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ModelsRGB.py
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ModelsRGB.py
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import resnetMod
from MyConvLSTMCell import *
class SupervisionHead(nn.Module):
def __init__(self, in_channels, out_channels, h, w, m):
super(SupervisionHead, self).__init__()
self.conv = nn.Sequential(nn.ReLU(),
nn.Conv2d(in_channels, out_channels,
kernel_size=1, padding=0))
self.fc = nn.Sequential(nn.Linear(7 * 7 * out_channels, m * h * w))
def forward(self, x):
x = self.conv(x)
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x
class ConvLSTM(nn.Module):
def __init__(self, supervision, num_classes=61, mem_size=512, loss_supervision="classification"):
super(ConvLSTM, self).__init__()
self.num_classes = num_classes
self.supervision = supervision
self.resNet = resnetMod.resnet34(True, True)
self.mem_size = mem_size
self.weight_softmax = self.resNet.fc.weight
self.lstm_cell = MyConvLSTMCell(512, mem_size)
self.avgpool = nn.AvgPool2d(7)
self.dropout = nn.Dropout(0.7)
self.fc = nn.Linear(mem_size, self.num_classes)
self.classifier = nn.Sequential(self.dropout, self.fc)
if loss_supervision == "regression":
self.sup_head = SupervisionHead(512, 100, 7, 7, 1)
if loss_supervision == "classification":
self.sup_head = SupervisionHead(512, 100, 7, 7, 2)
def forward(self, x):
state = (torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda(),
torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda())
superv_x = []
for t in range(x.size(0)):
logit, feature_conv, feature_convNBN = self.resNet(x[t])
state = self.lstm_cell(feature_convNBN, state)
if self.supervision:
superv_x.append(self.sup_head(feature_convNBN))
if self.supervision:
superv_x = torch.stack(superv_x)
superv_x = superv_x.reshape(superv_x.shape[0] * superv_x.shape[1], -1, 7, 7)
feats1 = self.avgpool(state[1]).view(state[1].size(0), -1)
feats = self.classifier(feats1)
return feats, feats1, superv_x
class ConvLSTMAttention(nn.Module):
def __init__(self, supervision, num_classes=61, mem_size=512, loss_supervision="classification"):
super(ConvLSTMAttention, self).__init__()
self.supervision = supervision
self.num_classes = num_classes
self.resNet = resnetMod.resnet34(True, True)
self.mem_size = mem_size
self.weight_softmax = self.resNet.fc.weight
self.lstm_cell = MyConvLSTMCell(512, mem_size)
self.avgpool = nn.AvgPool2d(7)
self.dropout = nn.Dropout(0.7)
self.fc = nn.Linear(mem_size, self.num_classes)
self.classifier = nn.Sequential(self.dropout, self.fc)
if loss_supervision == "regression":
self.sup_head = SupervisionHead(512, 100, 7, 7, 1)
if loss_supervision == "classification":
self.sup_head = SupervisionHead(512, 100, 7, 7, 2)
def forward(self, x):
state = (torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda(),
torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda())
superv_x = []
for t in range(x.size(0)):
logit, feature_conv, feature_convNBN = self.resNet(x[t])
bz, nc, h, w = feature_conv.size()
feature_conv1 = feature_conv.view(bz, nc, h * w)
probs, idxs = logit.sort(1, True)
class_idx = idxs[:, 0]
cam = torch.bmm(self.weight_softmax[class_idx].unsqueeze(1), feature_conv1)
attention_map = F.softmax(cam.squeeze(1), dim=1)
attention_map = attention_map.view(attention_map.size(0), 1, 7, 7)
attention_feat = feature_convNBN * attention_map.expand_as(feature_conv)
state = self.lstm_cell(attention_feat, state)
if self.supervision:
superv_x.append(self.sup_head(feature_convNBN))
if self.supervision:
superv_x = torch.stack(superv_x)
superv_x = superv_x.reshape(superv_x.shape[0] * superv_x.shape[1], -1, 7, 7)
feats1 = self.avgpool(state[1]).view(state[1].size(0), -1)
feats = self.classifier(feats1)
return feats, feats1, superv_x
class DynamicFilters(nn.Module):
def __init__(self, in_channels, out_channels):
super(DynamicFilters, self).__init__()
self.out_channels = out_channels
self.conv = nn.Sequential(nn.ReLU(),
nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0))
self.conv2 =nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
def forward(self, x):
x = self.conv(x)
# batch,out_channel,in channel,size,size
x_n = x.reshape(x.shape[0], self.out_channels // 3, 3, x.shape[2], x.shape[3])
return x_n
class MyNet(nn.Module):
def __init__(self, num_classes=61, mem_size=512):
super(MyNet, self).__init__()
self.num_classes = num_classes
self.resNet = resnetMod.resnet34(True, True)
self.mem_size = mem_size
self.weight_softmax = self.resNet.fc.weight
self.lstm_cell = MyConvLSTMCell(512, mem_size)
self.avgpool = nn.AvgPool2d(7)
self.dropout = nn.Dropout(0.7)
self.fc = nn.Linear(mem_size, self.num_classes)
self.classifier = nn.Sequential(self.dropout, self.fc)
self.dinam = DynamicFilters(512, 3 * 3)
