def forward(self, x, xr):
'''
the forward function
:param x: the first input
:param xr: the second input
:return: the output
'''
# convolution
x = self.Conv_classifer(x)
if self.maxpooling_dir:
# global average polling of cnn features map in direction of channels
x = F.adaptive_avg_pool2d(x, (x.size()[1], 1))
else:
# global max polling of cnn features map in direction of words
x = F.adaptive_max_pool2d(x, (x.size()[2], 1))
x = x.view(x.size()[0], -1)
if self.padding:
x = nn.ConstantPad1d((0, self.w_size - x.size()[1]), 0)(x)
if self.xr:
x = torch.cat((x, xr), 1)
x = self.FC_classifier(x)
return x
def forward(self, x, xr):
activity_layers, _ = self.bert_model(x)
if not isinstance(activity_layers, torch.Tensor):
activity_layers = activity_layers[-1]
activity_layers = activity_layers.permute(0, 2, 1)
y1 = self.cnn1(activity_layers[:, :, :], xr)
activity_layers = torch.stack(activity_layers)
activity_layers = activity_layers.permute(1, 3, 2, 0)
activity_layers = activity_layers[:, :, 0, :]
activity_layers = activity_layers.new_tensor()
y2 = self.cnn2(activity_layers[:, :, :], xr)
y = self.fc(torch.cat([y1, y2], dim=0))
return y
def forward(self, x, xr):
conv1 = self.Conv_classifer(x)
# x = F.adaptive_avg_pool2d(x, (x.size()[1], 1))
x1 = F.adaptive_max_pool2d(conv1, (conv1.size()[1], 1))
x1 = x1.view(x1.size()[0], -1)
conv2 = self.Conv_classifer2(conv1)
x2 = F.adaptive_max_pool2d(conv2, (conv2.size()[1], 1))
x2 = x2.view(x2.size()[0], -1)
x = torch.cat((x1, x2, xr), 1)
x = self.FC_classifier(x)
return x
def forward(self, x, xr):
activity_layers, _ = self.bert_model(x)
a1, e1, a2, e2, a3 = [], [], [], [], []
for i in range(xr.size()[0]):
e1start, e1end, e2start, e2end = xr[i, 0], xr[i, 1], xr[i,
2], xr[i,
3]
a1.append(activity_layers[i, :e1start + 1, :])
e1.append(activity_layers[i, e1start:e1end + 1, :])
a2.append(activity_layers[i, e1end - 1:e2start + 1, :])
e2.append(activity_layers[i, e2start:e2end + 1, :])
a3.append(activity_layers[i, e2end:, :])
if a2[-1].size()[0] == 0:
a2[-1] = torch.FloatTensor([[0.] * (768 * 1)] * 3).cuda()
a1 = torch.nn.utils.rnn.pad_sequence(a1)
e1 = torch.nn.utils.rnn.pad_sequence(e1)
a2 = torch.nn.utils.rnn.pad_sequence(a2)
e2 = torch.nn.utils.rnn.pad_sequence(e2)
a3 = torch.nn.utils.rnn.pad_sequence(a3)
a1 = a1.permute(1, 2, 0)
e1 = e1.permute(1, 2, 0)
a2 = a2.permute(1, 2, 0)
e2 = e2.permute(1, 2, 0)
a3 = a3.permute(1, 2, 0)
y1 = self.cnn1(a1, xr)
y2 = self.cnn2(e1, xr)
y3 = self.cnn3(a2, xr)
y4 = self.cnn4(e2, xr)
y5 = self.cnn5(a3, xr)
y = self.fc(torch.cat([y1, y2, y3, y4, y5], dim=1))
return y
def forward(self, x, xr):
# print(xr)
activity_layers, _ = self.bert_model(x)
#activity_layers, _, a = self.bert_model(x)
if not isinstance(activity_layers, torch.Tensor):
activity_layers = activity_layers[-1]
# activity_layers=activity_layers.permute(0,2,1)
# print(activity_layers.size())
#activity_layers = a[-1]
# print(out.size())
a1, e1, a2, e2, a3 = [], [], [], [], []
# activity_layers=torch.cat([a[-1],a[-2]],dim=2)
for i in range(xr.size()[0]):
e1start, e1end, e2start, e2end = xr[i, 0], xr[i, 1], xr[i,
2], xr[i,
3]
# print(e1start,e1end,e2start,e2end)
a1.append(activity_layers[i, :e1start + 1, :])
e1.append(activity_layers[i, e1start:e1end + 1, :])
a2.append(activity_layers[i, e1end - 1:e2start + 1, :])
e2.append(activity_layers[i, e2start:e2end + 1, :])
a3.append(activity_layers[i, e2end:, :])
if a2[-1].size()[0] == 0:
a2[-1] = torch.FloatTensor([[0.] * (768 * 1)] * 3).cuda()
#a2[-1] = torch.FloatTensor([[0.] * (768 * 1)] * 3)
'''
print(a1[-1].size())
print(e1[-1].size())
print(a2[-1].size())
print(e2[-1].size())
print(a3[-1].size())'''
# activity_layers[i,:,:e1]=activity_layers[i,:,0:e1]*0
# activity_layers[i,:,e2:]=activity_layers[i,:,e2:]*0
# print(activity_layers[i,:,e1:e2].size())
a1 = torch.nn.utils.rnn.pad_sequence(a1)
e1 = torch.nn.utils.rnn.pad_sequence(e1)
a2 = torch.nn.utils.rnn.pad_sequence(a2)
e2 = torch.nn.utils.rnn.pad_sequence(e2)
a3 = torch.nn.utils.rnn.pad_sequence(a3)
# e=torch.cat(e,dim=0)
# print(e.size())
# activity_layers=activity_layers.permute(0,2,1)
a1 = a1.permute(1, 2, 0)
e1 = e1.permute(1, 2, 0)
a2 = a2.permute(1, 2, 0)
e2 = e2.permute(1, 2, 0)
a3 = a3.permute(1, 2, 0)
# activity_layers=torch.cat([activity_layers[xr[i,0],xr[i,1]] for i in range(32)])
# print(activity_layers.size())
y1 = self.cnn1(a1, xr)
y2 = self.cnn2(e1, xr)
y3 = self.cnn3(a2, xr)
y4 = self.cnn4(e2, xr)
y5 = self.cnn5(a3, xr)
y = self.fc(torch.cat([y1, y2, y3, y4, y5], dim=1))
# y=self.cnn(activity_layers[:,:,:].detach(),out)
return y
