init

transformers/tests/models/poolformer/test_image_processing_poolformer.py

@@ -0,0 +1,127 @@
+# Copyright 2022 HuggingFace Inc.
+#
+# 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 unittest
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_vision_available():
+    from transformers import PoolFormerImageProcessor
+
+    if is_torchvision_available():
+        from transformers import PoolFormerImageProcessorFast
+
+
+class PoolFormerImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize_and_center_crop=True,
+        size=None,
+        crop_pct=0.9,
+        crop_size=None,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+    ):
+        size = size if size is not None else {"shortest_edge": 30}
+        crop_size = crop_size if crop_size is not None else {"height": 30, "width": 30}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize_and_center_crop = do_resize_and_center_crop
+        self.size = size
+        self.crop_pct = crop_pct
+        self.crop_size = crop_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_image_processor_dict(self):
+        return {
+            "size": self.size,
+            "do_resize_and_center_crop": self.do_resize_and_center_crop,
+            "crop_pct": self.crop_pct,
+            "crop_size": self.crop_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.crop_size["height"], self.crop_size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class PoolFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = PoolFormerImageProcessor if is_vision_available() else None
+    fast_image_processing_class = PoolFormerImageProcessorFast if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PoolFormerImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize_and_center_crop"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "crop_pct"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"shortest_edge": 30})
+            self.assertEqual(image_processor.crop_size, {"height": 30, "width": 30})
+
+            image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
+            self.assertEqual(image_processor.size, {"shortest_edge": 42})
+            self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+
+
+@require_torch
+@require_vision
+class PoolFormerImageProcessingNoCropPctTest(PoolFormerImageProcessingTest):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PoolFormerImageProcessingTester(self, crop_pct=None)

transformers/tests/models/poolformer/test_modeling_poolformer.py

@@ -0,0 +1,248 @@
+# 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 PoolFormer model."""
+
+import unittest
+
+from transformers import is_torch_available, is_vision_available
+from transformers.models.auto import get_values
+from transformers.testing_utils import Expectations, require_torch, slow, torch_device
+
+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 MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import PoolFormerImageProcessor
+
+
+class PoolFormerConfigTester(ConfigTester):
+    def create_and_test_config_common_properties(self):
+        config = self.config_class(**self.inputs_dict)
+        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
+        self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
+
+
+class PoolFormerModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=64,
+        num_channels=3,
+        num_encoder_blocks=4,
+        depths=[2, 2, 2, 2],
+        sr_ratios=[8, 4, 2, 1],
+        hidden_sizes=[16, 32, 64, 128],
+        downsampling_rates=[1, 4, 8, 16],
+        is_training=False,
+        use_labels=True,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        initializer_range=0.02,
+        num_labels=3,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.num_encoder_blocks = num_encoder_blocks
+        self.sr_ratios = sr_ratios
+        self.depths = depths
+        self.hidden_sizes = hidden_sizes
+        self.downsampling_rates = downsampling_rates
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        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
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
+
+        config = PoolFormerConfig(
+            image_size=self.image_size,
+            num_channels=self.num_channels,
+            num_encoder_blocks=self.num_encoder_blocks,
+            depths=self.depths,
+            hidden_sizes=self.hidden_sizes,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            initializer_range=self.initializer_range,
+        )
+
+        return config, pixel_values, labels
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = PoolFormerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        expected_height = expected_width = self.image_size // 32.0
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class PoolFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"image-feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
+        if is_torch_available()
+        else {}
+    )
+
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_torchscript = False
+    has_attentions = False
+    test_torch_exportable = True
+
+    def setUp(self):
+        self.model_tester = PoolFormerModelTester(self)
+        self.config_tester = PoolFormerConfigTester(self, config_class=PoolFormerConfig)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    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_batching_equivalence(self, atol=2e-4, rtol=2e-4):
+        super().test_batching_equivalence(atol=atol, rtol=rtol)
+
+    @unittest.skip(reason="PoolFormer does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="PoolFormer does not have get_input_embeddings method and get_output_embeddings methods")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    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_layers = self.model_tester.num_encoder_blocks
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            # verify the first hidden states (first block)
+            self.assertListEqual(
+                list(hidden_states[0].shape[-3:]),
+                [
+                    self.model_tester.hidden_sizes[0],
+                    self.model_tester.image_size // 4,
+                    self.model_tester.image_size // 4,
+                ],
+            )
+
+        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_training(self):
+        if not self.model_tester.is_training:
+            self.skipTest(reason="model_tester.is_training is set to False")
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            if model_class in get_values(MODEL_MAPPING):
+                continue
+            model = model_class(config)
+            model.to(torch_device)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "sail/poolformer_s12"
+        model = PoolFormerModel.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
+class PoolFormerModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_image_classification_head(self):
+        image_processor = PoolFormerImageProcessor()
+        model = PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12").to(torch_device)
+
+        inputs = image_processor(images=prepare_img(), return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1000))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expectations = Expectations(
+            {
+                (None, None): [-0.6113, 0.1685, -0.0492],
+                ("cuda", 8): [-0.6113, 0.1685, -0.0492],
+            }
+        )
+        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)