import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from utils import clones, is_list_or_tuple
from torchvision.ops import RoIAlign
class HorizontalPoolingPyramid():
"""
Horizontal Pyramid Matching for Person Re-identification
Arxiv: https://arxiv.org/abs/1804.05275
Github: https://github.com/SHI-Labs/Horizontal-Pyramid-Matching
"""
def __init__(self, bin_num=None):
if bin_num is None:
bin_num = [16, 8, 4, 2, 1]
self.bin_num = bin_num
def __call__(self, x):
"""
x : [n, c, h, w]
ret: [n, c, p]
"""
n, c = x.size()[:2]
features = []
for b in self.bin_num:
z = x.view(n, c, b, -1)
z = z.mean(-1) + z.max(-1)[0]
features.append(z)
return torch.cat(features, -1)
class SetBlockWrapper(nn.Module):
def __init__(self, forward_block):
super(SetBlockWrapper, self).__init__()
self.forward_block = forward_block
def forward(self, x, *args, **kwargs):
"""
In x: [n, c_in, s, h_in, w_in]
Out x: [n, c_out, s, h_out, w_out]
"""
n, c, s, h, w = x.size()
x = self.forward_block(x.transpose(
1, 2).reshape(-1, c, h, w), *args, **kwargs)
output_size = x.size()
return x.reshape(n, s, *output_size[1:]).transpose(1, 2).contiguous()
class PackSequenceWrapper(nn.Module):
def __init__(self, pooling_func):
super(PackSequenceWrapper, self).__init__()
self.pooling_func = pooling_func
def forward(self, seqs, seqL, dim=2, options={}):
"""
In seqs: [n, c, s, ...]
Out rets: [n, ...]
"""
if seqL is None:
return self.pooling_func(seqs, **options)
seqL = seqL[0].data.cpu().numpy().tolist()
start = [0] + np.cumsum(seqL).tolist()[:-1]
rets = []
for curr_start, curr_seqL in zip(start, seqL):
narrowed_seq = seqs.narrow(dim, curr_start, curr_seqL)
rets.append(self.pooling_func(narrowed_seq, **options))
if len(rets) > 0 and is_list_or_tuple(rets[0]):
return [torch.cat([ret[j] for ret in rets])
for j in range(len(rets[0]))]
return torch.cat(rets)
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, bias=False, **kwargs)
def forward(self, x):
x = self.conv(x)
return x
class SeparateFCs(nn.Module):
def __init__(self, parts_num, in_channels, out_channels, norm=False):
super(SeparateFCs, self).__init__()
self.p = parts_num
self.fc_bin = nn.Parameter(
nn.init.xavier_uniform_(
torch.zeros(parts_num, in_channels, out_channels)))
self.norm = norm
def forward(self, x):
"""
x: [n, c_in, p]
out: [n, c_out, p]
"""
x = x.permute(2, 0, 1).contiguous()
if self.norm:
out = x.matmul(F.normalize(self.fc_bin, dim=1))
else:
out = x.matmul(self.fc_bin)
return out.permute(1, 2, 0).contiguous()
class SeparateBNNecks(nn.Module):
"""
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
CVPR Workshop: https://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Luo_Bag_of_Tricks_and_a_Strong_Baseline_for_Deep_Person_CVPRW_2019_paper.pdf
Github: https://github.com/michuanhaohao/reid-strong-baseline
"""
def __init__(self, parts_num, in_channels, class_num, norm=True, parallel_BN1d=True):
super(SeparateBNNecks, self).__init__()
self.p = parts_num
self.class_num = class_num
self.norm = norm
