608 lines
22 KiB
Python
608 lines
22 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os
|
|
import numpy as np
|
|
import torch
|
|
from functools import partial
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.utils.checkpoint as checkpoint
|
|
|
|
from .layers import drop_path, to_2tuple, trunc_normal_
|
|
|
|
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
|
|
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
|
|
|
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
|
|
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
|
|
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
|
|
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
|
|
'survival rate' as the argument.
|
|
|
|
"""
|
|
if drop_prob == 0. or not training:
|
|
return x
|
|
keep_prob = 1 - drop_prob
|
|
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
|
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
|
if keep_prob > 0.0 and scale_by_keep:
|
|
random_tensor.div_(keep_prob)
|
|
return x * random_tensor
|
|
|
|
def get_abs_pos(abs_pos, h, w, ori_h, ori_w, has_cls_token=True):
|
|
"""
|
|
Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token
|
|
dimension for the original embeddings.
|
|
Args:
|
|
abs_pos (Tensor): absolute positional embeddings with (1, num_position, C).
|
|
has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token.
|
|
hw (Tuple): size of input image tokens.
|
|
|
|
Returns:
|
|
Absolute positional embeddings after processing with shape (1, H, W, C)
|
|
"""
|
|
cls_token = None
|
|
B, L, C = abs_pos.shape
|
|
if has_cls_token:
|
|
cls_token = abs_pos[:, 0:1]
|
|
abs_pos = abs_pos[:, 1:]
|
|
|
|
if ori_h != h or ori_w != w:
|
|
new_abs_pos = F.interpolate(
|
|
abs_pos.reshape(1, ori_h, ori_w, -1).permute(0, 3, 1, 2),
|
|
size=(h, w),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
).permute(0, 2, 3, 1).reshape(B, -1, C)
|
|
|
|
else:
|
|
new_abs_pos = abs_pos
|
|
|
|
if cls_token is not None:
|
|
new_abs_pos = torch.cat([cls_token, new_abs_pos], dim=1)
|
|
return new_abs_pos
|
|
|
|
class DropPath(nn.Module):
|
|
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
|
"""
|
|
def __init__(self, drop_prob=None):
|
|
super(DropPath, self).__init__()
|
|
self.drop_prob = drop_prob
|
|
|
|
def forward(self, x):
|
|
return drop_path(x, self.drop_prob, self.training)
|
|
|
|
def extra_repr(self):
|
|
return 'p={}'.format(self.drop_prob)
|
|
|
|
class Mlp(nn.Module):
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Linear(hidden_features, out_features)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
def forward(self, x):
|
|
x = self.fc1(x)
|
|
x = self.act(x)
|
|
x = self.fc2(x)
|
|
x = self.drop(x)
|
|
return x
|
|
|
|
class MoEMlp(nn.Module):
|
|
def __init__(self, num_expert=1, in_features=1024, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., part_features=256):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.part_features = part_features
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Linear(hidden_features, out_features - part_features)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
self.num_expert = num_expert
|
|
experts = []
|
|
|
|
for i in range(num_expert):
|
|
experts.append(
|
|
nn.Linear(hidden_features, part_features)
|
|
)
|
|
self.experts = nn.ModuleList(experts)
|
|
|
|
def forward(self, x, indices):
|
|
|
|
expert_x = torch.zeros_like(x[:, :, -self.part_features:], device=x.device, dtype=x.dtype)
|
|
|
|
x = self.fc1(x)
|
|
x = self.act(x)
|
|
shared_x = self.fc2(x)
|
|
indices = indices.view(-1, 1, 1)
|
|
|
|
# to support ddp training
|
|
for i in range(self.num_expert):
|
|
selectedIndex = (indices == i)
|
|
current_x = self.experts[i](x) * selectedIndex
|
|
expert_x = expert_x + current_x
|
|
|
|
x = torch.cat([shared_x, expert_x], dim=-1)
|
|
|
|
return x
|
|
|
|
class Attention(nn.Module):
