transformers/tests/models/vitpose/test_image_processing_vitpose.py
NielsRogge 8490d3159c
Add ViTPose (#30530)
* First draft

* Make fixup

* Make forward pass worké

* Improve code

* More improvements

* More improvements

* Make predictions match

* More improvements

* Improve image processor

* Fix model tests

* Add classic decoder

* Convert classic decoder

* Verify image processor

* Fix classic decoder logits

* Clean up

* Add post_process_pose_estimation

* Improve post_process_pose_estimation

* Use AutoBackbone

* Add support for MoE models

* Fix tests, improve num_experts%

* Improve variable names

* Make fixup

* More improvements

* Improve post_process_pose_estimation

* Compute centers and scales

* Improve postprocessing

* More improvements

* Fix ViTPoseBackbone tests

* Add docstrings, fix image processor tests

* Update index

* Use is_cv2_available

* Add model to toctree

* Add cv2 to doc tests

* Remove script

* Improve conversion script

* Add coco_to_pascal_voc

* Add box_to_center_and_scale to image_transforms

* Update tests

* Add integration test

* Fix merge

* Address comments

* Replace numpy by pytorch, improve docstrings

* Remove get_input_embeddings

* Address comments

* Move coco_to_pascal_voc

* Address comment

* Fix style

* Address comments

* Fix test

* Address comment

* Remove udp

* Remove comment

* [WIP] need to check if the numpy function is same as cv

* add scipy affine_transform

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* refactor convert

* add output_shape

* add atol 5e-2

* Use hf_hub_download in conversion script

* make box_to_center more applicable

* skipt test_get_set_embedding

* fix to accept array and fix CI

* add co-contributor

* make it to tensor type output

* add torch

* change to torch tensor

* add more test

* minor change

* CI test change

* import torch should be above ImageProcessor

* make style

* try not use torch in def

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/vitpose/modeling_vitpose.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* fix

* fix

* add caution

* make more detail about dataset_index

* Update src/transformers/models/vitpose/modeling_vitpose.py

Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>

* add docs

* Update docs/source/en/model_doc/vitpose.md

* Update src/transformers/models/vitpose/configuration_vitpose.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/__init__.py

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Revert "Update src/transformers/__init__.py"

This reverts commit 7ffa504450.

* change name

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/vitpose/test_modeling_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/vitpose.md

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/vitpose/modeling_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* move vitpose only function to image_processor

* raise valueerror when using timm backbone

* use out_indices

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* remove camel-case of def flip_back

* rename vitposeEstimatorOutput

* Update src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* fix confused camelcase of MLP

* remove in-place logic

* clear scale description

* make consistent batch format

* docs update

* formatting docstring

* add batch tests

* test docs change

* Update src/transformers/models/vitpose/image_processing_vitpose.py

* Update src/transformers/models/vitpose/configuration_vitpose.py

* chagne ViT to Vit

* change to enable MoE

* make fix-copies

* Update docs/source/en/model_doc/vitpose.md

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* extract udp

* add more described docs

* simple fix

* change to accept target_size

* make style

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/vitpose/configuration_vitpose.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* change to `verify_backbone_config_arguments`

* Update docs/source/en/model_doc/vitpose.md

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* remove unnecessary copy

* make config immutable

* enable gradient checkpointing

* update inappropriate docstring

* linting docs

* split function for visibility

* make style

* check isinstances

* change to acceptable use_pretrained_backbone

* make style

* remove copy in docs

* Update src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* Update docs/source/en/model_doc/vitpose.md

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* Update src/transformers/models/vitpose/modeling_vitpose.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* simple fix + make style

* change input config of activation function to string

* Update docs/source/en/model_doc/vitpose.md

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* tmp docs

* delete index.md

* make fix-copies

* simple fix

* change conversion to sam2/mllama style

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* Update src/transformers/models/vitpose/image_processing_vitpose.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* refactor convert

* add supervision

* Update src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* remove reduntant def

* seperate code block for visualization

* add validation for num_moe

* final commit

* add labels

* [run-slow] vitpose, vitpose_backbone

* Update src/transformers/models/vitpose/convert_vitpose_to_hf.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* enable all conversion

* final commit

* [run-slow] vitpose, vitpose_backbone

* ruff check --fix

* [run-slow] vitpose, vitpose_backbone

* rename split module

* [run-slow] vitpose, vitpose_backbone

* fix pos_embed

* Simplify init

* Revert "fix pos_embed"

This reverts commit 2c56a4806e.

* refactor single loop

* allow flag to enable custom model

* efficiency of MoE to not use unused experts

* make style

* Fix range -> arange to avoid warning

* Revert MOE router, a new one does not work

* Fix postprocessing a bit (labels)

* Fix type hint

* Fix docs snippets

* Fix links to checkpoints

* Fix checkpoints in tests

* Fix test

* Add image to docs

---------

Co-authored-by: Niels Rogge <nielsrogge@nielss-mbp.home>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: sangbumchoi <danielsejong55@gmail.com>
Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
2025-01-08 16:02:14 +00:00

230 lines
9.7 KiB
Python

# coding=utf-8
# 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(unittest.TestCase):
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),
)