self.fc_bin = nn.Parameter(
nn.init.xavier_uniform_(
torch.zeros(parts_num, in_channels, class_num)))
if parallel_BN1d:
self.bn1d = nn.BatchNorm1d(in_channels * parts_num)
else:
self.bn1d = clones(nn.BatchNorm1d(in_channels), parts_num)
self.parallel_BN1d = parallel_BN1d
def forward(self, x):
"""
x: [n, c, p]
"""
if self.parallel_BN1d:
n, c, p = x.size()
x = x.view(n, -1) # [n, c*p]
x = self.bn1d(x)
x = x.view(n, c, p)
else:
x = torch.cat([bn(_x) for _x, bn in zip(
x.split(1, 2), self.bn1d)], 2) # [p, n, c]
feature = x.permute(2, 0, 1).contiguous()
if self.norm:
feature = F.normalize(feature, dim=-1) # [p, n, c]
logits = feature.matmul(F.normalize(
self.fc_bin, dim=1)) # [p, n, c]
else:
logits = feature.matmul(self.fc_bin)
return feature.permute(1, 2, 0).contiguous(), logits.permute(1, 2, 0).contiguous()
class FocalConv2d(nn.Module):
"""
GaitPart: Temporal Part-based Model for Gait Recognition
CVPR2020: https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.pdf
Github: https://github.com/ChaoFan96/GaitPart
"""
def __init__(self, in_channels, out_channels, kernel_size, halving, **kwargs):
super(FocalConv2d, self).__init__()
self.halving = halving
self.conv = nn.Conv2d(in_channels, out_channels,
kernel_size, bias=False, **kwargs)
def forward(self, x):
if self.halving == 0:
z = self.conv(x)
else:
h = x.size(2)
split_size = int(h // 2**self.halving)
z = x.split(split_size, 2)
z = torch.cat([self.conv(_) for _ in z], 2)
return z
class BasicConv3d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False, **kwargs):
super(BasicConv3d, self).__init__()
self.conv3d = nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, bias=bias, **kwargs)
def forward(self, ipts):
'''
ipts: [n, c, s, h, w]
outs: [n, c, s, h, w]
'''
outs = self.conv3d(ipts)
return outs
class GaitAlign(nn.Module):
"""
GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality
ECCV2022: https://arxiv.org/pdf/2203.03972v2.pdf
Github: https://github.com/ShiqiYu/OpenGait/tree/master/configs/gaitedge
"""
def __init__(self, H=64, W=44, eps=1, **kwargs):
super(GaitAlign, self).__init__()
self.H, self.W, self.eps = H, W, eps
self.Pad = nn.ZeroPad2d((int(self.W / 2), int(self.W / 2), 0, 0))
self.RoiPool = RoIAlign((self.H, self.W), 1, sampling_ratio=-1)
def forward(self, feature_map, binary_mask, w_h_ratio):
"""
In sils: [n, c, h, w]
w_h_ratio: [n, 1]
Out aligned_sils: [n, c, H, W]
"""
n, c, h, w = feature_map.size()
# w_h_ratio = w_h_ratio.repeat(1, 1) # [n, 1]
w_h_ratio = w_h_ratio.view(-1, 1) # [n, 1]
h_sum = binary_mask.sum(-1) # [n, c, h]
_ = (h_sum >= self.eps).float().cumsum(axis=-1) # [n, c, h]
h_top = (_ == 0).float().sum(-1) # [n, c]
h_bot = (_ != torch.max(_, dim=-1, keepdim=True)
[0]).float().sum(-1) + 1. # [n, c]
w_sum = binary_mask.sum(-2) # [n, c, w]
w_cumsum = w_sum.cumsum(axis=-1) # [n, c, w]
w_h_sum = w_sum.sum(-1).unsqueeze(-1) # [n, c, 1]
w_center = (w_cumsum < w_h_sum / 2.).float().sum(-1) # [n, c]
p1 = self.W - self.H * w_h_ratio
p1 = p1 / 2.
