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Visualhead.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import PositionalEncoding, MaskedNorm, PositionwiseFeedForward, MLPHead
class VisualHead(torch.nn.Module):
def __init__(self,
cls_num, input_size=512, hidden_size=1024, ff_size=2048, pe=True,
ff_kernelsize=[3,3], pretrained_ckpt=None, is_empty=False, frozen=False,
plus_conv_cfg={},
ssl_projection_cfg={}):
super().__init__()
self.is_empty = is_empty
self.plus_conv_cfg = plus_conv_cfg
self.ssl_projection_cfg = ssl_projection_cfg
if is_empty==False:
self.frozen = frozen
self.hidden_size = hidden_size
if input_size is None:
self.fc1 = nn.Identity()
else:
self.fc1 = torch.nn.Linear(input_size, self.hidden_size)
self.bn1 = MaskedNorm(num_features=self.hidden_size, norm_type='batch')
self.relu1 = torch.nn.ReLU()
self.dropout1 = torch.nn.Dropout(p=0.1)
if pe:
self.pe = PositionalEncoding(self.hidden_size)
else:
self.pe = torch.nn.Identity()
self.feedforward = PositionwiseFeedForward(input_size=self.hidden_size,
ff_size=ff_size,
dropout=0.1, kernel_size=ff_kernelsize, skip_connection=True)
self.layer_norm = torch.nn.LayerNorm(self.hidden_size, eps=1e-6)
if plus_conv_cfg!={}:
plus_convs = []
for i in range(plus_conv_cfg['num_layer']):
plus_convs.append(nn.Conv1d(self.hidden_size, self.hidden_size,
kernel_size=plus_conv_cfg['kernel_size'], stride=plus_conv_cfg['stride'], padding_mode='replicate'))
self.plus_conv = nn.Sequential(*plus_convs)
else:
self.plus_conv = nn.Identity()
if ssl_projection_cfg!={}:
self.ssl_projection = MLPHead(embedding_size=self.hidden_size,
projection_hidden_size=ssl_projection_cfg['hidden_size'])
self.gloss_output_layer = torch.nn.Linear(self.hidden_size, cls_num)
if self.frozen:
self.frozen_layers = [self.fc1, self.bn1, self.relu1, self.pe, self.dropout1, self.feedforward, self.layer_norm]
for layer in self.frozen_layers:
for name, param in layer.named_parameters():
param.requires_grad = False
layer.eval()
else:
self.gloss_output_layer = torch.nn.Linear(input_size, cls_num)
if pretrained_ckpt:
self.load_from_pretrained_ckpt(pretrained_ckpt)
def load_from_pretrained_ckpt(self, pretrained_ckpt):
logger = get_logger()
checkpoint = torch.load(pretrained_ckpt, map_location='cpu')['model_state']
load_dict = {}
for k,v in checkpoint.items():
if 'recognition_network.visual_head.' in k:
load_dict[k.replace('recognition_network.visual_head.','')] = v
self.load_state_dict(load_dict)
logger.info('Load Visual Head from pretrained ckpt {}'.format(pretrained_ckpt))
def forward(self, x, mask, valid_len_in=None):
B, Tin, D = x.shape
if self.is_empty==False:
if not self.frozen:
#projection 1
x = self.fc1(x)
x = self.bn1(x, mask)
x = self.relu1(x)
#pe
x = self.pe(x)
x = self.dropout1(x)
#feedforward
x = self.feedforward(x)
x = self.layer_norm(x)
x = x.transpose(1,2)
x = self.plus_conv(x)
x = x.transpose(1,2)
else:
with torch.no_grad():
for ii, layer in enumerate(self.frozen_layers):
layer.eval()
if ii==1:
x = layer(x, mask)
else:
x = layer(x)
x = x.transpose(1,2)
x = self.plus_conv(x)
x = x.transpose(1,2)
#classification
logits = self.gloss_output_layer(x) #B,T,V
gloss_probabilities_log = logits.log_softmax(2)
gloss_probabilities = logits.softmax(2)
if self.plus_conv_cfg!={}:
B, Tout, D = x.shape
valid_len_out = torch.floor(valid_len_in*Tout/Tin).long() #B,
else:
valid_len_out = valid_len_in
if self.ssl_projection_cfg!={}:
x_ssl = self.ssl_projection(x)
if self.ssl_projection_cfg['normalize']==True:
x_ssl = F.normalize(x_ssl, dim=-1)
else:
x_ssl = None
return {'gloss_feature_ssl':x_ssl,
'gloss_feature': x,
'gloss_feature_norm': F.normalize(x, dim=-1),
'gloss_logits':logits,
'gloss_probabilities_log':gloss_probabilities_log,
'gloss_probabilities': gloss_probabilities,
'valid_len_out':valid_len_out}