self.assertIsInstance(backbone.backbone_type, BackboneType)
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# Verify num_features has been initialized in the backbone init
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self.assertIsNotNone(backbone.num_features)
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self.assertTrue(len(backbone.channels) == len(backbone.out_indices))
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self.assertTrue(len(backbone.stage_names) == len(backbone.num_features))
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self.assertTrue(len(backbone.channels) <= len(backbone.num_features))
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self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names))
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def test_backbone_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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batch_size = inputs_dict["pixel_values"].shape[0]
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for backbone_class in self.all_model_classes:
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backbone = backbone_class(config)
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backbone.to(torch_device)
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backbone.eval()
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outputs = backbone(**inputs_dict)
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# Test default outputs and verify feature maps
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self.assertIsInstance(outputs.feature_maps, tuple)
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self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
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for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
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self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
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self.assertIsNone(outputs.hidden_states)
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self.assertIsNone(outputs.attentions)
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# Test output_hidden_states=True
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outputs = backbone(**inputs_dict, output_hidden_states=True)
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self.assertIsNotNone(outputs.hidden_states)
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self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names))
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for hidden_state, n_channels in zip(outputs.hidden_states, backbone.channels):
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self.assertTrue(hidden_state.shape[:2], (batch_size, n_channels))
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# Test output_attentions=True
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if self.has_attentions:
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outputs = backbone(**inputs_dict, output_attentions=True)
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self.assertIsNotNone(outputs.attentions)
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def test_backbone_stage_selection(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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batch_size = inputs_dict["pixel_values"].shape[0]
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for backbone_class in self.all_model_classes:
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config.out_indices = [-2, -1]
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backbone = backbone_class(config)
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backbone.to(torch_device)
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backbone.eval()
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outputs = backbone(**inputs_dict)
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# Test number of feature maps returned
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self.assertIsInstance(outputs.feature_maps, tuple)
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self.assertTrue(len(outputs.feature_maps) == 2)
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# Order of channels returned is same as order of channels iterating over stage names
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channels_from_stage_names = [
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backbone.out_feature_channels[name] for name in backbone.stage_names if name in backbone.out_features
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]
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self.assertEqual(backbone.channels, channels_from_stage_names)
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for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
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self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
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