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# Code adapted from: https://github.com/gpastal24/ViTPose-Pytorch
from .topdown_heatmap_simple_head import TopdownHeatmapSimpleHead
import torch
from .backbone import ViT
import torchvision.transforms.functional as F
import torch.nn as nn
import tops
model_large = dict(
type="TopDown",
pretrained=None,
backbone=dict(
type="ViT",
img_size=(256, 192),
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.5,
),
keypoint_head=dict(
type="TopdownHeatmapSimpleHead",
in_channels=1024,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(
final_conv_kernel=1,
),
out_channels=17,
loss_keypoint=dict(type="JointsMSELoss", use_target_weight=True),
),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process="default",
shift_heatmap=False,
target_type="GaussianHeatmap",
modulate_kernel=11,
use_udp=True,
),
)
model_base = dict(
type="TopDown",
pretrained=None,
backbone=dict(
type="ViT",
img_size=(256, 192),
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type="TopdownHeatmapSimpleHead",
in_channels=768,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(
final_conv_kernel=1,
),
out_channels=17,
loss_keypoint=dict(type="JointsMSELoss", use_target_weight=True),
),
train_cfg=dict(),
test_cfg=dict(),
)
model_huge = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1280,
depth=32,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.55,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1280,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=17,
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=False,
target_type="GaussianHeatmap",
modulate_kernel=11,
use_udp=True))
class VitPoseModel(nn.Module):
def __init__(self, model_name):
super().__init__()
assert model_name in ["vit_base", "vit_large", "vit_huge"]
model = {
"vit_base": model_base,
"vit_large": model_large,
"vit_huge": model_huge
}[model_name]
weight_url = {
"vit_base": "https://api.loke.aws.unit.no/dlr-gui-backend-resources-content/v2/contents/links/90235a26-3b8c-427d-a264-c68155abecdcfcfcd8a9-0388-4575-b85b-607d3c0a9b149bef8f0f-a0f9-4662-a561-1b47ba5f1636",
"vit_large": "https://api.loke.aws.unit.no/dlr-gui-backend-resources-content/v2/contents/links/a580a44c-0afd-43ac-a2cb-9956c32b1d1a78c51ecb-81bb-4345-8710-13904cb9dbbe0703db2d-8534-42e0-ac4d-518ab51fe7db",
"vit_huge": "https://api.loke.aws.unit.no/dlr-gui-backend-resources-content/v2/contents/links/a33b6ada-4d2f-4ef7-8f83-b33f58b69f5b2a62e181-2131-467d-a900-027157a08571d761fad4-785b-4b84-8596-8932c7857e44"
}[model_name]
file_name = {
"vit_base": "vit-b-multi-coco-595b5e128b.pth",
"vit_large": "vit-l-multi-coco-9475d27cec.pth",
"vit_huge": "vit-h-multi-coco-dbc06d4337.pth",
}[model_name]
# Set check_hash to true if you suspect a download error.
weight_path = tops.download_file(
weight_url, file_name=file_name, check_hash=True)
self.keypoint_head = tops.to_cuda(TopdownHeatmapSimpleHead(
in_channels=model["keypoint_head"]["in_channels"],
out_channels=model["keypoint_head"]["out_channels"],
num_deconv_filters=model["keypoint_head"]["num_deconv_filters"],
num_deconv_kernels=model["keypoint_head"]["num_deconv_kernels"],
num_deconv_layers=model["keypoint_head"]["num_deconv_layers"],
extra=model["keypoint_head"]["extra"],
))
# print(head)
self.backbone = tops.to_cuda(ViT(
img_size=model["backbone"]["img_size"],
patch_size=model["backbone"]["patch_size"],
embed_dim=model["backbone"]["embed_dim"],
depth=model["backbone"]["depth"],
num_heads=model["backbone"]["num_heads"],
ratio=model["backbone"]["ratio"],
mlp_ratio=model["backbone"]["mlp_ratio"],
qkv_bias=model["backbone"]["qkv_bias"],
drop_path_rate=model["backbone"]["drop_path_rate"],
))
ckpt = torch.load(weight_path, map_location=tops.get_device())
self.load_state_dict(ckpt["state_dict"])
self.backbone.eval()
self.keypoint_head.eval()
def forward(self, img: torch.Tensor, boxes_ltrb: torch.Tensor):
assert img.ndim == 3
assert img.dtype == torch.uint8
assert boxes_ltrb.ndim == 2 and boxes_ltrb.shape[1] == 4
assert boxes_ltrb.dtype == torch.long
boxes_ltrb = boxes_ltrb.clamp(0)
padded_boxes = torch.zeros_like(boxes_ltrb)
images = torch.zeros((len(boxes_ltrb), 3, 256, 192), device=img.device, dtype=torch.float32)
for i, (x0, y0, x1, y1) in enumerate(boxes_ltrb):
x1 = min(img.shape[-1], x1)
y1 = min(img.shape[-2], y1)
correction_factor = 256 / 192 * (x1 - x0) / (y1 - y0)
if correction_factor > 1:
# increase y side
center = y0 + (y1 - y0) // 2
length = (y1-y0).mul(correction_factor).round().long()
y0_new = center - length.div(2).long()
y1_new = center + length.div(2).long()
image_crop = img[:, y0:y1, x0:x1]
# print(y1,y2,x1,x2)
pad = ((y0_new-y0).abs(), (y1_new-y1).abs())
# pad = (int(abs(y0_new-y0))), int(abs(y1_new-y1))
image_crop = torch.nn.functional.pad(image_crop, [*(0, 0), *pad])
padded_boxes[i] = torch.tensor([x0, y0_new, x1, y1_new])
else:
center = x0 + (x1 - x0) // 2
length = (x1-x0).div(correction_factor).round().long()
x0_new = center - length.div(2).long()
x1_new = center + length.div(2).long()
image_crop = img[:, y0:y1, x0:x1]
pad = ((x0_new-x0).abs(), (x1_new-x1).abs())
image_crop = torch.nn.functional.pad(image_crop, [*pad, ])
padded_boxes[i] = torch.tensor([x0_new, y0, x1_new, y1])
image_crop = F.resize(image_crop.float(), (256, 192), antialias=True)
image_crop = F.normalize(image_crop, mean=[0.485*255, 0.456*255,
0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
images[i] = image_crop
x = self.backbone(images)
out = self.keypoint_head(x)
pts = torch.empty((out.shape[0], out.shape[1], 3), dtype=torch.float32, device=img.device)
# For each human, for each joint: y, x, confidence
b, indices = torch.max(out, dim=2)
b, indices = torch.max(b, dim=2)
c, indicesc = torch.max(out, dim=3)
c, indicesc = torch.max(c, dim=2)
dim1 = torch.tensor(1./64, device=img.device)
dim2 = torch.tensor(1./48, device=img.device)
for i in range(0, out.shape[0]):
pts[i, :, 0] = indicesc[i, :] * dim1 * (padded_boxes[i][3] - padded_boxes[i][1]) + padded_boxes[i][1]
pts[i, :, 1] = indices[i, :] * dim2 * (padded_boxes[i][2] - padded_boxes[i][0]) + padded_boxes[i][0]
pts[i, :, 2] = c[i, :]
pts = pts[:, :, [1, 0, 2]]
return pts
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