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transformers/tests/models/vitpose/__init__.py Normal file
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transformers/tests/models/vitpose/test_image_processing_vitpose.py Normal file
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# Copyright 2024 HuggingFace Inc.
#
# 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.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VitPoseImageProcessor
class VitPoseImageProcessingTester:
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_affine_transform=True,
size=None,
do_rescale=True,
rescale_factor=1 / 255,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 20, "width": 20}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_affine_transform = do_affine_transform
self.size = size
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"do_affine_transform": self.do_affine_transform,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class VitPoseImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = VitPoseImageProcessor if is_vision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = VitPoseImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_affine_transform"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "rescale_factor"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 20, "width": 20})
image_processor = self.image_processing_class.from_dict(
self.image_processor_dict, size={"height": 42, "width": 42}
)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]]
encoded_images = image_processing(image_inputs[0], boxes=boxes, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (2, *expected_output_image_shape))
# Test batched
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size
encoded_images = image_processing(image_inputs, boxes=boxes, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size * 2, *expected_output_image_shape)
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]]
encoded_images = image_processing(image_inputs[0], boxes=boxes, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (2, *expected_output_image_shape))
# Test batched
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size
encoded_images = image_processing(image_inputs, boxes=boxes, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size * 2, *expected_output_image_shape)
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]]
encoded_images = image_processing(image_inputs[0], boxes=boxes, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (2, *expected_output_image_shape))
# Test batched
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size
encoded_images = image_processing(image_inputs, boxes=boxes, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size * 2, *expected_output_image_shape)
)
def test_call_numpy_4_channels(self):
# Test that can process images which have an arbitrary number of channels
# Initialize image_processing
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
self.image_processor_tester.num_channels = 4
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
# Test not batched input
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]]
encoded_images = image_processor(
image_inputs[0],
boxes=boxes,
return_tensors="pt",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (len(boxes[0]), *expected_output_image_shape))
# Test batched
boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size
encoded_images = image_processor(
image_inputs,
boxes=boxes,
return_tensors="pt",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape),
(self.image_processor_tester.batch_size * len(boxes[0]), *expected_output_image_shape),
)

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transformers/tests/models/vitpose/test_modeling_vitpose.py Normal file
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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 model."""
import inspect
import unittest
from functools import cached_property
import requests
from transformers import VitPoseBackboneConfig, VitPoseConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from transformers.utils.import_utils import get_torch_major_and_minor_version
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 VitPoseForPoseEstimation
if is_vision_available():
from PIL import Image
from transformers import VitPoseImageProcessor
class VitPoseModelTester:
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,
scale_factor=4,
out_indices=[-1],
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.scale_factor = scale_factor
self.out_indices = out_indices
self.scope = scope
# in VitPose, 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 VitPoseConfig(
backbone_config=self.get_backbone_config(),
)
def get_backbone_config(self):
return VitPoseBackboneConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_hidden_layers=self.num_hidden_layers,
hidden_size=self.hidden_size,
intermediate_size=self.intermediate_size,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
out_indices=self.out_indices,
)
def create_and_check_for_pose_estimation(self, config, pixel_values, labels):
