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# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner.base_module import BaseModule
from mmcv.utils import to_2tuple
from mmpose.models.builder import TRANSFORMER
from easydict import EasyDict
from einops import rearrange, repeat
from mmcv.runner import force_fp32
from mmcv.cnn.bricks.transformer import (BaseTransformerLayer,
TransformerLayerSequence,
build_transformer_layer_sequence)
from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER,
TRANSFORMER_LAYER_SEQUENCE)
import torch.distributions as distributions
from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
from torch.nn.init import normal_
import copy
import warnings
from mmcv.cnn import build_activation_layer, build_norm_layer, xavier_init
from utils.human_models import smpl_x
from config import cfg
def point_sample(input, point_coords, **kwargs):
"""
A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors.
Unlike :function:`torch.nn.functional.grid_sample` it assumes `point_coords` to lie inside
[0, 1] x [0, 1] square.
Args:
input (Tensor): A tensor of shape (N, C, H, W) that contains features map on a H x W grid.
point_coords (Tensor): A tensor of shape (N, P, 2) or (N, Hgrid, Wgrid, 2) that contains
[0, 1] x [0, 1] normalized point coordinates.
Returns:
output (Tensor): A tensor of shape (N, C, P) or (N, C, Hgrid, Wgrid) that contains
features for points in `point_coords`. The features are obtained via bilinear
interplation from `input` the same way as :function:`torch.nn.functional.grid_sample`.
"""
add_dim = False
if point_coords.dim() == 3:
add_dim = True
point_coords = point_coords.unsqueeze(2)
output = F.grid_sample(input, 2.0 * point_coords - 1.0, **kwargs)
if add_dim:
output = output.squeeze(3)
return output
def nlc_to_nchw(x, hw_shape):
"""Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
Args:
x (Tensor): The input tensor of shape [N, L, C] before conversion.
hw_shape (Sequence[int]): The height and width of output feature map.
Returns:
Tensor: The output tensor of shape [N, C, H, W] after conversion.
"""
H, W = hw_shape
assert len(x.shape) == 3
B, L, C = x.shape
assert L == H * W, 'The seq_len does not match H, W'
return x.transpose(1, 2).reshape(B, C, H, W).contiguous()
def nchw_to_nlc(x):
"""Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.
Args:
x (Tensor): The input tensor of shape [N, C, H, W] before conversion.
Returns:
Tensor: The output tensor of shape [N, L, C] after conversion.
"""
assert len(x.shape) == 4
return x.flatten(2).transpose(1, 2).contiguous()
class AdaptivePadding(nn.Module):
"""Applies padding to input (if needed) so that input can get fully covered
by filter you specified. It support two modes "same" and "corner". The
"same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
input. The "corner" mode would pad zero to bottom right.
Args:
kernel_size (int | tuple): Size of the kernel:
stride (int | tuple): Stride of the filter. Default: 1:
dilation (int | tuple): Spacing between kernel elements.
Default: 1
padding (str): Support "same" and "corner", "corner" mode
would pad zero to bottom right, and "same" mode would
pad zero around input. Default: "corner".
Example:
>>> kernel_size = 16
>>> stride = 16
>>> dilation = 1
>>> input = torch.rand(1, 1, 15, 17)
>>> adap_pad = AdaptivePadding(
>>> kernel_size=kernel_size,
>>> stride=stride,
>>> dilation=dilation,
>>> padding="corner")
>>> out = adap_pad(input)
>>> assert (out.shape[2], out.shape[3]) == (16, 32)
>>> input = torch.rand(1, 1, 16, 17)
>>> out = adap_pad(input)
>>> assert (out.shape[2], out.shape[3]) == (16, 32)
"""
def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
super(AdaptivePadding, self).__init__()
assert padding in ('same', 'corner')
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
padding = to_2tuple(padding)
dilation = to_2tuple(dilation)
self.padding = padding
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
def get_pad_shape(self, input_shape):
input_h, input_w = input_shape
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.stride
output_h = math.ceil(input_h / stride_h)
output_w = math.ceil(input_w / stride_w)
pad_h = max((output_h - 1) * stride_h +
(kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
pad_w = max((output_w - 1) * stride_w +
(kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
return pad_h, pad_w
def forward(self, x):
pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
if pad_h > 0 or pad_w > 0:
if self.padding == 'corner':
x = F.pad(x, [0, pad_w, 0, pad_h])
elif self.padding == 'same':
x = F.pad(x, [
pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
pad_h - pad_h // 2
])
return x
class PatchEmbed(BaseModule):
"""Image to Patch Embedding.
