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import numpy as np
import torch
import math
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import init
from torch.nn.functional import normalize
class PositionalEncoding(nn.Module):
def __init__(self,
emb_size: int,
dropout: float = 0.1,
maxlen: int = 750):
super(PositionalEncoding, self).__init__()
den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)
pos = torch.arange(0, maxlen).reshape(maxlen, 1)
pos_embedding = torch.zeros((maxlen, emb_size))
pos_embedding[:, 0::2] = torch.sin(pos * den)
pos_embedding[:, 1::2] = torch.cos(pos * den)
pos_embedding = pos_embedding.unsqueeze(-2)
self.dropout = nn.Dropout(dropout)
self.register_buffer('pos_embedding', pos_embedding)
def forward(self, token_embedding: torch.Tensor):
return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])
class HistoryUnit(torch.nn.Module):
def __init__(self, opt):
super(HistoryUnit, self).__init__()
self.n_feature=opt["feat_dim"]
n_class=opt["num_of_class"]
n_embedding_dim=opt["hidden_dim"]
n_hist_dec_head = 4
n_hist_dec_layer = 5
n_hist_dec_head_2 = 4
n_hist_dec_layer_2 = 2
self.anchors=opt["anchors"]
self.history_tokens = 16
self.short_window_size = 16
self.anchors_stride=[]
dropout=0.3
self.best_loss=1000000
self.best_map=0
self.history_positional_encoding = PositionalEncoding(n_embedding_dim, dropout, maxlen=400)
self.history_encoder_block1 = nn.TransformerDecoder(
nn.TransformerDecoderLayer(d_model=n_embedding_dim,
nhead=n_hist_dec_head,
dropout=dropout,
activation='gelu'),
n_hist_dec_layer,
nn.LayerNorm(n_embedding_dim))
self.history_encoder_block2 = nn.TransformerDecoder(
nn.TransformerDecoderLayer(d_model=n_embedding_dim,
nhead=n_hist_dec_head_2,
dropout=dropout,
activation='gelu'),
n_hist_dec_layer_2,
nn.LayerNorm(n_embedding_dim))
self.snip_head = nn.Sequential(nn.Linear(n_embedding_dim,n_embedding_dim//4), nn.ReLU())
self.snip_classifier = nn.Sequential(nn.Linear(self.history_tokens*n_embedding_dim//4, (self.history_tokens*n_embedding_dim//4)//4), nn.ReLU(), nn.Linear((self.history_tokens*n_embedding_dim//4)//4,n_class))
self.history_token = nn.Parameter(torch.zeros(self.history_tokens, 1, n_embedding_dim))
# self.history_token_extra = nn.Parameter(torch.zeros(self.history_tokens*2, 1, n_embedding_dim))
self.norm2 = nn.LayerNorm(n_embedding_dim)
self.dropout2 = nn.Dropout(0.1)
def forward(self, long_x, encoded_x):
## History Encoder
hist_pe_x = self.history_positional_encoding(long_x)
history_token = self.history_token.expand(-1, hist_pe_x.shape[1], -1)
hist_encoded_x_1 = self.history_encoder_block1(history_token, hist_pe_x)
hist_encoded_x_2 = self.history_encoder_block2(hist_encoded_x_1, encoded_x)
hist_encoded_x_2 = hist_encoded_x_2 + self.dropout2(hist_encoded_x_1)
hist_encoded_x = self.norm2(hist_encoded_x_2)
## Snippet Classfication Head
snippet_feat = self.snip_head(hist_encoded_x_1)
snippet_feat = torch.flatten(snippet_feat.permute(1, 0, 2), start_dim=1)
snip_cls = self.snip_classifier(snippet_feat)
return hist_encoded_x, snip_cls
class MYNET(torch.nn.Module):
def __init__(self, opt):
super(MYNET, self).__init__()
self.n_feature=opt["feat_dim"]
n_class=opt["num_of_class"]
n_embedding_dim=opt["hidden_dim"]
n_enc_layer=opt["enc_layer"]
n_enc_head=opt["enc_head"]
n_dec_layer=opt["dec_layer"]
n_dec_head=opt["dec_head"]
n_comb_dec_head = 4
n_comb_dec_layer = 5
n_seglen=opt["segment_size"]
self.anchors=opt["anchors"]
self.history_tokens = 16
self.short_window_size = 16