model = VitPoseForPoseEstimation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_height = (self.image_size[0] // self.patch_size[0]) * self.scale_factor
expected_width = (self.image_size[1] // self.patch_size[1]) * self.scale_factor
self.parent.assertEqual(
result.heatmaps.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
)
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 VitPoseModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as VitPose does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (VitPoseForPoseEstimation,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torch_exportable = True
test_torch_exportable_strictly = get_torch_major_and_minor_version() != "2.7"
def setUp(self):
self.model_tester = VitPoseModelTester(self)
self.config_tester = ConfigTester(self, config_class=VitPoseConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def test_batching_equivalence(self, atol=3e-4, rtol=3e-4):
super().test_batching_equivalence(atol=atol, rtol=rtol)
@unittest.skip(reason="VitPose does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="VitPose does not support input and output embeddings")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="VitPose does not support input and output embeddings")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="VitPose does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="VitPose does not support training yet")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="VitPose does not support training yet")
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(reason="VitPose 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)
def test_for_pose_estimation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pose_estimation(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model_name = "usyd-community/vitpose-base-simple"
model = VitPoseForPoseEstimation.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of people in house
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000000139.jpg"
image = Image.open(requests.get(url, stream=True).raw)
return image
@require_torch
@require_vision
class VitPoseModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
VitPoseImageProcessor.from_pretrained("usyd-community/vitpose-base-simple")
if is_vision_available()
else None
)
@slow
def test_inference_pose_estimation(self):
image_processor = self.default_image_processor
model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map=torch_device)
image = prepare_img()
boxes = [[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]]]
inputs = image_processor(images=image, boxes=boxes, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
heatmaps = outputs.heatmaps
assert heatmaps.shape == (2, 17, 64, 48)
expected_slice = torch.tensor(
[
[9.9330e-06, 9.9330e-06, 9.9330e-06],
[9.9330e-06, 9.9330e-06, 9.9330e-06],
[9.9330e-06, 9.9330e-06, 9.9330e-06],
]
).to(torch_device)
assert torch.allclose(heatmaps[0, 0, :3, :3], expected_slice, atol=1e-4)
pose_results = image_processor.post_process_pose_estimation(outputs, boxes=boxes)[0]
expected_bbox = torch.tensor([391.9900, 190.0800, 391.1575, 189.3034])
expected_keypoints = torch.tensor(
[
[3.9813e02, 1.8184e02],
[3.9828e02, 1.7981e02],
[3.9596e02, 1.7948e02],
]
)
expected_scores = torch.tensor([8.7529e-01, 8.4315e-01, 9.2678e-01])
self.assertEqual(len(pose_results), 2)
torch.testing.assert_close(pose_results[1]["bbox"].cpu(), expected_bbox, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(pose_results[1]["keypoints"][:3].cpu(), expected_keypoints, rtol=1e-2, atol=1e-2)
torch.testing.assert_close(pose_results[1]["scores"][:3].cpu(), expected_scores, rtol=1e-4, atol=1e-4)
@slow
def test_batched_inference(self):
image_processor = self.default_image_processor
model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map=torch_device)
image = prepare_img()
boxes = [
[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]],
[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]],
]
inputs = image_processor(images=[image, image], boxes=boxes, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
heatmaps = outputs.heatmaps
assert heatmaps.shape == (4, 17, 64, 48)
expected_slice = torch.tensor(
[
[9.9330e-06, 9.9330e-06, 9.9330e-06],
[9.9330e-06, 9.9330e-06, 9.9330e-06],
[9.9330e-06, 9.9330e-06, 9.9330e-06],
]
).to(torch_device)
assert torch.allclose(heatmaps[0, 0, :3, :3], expected_slice, atol=1e-4)
pose_results = image_processor.post_process_pose_estimation(outputs, boxes=boxes)
print(pose_results)
expected_bbox = torch.tensor([391.9900, 190.0800, 391.1575, 189.3034])
expected_keypoints = torch.tensor(
[
[3.9813e02, 1.8184e02],
[3.9828e02, 1.7981e02],
[3.9596e02, 1.7948e02],
]
)
expected_scores = torch.tensor([8.7529e-01, 8.4315e-01, 9.2678e-01])
self.assertEqual(len(pose_results), 2)
self.assertEqual(len(pose_results[0]), 2)
torch.testing.assert_close(pose_results[0][1]["bbox"].cpu(), expected_bbox, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(pose_results[0][1]["keypoints"][:3].cpu(), expected_keypoints, rtol=1e-2, atol=1e-2)
torch.testing.assert_close(pose_results[0][1]["scores"][:3].cpu(), expected_scores, rtol=1e-4, atol=1e-4)