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model_tri_c.py
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model_tri_c.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch.nn as nn
import torch as th
import torch.nn.functional as F
from model_davenet import load_DAVEnet
class Net(nn.Module):
def __init__(
self,
embd_dim=4096,
video_dim=4096,
we_dim=300,
tri_modal=False,
tri_modal_fuse=False,
cluster_size=512,
layer=0,
project=0,
project_dim=6000,
multi_cluster=0,
finetune_video=False
):
super(Net, self).__init__()
self.DAVEnet = load_DAVEnet()
self.DAVEnet_projection = nn.Linear(1024, embd_dim)
self.GU_audio = Gated_Embedding_Unit(1024, 1024)
self.GU_video = Gated_Embedding_Unit(video_dim, embd_dim)
if tri_modal and not tri_modal_fuse:
self.text_pooling_caption = Sentence_Maxpool(we_dim, embd_dim)
self.GU_text_captions = Gated_Embedding_Unit(embd_dim, embd_dim)
elif tri_modal_fuse:
self.DAVEnet_projection = nn.Linear(1024, embd_dim // 2)
self.text_pooling_caption = Sentence_Maxpool(we_dim, embd_dim // 2)
self.GU_audio_text = Fused_Gated_Unit(embd_dim // 2, embd_dim)
self.tri_modal = tri_modal
self.tri_modal_fuse = tri_modal_fuse
self.project = project
if project==0:
self.classification = nn.Linear(embd_dim, project_dim, bias=False) #4096,256
else:
self.projection_head = nn.Sequential(
nn.Linear(embd_dim, embd_dim),
nn.BatchNorm1d(embd_dim),
nn.ReLU(inplace=True),
nn.Linear(embd_dim, cluster_size),
)
self.classification = nn.Linear(cluster_size, project_dim, bias=False)
if project_dim==8000:
self.classification2 = nn.Linear(embd_dim, cluster_size)
self.classification3 = nn.Linear(embd_dim, cluster_size)
self.layer=layer
if self.layer==1:
self.layer1 = nn.Sequential(
nn.Linear(embd_dim, 4096),
nn.BatchNorm1d(4096),
nn.ReLU(True),
nn.Linear(4096, 256)
)
self.layer2 = nn.Sequential(
nn.Linear(embd_dim, 4096),
nn.BatchNorm1d(4096),
nn.ReLU(True),
nn.Linear(4096, 256)
)
self.layer3 = nn.Sequential(
nn.Linear(embd_dim, 4096),
nn.BatchNorm1d(4096),
nn.ReLU(True),
nn.Linear(4096, 256)
)
self.finetune_video = finetune_video
if self.finetune_video:
self.video_encoder = nn.Sequential(
nn.Conv1d(video_dim, embd_dim, 3, 2),
nn.ReLU(inplace=True),
nn.Conv1d(embd_dim, video_dim, 3, 2),
nn.ReLU(inplace=True)
)
def save_checkpoint(self, path):
th.save(self.state_dict(), path)
def load_checkpoint(self, path):
try:
self.load_state_dict(th.load(path, map_location='cpu'))
except Exception as e:
print(e)
print("IGNORING ERROR, LOADING MODEL USING STRICT=FALSE")
self.load_state_dict(th.load(path, map_location='cpu'), strict=False)
print("Loaded model checkpoint from {}".format(path))
def forward(self, video, audio_input, nframes, text=None):
if self.finetune_video:
video = self.video_encoder(video)
video = th.max(video, -1)[0] # Max pools along the last dimension
video = self.GU_video(video)
audio = self.DAVEnet(audio_input)
if not self.training: # controlled by net.train() / net.eval() (use for downstream tasks)
# Mean-pool audio embeddings and disregard embeddings from input 0 padding
pooling_ratio = round(audio_input.size(-1) / audio.size(-1))
nframes.div_(pooling_ratio)
audioPoolfunc = th.nn.AdaptiveAvgPool2d((1, 1)) #
#audioPoolfunc = th.nn.AdaptiveMaxPool2d((1, 1))
audio_outputs = audio.unsqueeze(2)
pooled_audio_outputs_list = []
for idx in range(audio.shape[0]):
nF = max(1, nframes[idx])
pooled_audio_outputs_list.append(audioPoolfunc(audio_outputs[idx][:, :, 0:nF]).unsqueeze(0))
audio = th.cat(pooled_audio_outputs_list).squeeze(3).squeeze(2)
else:
audio = audio.mean(dim=2) # this averages features from 0 padding too
if self.tri_modal_fuse:
text = self.text_pooling_caption(text)
audio = self.DAVEnet_projection(audio)
audio_text = self.GU_audio_text(audio, text)
return audio_text, video
# Gating in lower embedding dimension (1024 vs 4096) for stability with mixed-precision training
audio = self.GU_audio(audio)
audio = self.DAVEnet_projection(audio)
if self.tri_modal and not self.tri_modal_fuse:
text = self.GU_text_captions(self.text_pooling_caption(text))
# video_c2 = self.layer2(video)
#"""
#"""
if self.layer==1:
video_c = self.layer1(video)
audio_c = self.layer2(audio)
text_c = self.layer3(text)
else:
if self.project==1:
video_c = self.projection_head(video)
video_c2 = nn.functional.normalize(video_c, dim=1, p=2)
else:
video_c = nn.functional.normalize(video, dim=1, p=2)
video_c = self.classification(video_c2)
#
if self.project == 1:
audio_c = self.projection_head(audio)
audio_c2 = nn.functional.normalize(audio_c, dim=1, p=2)
else:
audio_c = nn.functional.normalize(audio, dim=1, p=2)
audio_c = self.classification(audio_c2)
#text_c = self.projection_head(text)
if self.project == 1:
text_c = self.projection_head(text)
text_c2 = nn.functional.normalize(text_c, dim=1, p=2)
else:
text_c = nn.functional.normalize(text, dim=1, p=2)
text_c = self.classification(text_c2)
return audio, video, text, audio_c, video_c, text_c, audio_c2, video_c2, text_c2
return audio, video
class Gated_Embedding_Unit(nn.Module):
def __init__(self, input_dimension, output_dimension):
super(Gated_Embedding_Unit, self).__init__()
self.fc = nn.Linear(input_dimension, output_dimension)
self.cg = Context_Gating(output_dimension)
def forward(self, x):
x = self.fc(x)
x = self.cg(x)
return x
class Fused_Gated_Unit(nn.Module):
def __init__(self, input_dimension, output_dimension):
super(Fused_Gated_Unit, self).__init__()
self.fc_audio = nn.Linear(input_dimension, output_dimension)
self.fc_text = nn.Linear(input_dimension, output_dimension)
self.cg = Context_Gating(output_dimension)
def forward(self, audio, text):
audio = self.fc_audio(audio)
text = self.fc_text(text)
x = audio + text
x = self.cg(x)
return x
class Context_Gating(nn.Module):
def __init__(self, dimension):
super(Context_Gating, self).__init__()
self.fc = nn.Linear(dimension, dimension)
def forward(self, x):
x1 = self.fc(x)
x = th.cat((x, x1), 1)
return F.glu(x, 1)
class Sentence_Maxpool(nn.Module):
def __init__(self, word_dimension, output_dim):
super(Sentence_Maxpool, self).__init__()
self.fc = nn.Linear(word_dimension, output_dim)
def forward(self, x):
x = self.fc(x)
x = F.relu(x)
return th.max(x, dim=1)[0]