def __init__(self, num_classes):
super(Decoder, self).__init__()
low_level_in_planes = 256
self.conv1 = nn.Conv2d(low_level_in_planes, 48, 1, bias=False)
self.bn1 = nn.BatchNorm2d(48)
self.relu = nn.ReLU()
self.last_conv = nn.Sequential(
nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1,
bias=False), nn.BatchNorm2d(256), nn.ReLU(),
nn.Dropout(0.5),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1,
bias=False), nn.BatchNorm2d(256), nn.ReLU(),
nn.Dropout(0.1),
nn.Conv2d(256, num_classes, kernel_size=1, stride=1))
self._init_weight()
def __init__(self,
input_size,
hidden_size,
output_size,
num_layers=1,
dropout=0,
bidirectional=False):
super(RNNModel, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layers = num_layers
self.dropout = dropout
self.bidirectional = bidirectional
if bidirectional:
num_directions = 2
else:
num_directions = 1
# 创建模型
self.rnn = modules.LSTM(input_size,
hidden_size,
num_layers,
batch_first=True,
dropout=self.dropout,
bidirectional=self.bidirectional)
if self.dropout != 0:
self.drop = modules.Dropout(dropout)
# 创建全连接层
self.fc = modules.Linear(hidden_size * num_directions, output_size)
# 设置激活函数
self.activation = modules.LogSoftmax(dim=1)
def __init__(self, model_name, input_size, hidden_size, batch_size, kernel_size,
out_channels, num_layers=1, dropout=0, bidirectional=False, bn=False):
super(LACModelUnit, self).__init__()
# 获得GPU数量
self.cuda_ids = np.arange(torch.cuda.device_count())
self.model_name = model_name
self.input_size = input_size
self.hidden_size = hidden_size
self.batch_size = int(batch_size / len(self.cuda_ids))
self.out_channels = out_channels
self.kernel_size = kernel_size
self.num_layers = num_layers
self.dropout = dropout
self.bidirectional = bidirectional
self.bn = bn
# 创建LSTM
self.rnn = modules.LSTM(self.input_size, self.hidden_size, self.num_layers,
batch_first=True, bidirectional=self.bidirectional)
# LSTM激活函数
self.rnn_act = modules.ReLU()
# 创建1D-CNN
self.cnn = modules.Conv1d(1, self.out_channels, self.kernel_size)
# BN层
self.bn = modules.BatchNorm1d(self.out_channels)
# 1D-CNN激活函数
self.cnn_act = modules.Tanh()
# Dropout层
self.drop = modules.Dropout(dropout)
# 初始化LSTM参数
self.lstm_hidden, self.lstm_cell = self.init_hidden_cell(self.batch_size)
def __init__(self):
super(ASPP, self).__init__()
in_planes = 2048
dilations = [1, 6, 12, 18]
# all aspp module output feature maps with channel 256
self.aspp1 = _ASPPModule(in_planes, planes=256, kernel_size=1, padding=0, dilation=dilations[0])
self.aspp2 = _ASPPModule(in_planes, planes=256, kernel_size=3, padding=dilations[1], dilation=dilations[1])
self.aspp3 = _ASPPModule(in_planes, planes=256, kernel_size=3, padding=dilations[2], dilation=dilations[2])
self.aspp4 = _ASPPModule(in_planes, planes=256, kernel_size=3, padding=dilations[3], dilation=dilations[3])
# perform global average pooling on the last feature map of the backbone
# batchsize must be greater than 1, otherwise exception will be thrown in calculating BatchNorm
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(in_planes, 256, 1, stride=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU())