We use a conv layer to implement PatchEmbed.
Args:
in_channels (int): The num of input channels. Default: 3
embed_dims (int): The dimensions of embedding. Default: 768
conv_type (str): The config dict for embedding
conv layer type selection. Default: "Conv2d.
kernel_size (int): The kernel_size of embedding conv. Default: 16.
stride (int): The slide stride of embedding conv.
Default: None (Would be set as `kernel_size`).
padding (int | tuple | string ): The padding length of
embedding conv. When it is a string, it means the mode
of adaptive padding, support "same" and "corner" now.
Default: "corner".
dilation (int): The dilation rate of embedding conv. Default: 1.
bias (bool): Bias of embed conv. Default: True.
norm_cfg (dict, optional): Config dict for normalization layer.
Default: None.
input_size (int | tuple | None): The size of input, which will be
used to calculate the out size. Only work when `dynamic_size`
is False. Default: None.
init_cfg (`mmcv.ConfigDict`, optional): The Config for initialization.
Default: None.
"""
def __init__(
self,
in_channels=3,
embed_dims=768,
conv_type='Conv2d',
kernel_size=16,
stride=16,
padding='corner',
dilation=1,
bias=True,
norm_cfg=None,
input_size=None,
init_cfg=None,
):
super(PatchEmbed, self).__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
if stride is None:
stride = kernel_size
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
dilation = to_2tuple(dilation)
if isinstance(padding, str):
self.adap_padding = AdaptivePadding(
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
padding=padding)
# disable the padding of conv
padding = 0
else:
self.adap_padding = None
padding = to_2tuple(padding)
self.projection = build_conv_layer(
dict(type=conv_type),
in_channels=in_channels,
out_channels=embed_dims,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias)
if norm_cfg is not None:
self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
else:
self.norm = None
if input_size:
input_size = to_2tuple(input_size)
# `init_out_size` would be used outside to
# calculate the num_patches
# when `use_abs_pos_embed` outside
self.init_input_size = input_size
if self.adap_padding:
pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
input_h, input_w = input_size
input_h = input_h + pad_h
input_w = input_w + pad_w
input_size = (input_h, input_w)
# https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
(kernel_size[0] - 1) - 1) // stride[0] + 1
w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
(kernel_size[1] - 1) - 1) // stride[1] + 1
self.init_out_size = (h_out, w_out)
else:
self.init_input_size = None
self.init_out_size = None
def forward(self, x):
"""
Args:
x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
Returns:
tuple: Contains merged results and its spatial shape.
- x (Tensor): Has shape (B, out_h * out_w, embed_dims)
- out_size (tuple[int]): Spatial shape of x, arrange as
(out_h, out_w).
"""
if self.adap_padding:
x = self.adap_padding(x)
x = self.projection(x)
out_size = (x.shape[2], x.shape[3])
x = x.flatten(2).transpose(1, 2)
if self.norm is not None:
x = self.norm(x)
return x, out_size
class PatchMerging(BaseModule):
"""Merge patch feature map.
This layer groups feature map by kernel_size, and applies norm and linear
layers to the grouped feature map. Our implementation uses `nn.Unfold` to
merge patch, which is about 25% faster than original implementation.
Instead, we need to modify pretrained models for compatibility.
Args:
in_channels (int): The num of input channels.
to gets fully covered by filter and stride you specified..
Default: True.
out_channels (int): The num of output channels.
kernel_size (int | tuple, optional): the kernel size in the unfold
layer. Defaults to 2.
stride (int | tuple, optional): the stride of the sliding blocks in the
unfold layer. Default: None. (Would be set as `kernel_size`)
padding (int | tuple | string ): The padding length of
embedding conv. When it is a string, it means the mode
of adaptive padding, support "same" and "corner" now.
Default: "corner".
dilation (int | tuple, optional): dilation parameter in the unfold
layer. Default: 1.
bias (bool, optional): Whether to add bias in linear layer or not.
Defaults: False.
norm_cfg (dict, optional): Config dict for normalization layer.
Default: dict(type='LN').
init_cfg (dict, optional): The extra config for initialization.