self.conv = nn.Sequential(nn.Conv2d(512, 512, kernel_size=1, padding=0),
nn.ReLU())
# self.convDynamic= nn.Conv2d(3, 3, 3, stride=1,padding=2, bias=False)
def forward(self, x):
state = (torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda(),
torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda())
output_t = []
for t in range(x.size(0)):
output = []
logit, feature_conv, feature_convNBN = self.resNet(x[t])
bz, nc, h, w = feature_conv.size()
feature_conv1 = feature_conv.view(bz, nc, h * w)
probs, idxs = logit.sort(1, True)
class_idx = idxs[:, 0]
cam = torch.bmm(self.weight_softmax[class_idx].unsqueeze(1), feature_conv1)
attention_map = F.softmax(cam.squeeze(1), dim=1)
attention_map = attention_map.view(attention_map.size(0), 1, 7, 7)
attention_feat = feature_convNBN * attention_map.expand_as(feature_conv)
state = self.lstm_cell(attention_feat, state)
dynamic_filter = self.dinam(state[1])
for i in range(x.shape[1]):
output.append(F.conv2d((x[t][i].unsqueeze(0)),
dynamic_filter[i].float(),
padding=3))
output_t.append(torch.stack(output))
output_t = torch.stack(output_t)
feats1 = self.avgpool(state[1]).view(state[1].size(0), -1)
feats = self.classifier(feats1)
return feats, feats1, output_t.squeeze(2)
class MyNetIDT(nn.Module):
def __init__(self, supervision, num_classes=61, mem_size=512, loss_supervision="classification"):
super(MyNetIDT, self).__init__()
self.supervision = supervision
self.num_classes = num_classes
self.resNet = resnetMod.resnet34(True, True)
self.mem_size = mem_size
self.weight_softmax = self.resNet.fc.weight
self.lstm_cell = MyConvLSTMCell(512, mem_size)
self.avgpool = nn.AvgPool2d(7)
self.dropout = nn.Dropout(0.7)
self.fc = nn.Linear(mem_size, self.num_classes)
self.classifier = nn.Sequential(self.dropout, self.fc)
if loss_supervision == "regression":
self.sup_head = SupervisionHead(512, 100, 7, 7, 1)
if loss_supervision == "classification":
self.sup_head = SupervisionHead(512, 100, 7, 7, 2)
def forward(self, x):
state = (torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda(),
torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda())
superv_x = []
for t in range(x.size(0)):
logit, feature_conv, feature_convNBN = self.resNet(x[t])
bz, nc, h, w = feature_conv.size()
feature_conv1 = feature_conv.view(bz, nc, h * w)
probs, idxs = logit.sort(1, True)
class_idx = idxs[:, 0]
cam = torch.bmm(self.weight_softmax[class_idx].unsqueeze(1), feature_conv1)
attention_map = F.softmax(cam.squeeze(1), dim=1)
attention_map = attention_map.view(attention_map.size(0), 1, 7, 7)
attention_feat = feature_convNBN * attention_map.expand_as(feature_conv)
state = self.lstm_cell(attention_feat, state)
if self.supervision:
superv_x.append(self.sup_head(state[1]))
if self.supervision:
superv_x = torch.stack(superv_x)
superv_x = superv_x.reshape(superv_x.shape[0] * superv_x.shape[1], -1, 7, 7)
feats1 = self.avgpool(state[1]).view(state[1].size(0), -1)
feats = self.classifier(feats1)
return feats, feats1, superv_x
class ConvLSTMDynamic(nn.Module):
def __init__(self, num_classes=61, mem_size=512):
super(ConvLSTMDynamic, self).__init__()
self.num_classes = num_classes
self.resNet = resnetMod.resnet34(True, True)
self.mem_size = mem_size
self.weight_softmax = self.resNet.fc.weight
self.lstm_cell = MyConvLSTMCell(512, mem_size)
self.avgpool = nn.AvgPool2d(7)
self.dropout = nn.Dropout(0.7)
self.fc = nn.Linear(mem_size, self.num_classes)
self.classifier = nn.Sequential(self.dropout, self.fc)
self.dinam = DynamicFilters(512, 3 * 3)
self.conv = nn.Sequential(nn.Conv2d(512, 512, kernel_size=1, padding=0),
nn.ReLU())
# self.convDynamic= nn.Conv2d(3, 3, 3, stride=1,padding=2, bias=False)
def forward(self, x):
state = (torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda(),
torch.zeros((x.size(1), self.mem_size, 7, 7)).cuda())
output_t = []
for t in range(x.size(0)):
output = []
logit, feature_conv, feature_convNBN = self.resNet(x[t])
bz, nc, h, w = feature_conv.size()
feature_conv1 = feature_conv.view(bz, nc, h * w)
probs, idxs = logit.sort(1, True)
class_idx = idxs[:, 0]
cam = torch.bmm(self.weight_softmax[class_idx].unsqueeze(1), feature_conv1)
attention_map = F.softmax(cam.squeeze(1), dim=1)
attention_map = attention_map.view(attention_map.size(0), 1, 7, 7)
attention_feat = feature_convNBN * attention_map.expand_as(feature_conv)
state = self.lstm_cell(attention_feat, state)
dynamic_filter = self.dinam(feature_convNBN)
for i in range(x.shape[1]):
output.append(F.conv2d((x[t][i].unsqueeze(0)),
dynamic_filter[i].float(),
padding=3))
output_t.append(torch.stack(output))
output_t = torch.stack(output_t)
feats1 = self.avgpool(state[1]).view(state[1].size(0), -1)
feats = self.classifier(feats1)
return feats, feats1, output_t.squeeze(2)