|
|
def __init__(
|
|
self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
|
|
proj_drop=0., attn_head_dim=None,):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
self.dim = dim
|
|
|
|
if attn_head_dim is not None:
|
|
head_dim = attn_head_dim
|
|
all_head_dim = head_dim * self.num_heads
|
|
|
|
self.scale = qk_scale or head_dim ** -0.5
|
|
|
|
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias)
|
|
|
|
self.attn_drop = nn.Dropout(attn_drop)
|
|
self.proj = nn.Linear(all_head_dim, dim)
|
|
self.proj_drop = nn.Dropout(proj_drop)
|
|
|
|
def forward(self, x):
|
|
B, N, C = x.shape
|
|
qkv = self.qkv(x)
|
|
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
|
|
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
|
|
|
q = q * self.scale
|
|
attn = (q @ k.transpose(-2, -1))
|
|
|
|
attn = attn.softmax(dim=-1)
|
|
attn = self.attn_drop(attn)
|
|
|
|
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
|
|
return x
|
|
|
|
class Block(nn.Module):
|
|
|
|
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
|
|
drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU,
|
|
norm_layer=nn.LayerNorm, attn_head_dim=None, num_expert=1, part_features=None
|
|
):
|
|
super().__init__()
|
|
|
|
self.norm1 = norm_layer(dim)
|
|
self.attn = Attention(
|
|
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
|
attn_drop=attn_drop, proj_drop=drop, attn_head_dim=attn_head_dim
|
|
)
|
|
|
|
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
self.norm2 = norm_layer(dim)
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = MoEMlp(num_expert=num_expert, in_features=dim, hidden_features=mlp_hidden_dim,
|
|
act_layer=act_layer, drop=drop, part_features=part_features)
|
|
|
|
def forward(self, x, indices=None):
|
|
|
|
x = x + self.drop_path(self.attn(self.norm1(x)))
|
|
x = x + self.drop_path(self.mlp(self.norm2(x), indices))
|
|
return x
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
""" Image to Patch Embedding
|
|
"""
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, ratio=1):
|
|
super().__init__()
|
|
img_size = to_2tuple(img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (ratio ** 2)
|
|
self.patch_shape = (int(img_size[0] // patch_size[0] * ratio), int(img_size[1] // patch_size[1] * ratio))
|
|
self.origin_patch_shape = (int(img_size[0] // patch_size[0]), int(img_size[1] // patch_size[1]))
|
|
self.img_size = img_size
|
|
self.patch_size = patch_size
|
|
self.num_patches = num_patches
|
|
|
|
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=(patch_size[0] // ratio), padding=4 + 2 * (ratio//2-1))
|
|
|
|
def forward(self, x, **kwargs):
|
|
B, C, H, W = x.shape
|
|
x = self.proj(x)
|
|
Hp, Wp = x.shape[2], x.shape[3]
|
|
|
|
x = x.flatten(2).transpose(1, 2)
|
|
return x, (Hp, Wp)
|
|
|
|
|
|
class HybridEmbed(nn.Module):
|
|
""" CNN Feature Map Embedding
|
|
Extract feature map from CNN, flatten, project to embedding dim.
|
|
"""
|
|
def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
|
|
super().__init__()
|
|
assert isinstance(backbone, nn.Module)
|
|
img_size = to_2tuple(img_size)
|
|
self.img_size = img_size
|
|
self.backbone = backbone
|
|
if feature_size is None:
|
|
with torch.no_grad():
|
|
training = backbone.training
|
|
if training:
|
|
backbone.eval()
|
|
o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
|
|
feature_size = o.shape[-2:]
|
|
feature_dim = o.shape[1]
|
|
backbone.train(training)
|
|
else:
|
|
feature_size = to_2tuple(feature_size)
|
|
feature_dim = self.backbone.feature_info.channels()[-1]
|
|
self.num_patches = feature_size[0] * feature_size[1]
|
|
self.proj = nn.Linear(feature_dim, embed_dim)
|
|
|
|
def forward(self, x):
|
|
x = self.backbone(x)[-1]
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.proj(x)
|
|
return x
|
|
|
|
class ViTMoE(nn.Module):
|
|
def __init__(self,