Default: None.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size=2,
stride=None,
padding='corner',
dilation=1,
bias=False,
norm_cfg=dict(type='LN'),
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
if stride:
stride = stride
else:
stride = kernel_size
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
dilation = to_2tuple(dilation)
if isinstance(padding, str):
self.adap_padding = AdaptivePadding(
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
padding=padding)
# disable the padding of unfold
padding = 0
else:
self.adap_padding = None
padding = to_2tuple(padding)
self.sampler = nn.Unfold(
kernel_size=kernel_size,
dilation=dilation,
padding=padding,
stride=stride)
sample_dim = kernel_size[0] * kernel_size[1] * in_channels
if norm_cfg is not None:
self.norm = build_norm_layer(norm_cfg, sample_dim)[1]
else:
self.norm = None
self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)
def forward(self, x, input_size):
"""
Args:
x (Tensor): Has shape (B, H*W, C_in).
input_size (tuple[int]): The spatial shape of x, arrange as (H, W).
Default: None.
Returns:
tuple: Contains merged results and its spatial shape.
- x (Tensor): Has shape (B, Merged_H * Merged_W, C_out)
- out_size (tuple[int]): Spatial shape of x, arrange as
(Merged_H, Merged_W).
"""
B, L, C = x.shape
assert isinstance(input_size, Sequence), f'Expect ' \
f'input_size is ' \
f'`Sequence` ' \
f'but get {input_size}'
H, W = input_size
assert L == H * W, 'input feature has wrong size'
x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W
# Use nn.Unfold to merge patch. About 25% faster than original method,
# but need to modify pretrained model for compatibility
if self.adap_padding:
x = self.adap_padding(x)
H, W = x.shape[-2:]
x = self.sampler(x)
# if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] *
(self.sampler.kernel_size[0] - 1) -
1) // self.sampler.stride[0] + 1
out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] *
(self.sampler.kernel_size[1] - 1) -
1) // self.sampler.stride[1] + 1
output_size = (out_h, out_w)
x = x.transpose(1, 2) # B, H/2*W/2, 4*C
x = self.norm(x) if self.norm else x
x = self.reduction(x)
return x, output_size
def inverse_sigmoid(x, eps=1e-5):
"""Inverse function of sigmoid.
Args:
x (Tensor): The tensor to do the
inverse.
eps (float): EPS avoid numerical
overflow. Defaults 1e-5.
Returns:
Tensor: The x has passed the inverse
function of sigmoid, has same
shape with input.
"""
x = x.clamp(min=0, max=1)
x1 = x.clamp(min=eps)
x2 = (1 - x).clamp(min=eps)
return torch.log(x1 / x2)
@TRANSFORMER_LAYER_SEQUENCE.register_module()
class DetrTransformerEncoder_zero_layer():
def __init__(self, *args, post_norm_cfg=dict(type='LN'), **kwargs):
pass
def __call__(self,
query,
key,
value,
query_pos=None,
key_pos=None,
attn_masks=None,
query_key_padding_mask=None,
key_padding_mask=None,
**kwargs):
query = query + query_pos
return query
@TRANSFORMER_LAYER.register_module()
class DetrTransformerDecoderLayer_grouped(BaseTransformerLayer):
def __init__(self,
attn_cfgs,
feedforward_channels,
ffn_dropout=0.0,
operation_order=None,
act_cfg=dict(type='ReLU', inplace=True),
norm_cfg=dict(type='LN'),
ffn_num_fcs=2,
num_joints=17,
**kwargs):
super(DetrTransformerDecoderLayer_grouped, self).__init__(
attn_cfgs=attn_cfgs,
feedforward_channels=feedforward_channels,
ffn_dropout=ffn_dropout,
operation_order=operation_order,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
ffn_num_fcs=ffn_num_fcs,
**kwargs)
# assert len(operation_order) == 6
# assert set(operation_order) == set(
# ['self_attn', 'norm', 'cross_attn', 'ffn'])
self.num_joints = num_joints
# self.num_joints = len(smpl_x.pos_joint_part['rhand'])
# self.num_joints = len(smpl_x.pos_joint_part['body']) + len(smpl_x.pos_joint_part['rhand']) + len(smpl_x.pos_joint_part['lhand'])
def forward(self,
query,
key=None,
value=None,
query_pos=None,
key_pos=None,
attn_masks=None,
query_key_padding_mask=None,
key_padding_mask=None,
**kwargs):
norm_index = 0
attn_index = 0
ffn_index = 0
identity = query
if attn_masks is None:
attn_masks = [None for _ in range(self.num_attn)]
elif isinstance(attn_masks, torch.Tensor):
attn_masks = [
copy.deepcopy(attn_masks) for _ in range(self.num_attn)
]
warnings.warn(f'Use same attn_mask in all attentions in '
f'{self.__class__.__name__} ')
else:
assert len(attn_masks) == self.num_attn, f'The length of ' \
f'attn_masks {len(attn_masks)} must be equal ' \
f'to the number of attention in ' \
f'operation_order {self.num_attn}'
for layer in self.operation_order:
if layer == 'self_attn':
# print(query.shape)
assert query.size(0) % self.num_joints == 0, f'query.shape: {query.shape}, num_joints: {self.num_joints}'
num_group = query.size(0) // self.num_joints
bs = query.size(1)
temp_query = rearrange(query, '(g k) b c -> k (g b) c',
g=num_group, k=self.num_joints)
temp_identity = rearrange(identity, '(g k) b c -> k (g b) c',
g=num_group, k=self.num_joints)
temp_query_pos = rearrange(query_pos, '(g k) b c -> k (g b) c',
g=num_group, k=self.num_joints)
temp_key = temp_value = temp_query
query = self.attentions[attn_index](
temp_query,
temp_key,
temp_value,
temp_identity if self.pre_norm else None,
query_pos=temp_query_pos,
key_pos=temp_query_pos,
attn_mask=attn_masks[attn_index],
key_padding_mask=query_key_padding_mask,
**kwargs)
query = rearrange(query, 'k (g b) c -> (g k) b c',
g=num_group, b=bs)
attn_index += 1
identity = query
elif layer == 'norm':
query = self.norms[norm_index](query)
norm_index += 1
elif layer == 'cross_attn':
query = self.attentions[attn_index](
query,
key,
value,
identity if self.pre_norm else None,
query_pos=query_pos,
key_pos=key_pos,
attn_mask=attn_masks[attn_index],
key_padding_mask=key_padding_mask,
**kwargs)
attn_index += 1
identity = query
elif layer == 'ffn':
query = self.ffns[ffn_index](
query, identity if self.pre_norm else None)
ffn_index += 1
if 'cross_attn' not in self.operation_order:
query = query + value.sum() * 0
return query
@TRANSFORMER_LAYER_SEQUENCE.register_module()
class DeformableDetrTransformerDecoder(TransformerLayerSequence):
"""Implements the decoder in DETR transformer.
Args:
return_intermediate (bool): Whether to return intermediate outputs.
coder_norm_cfg (dict): Config of last normalization layer. Default：
`LN`.
"""
def __init__(self, *args, return_intermediate=False, **kwargs):
super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs)
self.return_intermediate = return_intermediate
def forward(self,
query,
*args,
reference_points=None,
valid_ratios=None,
reg_branches=None,
fc_coord=None,
**kwargs):
output = query
intermediate = []
intermediate_reference_points = []
for lid, layer in enumerate(self.layers):
if reference_points.shape[-1] == 4:
reference_points_input = reference_points[:, :, None] * \
torch.cat([valid_ratios, valid_ratios], -1)[:, None]
else:
assert reference_points.shape[-1] == 3
# print(reference_points.shape, valid_ratios.shape) # [48,65,3], [48,4,3]
reference_points_input = reference_points[:, :, None, :2] * \
valid_ratios[:, None]
# assert reference_points.shape[-1] == 2
# reference_points_input = reference_points[:, :, None] * \
# valid_ratios[:, None]
# print(output.shape, reference_points_input.shape)
output = layer(
output,
*args,
reference_points=reference_points_input,
**kwargs)
output = output.permute(1, 0, 2)
# if reg_branches is not None:
# tmp = reg_branches[lid](output)
#
# if fc_coord is not None:
# tmp = fc_coord(tmp)
#
# if reference_points.shape[-1] == 4:
# new_reference_points = tmp + inverse_sigmoid(
# reference_points)
# new_reference_points = new_reference_points.sigmoid()
# else:
# assert reference_points.shape[-1] == 3
# new_reference_points = tmp
# new_reference_points[..., :3] = tmp[
# ..., :3] + inverse_sigmoid(reference_points)
# new_reference_points = new_reference_points.sigmoid()
# # else:
# # assert reference_points.shape[-1] == 2
# # new_reference_points = tmp
# # new_reference_points[..., :2] = tmp[
# # ..., :2] + inverse_sigmoid(reference_points)
# # new_reference_points = new_reference_points.sigmoid()
# # # reference_points = new_reference_points.detach()
# # reference_points = new_reference_points
# reference_points = new_reference_points
output = output.permute(1, 0, 2)
if self.return_intermediate:
intermediate.append(output)
intermediate_reference_points.append(reference_points)
if self.return_intermediate:
return torch.stack(intermediate), torch.stack(
intermediate_reference_points)
return output, reference_points
class Linear_with_norm(nn.Module):
def __init__(self, in_channel, out_channel, bias=True, norm=True):
super(Linear_with_norm, self).__init__()
self.bias = bias
self.norm = norm
self.linear = nn.Linear(in_channel, out_channel, bias)
nn.init.xavier_uniform_(self.linear.weight, gain=0.01)
def forward(self, x):
y = x.matmul(self.linear.weight.t())
if self.norm:
x_norm = torch.norm(x, dim=1, keepdim=True)
y = y / x_norm
if self.bias:
y = y + self.linear.bias
return y
@TRANSFORMER.register_module()
class Transformer(BaseModule):
"""Implements the DETR transformer.
