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344 lines
12 KiB
Python
344 lines
12 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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import numpy as np
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from ..registry import BACKBONES
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from ..weight_init import weight_init_
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def zero(x):
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return 0
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def iden(x):
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return x
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def einsum(x, A):
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"""paddle.einsum will be implemented in release/2.2.
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"""
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x = x.transpose((0, 2, 3, 1, 4))
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n, c, t, k, v = x.shape
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k2, v2, w = A.shape
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assert (k == k2 and v == v2), "Args of einsum not match!"
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x = x.reshape((n, c, t, k * v))
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A = A.reshape((k * v, w))
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y = paddle.matmul(x, A)
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return y
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def get_hop_distance(num_node, edge, max_hop=1):
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A = np.zeros((num_node, num_node))
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for i, j in edge:
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A[j, i] = 1
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A[i, j] = 1
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# compute hop steps
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hop_dis = np.zeros((num_node, num_node)) + np.inf
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transfer_mat = [np.linalg.matrix_power(A, d) for d in range(max_hop + 1)]
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arrive_mat = (np.stack(transfer_mat) > 0)
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for d in range(max_hop, -1, -1):
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hop_dis[arrive_mat[d]] = d
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return hop_dis
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def normalize_digraph(A):
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Dl = np.sum(A, 0)
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num_node = A.shape[0]
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Dn = np.zeros((num_node, num_node))
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for i in range(num_node):
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if Dl[i] > 0:
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Dn[i, i] = Dl[i]**(-1)
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AD = np.dot(A, Dn)
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return AD
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class Graph():
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def __init__(self,
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layout='openpose',
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strategy='uniform',
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max_hop=1,
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dilation=1):
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self.max_hop = max_hop
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self.dilation = dilation
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self.get_edge(layout)
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self.hop_dis = get_hop_distance(self.num_node,
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self.edge,
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max_hop=max_hop)
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self.get_adjacency(strategy)
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def __str__(self):
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return self.A
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def get_edge(self, layout):
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# edge is a list of [child, parent] paris
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if layout == 'fsd10':
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self.num_node = 25
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self_link = [(i, i) for i in range(self.num_node)]
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neighbor_link = [(1, 8), (0, 1), (15, 0), (17, 15), (16, 0),
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(18, 16), (5, 1), (6, 5), (7, 6), (2, 1), (3, 2),
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(4, 3), (9, 8), (10, 9), (11, 10), (24, 11),
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(22, 11), (23, 22), (12, 8), (13, 12), (14, 13),
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(21, 14), (19, 14), (20, 19)]
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self.edge = self_link + neighbor_link
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self.center = 8
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elif layout == 'ntu-rgb+d':
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self.num_node = 25
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self_link = [(i, i) for i in range(self.num_node)]
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neighbor_1base = [(1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5),
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(7, 6), (8, 7), (9, 21), (10, 9), (11, 10),
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(12, 11), (13, 1), (14, 13), (15, 14), (16, 15),
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(17, 1), (18, 17), (19, 18), (20, 19), (22, 23),
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(23, 8), (24, 25), (25, 12)]
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neighbor_link = [(i - 1, j - 1) for (i, j) in neighbor_1base]
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self.edge = self_link + neighbor_link
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self.center = 21 - 1
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elif layout == 'coco_keypoint':
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self.num_node = 17
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self_link = [(i, i) for i in range(self.num_node)]
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neighbor_1base = [(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6),
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(5, 7), (6, 8), (7, 9), (8, 10), (5, 11), (6, 12),
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(11, 13), (12, 14), (13, 15), (14, 16), (11, 12)]
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neighbor_link = [(i, j) for (i, j) in neighbor_1base]
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self.edge = self_link + neighbor_link
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self.center = 11
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else:
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raise ValueError("Do Not Exist This Layout.")
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def get_adjacency(self, strategy):
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valid_hop = range(0, self.max_hop + 1, self.dilation)
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adjacency = np.zeros((self.num_node, self.num_node))
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for hop in valid_hop:
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adjacency[self.hop_dis == hop] = 1
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normalize_adjacency = normalize_digraph(adjacency)
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if strategy == 'spatial':
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A = []
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for hop in valid_hop:
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a_root = np.zeros((self.num_node, self.num_node))
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a_close = np.zeros((self.num_node, self.num_node))
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a_further = np.zeros((self.num_node, self.num_node))
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for i in range(self.num_node):
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for j in range(self.num_node):
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if self.hop_dis[j, i] == hop:
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if self.hop_dis[j, self.center] == self.hop_dis[
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i, self.center]:
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a_root[j, i] = normalize_adjacency[j, i]
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elif self.hop_dis[j, self.center] > self.hop_dis[
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i, self.center]:
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a_close[j, i] = normalize_adjacency[j, i]
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else:
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a_further[j, i] = normalize_adjacency[j, i]
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if hop == 0:
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A.append(a_root)
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else:
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A.append(a_root + a_close)
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A.append(a_further)
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A = np.stack(A)
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self.A = A
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else:
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raise ValueError("Do Not Exist This Strategy")
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class ConvTemporalGraphical(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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t_kernel_size=1,
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t_stride=1,
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t_padding=0,
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t_dilation=1):
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super().__init__()
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self.kernel_size = kernel_size
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self.conv = nn.Conv2D(in_channels,
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out_channels * kernel_size,
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kernel_size=(t_kernel_size, 1),
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padding=(t_padding, 0),
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stride=(t_stride, 1),
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dilation=(t_dilation, 1))
