File size: 2,121 Bytes
af2130e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import pdb
import copy
import torch
import collections
import torch.nn as nn
import torch.nn.functional as F
class TemporalConv(nn.Module):
def __init__(self, input_size, hidden_size, conv_type=2, use_bn=False, num_classes=-1):
super(TemporalConv, self).__init__()
self.use_bn = use_bn
self.input_size = input_size
self.hidden_size = hidden_size
self.num_classes = num_classes
self.conv_type = conv_type
if self.conv_type == 0:
self.kernel_size = ['K3']
elif self.conv_type == 1:
self.kernel_size = ['K5', "P2"]
elif self.conv_type == 2:
self.kernel_size = ['K5', "P2", 'K5', "P2"]
modules = []
for layer_idx, ks in enumerate(self.kernel_size):
input_sz = self.input_size if layer_idx == 0 else self.hidden_size
if ks[0] == 'P':
modules.append(nn.MaxPool1d(kernel_size=int(ks[1]), ceil_mode=False))
elif ks[0] == 'K':
modules.append(
nn.Conv1d(input_sz, self.hidden_size, kernel_size=int(ks[1]), stride=1, padding=0)
)
modules.append(nn.BatchNorm1d(self.hidden_size))
modules.append(nn.ReLU(inplace=True))
self.temporal_conv = nn.Sequential(*modules)
if self.num_classes != -1:
self.fc = nn.Linear(self.hidden_size, self.num_classes)
def update_lgt(self, lgt):
feat_len = copy.deepcopy(lgt)
for ks in self.kernel_size:
if ks[0] == 'P':
feat_len = torch.div(feat_len, 2)
else:
feat_len -= int(ks[1]) - 1
return feat_len
def forward(self, frame_feat, lgt):
visual_feat = self.temporal_conv(frame_feat)
lgt = self.update_lgt(lgt)
logits = None if self.num_classes == -1 \
else self.fc(visual_feat.transpose(1, 2)).transpose(1, 2)
return {
"visual_feat": visual_feat.permute(2, 0, 1),
"conv_logits": logits.permute(2, 0, 1),
"feat_len": lgt.cpu(),
}
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