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TRNmodule.py
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TRNmodule.py
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# the relation consensus module by Bolei
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
import pdb
class RelationModule(torch.nn.Module):
# this is the naive implementation of the n-frame relation module, as num_frames == num_frames_relation
def __init__(self, img_feature_dim, num_frames, num_class):
super(RelationModule, self).__init__()
self.num_frames = num_frames
self.num_class = num_class
self.img_feature_dim = img_feature_dim
self.classifier = self.fc_fusion()
def fc_fusion(self):
# naive concatenate
num_bottleneck = 512
classifier = nn.Sequential(
nn.ReLU(),
nn.Linear(self.num_frames * self.img_feature_dim, num_bottleneck),
nn.ReLU(),
nn.Linear(num_bottleneck,self.num_class),
)
return classifier
def forward(self, input):
input = input.view(input.size(0), self.num_frames*self.img_feature_dim)
input = self.classifier(input)
return input
class RelationModuleMultiScale(torch.nn.Module):
# Temporal Relation module in multiply scale, suming over [2-frame relation, 3-frame relation, ..., n-frame relation]
def __init__(self, img_feature_dim, num_frames, num_class):
super(RelationModuleMultiScale, self).__init__()
self.subsample_num = 3 # how many relations selected to sum up
self.img_feature_dim = img_feature_dim
self.scales = [i for i in range(num_frames, 1, -1)] # generate the multiple frame relations
self.relations_scales = []
self.subsample_scales = []
for scale in self.scales:
relations_scale = self.return_relationset(num_frames, scale)
self.relations_scales.append(relations_scale)
self.subsample_scales.append(min(self.subsample_num, len(relations_scale))) # how many samples of relation to select in each forward pass
self.num_class = num_class
self.num_frames = num_frames
num_bottleneck = 256
self.fc_fusion_scales = nn.ModuleList() # high-tech modulelist
for i in range(len(self.scales)):
scale = self.scales[i]
fc_fusion = nn.Sequential(
nn.ReLU(),
nn.Linear(scale * self.img_feature_dim, num_bottleneck),
nn.ReLU(),
nn.Linear(num_bottleneck, self.num_class),
)
self.fc_fusion_scales += [fc_fusion]
print('Multi-Scale Temporal Relation Network Module in use', ['%d-frame relation' % i for i in self.scales])
def forward(self, input):
# the first one is the largest scale
act_all = input[:, self.relations_scales[0][0] , :]
act_all = act_all.view(act_all.size(0), self.scales[0] * self.img_feature_dim)
act_all = self.fc_fusion_scales[0](act_all)
for scaleID in range(1, len(self.scales)):
# iterate over the scales
idx_relations_randomsample = np.random.choice(len(self.relations_scales[scaleID]), self.subsample_scales[scaleID], replace=False)
for idx in idx_relations_randomsample:
act_relation = input[:, self.relations_scales[scaleID][idx], :]
act_relation = act_relation.view(act_relation.size(0), self.scales[scaleID] * self.img_feature_dim)
act_relation = self.fc_fusion_scales[scaleID](act_relation)
act_all += act_relation
return act_all
def return_relationset(self, num_frames, num_frames_relation):
import itertools
return list(itertools.combinations([i for i in range(num_frames)], num_frames_relation))
class RelationModuleMultiScaleWithClassifier(torch.nn.Module):
# relation module in multi-scale with a classifier at the end
def __init__(self, img_feature_dim, num_frames, num_class):
super(RelationModuleMultiScaleWithClassifier, self).__init__()
self.subsample_num = 3 # how many relations selected to sum up
self.img_feature_dim = img_feature_dim
self.scales = [i for i in range(num_frames, 1, -1)] #
self.relations_scales = []
self.subsample_scales = []
for scale in self.scales:
relations_scale = self.return_relationset(num_frames, scale)
self.relations_scales.append(relations_scale)
self.subsample_scales.append(min(self.subsample_num, len(relations_scale))) # how many samples of relation to select in each forward pass
self.num_class = num_class
self.num_frames = num_frames
num_bottleneck = 256
self.fc_fusion_scales = nn.ModuleList() # high-tech modulelist
self.classifier_scales = nn.ModuleList()
for i in range(len(self.scales)):
scale = self.scales[i]
fc_fusion = nn.Sequential(
nn.ReLU(),
nn.Linear(scale * self.img_feature_dim, num_bottleneck),
nn.ReLU(),
nn.Dropout(p=0.6),# this is the newly added thing
nn.Linear(num_bottleneck, num_bottleneck),
nn.ReLU(),
nn.Dropout(p=0.6),
)
classifier = nn.Linear(num_bottleneck, self.num_class)
self.fc_fusion_scales += [fc_fusion]
self.classifier_scales += [classifier]
# maybe we put another fc layer after the summed up results???
print('Multi-Scale Temporal Relation with classifier in use')
print(['%d-frame relation' % i for i in self.scales])
def forward(self, input):
# the first one is the largest scale
act_all = input[:, self.relations_scales[0][0] , :]
act_all = act_all.view(act_all.size(0), self.scales[0] * self.img_feature_dim)
act_all = self.fc_fusion_scales[0](act_all)
act_all = self.classifier_scales[0](act_all)
for scaleID in range(1, len(self.scales)):
# iterate over the scales
idx_relations_randomsample = np.random.choice(len(self.relations_scales[scaleID]), self.subsample_scales[scaleID], replace=False)
for idx in idx_relations_randomsample:
act_relation = input[:, self.relations_scales[scaleID][idx], :]
act_relation = act_relation.view(act_relation.size(0), self.scales[scaleID] * self.img_feature_dim)
act_relation = self.fc_fusion_scales[scaleID](act_relation)
act_relation = self.classifier_scales[scaleID](act_relation)
act_all += act_relation
return act_all
def return_relationset(self, num_frames, num_frames_relation):
import itertools
return list(itertools.combinations([i for i in range(num_frames)], num_frames_relation))
def return_TRN(relation_type, img_feature_dim, num_frames, num_class):
if relation_type == 'TRN':
TRNmodel = RelationModule(img_feature_dim, num_frames, num_class)
elif relation_type == 'TRNmultiscale':
TRNmodel = RelationModuleMultiScale(img_feature_dim, num_frames, num_class)
else:
raise ValueError('Unknown TRN' + relation_type)
return TRNmodel
if __name__ == "__main__":
batch_size = 10
num_frames = 5
num_class = 174
img_feature_dim = 512
input_var = Variable(torch.randn(batch_size, num_frames, img_feature_dim))
model = RelationModuleMultiScale(img_feature_dim, num_frames, num_class)
output = model(input_var)
print(output)