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89c46b648d
* skip * fix * better check * Update import_utils.py --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
331 lines
12 KiB
Python
331 lines
12 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 model."""
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import inspect
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import unittest
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import requests
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from transformers import VitPoseBackboneConfig, VitPoseConfig
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from transformers.utils.import_utils import get_torch_major_and_minor_version
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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 VitPoseForPoseEstimation
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if is_vision_available():
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from PIL import Image
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from transformers import VitPoseImageProcessor
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class VitPoseModelTester:
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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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scale_factor=4,
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out_indices=[-1],
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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.scale_factor = scale_factor
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self.out_indices = out_indices
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self.scope = scope
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# in VitPose, 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 VitPoseConfig(
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backbone_config=self.get_backbone_config(),
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)
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def get_backbone_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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num_hidden_layers=self.num_hidden_layers,
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hidden_size=self.hidden_size,
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intermediate_size=self.intermediate_size,
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num_attention_heads=self.num_attention_heads,
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hidden_act=self.hidden_act,
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out_indices=self.out_indices,
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)
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def create_and_check_for_pose_estimation(self, config, pixel_values, labels):
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model = VitPoseForPoseEstimation(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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expected_height = (self.image_size[0] // self.patch_size[0]) * self.scale_factor
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expected_width = (self.image_size[1] // self.patch_size[1]) * self.scale_factor
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self.parent.assertEqual(
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result.heatmaps.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
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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 VitPoseModelTest(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 VitPose 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 = (VitPoseForPoseEstimation,) 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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test_torch_exportable_strictly = not get_torch_major_and_minor_version() == "2.7"
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def setUp(self):
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self.model_tester = VitPoseModelTester(self)
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self.config_tester = ConfigTester(self, config_class=VitPoseConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.create_and_test_config_to_json_string()
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self.config_tester.create_and_test_config_to_json_file()
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self.config_tester.create_and_test_config_from_and_save_pretrained()
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self.config_tester.create_and_test_config_with_num_labels()
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self.config_tester.check_config_can_be_init_without_params()
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self.config_tester.check_config_arguments_init()
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@unittest.skip(reason="VitPose 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="VitPose 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="VitPose 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="VitPose 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="VitPose 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="VitPose 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="VitPose 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_for_pose_estimation(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_pose_estimation(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "usyd-community/vitpose-base-simple"
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model = VitPoseForPoseEstimation.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# We will verify our results on an image of people in house
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def prepare_img():
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url = "http://images.cocodataset.org/val2017/000000000139.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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return image
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@require_torch
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@require_vision
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class VitPoseModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return (
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VitPoseImageProcessor.from_pretrained("usyd-community/vitpose-base-simple")
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if is_vision_available()
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else None
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)
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@slow
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def test_inference_pose_estimation(self):
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image_processor = self.default_image_processor
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model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map=torch_device)
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image = prepare_img()
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boxes = [[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]]]
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inputs = image_processor(images=image, boxes=boxes, return_tensors="pt").to(torch_device)
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with torch.no_grad():
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outputs = model(**inputs)
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heatmaps = outputs.heatmaps
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assert heatmaps.shape == (2, 17, 64, 48)
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expected_slice = torch.tensor(
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[
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[9.9330e-06, 9.9330e-06, 9.9330e-06],
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[9.9330e-06, 9.9330e-06, 9.9330e-06],
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[9.9330e-06, 9.9330e-06, 9.9330e-06],
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]
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).to(torch_device)
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assert torch.allclose(heatmaps[0, 0, :3, :3], expected_slice, atol=1e-4)
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pose_results = image_processor.post_process_pose_estimation(outputs, boxes=boxes)[0]
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expected_bbox = torch.tensor([391.9900, 190.0800, 391.1575, 189.3034])
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expected_keypoints = torch.tensor(
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[
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[3.9813e02, 1.8184e02],
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[3.9828e02, 1.7981e02],
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[3.9596e02, 1.7948e02],
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]
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)
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expected_scores = torch.tensor([8.7529e-01, 8.4315e-01, 9.2678e-01])
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self.assertEqual(len(pose_results), 2)
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torch.testing.assert_close(pose_results[1]["bbox"].cpu(), expected_bbox, rtol=1e-4, atol=1e-4)
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torch.testing.assert_close(pose_results[1]["keypoints"][:3].cpu(), expected_keypoints, rtol=1e-2, atol=1e-2)
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torch.testing.assert_close(pose_results[1]["scores"][:3].cpu(), expected_scores, rtol=1e-4, atol=1e-4)
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@slow
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def test_batched_inference(self):
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image_processor = self.default_image_processor
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model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map=torch_device)
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image = prepare_img()
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boxes = [
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[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]],
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[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]],
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]
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inputs = image_processor(images=[image, image], boxes=boxes, return_tensors="pt").to(torch_device)
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with torch.no_grad():
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outputs = model(**inputs)
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heatmaps = outputs.heatmaps
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assert heatmaps.shape == (4, 17, 64, 48)
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expected_slice = torch.tensor(
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[
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[9.9330e-06, 9.9330e-06, 9.9330e-06],
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[9.9330e-06, 9.9330e-06, 9.9330e-06],
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[9.9330e-06, 9.9330e-06, 9.9330e-06],
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]
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).to(torch_device)
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assert torch.allclose(heatmaps[0, 0, :3, :3], expected_slice, atol=1e-4)
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pose_results = image_processor.post_process_pose_estimation(outputs, boxes=boxes)
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print(pose_results)
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expected_bbox = torch.tensor([391.9900, 190.0800, 391.1575, 189.3034])
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expected_keypoints = torch.tensor(
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[
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[3.9813e02, 1.8184e02],
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[3.9828e02, 1.7981e02],
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[3.9596e02, 1.7948e02],
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]
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)
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expected_scores = torch.tensor([8.7529e-01, 8.4315e-01, 9.2678e-01])
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self.assertEqual(len(pose_results), 2)
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self.assertEqual(len(pose_results[0]), 2)
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torch.testing.assert_close(pose_results[0][1]["bbox"].cpu(), expected_bbox, rtol=1e-4, atol=1e-4)
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torch.testing.assert_close(pose_results[0][1]["keypoints"][:3].cpu(), expected_keypoints, rtol=1e-2, atol=1e-2)
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torch.testing.assert_close(pose_results[0][1]["scores"][:3].cpu(), expected_scores, rtol=1e-4, atol=1e-4)
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