def extract(self, x, label):
conv1 = self.conv_block1(x)
conv2 = self.conv_block2(conv1)
conv3 = self.conv_block3(conv2)
conv4 = self.conv_block4(conv3)
conv5 = self.conv_block5(conv4)
fconv = self.fconv_block(conv5)
if self.use_norm_weights:
fconv_norm = l2_norm(fconv)
conv3_norm = l2_norm(conv3)
conv4_norm = l2_norm(conv4)
else:
fconv_norm = fconv
conv3_norm = conv3
conv4_norm = conv4
if self.weighted_mask:
fconv_pooled = weighted_masked_embeddings(fconv_norm.shape, label,
fconv_norm, self.n_classes)
conv3_pooled = weighted_masked_embeddings(conv3_norm.shape, label,
conv3_norm, self.n_classes)
conv4_pooled = weighted_masked_embeddings(conv4_norm.shape, label,
conv4_norm, self.n_classes)
else:
fconv_pooled = masked_embeddings(fconv_norm.shape, label, fconv_norm,
self.n_classes)
conv3_pooled = masked_embeddings(conv3_norm.shape, label, conv3_norm,
self.n_classes)
conv4_pooled = masked_embeddings(conv4_norm.shape, label, conv4_norm,
self.n_classes)
return fconv_pooled, conv4_pooled, conv3_pooled
def extract(self, x, label):
conv1 = self.conv_block1(x)
conv2 = self.conv_block2(conv1)
conv3 = self.conv_block3(conv2)
conv4 = self.conv_block4(conv3)
conv5 = self.conv_block5(conv4)
fconv = self.fconv_block(conv5)
fconv_pooled = masked_embeddings(fconv.shape, label, fconv,
self.n_classes)
conv3_pooled = masked_embeddings(conv3.shape, label, conv3,
self.n_classes)
conv4_pooled = masked_embeddings(conv4.shape, label, conv4,
self.n_classes)
return fconv_pooled, conv4_pooled, conv3_pooled
def extract(self, x, label):
crop_h, crop_w = int(x.size()[-2]), int(x.size()[-1])
x = self.stages[0](x)
side = []
side_out = []
for i in range(1, len(self.stages)):
x = self.stages[i](x)
side_temp = self.side_prep[i - 1](x)
side.append(
center_crop(self.upscale[i - 1](side_temp), crop_h, crop_w))
side_out.append(
center_crop(
self.upscale_[i - 1](self.score_dsn[i - 1](side_temp)),
crop_h, crop_w))
out = torch.cat(side[:], dim=1)
out = masked_embeddings(out.shape,
label,
out,
self.n_classes,
pad=True)
return out