|
|
img_size=224, patch_size=16, in_chans=3, num_classes=80, embed_dim=768, depth=12,
|
|
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
|
|
drop_path_rate=0., hybrid_backbone=None, norm_layer=None, use_checkpoint=False,
|
|
frozen_stages=-1, ratio=1, last_norm=True,
|
|
patch_padding='pad', freeze_attn=False, freeze_ffn=False,
|
|
num_expert=1, part_features=None
|
|
):
|
|
# Protect mutable default arguments
|
|
super(ViTMoE, self).__init__()
|
|
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
|
self.num_classes = num_classes
|
|
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
|
self.frozen_stages = frozen_stages
|
|
self.use_checkpoint = use_checkpoint
|
|
self.patch_padding = patch_padding
|
|
self.freeze_attn = freeze_attn
|
|
self.freeze_ffn = freeze_ffn
|
|
self.depth = depth
|
|
|
|
if hybrid_backbone is not None:
|
|
self.patch_embed = HybridEmbed(
|
|
hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
|
|
else:
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ratio=ratio)
|
|
num_patches = self.patch_embed.num_patches
|
|
|
|
self.part_features = part_features
|
|
|
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
|
|
|
self.blocks = nn.ModuleList([
|
|
Block(
|
|
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
|
|
num_expert=num_expert, part_features=part_features
|
|
)
|
|
for i in range(depth)])
|
|
|
|
self.last_norm = norm_layer(embed_dim) if last_norm else nn.Identity()
|
|
|
|
if self.pos_embed is not None:
|
|
trunc_normal_(self.pos_embed, std=.02)
|
|
|
|
self._freeze_stages()
|
|
|
|
def _freeze_stages(self):
|
|
"""Freeze parameters."""
|
|
if self.frozen_stages >= 0:
|
|
self.patch_embed.eval()
|
|
for param in self.patch_embed.parameters():
|
|
param.requires_grad = False
|
|
|
|
for i in range(1, self.frozen_stages + 1):
|
|
m = self.blocks[i]
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
if self.freeze_attn:
|
|
for i in range(0, self.depth):
|
|
m = self.blocks[i]
|
|
m.attn.eval()
|
|
m.norm1.eval()
|
|
for param in m.attn.parameters():
|
|
param.requires_grad = False
|
|
for param in m.norm1.parameters():
|
|
param.requires_grad = False
|
|
|
|
if self.freeze_ffn:
|
|
self.pos_embed.requires_grad = False
|
|
self.patch_embed.eval()
|
|
for param in self.patch_embed.parameters():
|
|
param.requires_grad = False
|
|
for i in range(0, self.depth):
|
|
m = self.blocks[i]
|
|
m.mlp.eval()
|
|
m.norm2.eval()
|
|
for param in m.mlp.parameters():
|
|
param.requires_grad = False
|
|
for param in m.norm2.parameters():
|
|
param.requires_grad = False
|
|
|
|
def init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
super().init_weights(pretrained, patch_padding=self.patch_padding, part_features=self.part_features)
|
|
|
|
if pretrained is None:
|
|
def _init_weights(m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
self.apply(_init_weights)
|
|
|
|
def get_num_layers(self):
|
|
return len(self.blocks)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'pos_embed', 'cls_token'}
|
|
|
|
def forward_features(self, x, dataset_source=None):
|
|
B, C, H, W = x.shape
|
|
x, (Hp, Wp) = self.patch_embed(x)
|
|
|
|
if self.pos_embed is not None:
|
|
# fit for multiple GPU training
|
|
# since the first element for pos embed (sin-cos manner) is zero, it will cause no difference
|
|
x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1]
|
|
|
|
for blk in self.blocks:
|
|
if self.use_checkpoint:
|
|
x = checkpoint.checkpoint(blk, x, dataset_source)
|
|
else:
|
|
x = blk(x, dataset_source)
|
|
|
|
x = self.last_norm(x)
|
|
|
|
xp = x.permute(0, 2, 1).reshape(B, -1, Hp, Wp).contiguous()
|
|
|
|
return xp
|
|
|
|
def forward(self, x, dataset_source=None):
|
|
x = self.forward_features(x, dataset_source)
|
|
return x
|
|
|
|
def train(self, mode=True):
|
|
"""Convert the model into training mode."""
