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d21c97cc0f
* add model files etc for MobileNetV2 rename files for MobileNetV1 initial implementation of MobileNetV1 fix conversion script cleanup write docs tweaks fix conversion script extract hidden states fix test cases make fixup fixup it all remove main from doc link fixes fix tests fix up use google org fix weird assert * fixup * use google organization for checkpoints
263 lines
9.3 KiB
Python
263 lines
9.3 KiB
Python
# coding=utf-8
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# Copyright 2022 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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""" Testing suite for the PyTorch MobileNetV1 model. """
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import inspect
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import unittest
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from transformers import MobileNetV1Config
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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from transformers import MobileNetV1ForImageClassification, MobileNetV1Model
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from transformers.models.mobilenet_v1.modeling_mobilenet_v1 import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
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if is_vision_available():
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from PIL import Image
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from transformers import MobileNetV1FeatureExtractor
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class MobileNetV1ConfigTester(ConfigTester):
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def create_and_test_config_common_properties(self):
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config = self.config_class(**self.inputs_dict)
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self.parent.assertTrue(hasattr(config, "tf_padding"))
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self.parent.assertTrue(hasattr(config, "depth_multiplier"))
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class MobileNetV1ModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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num_channels=3,
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image_size=32,
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depth_multiplier=0.25,
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min_depth=8,
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tf_padding=True,
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last_hidden_size=1024,
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output_stride=32,
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hidden_act="relu6",
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classifier_dropout_prob=0.1,
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initializer_range=0.02,
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is_training=True,
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use_labels=True,
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num_labels=10,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.depth_multiplier = depth_multiplier
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self.min_depth = min_depth
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self.tf_padding = tf_padding
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self.last_hidden_size = int(last_hidden_size * depth_multiplier)
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self.output_stride = output_stride
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self.hidden_act = hidden_act
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self.classifier_dropout_prob = classifier_dropout_prob
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self.use_labels = use_labels
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self.is_training = is_training
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self.num_labels = num_labels
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self.initializer_range = initializer_range
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self.scope = scope
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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labels = None
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pixel_labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.num_labels)
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pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels, pixel_labels
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def get_config(self):
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return MobileNetV1Config(
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num_channels=self.num_channels,
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image_size=self.image_size,
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depth_multiplier=self.depth_multiplier,
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min_depth=self.min_depth,
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tf_padding=self.tf_padding,
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hidden_act=self.hidden_act,
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classifier_dropout_prob=self.classifier_dropout_prob,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
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model = MobileNetV1Model(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(
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result.last_hidden_state.shape,
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(
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self.batch_size,
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self.last_hidden_size,
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self.image_size // self.output_stride,
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self.image_size // self.output_stride,
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),
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)
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def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.num_labels
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model = MobileNetV1ForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels, pixel_labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class MobileNetV1ModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as MobileNetV1 does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else ()
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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has_attentions = False
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def setUp(self):
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self.model_tester = MobileNetV1ModelTester(self)
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self.config_tester = MobileNetV1ConfigTester(self, config_class=MobileNetV1Config, has_text_modality=False)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="MobileNetV1 does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="MobileNetV1 does not support input and output embeddings")
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="MobileNetV1 does not output attentions")
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def test_attention_outputs(self):
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pass
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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_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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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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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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expected_num_stages = 26
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self.assertEqual(len(hidden_states), expected_num_stages)
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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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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_for_image_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = MobileNetV1Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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@require_vision
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class MobileNetV1ModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_feature_extractor(self):
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return (
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MobileNetV1FeatureExtractor.from_pretrained("google/mobilenet_v1_1.0_224")
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if is_vision_available()
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else None
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)
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@slow
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def test_inference_image_classification_head(self):
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model = MobileNetV1ForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224").to(torch_device)
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feature_extractor = self.default_feature_extractor
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image = prepare_img()
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inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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# verify the logits
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expected_shape = torch.Size((1, 1001))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor([-4.1739, -1.1233, 3.1205]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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