Following the official DETR implementation, this module copy-paste
from torch.nn.Transformer with modifications:
* positional encodings are passed in MultiheadAttention
* extra LN at the end of encoder is removed
* decoder returns a stack of activations from all decoding layers
See `paper: End-to-End Object Detection with Transformers
<https://arxiv.org/pdf/2005.12872>`_ for details.
Args:
encoder (`mmcv.ConfigDict` | Dict): Config of
TransformerEncoder. Defaults to None.
decoder ((`mmcv.ConfigDict` | Dict)): Config of
TransformerDecoder. Defaults to None
init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
Defaults to None.
"""
def __init__(self, encoder=None, decoder=None, init_cfg=None):
super(Transformer, self).__init__(init_cfg=init_cfg)
self.encoder = build_transformer_layer_sequence(encoder)
self.decoder = build_transformer_layer_sequence(decoder)
# self.embed_dims = self.encoder.embed_dims
def init_weights(self):
# follow the official DETR to init parameters
for m in self.modules():
if hasattr(m, 'weight') and m.weight.dim() > 1:
xavier_init(m, distribution='uniform')
self._is_init = True
def forward(self, x, mask, query_embed, pos_embed):
"""Forward function for `Transformer`.
Args:
x (Tensor): Input query with shape [bs, c, h, w] where
c = embed_dims.
mask (Tensor): The key_padding_mask used for encoder and decoder,
with shape [bs, h, w].
query_embed (Tensor): The query embedding for decoder, with shape
[num_query, c].
pos_embed (Tensor): The positional encoding for encoder and
decoder, with the same shape as `x`.
Returns:
tuple[Tensor]: results of decoder containing the following tensor.
- out_dec: Output from decoder. If return_intermediate_dec \
is True output has shape [num_dec_layers, bs,
num_query, embed_dims], else has shape [1, bs, \
num_query, embed_dims].
- memory: Output results from encoder, with shape \
[bs, embed_dims, h, w].
"""
bs, c, h, w = x.shape
# use `view` instead of `flatten` for dynamically exporting to ONNX
x = x.view(bs, c, -1).permute(2, 0, 1) # [bs, c, h, w] -> [h*w, bs, c]
pos_embed = pos_embed.view(bs, c, -1).permute(2, 0, 1)
query_embed = query_embed.unsqueeze(1).repeat(
1, bs, 1) # [num_query, dim] -> [num_query, bs, dim]
mask = mask.view(bs, -1) # [bs, h, w] -> [bs, h*w]
memory = self.encoder(
query=x,
key=None,
value=None,
query_pos=pos_embed,
query_key_padding_mask=mask)
target = torch.zeros_like(query_embed)
# out_dec: [num_layers, num_query, bs, dim]
out_dec = self.decoder(
query=target,
key=memory,
value=memory,
key_pos=pos_embed,
query_pos=query_embed,
key_padding_mask=mask)
out_dec = out_dec.transpose(1, 2)
memory = memory.permute(1, 2, 0).reshape(bs, c, h, w)
return out_dec, memory
@TRANSFORMER.register_module()
class PoseurTransformer_v3(Transformer):
""" add noise training """
def __init__(self,
as_two_stage=False,
num_feature_levels=4,
two_stage_num_proposals=300,
num_joints=17,
use_soft_argmax=False,
use_soft_argmax_def=False,
proposal_feature='backbone_s', # or encoder_memory
image_size=[192, 256],
init_q_sigmoid=False,
soft_arg_stride=4,
add_feat_2_query=False,
query_pose_emb=True,
num_noise_sample=3,
num_noise_point=4,
noise_sigma=0.2,
embed_dims=256,
**kwargs):
super(PoseurTransformer_v3, self).__init__(**kwargs)
assert query_pose_emb == True
# self.num_noise_sample = num_noise_sample
self.num_noise_sample = num_noise_sample
self.num_noise_point = num_noise_point
self.noise_sigma = noise_sigma
self.add_feat_2_query = add_feat_2_query
self.as_two_stage = as_two_stage
self.num_feature_levels = num_feature_levels
self.two_stage_num_proposals = two_stage_num_proposals
try:
self.embed_dims = self.encoder.embed_dims
except:
self.embed_dims = embed_dims
self.num_joints = num_joints
# self.num_joints = 17
# self.num_joints = len(smpl_x.pos_joint_part['rhand']) # body_joints+bboxes
# self.num_joints = len(smpl_x.pos_joint_part['body']) + len(smpl_x.pos_joint_part['rhand']) + len(smpl_x.pos_joint_part['lhand'])
self.use_soft_argmax = use_soft_argmax
self.use_soft_argmax_def = use_soft_argmax_def
assert not (self.use_soft_argmax & self.use_soft_argmax_def)
self.init_q_sigmoid = init_q_sigmoid
self.image_size = image_size
self.soft_arg_stride = soft_arg_stride
self.proposal_feature = proposal_feature
self.query_pose_emb = query_pose_emb
self.prior = distributions.MultivariateNormal(torch.zeros(2), torch.eye(2) * self.noise_sigma)
self.init_layers()
def init_layers(self):
"""Initialize layers of the DeformableDetrTransformer."""