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def forward(self, x, A):
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assert A.shape[0] == self.kernel_size
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x = self.conv(x)
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n, kc, t, v = x.shape
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x = x.reshape((n, self.kernel_size, kc // self.kernel_size, t, v))
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x = einsum(x, A)
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return x, A
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class st_gcn_block(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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dropout=0,
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residual=True):
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super(st_gcn_block, self).__init__()
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assert len(kernel_size) == 2
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assert kernel_size[0] % 2 == 1
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padding = ((kernel_size[0] - 1) // 2, 0)
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self.gcn = ConvTemporalGraphical(in_channels, out_channels,
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kernel_size[1])
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self.tcn = nn.Sequential(
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nn.BatchNorm2D(out_channels),
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nn.ReLU(),
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nn.Conv2D(
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out_channels,
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out_channels,
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(kernel_size[0], 1),
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(stride, 1),
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padding,
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),
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nn.BatchNorm2D(out_channels),
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nn.Dropout(dropout),
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)
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if not residual:
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self.residual = zero
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elif (in_channels == out_channels) and (stride == 1):
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self.residual = iden
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else:
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self.residual = nn.Sequential(
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nn.Conv2D(in_channels,
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out_channels,
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kernel_size=1,
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stride=(stride, 1)),
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nn.BatchNorm2D(out_channels),
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)
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self.relu = nn.ReLU()
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def forward(self, x, A):
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res = self.residual(x)
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x, A = self.gcn(x, A)
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x = self.tcn(x) + res
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return self.relu(x), A
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@BACKBONES.register()
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class STGCN(nn.Layer):
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"""
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ST-GCN model from:
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`"Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition" <https://arxiv.org/abs/1801.07455>`_
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Args:
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in_channels: int, channels of vertex coordinate. 2 for (x,y), 3 for (x,y,z). Default 2.
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edge_importance_weighting: bool, whether to use edge attention. Default True.
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data_bn: bool, whether to use data BatchNorm. Default True.
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"""
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def __init__(self,
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in_channels=2,
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edge_importance_weighting=True,
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data_bn=True,
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layout='fsd10',
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strategy='spatial',
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**kwargs):
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super(STGCN, self).__init__()
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self.data_bn = data_bn
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# load graph
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self.graph = Graph(
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layout=layout,
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strategy=strategy,
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)
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A = paddle.to_tensor(self.graph.A, dtype='float32')
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self.register_buffer('A', A)
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# build networks
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spatial_kernel_size = A.shape[0]
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temporal_kernel_size = 9
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kernel_size = (temporal_kernel_size, spatial_kernel_size)
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self.data_bn = nn.BatchNorm1D(in_channels *
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A.shape[1]) if self.data_bn else iden
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kwargs0 = {k: v for k, v in kwargs.items() if k != 'dropout'}
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self.st_gcn_networks = nn.LayerList((
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st_gcn_block(in_channels,
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64,
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kernel_size,
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1,
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residual=False,
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**kwargs0),
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st_gcn_block(64, 64, kernel_size, 1, **kwargs),
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st_gcn_block(64, 64, kernel_size, 1, **kwargs),
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st_gcn_block(64, 64, kernel_size, 1, **kwargs),
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st_gcn_block(64, 128, kernel_size, 2, **kwargs),
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st_gcn_block(128, 128, kernel_size, 1, **kwargs),
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st_gcn_block(128, 128, kernel_size, 1, **kwargs),
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st_gcn_block(128, 256, kernel_size, 2, **kwargs),
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st_gcn_block(256, 256, kernel_size, 1, **kwargs),
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st_gcn_block(256, 256, kernel_size, 1, **kwargs),
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))
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# initialize parameters for edge importance weighting
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if edge_importance_weighting:
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self.edge_importance = nn.ParameterList([
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self.create_parameter(
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shape=self.A.shape,
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default_initializer=nn.initializer.Constant(1))
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for i in self.st_gcn_networks
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])
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else:
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self.edge_importance = [1] * len(self.st_gcn_networks)
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self.pool = nn.AdaptiveAvgPool2D(output_size=(1, 1))
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def init_weights(self):
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"""Initiate the parameters.
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"""
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for layer in self.sublayers():
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if isinstance(layer, nn.Conv2D):
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weight_init_(layer, 'Normal', mean=0.0, std=0.02)
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elif isinstance(layer, nn.BatchNorm2D):
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weight_init_(layer, 'Normal', mean=1.0, std=0.02)
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elif isinstance(layer, nn.BatchNorm1D):
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weight_init_(layer, 'Normal', mean=1.0, std=0.02)
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def forward(self, x):
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# data normalization
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N, C, T, V, M = x.shape
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x = x.transpose((0, 4, 3, 1, 2)) # N, M, V, C, T
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x = x.reshape((N * M, V * C, T))
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if self.data_bn:
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x.stop_gradient = False
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x = self.data_bn(x)
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x = x.reshape((N, M, V, C, T))
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x = x.transpose((0, 1, 3, 4, 2)) # N, M, C, T, V
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x = x.reshape((N * M, C, T, V))
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# forward
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for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
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x, _ = gcn(x, paddle.multiply(self.A, importance))
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x = self.pool(x) # NM,C,T,V --> NM,C,1,1
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C = x.shape[1]
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x = paddle.reshape(x, (N, M, C, 1, 1)).mean(axis=1) # N,C,1,1
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return x
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