p1 = torch.clamp(p1, min=0) # [n, c]
t_w = w_h_ratio * self.H / w
p2 = p1 / t_w # [n, c]
height = h_bot - h_top # [n, c]
width = height * w / h # [n, c]
width_p = int(self.W / 2)
feature_map = self.Pad(feature_map)
w_center = w_center + width_p # [n, c]
w_left = w_center - width / 2 - p2 # [n, c]
w_right = w_center + width / 2 + p2 # [n, c]
w_left = torch.clamp(w_left, min=0., max=w+2*width_p)
w_right = torch.clamp(w_right, min=0., max=w+2*width_p)
boxes = torch.cat([w_left, h_top, w_right, h_bot], dim=-1)
# index of bbox in batch
box_index = torch.arange(n, device=feature_map.device)
rois = torch.cat([box_index.view(-1, 1), boxes], -1)
crops = self.RoiPool(feature_map, rois) # [n, c, H, W]
return crops
def RmBN2dAffine(model):
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.requires_grad = False
m.bias.requires_grad = False
'''
Modifed from https://github.com/BNU-IVC/FastPoseGait/blob/main/fastposegait/modeling/components/units
'''
class Graph():
"""
# Thanks to YAN Sijie for the released code on Github (https://github.com/yysijie/st-gcn)
"""
def __init__(self, joint_format='coco', max_hop=2, dilation=1):
self.joint_format = joint_format
self.max_hop = max_hop
self.dilation = dilation
# get edges
self.num_node, self.edge, self.connect_joint, self.parts = self._get_edge()
# get adjacency matrix
self.A = self._get_adjacency()
def __str__(self):
return self.A
def _get_edge(self):
if self.joint_format == 'coco':
# keypoints = {
# 0: "nose",
# 1: "left_eye",
# 2: "right_eye",
# 3: "left_ear",
# 4: "right_ear",
# 5: "left_shoulder",
# 6: "right_shoulder",
# 7: "left_elbow",
# 8: "right_elbow",
# 9: "left_wrist",
# 10: "right_wrist",
# 11: "left_hip",
# 12: "right_hip",
# 13: "left_knee",
# 14: "right_knee",
# 15: "left_ankle",
# 16: "right_ankle"
# }
num_node = 17
self_link = [(i, i) for i in range(num_node)]
neighbor_link = [(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6), (5, 6),
(5, 7), (7, 9), (6, 8), (8, 10), (5, 11), (6, 12), (11, 12),
(11, 13), (13, 15), (12, 14), (14, 16)]
self.edge = self_link + neighbor_link
self.center = 0
self.flip_idx = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
connect_joint = np.array([5,0,0,1,2,0,0,5,6,7,8,5,6,11,12,13,14])
parts = [
np.array([5, 7, 9]), # left_arm
np.array([6, 8, 10]), # right_arm
np.array([11, 13, 15]), # left_leg
np.array([12, 14, 16]), # right_leg
np.array([0, 1, 2, 3, 4]), # head
]
elif self.joint_format == 'coco-no-head':
num_node = 12
self_link = [(i, i) for i in range(num_node)]
neighbor_link = [(0, 1),
(0, 2), (2, 4), (1, 3), (3, 5), (0, 6), (1, 7), (6, 7),
(6, 8), (8, 10), (7, 9), (9, 11)]
self.edge = self_link + neighbor_link
self.center = 0
connect_joint = np.array([3,1,0,2,4,0,6,8,10,7,9,11])
parts =[
np.array([0, 2, 4]), # left_arm
np.array([1, 3, 5]), # right_arm
np.array([6, 8, 10]), # left_leg
np.array([7, 9, 11]) # right_leg
]
elif self.joint_format =='alphapose' or self.joint_format =='openpose':
num_node = 18
self_link = [(i, i) for i in range(num_node)]
neighbor_link = [(0, 1), (0, 14), (0, 15), (14, 16), (15, 17),
(1, 2), (2, 3), (3, 4), (1, 5), (5, 6), (6, 7),
(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13)]
self.edge = self_link + neighbor_link
self.center = 1
self.flip_idx = [0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16]
connect_joint = np.array([1,1,1,2,3,1,5,6,2,8,9,5,11,12,0,0,14,15])
parts = [
np.array([5, 6, 7]), # left_arm
np.array([2, 3, 4]), # right_arm
np.array([11, 12, 13]), # left_leg
np.array([8, 9, 10]), # right_leg
np.array([0, 1, 14, 15, 16, 17]), # head
]
else:
num_node, neighbor_link, connect_joint, parts = 0, [], [], []
raise ValueError('Error: Do NOT exist this dataset: {}!'.format(self.dataset))