self.level_embeds = nn.Parameter(
torch.Tensor(self.num_feature_levels, self.embed_dims))
if self.as_two_stage:
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# self.fc_sigma = Linear_with_norm(self.embed_dims, self.num_joints * 2, norm=False)
self.fc_sigma = Linear_with_norm(self.embed_dims, self.num_joints * 3, norm=False)
if self.use_soft_argmax:
self.soft_argmax_coord = Heatmap1DHead(in_channels=self.embed_dims, expand_ratio=2, hidden_dims=(512,),
image_size=self.image_size, stride=self.soft_arg_stride)
self.fc_layers = [self.fc_sigma]
elif self.use_soft_argmax_def:
self.soft_argmax_coord = Heatmap2DHead(in_channels=self.embed_dims,
image_size=self.image_size, stride=self.soft_arg_stride)
self.fc_layers = [self.fc_sigma]
else:
# self.fc_coord = Linear_with_norm(self.embed_dims, self.num_joints * 2)
self.fc_coord = Linear_with_norm(self.embed_dims, self.num_joints * 3)
self.fc_layers = [self.fc_coord, self.fc_sigma]
if self.query_pose_emb:
self.pos_trans = nn.Linear(self.embed_dims * 2,
self.embed_dims)
self.pos_trans_norm = nn.LayerNorm(self.embed_dims)
# self.pos_embed = nn.Embedding(17,self.embed_dims)
self.pos_embed = nn.Embedding(self.num_joints, self.embed_dims)
else:
self.pos_trans = nn.Linear(self.embed_dims * 2,
self.embed_dims * 2)
self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2)
else:
self.reference_points = nn.Linear(self.embed_dims, 2)
self.fp16_enabled = False
def init_weights(self):
"""Initialize the transformer weights."""
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
for m in self.modules():
if isinstance(m, MultiScaleDeformableAttention):
m.init_weights()
if not self.as_two_stage:
xavier_init(self.reference_points, distribution='uniform', bias=0.)
normal_(self.level_embeds)
if self.use_soft_argmax:
self.soft_argmax_coord.init_weights()
if self.as_two_stage:
for m in self.fc_layers:
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight, gain=0.01)
def gen_encoder_output_proposals(self, memory, memory_padding_mask,
spatial_shapes):
"""Generate proposals from encoded memory.
Args:
memory (Tensor) : The output of encoder,
has shape (bs, num_key, embed_dim). num_key is
equal the number of points on feature map from
all level.
memory_padding_mask (Tensor): Padding mask for memory.
has shape (bs, num_key).
spatial_shapes (Tensor): The shape of all feature maps.
has shape (num_level, 2).
Returns:
tuple: A tuple of feature map and bbox prediction.
- output_memory (Tensor): The input of decoder, \
has shape (bs, num_key, embed_dim). num_key is \
equal the number of points on feature map from \
all levels.
- output_proposals (Tensor): The normalized proposal \
after a inverse sigmoid, has shape \
(bs, num_keys, 4).
