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218 lines
7.5 KiB
Python
218 lines
7.5 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch VitPose backbone model."""
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import inspect
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import unittest
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from transformers import VitPoseBackboneConfig
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from transformers.testing_utils import require_torch, torch_device
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_backbone_common import BackboneTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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from transformers import VitPoseBackbone
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if is_vision_available():
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pass
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class VitPoseBackboneModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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image_size=[16 * 8, 12 * 8],
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patch_size=[8, 8],
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num_channels=3,
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is_training=True,
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use_labels=True,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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type_sequence_label_size=10,
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initializer_range=0.02,
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num_labels=2,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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# in VitPoseBackbone, the seq length equals the number of patches
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num_patches = (image_size[0] // patch_size[0]) * (image_size[1] // patch_size[1])
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self.seq_length = num_patches
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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return VitPoseBackboneConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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initializer_range=self.initializer_range,
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num_labels=self.num_labels,
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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pixel_values,
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labels,
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) = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class VitPoseBackboneModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as VitPoseBackbone does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (VitPoseBackbone,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_torch_exportable = True
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def setUp(self):
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self.model_tester = VitPoseBackboneModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=VitPoseBackboneConfig, has_text_modality=False, hidden_size=37
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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# TODO: @Pavel
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@unittest.skip(reason="currently failing")
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def test_initialization(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support input and output embeddings")
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support input and output embeddings")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support input and output embeddings")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support feedforward chunking")
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def test_feed_forward_chunking(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not output a loss")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support training yet")
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def test_training(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support training yet")
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support training yet")
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(reason="VitPoseBackbone does not support training yet")
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_torch_export(self):
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# Dense architecture
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super().test_torch_export()
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# MOE architecture
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_experts = 2
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config.part_features = config.hidden_size // config.num_experts
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inputs_dict["dataset_index"] = torch.tensor([0] * self.model_tester.batch_size, device=torch_device)
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super().test_torch_export(config=config, inputs_dict=inputs_dict)
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@require_torch
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class VitPoseBackboneTest(unittest.TestCase, BackboneTesterMixin):
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all_model_classes = (VitPoseBackbone,) if is_torch_available() else ()
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config_class = VitPoseBackboneConfig
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has_attentions = False
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def setUp(self):
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self.model_tester = VitPoseBackboneModelTester(self)
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