self_link = [(i, i) for i in range(num_node)]
edge = self_link + neighbor_link
return num_node, edge, connect_joint, parts
def _get_hop_distance(self):
A = np.zeros((self.num_node, self.num_node))
for i, j in self.edge:
A[j, i] = 1
A[i, j] = 1
hop_dis = np.zeros((self.num_node, self.num_node)) + np.inf
transfer_mat = [np.linalg.matrix_power(A, d) for d in range(self.max_hop + 1)]
arrive_mat = (np.stack(transfer_mat) > 0)
for d in range(self.max_hop, -1, -1):
hop_dis[arrive_mat[d]] = d
return hop_dis
def _get_adjacency(self):
hop_dis = self._get_hop_distance()
valid_hop = range(0, self.max_hop + 1, self.dilation)
adjacency = np.zeros((self.num_node, self.num_node))
for hop in valid_hop:
adjacency[hop_dis == hop] = 1
normalize_adjacency = self._normalize_digraph(adjacency)
A = np.zeros((len(valid_hop), self.num_node, self.num_node))
for i, hop in enumerate(valid_hop):
A[i][hop_dis == hop] = normalize_adjacency[hop_dis == hop]
return A
def _normalize_digraph(self, A):
Dl = np.sum(A, 0)
num_node = A.shape[0]
Dn = np.zeros((num_node, num_node))
for i in range(num_node):
if Dl[i] > 0:
Dn[i, i] = Dl[i]**(-1)
AD = np.dot(A, Dn)
return AD
class TemporalBasicBlock(nn.Module):
"""
TemporalConv_Res_Block
Arxiv: https://arxiv.org/abs/2010.09978
Github: https://github.com/Thomas-yx/ResGCNv1
"""
def __init__(self, channels, temporal_window_size, stride=1, residual=False,reduction=0,get_res=False,tcn_stride=False):
super(TemporalBasicBlock, self).__init__()
padding = ((temporal_window_size - 1) // 2, 0)
if not residual:
self.residual = lambda x: 0
elif stride == 1:
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(channels, channels, 1, (stride,1)),
nn.BatchNorm2d(channels),
)
self.conv = nn.Conv2d(channels, channels, (temporal_window_size,1), (stride,1), padding)
self.bn = nn.BatchNorm2d(channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, res_module):
res_block = self.residual(x)
x = self.conv(x)
x = self.bn(x)
x = self.relu(x + res_block + res_module)
return x
class TemporalBottleneckBlock(nn.Module):
"""
TemporalConv_Res_Bottleneck
Arxiv: https://arxiv.org/abs/2010.09978
Github: https://github.com/Thomas-yx/ResGCNv1
"""
def __init__(self, channels, temporal_window_size, stride=1, residual=False, reduction=4,get_res=False, tcn_stride=False):
super(TemporalBottleneckBlock, self).__init__()
tcn_stride =False
padding = ((temporal_window_size - 1) // 2, 0)
inter_channels = channels // reduction
if get_res:
if tcn_stride:
stride =2
self.residual = nn.Sequential(
nn.Conv2d(channels, channels, 1, (2,1)),
nn.BatchNorm2d(channels),
)
tcn_stride= True
else:
if not residual:
self.residual = lambda x: 0
elif stride == 1:
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(channels, channels, 1, (2,1)),
nn.BatchNorm2d(channels),
)
tcn_stride= True
self.conv_down = nn.Conv2d(channels, inter_channels, 1)
self.bn_down = nn.BatchNorm2d(inter_channels)
if tcn_stride:
stride=2
self.conv = nn.Conv2d(inter_channels, inter_channels, (temporal_window_size,1), (stride,1), padding)
self.bn = nn.BatchNorm2d(inter_channels)
self.conv_up = nn.Conv2d(inter_channels, channels, 1)
self.bn_up = nn.BatchNorm2d(channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, res_module):
res_block = self.residual(x)
x = self.conv_down(x)
x = self.bn_down(x)
x = self.relu(x)
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
x = self.conv_up(x)
x = self.bn_up(x)
x = self.relu(x + res_block + res_module)
return x
class SpatialGraphConv(nn.Module):
"""
SpatialGraphConv_Basic_Block
Arxiv: https://arxiv.org/abs/1801.07455
Github: https://github.com/yysijie/st-gcn
"""
def __init__(self, in_channels, out_channels, max_graph_distance):
super(SpatialGraphConv, self).__init__()
