def forward(self, x):
out = self.conv1(x)
rp = F.adaptive_max_pool2d(out, (self.s, 1))
cp = F.adaptive_max_pool2d(out, (1, self.s))
p = paddle.reshape(self.conv_p(rp), (x.shape[0], self.k, self.s, self.s))
q = paddle.reshape(self.conv_q(cp), (x.shape[0], self.k, self.s, self.s))
p = F.sigmoid(p)
q = F.sigmoid(q)
p = p / paddle.sum(p, axis=3, keepdim=True)
q = q / paddle.sum(q, axis=2, keepdim=True)
p = paddle.reshape(p, (x.shape[0], self.k, 1, self.s, self.s))
p = paddle.expand(p, (x.shape[0], self.k, x.shape[1] // self.k, self.s, self.s))
p = paddle.reshape(p, (x.shape[0], x.shape[1], self.s, self.s))
q = paddle.reshape(q, (x.shape[0], self.k, 1, self.s, self.s))
q = paddle.expand(q, (x.shape[0], self.k, x.shape[1] // self.k, self.s, self.s))
q = paddle.reshape(q, (x.shape[0], x.shape[1], self.s, self.s))
p = self.resize_mat(p, x.shape[2] // self.s)
q = self.resize_mat(q, x.shape[2] // self.s)
y = paddle.matmul(p, x)
y = paddle.matmul(y, q)
y = self.conv2(y)
return y
def forward(self, input):
x = self.DownBlock(input)
gap = F.adaptive_avg_pool2d(x, 1)
gap_logit = self.gap_fc(gap.reshape([x.shape[0], -1]))
gap_weight = list(self.gap_fc.parameters())[0].transpose([1, 0])
gap = x * gap_weight.unsqueeze(2).unsqueeze(3)
gmp = F.adaptive_max_pool2d(x, 1)
gmp_logit = self.gmp_fc(gmp.reshape([x.shape[0], -1]))
gmp_weight = list(self.gmp_fc.parameters())[0].transpose([1, 0])
gmp = x * gmp_weight.unsqueeze(2).unsqueeze(3)
cam_logit = paddle.concat([gap_logit, gmp_logit], 1)
x = paddle.concat([gap, gmp], 1)
x = self.relu(self.conv1x1(x))
heatmap = paddle.sum(x, axis=1, keepdim=True)
if self.light:
x_ = F.adaptive_avg_pool2d(x, 1)
x_ = self.FC(x_.reshape([x_.shape[0], -1]))
else:
x_ = self.FC(x.reshape([x.shape[0], -1]))
gamma, beta = self.gamma(x_), self.beta(x_)
for i in range(self.n_blocks):
x = getattr(self, 'UpBlock1_' + str(i + 1))(x, gamma, beta)
out = self.UpBlock2(x)
return out, cam_logit, heatmap
def forward(self, fstudent, fteacher):
loss_all = 0.0
for fs, ft in zip(fstudent, fteacher):
h = fs.shape[2]
loss = F.mse_loss(fs, ft)
cnt = 1.0
tot = 1.0
for l in [4, 2, 1]:
if l >= h:
continue
if self.mode == "max":
tmpfs = F.adaptive_max_pool2d(fs, (l, l))
tmpft = F.adaptive_max_pool2d(ft, (l, l))
else:
tmpfs = F.adaptive_avg_pool2d(fs, (l, l))
tmpft = F.adaptive_avg_pool2d(ft, (l, l))
cnt /= 2.0
loss += F.mse_loss(tmpfs, tmpft) * cnt
tot += cnt
loss = loss / tot
loss_all = loss_all + loss
return loss_all
def forward(self, x):
bs = x.shape[0]
x = self.DownBlock(x)
content_features = []
for i in range(self.n_blocks):
x = getattr(self, 'EncodeBlock' + str(i + 1))(x)
content_features.append(
F.adaptive_avg_pool2d(x, 1).reshape([bs, -1]))
gap = F.adaptive_avg_pool2d(x, 1)
gap_logit = self.gap_fc(gap.reshape([bs, -1]))
gap_weight = list(self.gap_fc.parameters())[0].transpose([1, 0])
gap = x * gap_weight.unsqueeze(2).unsqueeze(3)
gmp = F.adaptive_max_pool2d(x, 1)
gmp_logit = self.gmp_fc(gmp.reshape([bs, -1]))
gmp_weight = list(self.gmp_fc.parameters())[0].transpose([1, 0])
gmp = x * gmp_weight.unsqueeze(2).unsqueeze(3)
cam_logit = paddle.concat([gap_logit, gmp_logit], 1)
x = paddle.concat([gap, gmp], 1)
x = self.relu(self.conv1x1(x))
heatmap = paddle.sum(x, axis=1, keepdim=True)
if self.light:
x_ = F.adaptive_avg_pool2d(x, 1)
style_features = self.FC(x_.reshape([bs, -1]))
else:
style_features = self.FC(x.reshape([bs, -1]))
for i in range(self.n_blocks):
x = getattr(self, 'DecodeBlock' + str(i + 1))(
x, content_features[4 - i - 1], style_features)
out = self.UpBlock(x)
return out, cam_logit, heatmap
def forward(self, x):
x = self.model(x)
gap = F.adaptive_avg_pool2d(x, 1)
gap_logit = self.gap_fc(gap.reshape([x.shape[0], -1]))
gap_weight = list(self.gap_fc.parameters())[0].transpose([1, 0])
gap = x * gap_weight.unsqueeze(2).unsqueeze(3)
gmp = F.adaptive_max_pool2d(x, 1)
gmp_logit = self.gmp_fc(gmp.reshape([x.shape[0], -1]))
gmp_weight = list(self.gmp_fc.parameters())[0].transpose([1, 0])
gmp = x * gmp_weight.unsqueeze(2).unsqueeze(3)
cam_logit = paddle.concat([gap_logit, gmp_logit], 1)
x = paddle.concat([gap, gmp], 1)
x = self.leaky_relu(self.conv1x1(x))
heatmap = paddle.sum(x, axis=1, keepdim=True)
x = self.pad(x)
out = self.conv(x)
return out, cam_logit, heatmap
