transformers
228 строк · 8.3 Кб
1# Copyright 2023 The HuggingFace Team. All rights reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14
15import inspect
16import unittest
17
18from transformers import ResNetConfig, is_flax_available
19from transformers.testing_utils import require_flax, slow
20from transformers.utils import cached_property, is_vision_available
21
22from ...test_configuration_common import ConfigTester
23from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
24
25
26if is_flax_available():
27import jax
28import jax.numpy as jnp
29
30from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel
31
32if is_vision_available():
33from PIL import Image
34
35from transformers import AutoImageProcessor
36
37
38class FlaxResNetModelTester(unittest.TestCase):
39def __init__(
40self,
41parent,
42batch_size=3,
43image_size=32,
44num_channels=3,
45embeddings_size=10,
46hidden_sizes=[10, 20, 30, 40],
47depths=[1, 1, 2, 1],
48is_training=True,
49use_labels=True,
50hidden_act="relu",
51num_labels=3,
52scope=None,
53):
54self.parent = parent
55self.batch_size = batch_size
56self.image_size = image_size
57self.num_channels = num_channels
58self.embeddings_size = embeddings_size
59self.hidden_sizes = hidden_sizes
60self.depths = depths
61self.is_training = is_training
62self.use_labels = use_labels
63self.hidden_act = hidden_act
64self.num_labels = num_labels
65self.scope = scope
66self.num_stages = len(hidden_sizes)
67
68def prepare_config_and_inputs(self):
69pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
70
71config = self.get_config()
72
73return config, pixel_values
74
75def get_config(self):
76return ResNetConfig(
77num_channels=self.num_channels,
78embeddings_size=self.embeddings_size,
79hidden_sizes=self.hidden_sizes,
80depths=self.depths,
81hidden_act=self.hidden_act,
82num_labels=self.num_labels,
83image_size=self.image_size,
84)
85
86def create_and_check_model(self, config, pixel_values):
87model = FlaxResNetModel(config=config)
88result = model(pixel_values)
89
90# Output shape (b, c, h, w)
91self.parent.assertEqual(
92result.last_hidden_state.shape,
93(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
94)
95
96def create_and_check_for_image_classification(self, config, pixel_values):
97config.num_labels = self.num_labels
98model = FlaxResNetForImageClassification(config=config)
99result = model(pixel_values)
100self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
101
102def prepare_config_and_inputs_for_common(self):
103config_and_inputs = self.prepare_config_and_inputs()
104config, pixel_values = config_and_inputs
105inputs_dict = {"pixel_values": pixel_values}
106return config, inputs_dict
107
108
109@require_flax
110class FlaxResNetModelTest(FlaxModelTesterMixin, unittest.TestCase):
111all_model_classes = (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()
112
113is_encoder_decoder = False
114test_head_masking = False
115has_attentions = False
116
117def setUp(self) -> None:
118self.model_tester = FlaxResNetModelTester(self)
119self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False)
120
121def test_config(self):
122self.create_and_test_config_common_properties()
123self.config_tester.create_and_test_config_to_json_string()
124self.config_tester.create_and_test_config_to_json_file()
125self.config_tester.create_and_test_config_from_and_save_pretrained()
126self.config_tester.create_and_test_config_with_num_labels()
127self.config_tester.check_config_can_be_init_without_params()
128self.config_tester.check_config_arguments_init()
129
130def create_and_test_config_common_properties(self):
131return
132
133def test_model(self):
134config_and_inputs = self.model_tester.prepare_config_and_inputs()
135self.model_tester.create_and_check_model(*config_and_inputs)
136
137def test_for_image_classification(self):
138config_and_inputs = self.model_tester.prepare_config_and_inputs()
139self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
140
141@unittest.skip(reason="ResNet does not use inputs_embeds")
142def test_inputs_embeds(self):
143pass
144
145@unittest.skip(reason="ResNet does not support input and output embeddings")
146def test_model_common_attributes(self):
147pass
148
149def test_forward_signature(self):
150config, _ = self.model_tester.prepare_config_and_inputs_for_common()
151
152for model_class in self.all_model_classes:
153model = model_class(config)
154signature = inspect.signature(model.__call__)
155# signature.parameters is an OrderedDict => so arg_names order is deterministic
156arg_names = [*signature.parameters.keys()]
157
158expected_arg_names = ["pixel_values"]
159self.assertListEqual(arg_names[:1], expected_arg_names)
160
161def test_hidden_states_output(self):
162def check_hidden_states_output(inputs_dict, config, model_class):
163model = model_class(config)
164
165outputs = model(**self._prepare_for_class(inputs_dict, model_class))
166
167hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
168
169expected_num_stages = self.model_tester.num_stages
170self.assertEqual(len(hidden_states), expected_num_stages + 1)
171
172@unittest.skip(reason="ResNet does not use feedforward chunking")
173def test_feed_forward_chunking(self):
174pass
175
176def test_jit_compilation(self):
177config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
178
179for model_class in self.all_model_classes:
180with self.subTest(model_class.__name__):
181prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
182model = model_class(config)
183
184@jax.jit
185def model_jitted(pixel_values, **kwargs):
186return model(pixel_values=pixel_values, **kwargs)
187
188with self.subTest("JIT Enabled"):
189jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
190
191with self.subTest("JIT Disabled"):
192with jax.disable_jit():
193outputs = model_jitted(**prepared_inputs_dict).to_tuple()
194
195self.assertEqual(len(outputs), len(jitted_outputs))
196for jitted_output, output in zip(jitted_outputs, outputs):
197self.assertEqual(jitted_output.shape, output.shape)
198
199
200# We will verify our results on an image of cute cats
201def prepare_img():
202image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
203return image
204
205
206@require_flax
207class FlaxResNetModelIntegrationTest(unittest.TestCase):
208@cached_property
209def default_image_processor(self):
210return AutoImageProcessor.from_pretrained("microsoft/resnet-50") if is_vision_available() else None
211
212@slow
213def test_inference_image_classification_head(self):
214model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")
215
216image_processor = self.default_image_processor
217image = prepare_img()
218inputs = image_processor(images=image, return_tensors="np")
219
220outputs = model(**inputs)
221
222# verify the logits
223expected_shape = (1, 1000)
224self.assertEqual(outputs.logits.shape, expected_shape)
225
226expected_slice = jnp.array([-11.1069, -9.7877, -8.3777])
227
228self.assertTrue(jnp.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
229