self.p1 = nn.AdaptiveAvgPool2d(1)
self.p2 = nn.Conv2d(in_planes, 256, 1, stride=1, bias=False)
self.p3 = nn.BatchNorm2d(256)
self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
self.bn1 = nn.BatchNorm2d(256)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.5)
self._init_weight()
def __init__(self,
n_inputs,
n_outputs,
kernel_size,
stride,
dilation,
padding,
dropout=0.2):
super(TemporalBlock, self).__init__()
# 定义残差模块的第一层扩张卷积
# 经过conv，输出的size为(Batch, input_channel, seq_len + padding)，并归一化模型的参数
self.conv1 = weight_norm(
modules.Conv1d(n_inputs,
n_outputs,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation))
# 裁剪掉多出来的padding部分，维持输出时间步为seq_len
self.chomp1 = Chomp1d(padding)
self.relu1 = modules.ReLU()
self.dropout1 = modules.Dropout(dropout)
#定义残差模块的第二层扩张卷积
self.conv2 = weight_norm(
modules.Conv1d(n_outputs,
n_outputs,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation))
self.chomp2 = Chomp1d(padding)
self.relu2 = modules.ReLU()
self.dropout2 = modules.Dropout(dropout)
# 将卷积模块进行串联构成序列
self.net = modules.Sequential(self.conv1, self.chomp1, self.relu1,
self.dropout1, self.conv2, self.chomp2,
self.relu2, self.dropout2)
# 如果输入通道和输出通道不相同，那么通过1x1卷积进行降维，保持通道相同
self.downsample = modules.Conv1d(n_inputs, n_outputs,
1) if n_inputs != n_outputs else None
self.relu = modules.ReLU()
self.init_weights()
def __init__(self, n_states, n_actions, n_hidden, lr, device):
super(DDPGActor, self).__init__()
self.device = device
self.input = nn.Linear(n_states, n_hidden)
self.l1 = nn.Linear(n_hidden, n_hidden)
self.out = nn.Linear(n_hidden, n_actions)
self.dropout = nn.Dropout(0.1)
self.optimizer = optim.SGD(self.parameters(), lr=lr)
self.to(device)
def __init__(self,
h,
d_model,
k,
last_feat_height,
last_feat_width,
scales=1,
dropout=0.1,
need_attn=False):
"""
:param h: number of self attention head
:param d_model: dimension of model
:param dropout:
:param k: number of keys
"""
super(DeformableHeadAttention, self).__init__()
assert h == 8 # currently header is fixed 8 in paper
assert d_model % h == 0
# We assume d_v always equals d_k, d_q = d_k = d_v = d_m / h
self.d_k = int(d_model / h)
self.h = h
self.q_proj = nn.Linear(d_model, d_model)
self.k_proj = nn.Linear(d_model, d_model)
self.scales_hw = []
for i in range(scales):
self.scales_hw.append(
[last_feat_height * 2**i, last_feat_width * 2**i])
self.dropout = None
if self.dropout:
self.dropout = nn.Dropout(p=dropout)
self.k = k
self.scales = scales
self.last_feat_height = last_feat_height
self.last_feat_width = last_feat_width
self.offset_dims = 2 * self.h * self.k * self.scales
self.A_dims = self.h * self.k * self.scales
# 2MLK for offsets MLK for A_mlqk
self.offset_proj = nn.Linear(d_model, self.offset_dims)
self.A_proj = nn.Linear(d_model, self.A_dims)
self.wm_proj = nn.Linear(d_model, d_model)
self.need_attn = need_attn
self.attns = []
self.offsets = []
def __init__(self, rnn_size, embedding_size, input_size, output_size,
grids_width, grids_height, dropout_par, device):
super(VPTLSTM, self).__init__()
######参数初始化##########
self.device = device
self.rnn_size = rnn_size # hidden size默认128
