transformers
256 строк · 9.3 Кб
1# coding=utf-8
2# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
3#
4# Licensed under the Apache License, Version 2.0 (the "License");
5# you may not use this file except in compliance with the License.
6# You may obtain a copy of the License at
7#
8# http://www.apache.org/licenses/LICENSE-2.0
9#
10# Unless required by applicable law or agreed to in writing, software
11# distributed under the License is distributed on an "AS IS" BASIS,
12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13# See the License for the specific language governing permissions and
14# limitations under the License.
15""" Testing suite for the Tensorflow ResNet model. """
16
17
18from __future__ import annotations
19
20import inspect
21import unittest
22
23import numpy as np
24
25from transformers import ResNetConfig
26from transformers.testing_utils import require_tf, require_vision, slow
27from transformers.utils import cached_property, is_tf_available, is_vision_available
28
29from ...test_configuration_common import ConfigTester
30from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
31from ...test_pipeline_mixin import PipelineTesterMixin
32
33
34if is_tf_available():
35import tensorflow as tf
36
37from transformers import TFResNetForImageClassification, TFResNetModel
38from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
39
40
41if is_vision_available():
42from PIL import Image
43
44from transformers import AutoImageProcessor
45
46
47class TFResNetModelTester:
48def __init__(
49self,
50parent,
51batch_size=3,
52image_size=32,
53num_channels=3,
54embeddings_size=10,
55hidden_sizes=[10, 20, 30, 40],
56depths=[1, 1, 2, 1],
57is_training=True,
58use_labels=True,
59hidden_act="relu",
60num_labels=3,
61scope=None,
62):
63self.parent = parent
64self.batch_size = batch_size
65self.image_size = image_size
66self.num_channels = num_channels
67self.embeddings_size = embeddings_size
68self.hidden_sizes = hidden_sizes
69self.depths = depths
70self.is_training = is_training
71self.use_labels = use_labels
72self.hidden_act = hidden_act
73self.num_labels = num_labels
74self.scope = scope
75self.num_stages = len(hidden_sizes)
76
77def prepare_config_and_inputs(self):
78pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
79
80labels = None
81if self.use_labels:
82labels = ids_tensor([self.batch_size], self.num_labels)
83
84config = self.get_config()
85
86return config, pixel_values, labels
87
88def get_config(self):
89return ResNetConfig(
90num_channels=self.num_channels,
91embeddings_size=self.embeddings_size,
92hidden_sizes=self.hidden_sizes,
93depths=self.depths,
94hidden_act=self.hidden_act,
95num_labels=self.num_labels,
96image_size=self.image_size,
97)
98
99def create_and_check_model(self, config, pixel_values, labels):
100model = TFResNetModel(config=config)
101result = model(pixel_values)
102# expected last hidden states: B, C, H // 32, W // 32
103self.parent.assertEqual(
104result.last_hidden_state.shape,
105(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
106)
107
108def create_and_check_for_image_classification(self, config, pixel_values, labels):
109config.num_labels = self.num_labels
110model = TFResNetForImageClassification(config)
111result = model(pixel_values, labels=labels)
112self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
113
114def prepare_config_and_inputs_for_common(self):
115config_and_inputs = self.prepare_config_and_inputs()
116config, pixel_values, labels = config_and_inputs
117inputs_dict = {"pixel_values": pixel_values}
118return config, inputs_dict
119
120
121@require_tf
122class TFResNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
123"""
124Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds,
125attention_mask and seq_length.
126"""
127
128all_model_classes = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
129pipeline_model_mapping = (
130{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
131if is_tf_available()
132else {}
133)
134
135test_pruning = False
136test_resize_embeddings = False
137test_head_masking = False
138test_onnx = False
139has_attentions = False
140
141def setUp(self):
142self.model_tester = TFResNetModelTester(self)
143self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False)
144
145def test_config(self):
146self.create_and_test_config_common_properties()
147self.config_tester.create_and_test_config_to_json_string()
148self.config_tester.create_and_test_config_to_json_file()
149self.config_tester.create_and_test_config_from_and_save_pretrained()
150self.config_tester.create_and_test_config_with_num_labels()
151self.config_tester.check_config_can_be_init_without_params()
152self.config_tester.check_config_arguments_init()
153
154def create_and_test_config_common_properties(self):
155return
156
157@unittest.skip(reason="ResNet does not use inputs_embeds")
158def test_inputs_embeds(self):
159pass
160
161@unittest.skip(reason="ResNet does not support input and output embeddings")
162def test_model_common_attributes(self):
163pass
164
165def test_forward_signature(self):
166config, _ = self.model_tester.prepare_config_and_inputs_for_common()
167
168for model_class in self.all_model_classes:
169model = model_class(config)
170signature = inspect.signature(model.call)
171# signature.parameters is an OrderedDict => so arg_names order is deterministic
172arg_names = [*signature.parameters.keys()]
173
174expected_arg_names = ["pixel_values"]
175self.assertListEqual(arg_names[:1], expected_arg_names)
176
177def test_model(self):
178config_and_inputs = self.model_tester.prepare_config_and_inputs()
179self.model_tester.create_and_check_model(*config_and_inputs)
180
181def test_hidden_states_output(self):
182def check_hidden_states_output(inputs_dict, config, model_class):
183model = model_class(config)
184outputs = model(**self._prepare_for_class(inputs_dict, model_class))
185
186hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
187
188expected_num_stages = self.model_tester.num_stages
189self.assertEqual(len(hidden_states), expected_num_stages + 1)
190
191# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
192self.assertListEqual(
193list(hidden_states[0].shape[-2:]),
194[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
195)
196
197config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
198layers_type = ["basic", "bottleneck"]
199for model_class in self.all_model_classes:
200for layer_type in layers_type:
201config.layer_type = layer_type
202inputs_dict["output_hidden_states"] = True
203check_hidden_states_output(inputs_dict, config, model_class)
204
205# check that output_hidden_states also work using config
206del inputs_dict["output_hidden_states"]
207config.output_hidden_states = True
208
209check_hidden_states_output(inputs_dict, config, model_class)
210
211def test_for_image_classification(self):
212config_and_inputs = self.model_tester.prepare_config_and_inputs()
213self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
214
215@slow
216def test_model_from_pretrained(self):
217for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
218model = TFResNetModel.from_pretrained(model_name)
219self.assertIsNotNone(model)
220
221
222# We will verify our results on an image of cute cats
223def prepare_img():
224image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
225return image
226
227
228@require_tf
229@require_vision
230class TFResNetModelIntegrationTest(unittest.TestCase):
231@cached_property
232def default_image_processor(self):
233return (
234AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
235if is_vision_available()
236else None
237)
238
239@slow
240def test_inference_image_classification_head(self):
241model = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
242
243image_processor = self.default_image_processor
244image = prepare_img()
245inputs = image_processor(images=image, return_tensors="tf")
246
247# forward pass
248outputs = model(**inputs)
249
250# verify the logits
251expected_shape = tf.TensorShape((1, 1000))
252self.assertEqual(outputs.logits.shape, expected_shape)
253
254expected_slice = tf.constant([-11.1069, -9.7877, -8.3777])
255
256self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))
257