init

transformers/tests/models/vitpose_backbone/test_modeling_vitpose_backbone.py

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+# Copyright 2024 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 VitPose backbone model."""
+
+import inspect
+import unittest
+
+import pytest
+
+from transformers import VitPoseBackboneConfig
+from transformers.testing_utils import require_torch, torch_device
+from transformers.utils import is_torch_available
+
+from ...test_backbone_common import BackboneTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import VitPoseBackbone
+
+
+class VitPoseBackboneModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=[16 * 8, 12 * 8],
+        patch_size=[8, 8],
+        num_channels=3,
+        is_training=True,
+        use_labels=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+        num_labels=2,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.scope = scope
+
+        # in VitPoseBackbone, the seq length equals the number of patches
+        num_patches = (image_size[0] // patch_size[0]) * (image_size[1] // patch_size[1])
+        self.seq_length = num_patches
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return VitPoseBackboneConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            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,
+            num_labels=self.num_labels,
+        )
+
+    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 VitPoseBackboneModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as VitPoseBackbone does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (VitPoseBackbone,) if is_torch_available() else ()
+    fx_compatible = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_torch_exportable = True
+
+    def setUp(self):
+        self.model_tester = VitPoseBackboneModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=VitPoseBackboneConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_batching_equivalence(self, atol=3e-4, rtol=3e-4):
+        super().test_batching_equivalence(atol=atol, rtol=rtol)
+
+    # TODO: @Pavel
+    @unittest.skip(reason="currently failing")
+    def test_initialization(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support input and output embeddings")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support feedforward chunking")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not output a loss")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support training yet")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support training yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support training yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="VitPoseBackbone does not support training yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    @pytest.mark.torch_export_test
+    def test_torch_export(self):
+        # Dense architecture
+        super().test_torch_export()
+
+        # MOE architecture
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_experts = 2
+        config.part_features = config.hidden_size // config.num_experts
+        inputs_dict["dataset_index"] = torch.tensor([0] * self.model_tester.batch_size, device=torch_device)
+        super().test_torch_export(config=config, inputs_dict=inputs_dict)
+
+
+@require_torch
+class VitPoseBackboneTest(unittest.TestCase, BackboneTesterMixin):
+    all_model_classes = (VitPoseBackbone,) if is_torch_available() else ()
+    config_class = VitPoseBackboneConfig
+
+    has_attentions = False
+
+    def setUp(self):
+        self.model_tester = VitPoseBackboneModelTester(self)