# spatial class number (distance = 0 for class 0, distance = 1 for class 1, ...)
self.s_kernel_size = max_graph_distance + 1
# weights of different spatial classes
self.gcn = nn.Conv2d(in_channels, out_channels*self.s_kernel_size, 1)
def forward(self, x, A):
# numbers in same class have same weight
x = self.gcn(x)
# divide nodes into different classes
n, kc, t, v = x.size()
x = x.view(n, self.s_kernel_size, kc//self.s_kernel_size, t, v).contiguous()
# spatial graph convolution
x = torch.einsum('nkctv,kvw->nctw', (x, A[:self.s_kernel_size])).contiguous()
return x
class SpatialBasicBlock(nn.Module):
"""
SpatialGraphConv_Res_Block
Arxiv: https://arxiv.org/abs/2010.09978
Github: https://github.com/Thomas-yx/ResGCNv1
"""
def __init__(self, in_channels, out_channels, max_graph_distance, residual=False,reduction=0):
super(SpatialBasicBlock, self).__init__()
if not residual:
self.residual = lambda x: 0
elif in_channels == out_channels:
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1),
nn.BatchNorm2d(out_channels),
)
self.conv = SpatialGraphConv(in_channels, out_channels, max_graph_distance)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, A):
res_block = self.residual(x)
x = self.conv(x, A)
x = self.bn(x)
x = self.relu(x + res_block)
return x
class SpatialBottleneckBlock(nn.Module):
"""
SpatialGraphConv_Res_Bottleneck
Arxiv: https://arxiv.org/abs/2010.09978
Github: https://github.com/Thomas-yx/ResGCNv1
"""
def __init__(self, in_channels, out_channels, max_graph_distance, residual=False, reduction=4):
super(SpatialBottleneckBlock, self).__init__()
inter_channels = out_channels // reduction
if not residual:
self.residual = lambda x: 0
elif in_channels == out_channels:
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1),
nn.BatchNorm2d(out_channels),
)
self.conv_down = nn.Conv2d(in_channels, inter_channels, 1)
self.bn_down = nn.BatchNorm2d(inter_channels)
self.conv = SpatialGraphConv(inter_channels, inter_channels, max_graph_distance)
self.bn = nn.BatchNorm2d(inter_channels)
self.conv_up = nn.Conv2d(inter_channels, out_channels, 1)
self.bn_up = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, A):
res_block = self.residual(x)
x = self.conv_down(x)
x = self.bn_down(x)
x = self.relu(x)
x = self.conv(x, A)
x = self.bn(x)
x = self.relu(x)
x = self.conv_up(x)
x = self.bn_up(x)
x = self.relu(x + res_block)
return x
class SpatialAttention(nn.Module):
"""
This class implements Spatial Transformer.
Function adapted from: https://github.com/leaderj1001/Attention-Augmented-Conv2d
"""
def __init__(self, in_channels, out_channel, A, num_point, dk_factor=0.25, kernel_size=1, Nh=8, num=4, stride=1):
super(SpatialAttention, self).__init__()
self.in_channels = in_channels
self.kernel_size = kernel_size
self.dk = int(dk_factor * out_channel)
self.dv = int(out_channel)
self.num = num
self.Nh = Nh
self.num_point=num_point
self.A = A[0] + A[1] + A[2]
self.stride = stride
self.padding = (self.kernel_size - 1) // 2
assert self.Nh != 0, "integer division or modulo by zero, Nh >= 1"
assert self.dk % self.Nh == 0, "dk should be divided by Nh. (example: out_channels: 20, dk: 40, Nh: 4)"
assert self.dv % self.Nh == 0, "dv should be divided by Nh. (example: out_channels: 20, dv: 4, Nh: 4)"
assert stride in [1, 2], str(stride) + " Up to 2 strides are allowed."