|
|
super().train(mode)
|
|
self._freeze_stages()
|
|
|
|
class Head(nn.Module):
|
|
def __init__(self, in_channels,
|
|
out_channels,
|
|
num_deconv_layers=3,
|
|
num_deconv_filters=(256, 256, 256),
|
|
num_deconv_kernels=(4, 4, 4),):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.deconv_layers = self._make_deconv_layer(num_deconv_layers, num_deconv_filters, num_deconv_kernels)
|
|
self.final_layer = nn.Conv2d(in_channels=num_deconv_filters[-1], out_channels=out_channels,
|
|
kernel_size=1, stride=1, padding=0)
|
|
|
|
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
|
|
layers = []
|
|
for i in range(num_layers):
|
|
kernel, padding, output_padding = \
|
|
self._get_deconv_cfg(num_kernels[i])
|
|
|
|
planes = num_filters[i]
|
|
layers.append(
|
|
nn.ConvTranspose2d(
|
|
in_channels=self.in_channels,
|
|
out_channels=planes,
|
|
kernel_size=kernel,
|
|
stride=2,
|
|
padding=padding,
|
|
output_padding=output_padding,
|
|
bias=False))
|
|
layers.append(nn.BatchNorm2d(planes))
|
|
layers.append(nn.ReLU(inplace=True))
|
|
self.in_channels = planes
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
@staticmethod
|
|
def _get_deconv_cfg(deconv_kernel):
|
|
"""Get configurations for deconv layers."""
|
|
if deconv_kernel == 4:
|
|
padding = 1
|
|
output_padding = 0
|
|
elif deconv_kernel == 3:
|
|
padding = 1
|
|
output_padding = 1
|
|
elif deconv_kernel == 2:
|
|
padding = 0
|
|
output_padding = 0
|
|
else:
|
|
raise ValueError(f'Not supported num_kernels ({deconv_kernel}).')
|
|
|
|
return deconv_kernel, padding, output_padding
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
x = self.deconv_layers(x)
|
|
x = self.final_layer(x)
|
|
return x
|
|
|
|
|
|
class ComposeVit(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
cfg_backbone = dict(
|
|
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,
|
|
num_expert=6,
|
|
part_features=192
|
|
)
|
|
cfg_head = dict(
|
|
in_channels=768,
|
|
out_channels=17,
|
|
num_deconv_layers=2,
|
|
num_deconv_filters=(256, 256),
|
|
num_deconv_kernels=(4, 4),
|
|
)
|
|
cfg_head_133 = dict(
|
|
in_channels=768,
|
|
out_channels=133,
|
|
num_deconv_layers=2,
|
|
num_deconv_filters=(256, 256),
|
|
num_deconv_kernels=(4, 4),
|
|
)
|
|
self.backbone = ViTMoE(**cfg_backbone)
|
|
self.keypoint_head = Head(**cfg_head)
|
|
self.associate_head = Head(**cfg_head_133)
|
|
|
|
def forward(self, x):
|
|
indices = torch.zeros((x.shape[0]), dtype=torch.long, device=x.device)
|
|
back_out = self.backbone(x, indices)
|
|
out = self.keypoint_head(back_out)
|
|
if True:
|
|
indices += 5 # 最后一个是whole body dataset
|
|
back_133 = self.backbone(x, indices)
|
|
out_133 = self.associate_head(back_133)
|
|
out_foot = out_133[:, 17:23]
|
|
out = torch.cat([out, out_foot], dim=1)
|
|
if False:
|
|
import cv2
|
|
vis = x[0].permute(1, 2, 0).cpu().numpy()
|
|
mean= np.array([0.485, 0.456, 0.406]).reshape(1, 1, 3)
|
|
std=np.array([0.229, 0.224, 0.225]).reshape(1, 1 ,3)
|
|
vis = np.clip(vis * std + mean, 0., 1.)
|
|
vis = (vis[:,:,::-1] * 255).astype(np.uint8)
|
|
value = out_133[0].detach().cpu().numpy()
|
|
vis_all = []
|
|
for i in range(value.shape[0]):
|
|
_val = np.clip(value[i], 0., 1.)