"""
N, S, C = memory.shape
proposals = []
_cur = 0
for lvl, (H, W) in enumerate(spatial_shapes):
mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H * W)].view(
N, H, W, 1)
valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
grid_y, grid_x = torch.meshgrid(
torch.linspace(
0, H - 1, H, dtype=torch.float32, device=memory.device),
torch.linspace(
0, W - 1, W, dtype=torch.float32, device=memory.device))
grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
scale = torch.cat([valid_W.unsqueeze(-1),
valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2)
grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale
wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
# proposal = torch.cat((grid, wh), -1).view(N, -1, 4)
proposal = grid.view(N, -1, 2)
proposals.append(proposal)
_cur += (H * W)
output_proposals = torch.cat(proposals, 1)
output_proposals_valid = ((output_proposals > 0.01) &
(output_proposals < 0.99)).all(
-1, keepdim=True)
output_proposals = torch.log(output_proposals / (1 - output_proposals))
output_proposals = output_proposals.masked_fill(
memory_padding_mask.unsqueeze(-1), float('inf'))
output_proposals = output_proposals.masked_fill(
~output_proposals_valid, float('inf'))
output_memory = memory
output_memory = output_memory.masked_fill(
memory_padding_mask.unsqueeze(-1), float(0))
output_memory = output_memory.masked_fill(~output_proposals_valid,
float(0))
output_memory = self.enc_output_norm(self.enc_output(output_memory))
return output_memory, output_proposals
@staticmethod
def get_reference_points(spatial_shapes, valid_ratios, device):
"""Get the reference points used in decoder.
Args:
spatial_shapes (Tensor): The shape of all
feature maps, has shape (num_level, 2).
valid_ratios (Tensor): The radios of valid
points on the feature map, has shape
(bs, num_levels, 2)
device (obj:`device`): The device where
reference_points should be.
Returns:
Tensor: reference points used in decoder, has \
shape (bs, num_keys, num_levels, 2).
"""
# print(spatial_shapes)
reference_points_list = []
for lvl, (H, W) in enumerate(spatial_shapes):
# TODO check this 0.5
ref_y, ref_x = torch.meshgrid(
torch.linspace(
0.5, H - 0.5, H, dtype=torch.float32, device=device),
torch.linspace(
0.5, W - 0.5, W, dtype=torch.float32, device=device))
ref_y = ref_y.reshape(-1)[None] / (
valid_ratios[:, None, lvl, 1] * H)
ref_x = ref_x.reshape(-1)[None] / (
valid_ratios[:, None, lvl, 0] * W)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
# print(reference_points_list[-1]) # range:(0,1)
# print(H, W) [8,6]
reference_points = torch.cat(reference_points_list, 1)
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
def get_valid_ratio(self, mask):
"""Get the valid radios of feature maps of all level."""
_, H, W = mask.shape
valid_H = torch.sum(~mask[:, :, 0], 1)
valid_W = torch.sum(~mask[:, 0, :], 1)
valid_ratio_h = valid_H.float() / H
valid_ratio_w = valid_W.float() / W
valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
return valid_ratio
def get_proposal_pos_embed(self,
proposals,
num_pos_feats=128,
temperature=10000):
"""Get the position embedding of proposal."""
num_pos_feats = self.embed_dims // 3 + 1
scale = 2 * math.pi
dim_t = torch.arange(
num_pos_feats, dtype=torch.float32, device=proposals.device)
dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
# N, L, 2
if self.init_q_sigmoid:
proposals = proposals.sigmoid() * scale
else:
proposals = proposals * scale
# N, L, 3, 86
pos = proposals[:, :, :, None] / dim_t
# N, L, 3, 43, 2
pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
return pos[:, :, :self.embed_dims]
@force_fp32(apply_to=('mlvl_feats', 'query_embed', 'mlvl_pos_embeds'))
def forward(self,
mlvl_feats,
mlvl_masks,
query_embed,
mlvl_pos_embeds,
reg_branches=None,
fc_coord=None,
cls_branches=None,
coord_init=None,
query_init=None,
**kwargs):
assert self.as_two_stage or query_embed is not None
feat_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes = []
for lvl, (feat, mask, pos_embed) in enumerate(
zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)):
bs, c, h, w = feat.shape
spatial_shape = (h, w)
spatial_shapes.append(spatial_shape)
feat = feat.flatten(2).transpose(1, 2)
mask = mask.flatten(1)
pos_embed = pos_embed.flatten(2).transpose(1, 2)
lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1)
lvl_pos_embed_flatten.append(lvl_pos_embed)
feat_flatten.append(feat)
mask_flatten.append(mask)
feat_flatten = torch.cat(feat_flatten, 1)
mask_flatten = torch.cat(mask_flatten, 1)
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
spatial_shapes = torch.as_tensor(
spatial_shapes, dtype=torch.long, device=feat_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros(
(1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack(
[self.get_valid_ratio(m) for m in mlvl_masks], 1)
# [bs, H*W, num_lvls, 2]
# print(spatial_shape)
reference_points = \
self.get_reference_points(spatial_shapes,
valid_ratios,
device=feat.device)
# print(reference_points.shape, valid_ratios.shape) # [bs, 4080, 4, 2]; [bs, 4, 2]
feat_flatten = feat_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims)
lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(
1, 0, 2) # (H*W, bs, embed_dims)
memory = self.encoder(
query=feat_flatten,
key=None,
value=None,
query_pos=lvl_pos_embed_flatten,
query_key_padding_mask=mask_flatten,
spatial_shapes=spatial_shapes,
reference_points=reference_points,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
**kwargs)
memory = memory.permute(1, 0, 2)
bs, _, c = memory.shape
if self.proposal_feature == 'backbone_l':
x = mlvl_feats[0]
elif self.proposal_feature == 'backbone_s':
x = mlvl_feats[-1]
point_sample_feat = mlvl_feats[-1]
elif self.proposal_feature == 'encoder_memory_l':
x = memory.permute(0, 2, 1)[:, :, :int(level_start_index[1])].view_as(mlvl_feats[0])
point_sample_feat = memory.permute(0, 2, 1)[:, :, :int(level_start_index[1])].view_as(mlvl_feats[0])
elif self.proposal_feature == 'encoder_memory_s':
x = memory.permute(0, 2, 1)[:, :, int(level_start_index[-1]):].view_as(mlvl_feats[-1])
else:
raise NotImplementedError
BATCH_SIZE = x.shape[0]
if coord_init is not None:
pred_jts = coord_init
enc_outputs = None
else:
if self.use_soft_argmax:
out_coord = self.soft_argmax_coord(x) # bs, 17, 2
assert out_coord.shape[2] == 2
x = self.avg_pool(x).reshape(BATCH_SIZE, -1)
out_sigma = self.fc_sigma(x).reshape(BATCH_SIZE, self.num_joints, -1)
elif self.use_soft_argmax_def:
out_coord = self.soft_argmax_coord(x) # bs, 17, 2
assert out_coord.shape[2] == 2
x = self.avg_pool(x).reshape(BATCH_SIZE, -1)
out_sigma = self.fc_sigma(x).reshape(BATCH_SIZE, self.num_joints, -1)
else:
x = self.avg_pool(x).reshape(BATCH_SIZE, -1)
out_coord = self.fc_coord(x).reshape(BATCH_SIZE, self.num_joints, 3)
assert out_coord.shape[2] == 3
out_sigma = self.fc_sigma(x).reshape(BATCH_SIZE, self.num_joints, -1)
# (B, N, 3)
pred_jts = out_coord.reshape(BATCH_SIZE, self.num_joints, 3)
sigma = out_sigma.reshape(BATCH_SIZE, self.num_joints, -1).sigmoid()
scores = 1 - sigma
scores = torch.mean(scores, dim=2, keepdim=True)
enc_outputs = EasyDict(
pred_jts=pred_jts,
sigma=sigma,
maxvals=scores.float(),
)
reference_points = pred_jts.detach()
reference_points_cliped = reference_points.clip(0, 1)
init_reference_out = reference_points_cliped
if query_init is not None:
query = query_init
else:
pred_jts_pos_embed = self.get_proposal_pos_embed(reference_points.detach())
reference_points_pos_embed = self.get_proposal_pos_embed(reference_points_cliped.detach()) # query init here
if self.add_feat_2_query:
query_feat = point_sample(point_sample_feat, init_reference_out, align_corners=False).permute(0, 2, 1)
reference_points_pos_embed = reference_points_pos_embed + query_feat
query_pos_emb = torch.cat([pred_jts_pos_embed, reference_points_pos_embed], dim=2)
pos_trans_out = self.pos_trans_norm(self.pos_trans(query_pos_emb))
query = pos_trans_out
query_pos = self.pos_embed.weight.clone().repeat(bs, 1, 1).contiguous()
# decoder
query = query.permute(1, 0, 2)
memory = memory.permute(1, 0, 2)
query_pos = query_pos.permute(1, 0, 2)
inter_states, inter_references = self.decoder(
query=query,
key=None,
value=memory,
query_pos=query_pos,
key_padding_mask=mask_flatten,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
reg_branches=reg_branches,
fc_coord=fc_coord,
**kwargs)
inter_references_out = inter_references
return memory.permute(1, 0, 2), spatial_shapes, level_start_index, inter_states, init_reference_out, \
inter_references_out, enc_outputs
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