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import torch
import torch.nn as nn
from torch.nn import Module, Linear
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[: x.size(0), :]
return self.dropout(x)
class ConcatSquashLinear(Module):
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = Linear(dim_in, dim_out)
self._hyper_bias = Linear(dim_ctx, dim_out, bias=False)
self._hyper_gate = Linear(dim_ctx, dim_out)
def forward(self, ctx, x):
# ctx: (B, 1, F+3)
# x: (B, T, 2)
gate = torch.sigmoid(self._hyper_gate(ctx))
bias = self._hyper_bias(ctx)
# if x.dim() == 3:
# gate = gate.unsqueeze(1)
# bias = bias.unsqueeze(1)
ret = self._layer(x) * gate + bias
return ret
def batch_generate(self, ctx, x):
# ctx: (B, n, 1, F+3)
# x: (B, n, T, 2)
gate = torch.sigmoid(self._hyper_gate(ctx))
bias = self._hyper_bias(ctx)
# if x.dim() == 3:
# gate = gate.unsqueeze(1)
# bias = bias.unsqueeze(1)
ret = self._layer(x) * gate + bias
return ret
class GAT(nn.Module):
def __init__(self, in_feat=2, out_feat=64, n_head=4, dropout=0.1, skip=True):
super(GAT, self).__init__()
self.in_feat = in_feat
self.out_feat = out_feat
self.n_head = n_head
self.skip = skip
self.w = nn.Parameter(torch.Tensor(n_head, in_feat, out_feat))
self.a_src = nn.Parameter(torch.Tensor(n_head, out_feat, 1))
self.a_dst = nn.Parameter(torch.Tensor(n_head, out_feat, 1))
self.bias = nn.Parameter(torch.Tensor(out_feat))
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(dropout)
nn.init.xavier_uniform_(self.w, gain=1.414)
nn.init.xavier_uniform_(self.a_src, gain=1.414)
nn.init.xavier_uniform_(self.a_dst, gain=1.414)
nn.init.constant_(self.bias, 0)
def forward(self, h, mask):
h_prime = h.unsqueeze(1) @ self.w
attn_src = h_prime @ self.a_src
attn_dst = h_prime @ self.a_dst
attn = attn_src @ attn_dst.permute(0, 1, 3, 2)
attn = self.leaky_relu(attn)
attn = self.softmax(attn)
attn = self.dropout(attn)
attn = attn * mask if mask is not None else attn
out = (attn @ h_prime).sum(dim=1) + self.bias
if self.skip:
out += h_prime.sum(dim=1)
return out, attn
class MLP(nn.Module):
def __init__(self, in_feat, out_feat, hid_feat=(1024, 512), activation=None, dropout=-1):
super(MLP, self).__init__()
dims = (in_feat, ) + hid_feat + (out_feat, )
self.layers = nn.ModuleList()
for i in range(len(dims) - 1):
self.layers.append(nn.Linear(dims[i], dims[i + 1]))
self.activation = activation if activation is not None else lambda x: x
self.dropout = nn.Dropout(dropout) if dropout != -1 else lambda x: x
def forward(self, x):
for i in range(len(self.layers)):
x = self.activation(x)
x = self.dropout(x)
x = self.layers[i](x)
return x
class social_transformer(nn.Module):
def __init__(self, past_len):
super(social_transformer, self).__init__()
self.encode_past = nn.Linear(past_len*6, 256, bias=False)
self.layer = nn.TransformerEncoderLayer(d_model=256, nhead=2, dim_feedforward=256)
self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=2)
def forward(self, h, mask):
'''
h: batch_size, t, 2
'''
h_feat = self.encode_past(h.reshape(h.size(0), -1)).unsqueeze(1)
# print(h_feat.shape)
# n_samples, 1, 64
h_feat_ = self.transformer_encoder(h_feat, mask)
h_feat = h_feat + h_feat_
return h_feat
class st_encoder(nn.Module):
def __init__(self):
super().__init__()
channel_in = 6
channel_out = 32
dim_kernel = 3
self.dim_embedding_key = 256
self.spatial_conv = nn.Conv1d(channel_in, channel_out, dim_kernel, stride=1, padding=1)
self.temporal_encoder = nn.GRU(channel_out, self.dim_embedding_key, 1, batch_first=True)
self.relu = nn.ReLU()
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_normal_(self.spatial_conv.weight)
nn.init.kaiming_normal_(self.temporal_encoder.weight_ih_l0)
nn.init.kaiming_normal_(self.temporal_encoder.weight_hh_l0)
nn.init.zeros_(self.spatial_conv.bias)
nn.init.zeros_(self.temporal_encoder.bias_ih_l0)
nn.init.zeros_(self.temporal_encoder.bias_hh_l0)
def forward(self, X):
'''
X: b, T, 2
return: b, F
'''
X_t = torch.transpose(X, 1, 2)
X_after_spatial = self.relu(self.spatial_conv(X_t))
X_embed = torch.transpose(X_after_spatial, 1, 2)
output_x, state_x = self.temporal_encoder(X_embed)
state_x = state_x.squeeze(0)
return state_x
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