|
|
_val = (_val * 255).astype(np.uint8)
|
|
_val = cv2.resize(_val, None, fx=4, fy=4)
|
|
_val = cv2.applyColorMap(_val, cv2.COLORMAP_JET)
|
|
_vis = cv2.addWeighted(vis, 0.5, _val, 0.5, 0)
|
|
vis_all.append(_vis)
|
|
from easymocap.mytools.vis_base import merge
|
|
cv2.imwrite('debug.jpg', merge(vis_all))
|
|
|
|
import ipdb; ipdb.set_trace()
|
|
return {
|
|
'output': out
|
|
}
|
|
|
|
from ..basetopdown import BaseTopDownModelCache
|
|
from ..topdown_keypoints import BaseKeypoints
|
|
|
|
class MyViT(BaseTopDownModelCache, BaseKeypoints):
|
|
def __init__(self, ckpt='data/models/vitpose+_base.pth', single_person=True, url='https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G', **kwargs):
|
|
super().__init__(name='myvit', bbox_scale=1.25,
|
|
res_input=[192, 256], **kwargs)
|
|
self.single_person = single_person
|
|
model = ComposeVit()
|
|
if not os.path.exists(ckpt):
|
|
print('')
|
|
print('{} not exists, please download it from {} and place it to {}'.format(ckpt, url, ckpt))
|
|
print('')
|
|
raise FileNotFoundError
|
|
ckpt = torch.load(ckpt, map_location='cpu')['state_dict']
|
|
ckpt_backbone = {key:val for key, val in ckpt.items() if key.startswith('backbone.')}
|
|
ckpt_head = {key:val for key, val in ckpt.items() if key.startswith('keypoint_head.')}
|
|
key_whole = 'associate_keypoint_heads.4.'
|
|
ckpt_head_133 = {key.replace(key_whole, 'associate_head.'):val for key, val in ckpt.items() if key.startswith(key_whole)}
|
|
ckpt_backbone.update(ckpt_head)
|
|
ckpt_backbone.update(ckpt_head_133)
|
|
state_dict = ckpt_backbone
|
|
self.load_checkpoint(model, state_dict, prefix='', strict=True)
|
|
model.eval()
|
|
self.model = model
|
|
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
|
self.model.to(self.device)
|
|
|
|
def dump(self, cachename, output):
|
|
_output = output['output']
|
|
kpts = self.get_max_preds(_output)
|
|
kpts_ori = self.batch_affine_transform(kpts, output['inv_trans'])
|
|
kpts = np.concatenate([kpts_ori, kpts[..., -1:]], axis=-1)
|
|
output = {'keypoints': kpts}
|
|
super().dump(cachename, output)
|
|
return output
|
|
|
|
def estimate_keypoints(self, bbox, images, imgnames):
|
|
squeeze = False
|
|
if not isinstance(images, list):
|
|
images = [images]
|
|
imgnames = [imgnames]
|
|
bbox = [bbox]
|
|
squeeze = True
|
|
nViews = len(images)
|
|
kpts_all = []
|
|
for nv in range(nViews):
|
|
_bbox = bbox[nv]
|
|
if _bbox.shape[0] == 0:
|
|
if self.single_person:
|
|
kpts = np.zeros((1, self.num_joints, 3))
|
|
else:
|
|
kpts = np.zeros((_bbox.shape[0], self.num_joints, 3))
|
|
else:
|
|
img = images[nv]
|
|
# TODO: add flip test
|
|
out = super().__call__(_bbox, img, imgnames[nv])
|
|
kpts = out['params']['keypoints']
|
|
if kpts.shape[-2] == 23:
|
|
kpts = self.coco23tobody25(kpts)
|
|
elif kpts.shape[-2] == 17:
|
|
kpts = self.coco17tobody25(kpts)
|
|
else:
|
|
raise NotImplementedError
|
|
kpts_all.append(kpts)
|
|
if self.single_person:
|
|
kpts_all = [k[0] for k in kpts_all]
|
|
kpts_all = np.stack(kpts_all)
|
|
if squeeze:
|
|
kpts_all = kpts_all[0]
|
|
return {
|
|
'keypoints': kpts_all
|
|
}
|
|
|
|
def __call__(self, bbox, images, imgnames):
|
|
return self.estimate_keypoints(bbox, images, imgnames)
|
|
|
|
if __name__ == '__main__':
|
|
# Load checkpoint
|
|
rand_input = torch.rand(1, 3, 256, 192)
|
|
model = MyViT()
|