self.anchors_stride=[]
dropout=0.3
self.best_loss=1000000
self.best_map=0
self.feature_reduction_rgb = nn.Linear(self.n_feature//2, n_embedding_dim//2)
self.feature_reduction_flow = nn.Linear(self.n_feature//2, n_embedding_dim//2)
self.positional_encoding = PositionalEncoding(n_embedding_dim, dropout, maxlen=400)
self.encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(d_model=n_embedding_dim,
nhead=n_enc_head,
dropout=dropout,
activation='gelu'),
n_enc_layer,
nn.LayerNorm(n_embedding_dim))
self.decoder = nn.TransformerDecoder(
nn.TransformerDecoderLayer(d_model=n_embedding_dim,
nhead=n_dec_head,
dropout=dropout,
activation='gelu'),
n_dec_layer,
nn.LayerNorm(n_embedding_dim))
self.history_unit = HistoryUnit(opt)
self.history_anchor_decoder_block1 = nn.TransformerDecoder(
nn.TransformerDecoderLayer(d_model=n_embedding_dim,
nhead=n_comb_dec_head,
dropout=dropout,
activation='gelu'),
n_comb_dec_layer,
nn.LayerNorm(n_embedding_dim))
self.classifier = nn.Sequential(nn.Linear(n_embedding_dim,n_embedding_dim), nn.ReLU(), nn.Linear(n_embedding_dim,n_class))
self.regressor = nn.Sequential(nn.Linear(n_embedding_dim,n_embedding_dim), nn.ReLU(), nn.Linear(n_embedding_dim,2))
self.decoder_token = nn.Parameter(torch.zeros(len(self.anchors), 1, n_embedding_dim))
self.norm1 = nn.LayerNorm(n_embedding_dim)
self.dropout1 = nn.Dropout(0.1)
self.relu = nn.ReLU(True)
self.softmaxd1 = nn.Softmax(dim=-1)
def forward(self, inputs):
# base_x_rgb = self.feature_reduction_rgb(inputs[:,:,:self.n_feature//2])
# base_x_flow = self.feature_reduction_flow(inputs[:,:,self.n_feature//2:])
base_x_rgb = self.feature_reduction_rgb(inputs[:,:,:self.n_feature//2].float())
base_x_flow = self.feature_reduction_flow(inputs[:,:,self.n_feature//2:].float())
base_x = torch.cat([base_x_rgb,base_x_flow],dim=-1)
base_x = base_x.permute([1,0,2])# seq_len x batch x featsize x
short_x = base_x[-self.short_window_size:]
long_x = base_x[:-self.short_window_size]
## Anchor Feature Generator
pe_x = self.positional_encoding(short_x)
encoded_x = self.encoder(pe_x)
decoder_token = self.decoder_token.expand(-1, encoded_x.shape[1], -1)
decoded_x = self.decoder(decoder_token, encoded_x)
decoded_x = decoded_x
## Future-Supervised History Module
hist_encoded_x, snip_cls = self.history_unit(long_x, encoded_x)
## History Driven Anchor Refinement
decoded_anchor_feat = self.history_anchor_decoder_block1(decoded_x, hist_encoded_x)
decoded_anchor_feat = decoded_anchor_feat + self.dropout1(decoded_x)
decoded_anchor_feat = self.norm1(decoded_anchor_feat)
decoded_anchor_feat = decoded_anchor_feat.permute([1, 0, 2])
# Predition Module
anc_cls = self.classifier(decoded_anchor_feat)
anc_reg = self.regressor(decoded_anchor_feat)
return anc_cls, anc_reg, snip_cls
class SuppressNet(torch.nn.Module):
def __init__(self, opt):
super(SuppressNet, self).__init__()
n_class=opt["num_of_class"]-1
n_seglen=opt["segment_size"]
n_embedding_dim=2*n_seglen
dropout=0.3
self.best_loss=1000000
self.best_map=0
# FC layers for the 2 streams
self.mlp1 = nn.Linear(n_seglen, n_embedding_dim)
self.mlp2 = nn.Linear(n_embedding_dim, 1)
self.norm = nn.InstanceNorm1d(n_class)
self.relu = nn.ReLU(True)
self.sigmoid = nn.Sigmoid()
def forward(self, inputs):
#inputs - batch x seq_len x class
base_x = inputs.permute([0,2,1])
base_x = self.norm(base_x)
x = self.relu(self.mlp1(base_x))
x = self.sigmoid(self.mlp2(x))
x = x.squeeze(-1)
return x
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