first commit

tests/models/mobilenet_v2/test_modeling_mobilenet_v2.py

@@ -0,0 +1,349 @@
+# Copyright 2022 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.
+"""Testing suite for the PyTorch MobileNetV2 model."""
+
+import unittest
+from functools import cached_property
+
+from transformers import MobileNetV2Config
+from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2Model
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import MobileNetV2ImageProcessorPil
+
+
+class MobileNetV2ConfigTester(ConfigTester):
+    def create_and_test_config_common_properties(self):
+        config = self.config_class(**self.inputs_dict)
+        self.parent.assertTrue(hasattr(config, "tf_padding"))
+        self.parent.assertTrue(hasattr(config, "depth_multiplier"))
+
+
+class MobileNetV2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        num_channels=3,
+        image_size=32,
+        depth_multiplier=0.25,
+        depth_divisible_by=8,
+        min_depth=8,
+        expand_ratio=6,
+        output_stride=32,
+        first_layer_is_expansion=True,
+        finegrained_output=True,
+        tf_padding=True,
+        hidden_act="relu6",
+        last_hidden_size=1280,
+        classifier_dropout_prob=0.1,
+        initializer_range=0.02,
+        is_training=True,
+        use_labels=True,
+        num_labels=10,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.depth_multiplier = depth_multiplier
+        self.depth_divisible_by = depth_divisible_by
+        self.min_depth = min_depth
+        self.expand_ratio = expand_ratio
+        self.tf_padding = tf_padding
+        self.output_stride = output_stride
+        self.first_layer_is_expansion = first_layer_is_expansion
+        self.finegrained_output = finegrained_output
+        self.hidden_act = hidden_act
+        self.last_hidden_size = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
+        self.classifier_dropout_prob = classifier_dropout_prob
+        self.use_labels = use_labels
+        self.is_training = is_training
+        self.num_labels = num_labels
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        pixel_labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.num_labels)
+            pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels, pixel_labels
+
+    def get_config(self):
+        return MobileNetV2Config(
+            num_channels=self.num_channels,
+            image_size=self.image_size,
+            depth_multiplier=self.depth_multiplier,
+            depth_divisible_by=self.depth_divisible_by,
+            min_depth=self.min_depth,
+            expand_ratio=self.expand_ratio,
+            output_stride=self.output_stride,
+            first_layer_is_expansion=self.first_layer_is_expansion,
+            finegrained_output=self.finegrained_output,
+            hidden_act=self.hidden_act,
+            tf_padding=self.tf_padding,
+            classifier_dropout_prob=self.classifier_dropout_prob,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
+        model = MobileNetV2Model(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (
+                self.batch_size,
+                self.last_hidden_size,
+                self.image_size // self.output_stride,
+                self.image_size // self.output_stride,
+            ),
+        )
+        self.parent.assertEqual(
+            result.pooler_output.shape,
+            (self.batch_size, self.last_hidden_size),
+        )
+
+    def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
+        config.num_labels = self.num_labels
+        model = MobileNetV2ForImageClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values, labels=labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
+        config.num_labels = self.num_labels
+        model = MobileNetV2ForSemanticSegmentation(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(
+            result.logits.shape,
+            (
+                self.batch_size,
+                self.num_labels,
+                self.image_size // self.output_stride,
+                self.image_size // self.output_stride,
+            ),
+        )
+        result = model(pixel_values, labels=pixel_labels)
+        self.parent.assertEqual(
+            result.logits.shape,
+            (
+                self.batch_size,
+                self.num_labels,
+                self.image_size // self.output_stride,
+                self.image_size // self.output_stride,
+            ),
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels, pixel_labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class MobileNetV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as MobileNetV2 does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (
+        (MobileNetV2Model, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation)
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "image-feature-extraction": MobileNetV2Model,
+            "image-classification": MobileNetV2ForImageClassification,
+            "image-segmentation": MobileNetV2ForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_resize_embeddings = False
+    has_attentions = False
+
+    def setUp(self):
+        self.model_tester = MobileNetV2ModelTester(self)
+        self.config_tester = MobileNetV2ConfigTester(self, config_class=MobileNetV2Config, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="MobileNetV2 does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="MobileNetV2 does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="MobileNetV2 does not output attentions")
+    def test_attention_outputs(self):
+        pass
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.hidden_states
+
+            expected_num_stages = 16
+            self.assertEqual(len(hidden_states), expected_num_stages)
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    def test_for_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
+
+    def test_for_semantic_segmentation(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/mobilenet_v2_1.4_224"
+        model = MobileNetV2Model.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class MobileNetV2ModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return (
+            MobileNetV2ImageProcessorPil.from_pretrained("google/mobilenet_v2_1.0_224")
+            if is_vision_available()
+            else None
+        )
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = MobileNetV2ForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1001))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expectations = Expectations(
+            {
+                (None, None): [0.2445, -1.1993, 0.1905],
+                ("cuda", 8): [0.2445, -1.1993, 0.1905],
+            }
+        )
+        expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
+
+        torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4)
+
+    @slow
+    def test_inference_semantic_segmentation(self):
+        model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
+        model = model.to(torch_device)
+
+        image_processor = MobileNetV2ImageProcessorPil.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
+
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+        logits = outputs.logits
+
+        # verify the logits
+        expected_shape = torch.Size((1, 21, 65, 65))
+        self.assertEqual(logits.shape, expected_shape)
+
+        expectations = Expectations(
+            {
+                (None, None): [
+                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
+                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
+                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
+                ],
+                ("cuda", 8): [
+                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
+                    [[-2.1595, -2.0977, -2.3742], [-2.4226, -2.3028, -2.6836], [-2.7820, -2.5991, -2.7706]],
+                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9645, 4.8734]],
+                ],
+            }
+        )
+        expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
+
+        torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)