self.qkv_conv = nn.Conv2d(self.in_channels, 2 * self.dk + self.dv, kernel_size=self.kernel_size,
stride=stride,
padding=self.padding)
self.attn_out = nn.Conv2d(self.dv, self.dv, kernel_size=1, stride=1)
def forward(self, x):
# Input x
# (batch_size, channels, 1, joints)
B, _, T, V = x.size()
# flat_q, flat_k, flat_v
# (batch_size, Nh, dvh or dkh, joints)
# dvh = dv / Nh, dkh = dk / Nh
# q, k, v obtained by doing 2D convolution on the input (q=XWq, k=XWk, v=XWv)
flat_q, flat_k, flat_v, q, k, v = self.compute_flat_qkv(x, self.dk, self.dv, self.Nh)
# Calculate the scores, obtained by doing q*k
# (batch_size, Nh, joints, dkh)*(batch_size, Nh, dkh, joints) = (batch_size, Nh, joints,joints)
# The multiplication can also be divided (multi_matmul) in case of space problems
logits = torch.matmul(flat_q.transpose(2, 3), flat_k)
weights = F.softmax(logits, dim=-1)
# attn_out
# (batch, Nh, joints, dvh)
# weights*V
# (batch, Nh, joints, joints)*(batch, Nh, joints, dvh)=(batch, Nh, joints, dvh)
attn_out = torch.matmul(weights, flat_v.transpose(2, 3))
attn_out = torch.reshape(attn_out, (B, self.Nh, T, V, self.dv // self.Nh))
attn_out = attn_out.permute(0, 1, 4, 2, 3)
# combine_heads_2d, combine heads only after having calculated each Z separately
# (batch, Nh*dv, 1, joints)
attn_out = self.combine_heads_2d(attn_out)
# Multiply for W0 (batch, out_channels, 1, joints) with out_channels=dv
attn_out = self.attn_out(attn_out)
return attn_out
def compute_flat_qkv(self, x, dk, dv, Nh):
qkv = self.qkv_conv(x)
# T=1 in this case, because we are considering each frame separately
N, _, T, V = qkv.size()
q, k, v = torch.split(qkv, [dk, dk, dv], dim=1)
q = self.split_heads_2d(q, Nh)
k = self.split_heads_2d(k, Nh)
v = self.split_heads_2d(v, Nh)
dkh = dk // Nh
q = q*(dkh ** -0.5)
flat_q = torch.reshape(q, (N, Nh, dkh, T * V))
flat_k = torch.reshape(k, (N, Nh, dkh, T * V))
flat_v = torch.reshape(v, (N, Nh, dv // self.Nh, T * V))
return flat_q, flat_k, flat_v, q, k, v
def split_heads_2d(self, x, Nh):
B, channels, T, V = x.size()
ret_shape = (B, Nh, channels // Nh, T, V)
split = torch.reshape(x, ret_shape)
return split
def combine_heads_2d(self, x):
batch, Nh, dv, T, V = x.size()
ret_shape = (batch, Nh * dv, T, V)
return torch.reshape(x, ret_shape)
from einops import rearrange
class ParallelBN1d(nn.Module):
def __init__(self, parts_num, in_channels, **kwargs):
super(ParallelBN1d, self).__init__()
self.parts_num = parts_num
self.bn1d = nn.BatchNorm1d(in_channels * parts_num, **kwargs)
def forward(self, x):
'''
x: [n, c, p]
'''
x = rearrange(x, 'n c p -> n (c p)')
x = self.bn1d(x)
x = rearrange(x, 'n (c p) -> n c p', p=self.parts_num)
return x
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock2D(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock2D, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError(
'BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError(
"Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class BasicBlockP3D(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlockP3D, self).__init__()
if norm_layer is None:
norm_layer2d = nn.BatchNorm2d
norm_layer3d = nn.BatchNorm3d
if groups != 1 or base_width != 64:
raise ValueError(
'BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError(
"Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.relu = nn.ReLU(inplace=True)
self.conv1 = SetBlockWrapper(
nn.Sequential(
conv3x3(inplanes, planes, stride),
norm_layer2d(planes),
nn.ReLU(inplace=True)
)
)
self.conv2 = SetBlockWrapper(
nn.Sequential(
conv3x3(planes, planes),
norm_layer2d(planes),
)
)
self.shortcut3d = nn.Conv3d(planes, planes, (3, 1, 1), (1, 1, 1), (1, 0, 0), bias=False)
self.sbn = norm_layer3d(planes)
self.downsample = downsample
def forward(self, x):
'''
x: [n, c, s, h, w]
'''
identity = x
out = self.conv1(x)
out = self.relu(out + self.sbn(self.shortcut3d(out)))
out = self.conv2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class BasicBlock3D(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=[1, 1, 1], downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock3D, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm3d
if groups != 1 or base_width != 64:
raise ValueError(
'BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError(
"Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
assert stride[0] in [1, 2, 3]
if stride[0] in [1, 2]:
tp = 1
else:
tp = 0
self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=(3, 3, 3), stride=stride, padding=[tp, 1, 1], bias=False)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv3d(planes, planes, kernel_size=(3, 3, 3), stride=[1, 1, 1], padding=[1, 1, 1], bias=False)
self.bn2 = norm_layer(planes)
self.downsample = downsample
def forward(self, x):
'''
x: [n, c, s, h, w]
'''
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
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