mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
172 lines
7.5 KiB
Python
172 lines
7.5 KiB
Python
# coding=utf-8
|
|
# 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
|
|
|
|
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)
|
|
expected_stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(config.depths) + 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_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)
|
|
|
|
config.out_features = None
|
|
config.out_indices = None
|
|
model = model_class(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))
|
|
|
|
# Check output of last stage is taken if out_features=None, out_indices=None
|
|
modified_config = copy.deepcopy(config)
|
|
modified_config.out_features = None
|
|
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)
|
|
|
|
# Check backbone can be initialized with fresh weights
|
|
modified_config = copy.deepcopy(config)
|
|
modified_config.use_pretrained_backbone = False
|
|
model = model_class(modified_config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(**inputs_dict)
|
|
|
|
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, "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"))
|
|
|
|
# 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)
|