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tests/models/segformer/test_modeling_segformer.py

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+# Copyright 2021 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 SegFormer model."""
+
+import unittest
+
+from transformers import SegformerConfig, is_torch_available, is_vision_available
+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 (
+        SegformerForImageClassification,
+        SegformerForSemanticSegmentation,
+        SegformerModel,
+    )
+    from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import SegformerImageProcessorPil
+
+
+class SegformerConfigTester(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_attention_heads"))
+        self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
+
+
+class SegformerModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=64,
+        num_channels=3,
+        num_encoder_blocks=4,
+        decoder_hidden_size=32,
+        depths=[1, 1, 1, 1],
+        sr_ratios=[8, 4, 2, 1],
+        hidden_sizes=[8, 8, 16, 16],
+        downsampling_rates=[1, 4, 8, 16],
+        num_attention_heads=[1, 1, 2, 2],
+        is_training=True,
+        use_labels=True,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_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.decoder_hidden_size = decoder_hidden_size
+        self.sr_ratios = sr_ratios
+        self.depths = depths
+        self.hidden_sizes = hidden_sizes
+        self.downsampling_rates = downsampling_rates
+        self.num_attention_heads = num_attention_heads
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_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 = self.get_config()
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return SegformerConfig(
+            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,
+            num_attention_heads=self.num_attention_heads,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            initializer_range=self.initializer_range,
+            decoder_hidden_size=self.decoder_hidden_size,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = SegformerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
+        )
+
+    def create_and_check_for_image_segmentation(self, config, pixel_values, labels):
+        config.num_labels = self.num_labels
+        model = SegformerForSemanticSegmentation(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 // 4, self.image_size // 4)
+        )
+        result = model(pixel_values, labels=labels)
+        self.parent.assertEqual(
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
+        )
+        self.parent.assertGreater(result.loss, 0.0)
+
+    def create_and_check_for_binary_image_segmentation(self, config, pixel_values, labels):
+        config.num_labels = 1
+        model = SegformerForSemanticSegmentation(config=config)
+        model.to(torch_device)
+        model.eval()
+        labels = torch.randint(0, 1, (self.batch_size, self.image_size, self.image_size)).to(torch_device)
+        result = model(pixel_values, labels=labels)
+        self.parent.assertGreater(result.loss, 0.0)
+
+    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 SegformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            SegformerModel,
+            SegformerForSemanticSegmentation,
+            SegformerForImageClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "image-feature-extraction": SegformerModel,
+            "image-classification": SegformerForImageClassification,
+            "image-segmentation": SegformerForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = SegformerModelTester(self)
+        self.config_tester = SegformerConfigTester(self, config_class=SegformerConfig)
+
+    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_for_binary_image_segmentation(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_binary_image_segmentation(*config_and_inputs)
+
+    def test_for_image_segmentation(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_segmentation(*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="SegFormer does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(
+        reason="SegFormer test config uses head_dim=8 which is below the minimum of 16 required by Flex attention"
+    )
+    def test_flex_attention_with_grads(self):
+        pass
+
+    @unittest.skip(reason="SegFormer does not have get_input_embeddings method and get_output_embeddings methods")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+
+            expected_num_attentions = sum(self.model_tester.depths)
+            self.assertEqual(len(attentions), expected_num_attentions)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+
+            self.assertEqual(len(attentions), expected_num_attentions)
+
+            # verify the first attentions (first block, first layer)
+            expected_seq_len = (self.model_tester.image_size // 4) ** 2
+            expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
+            )
+
+            # verify the last attentions (last block, last layer)
+            expected_seq_len = (self.model_tester.image_size // 32) ** 2
+            expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
+            self.assertListEqual(
+                list(attentions[-1].shape[-3:]),
+                [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],
+            )
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertEqual(out_len + 1, len(outputs))
+
+            self_attentions = outputs.attentions
+
+            self.assertEqual(len(self_attentions), expected_num_attentions)
+            # verify the first attentions (first block, first layer)
+            expected_seq_len = (self.model_tester.image_size // 4) ** 2
+            expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
+            )
+
+    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.__name__ in MODEL_MAPPING_NAMES.values():
+                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 = "nvidia/segformer-b0-finetuned-ade-512-512"
+        model = SegformerModel.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 SegformerModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_image_segmentation_ade(self):
+        # only resize + normalize
+        image_processor = SegformerImageProcessorPil(
+            image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
+        )
+        model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
+            torch_device
+        )
+
+        image = prepare_img()
+        encoded_inputs = image_processor(images=image, return_tensors="pt")
+        pixel_values = encoded_inputs.pixel_values.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(pixel_values)
+
+        expected_shape = torch.Size((1, model.config.num_labels, 128, 128))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expectations = Expectations(
+            {
+                (None, None): [
+                    [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
+                    [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
+                    [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
+                ],
+                ("cuda", 8): [
+                    [[-4.6310, -5.5232, -6.2361], [-5.1918, -6.1445, -6.5996], [-5.4427, -6.2792, -6.7580]],
+                    [[-12.1397, -13.3124, -13.9551], [-12.8736, -13.9347, -14.3569], [-12.9440, -13.8222, -14.2514]],
+                    [[-12.5135, -13.4682, -14.4913], [-12.8670, -14.4339, -14.7766], [-13.2519, -14.5800, -15.0685]],
+                ],
+            }
+        )
+        expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
+        torch.testing.assert_close(outputs.logits[0, :3, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
+
+    @slow
+    def test_inference_image_segmentation_city(self):
+        # only resize + normalize
+        image_processor = SegformerImageProcessorPil(
+            image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
+        )
+        model = SegformerForSemanticSegmentation.from_pretrained(
+            "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
+        ).to(torch_device)
+
+        image = prepare_img()
+        encoded_inputs = image_processor(images=image, return_tensors="pt")
+        pixel_values = encoded_inputs.pixel_values.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(pixel_values)
+
+        expected_shape = torch.Size((1, model.config.num_labels, 128, 128))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([]).to(torch_device)
+
+        expectations = Expectations(
+            {
+                (None, None): [
+                    [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
+                    [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
+                    [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
+                ],
+                ("cuda", 8): [
+                    [[-13.5728, -13.9089, -12.6492], [-14.3478, -15.3656, -14.2309], [-14.7512, -16.0394, -15.6065]],
+                    [[-17.1642, -15.8704, -12.9641], [-17.2572, -17.3701, -14.8214], [-16.6043, -16.8761, -16.7425]],
+                    [[-3.6444, -3.0189, -1.4195], [-3.0787, -3.1953, -1.9993], [-1.8755, -1.9219, -1.7002]],
+                ],
+            }
+        )
+        expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
+
+        torch.testing.assert_close(outputs.logits[0, :3, :3, :3], expected_slice, rtol=1e-1, atol=1e-1)
+
+    @slow
+    def test_post_processing_semantic_segmentation(self):
+        # only resize + normalize
+        image_processor = SegformerImageProcessorPil(
+            image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
+        )
+        model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
+            torch_device
+        )
+
+        image = prepare_img()
+        encoded_inputs = image_processor(images=image, return_tensors="pt")
+        pixel_values = encoded_inputs.pixel_values.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(pixel_values)
+
+        outputs.logits = outputs.logits.detach().cpu()
+
+        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
+        expected_shape = torch.Size((500, 300))
+        self.assertEqual(segmentation[0].shape, expected_shape)
+
+        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
+        expected_shape = torch.Size((128, 128))
+        self.assertEqual(segmentation[0].shape, expected_shape)