self.embedding_size = embedding_size # 空间坐标嵌入尺寸64，每个状态用64维向量表示
self.input_size = input_size # 输入尺寸6,特征向量长度
self.output_size = output_size # 输出尺寸5
self.grids_width = grids_width
self.grids_height = grids_height
self.dropout_par = dropout_par
############网络层初始化###############
# 输入embeded_input,hidden_states
self.cell = nn.LSTMCell(2 * self.embedding_size, self.rnn_size)
# 输入Embed层，将长度为input_size的vec映射到embedding_size
self.input_embedding_layer = nn.Linear(self.input_size,
self.embedding_size)
# 输入[vehicle_num,grids_height,grids_width,rnn_size] [26,39,5,128]
# 输出[vehicle_num,grids_height-12,grids_width-4,rnn_size*4] [26,27,1,32]
self.social_tensor_conv1 = nn.Conv2d(in_channels=self.rnn_size,
out_channels=self.rnn_size // 2,
kernel_size=(5, 3),
stride=(2, 1))
self.social_tensor_conv2 = nn.Conv2d(in_channels=self.rnn_size // 2,
out_channels=self.rnn_size // 4,
kernel_size=(5, 3),
stride=1)
self.social_tensor_embed = nn.Linear(
(self.grids_height - 15) * (self.grids_width - 4) *
self.rnn_size // 4, self.embedding_size)
# 输出Embed层，将长度为64的hidden_state映射到5
self.output_layer = nn.Linear(self.rnn_size, self.output_size)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(self.dropout_par)
def __init__(self, num_class=10):
super(VGG16, self).__init__()
self.feature = modules.Sequential(
# #1,
modules.Conv2d(3, 64, kernel_size=3, padding=1),
modules.BatchNorm2d(64),
modules.ReLU(True),
#2
modules.Conv2d(64, 64, kernel_size=3, padding=1),
modules.BatchNorm2d(64),
modules.ReLU(True),
modules.MaxPool2d(kernel_size=2, stride=2),
#3
modules.Conv2d(64, 128, kernel_size=3, padding=1),
modules.BatchNorm2d(128),
modules.ReLU(True),
# modules.MaxPool2d(kernel_size=2,stride=2),
#4
modules.Conv2d(128, 128, kernel_size=3, padding=1),
modules.BatchNorm2d(128),
modules.ReLU(True),
modules.MaxPool2d(kernel_size=2, stride=2),
#5
modules.Conv2d(128, 256, kernel_size=3, padding=1),
modules.BatchNorm2d(256),
modules.ReLU(True),
#6
modules.Conv2d(256, 256, kernel_size=3, padding=1),
modules.BatchNorm2d(256),
modules.ReLU(True),
#7
modules.Conv2d(256, 256, kernel_size=3, padding=1),
modules.BatchNorm2d(256),
modules.ReLU(True),
modules.MaxPool2d(kernel_size=2, stride=2),
#8
modules.Conv2d(256, 512, kernel_size=3, padding=1),
modules.BatchNorm2d(512),
modules.ReLU(True),
#9
modules.Conv2d(512, 512, kernel_size=3, padding=1),
modules.BatchNorm2d(512),
modules.ReLU(True),
#10
modules.Conv2d(512, 512, kernel_size=3, padding=1),
modules.BatchNorm2d(512),
modules.ReLU(True),
modules.MaxPool2d(kernel_size=2, stride=2),
#11
modules.Conv2d(512, 512, kernel_size=3, padding=1),
modules.BatchNorm2d(512),
modules.ReLU(True),
#12
modules.Conv2d(512, 512, kernel_size=3, padding=1),
modules.BatchNorm2d(512),
modules.ReLU(True),
#13
modules.Conv2d(512, 512, kernel_size=3, padding=1),
modules.BatchNorm2d(512),
modules.ReLU(True),
modules.MaxPool2d(kernel_size=2, stride=2),
modules.AvgPool2d(kernel_size=1, stride=1),
)
# 全连接层
self.classifier = modules.Sequential(
# #14
modules.Linear(512, 4096),
modules.ReLU(True),
modules.Dropout(),
#15
modules.Linear(4096, 4096),
modules.ReLU(True),
modules.Dropout(),
#16
modules.Linear(4096, num_class),
)
