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transformers/tests/models/swiftformer/test_modeling_swiftformer.py

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+# Copyright 2023 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 SwiftFormer model."""
+
+import unittest
+from functools import cached_property
+
+from transformers import SwiftFormerConfig
+from transformers.testing_utils import (
+    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, _config_zero_init, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import SwiftFormerForImageClassification, SwiftFormerModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ViTImageProcessor
+
+
+class SwiftFormerModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        num_channels=3,
+        is_training=True,
+        use_labels=True,
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        image_size=224,
+        num_labels=3,
+        layer_depths=[1, 1, 1, 1],
+        embed_dims=[16, 16, 32, 32],
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.num_labels = num_labels
+        self.image_size = image_size
+        self.layer_depths = layer_depths
+        self.embed_dims = embed_dims
+
+    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.num_labels)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return SwiftFormerConfig(
+            depths=self.layer_depths,
+            embed_dims=self.embed_dims,
+            mlp_ratio=4,
+            downsamples=[True, True, True, True],
+            hidden_act="gelu",
+            num_labels=self.num_labels,
+            down_patch_size=3,
+            down_stride=2,
+            down_pad=1,
+            drop_rate=0.0,
+            drop_path_rate=0.0,
+            use_layer_scale=True,
+            layer_scale_init_value=1e-5,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = SwiftFormerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dims[-1], 7, 7))
+
+    def create_and_check_for_image_classification(self, config, pixel_values, labels):
+        config.num_labels = self.num_labels
+        model = SwiftFormerForImageClassification(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))
+
+        model = SwiftFormerForImageClassification(config)
+        model.to(torch_device)
+        model.eval()
+
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        result = model(pixel_values)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        (config, pixel_values, labels) = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class SwiftFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as SwiftFormer does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"image-feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
+        if is_torch_available()
+        else {}
+    )
+
+    fx_compatible = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    has_attentions = False
+    test_torch_exportable = True
+
+    def setUp(self):
+        self.model_tester = SwiftFormerModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=SwiftFormerConfig,
+            has_text_modality=False,
+            hidden_size=37,
+            num_attention_heads=12,
+            num_hidden_layers=12,
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="SwiftFormer does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    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_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)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "MBZUAI/swiftformer-xs"
+        model = SwiftFormerModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @unittest.skip(reason="SwiftFormer does not output attentions")
+    def test_attention_outputs(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_stages = 8
+            self.assertEqual(len(hidden_states), expected_num_stages)  # TODO
+
+            # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
+            # with the width and height being successively divided by 2, after every 2 blocks
+            for i in range(len(hidden_states)):
+                self.assertEqual(
+                    hidden_states[i].shape,
+                    torch.Size(
+                        [
+                            self.model_tester.batch_size,
+                            self.model_tester.embed_dims[i // 2],
+                            (self.model_tester.image_size // 4) // 2 ** (i // 2),
+                            (self.model_tester.image_size // 4) // 2 ** (i // 2),
+                        ]
+                    ),
+                )
+
+        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_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if name.endswith(".w_g"):
+                    continue
+                if param.requires_grad:
+                    self.assertIn(
+                        ((param.data.mean() * 1e9) / 1e9).round().item(),
+                        [0.0, 1.0],
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+
+# 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 SwiftFormerModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").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, 1000))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]).to(torch_device)
+        torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)