def forward(self, x):
h, w = x.shape[2:]
# H, W -> H/2, W/2
x_sub2 = F.interpolate(
x, size=get_interp_size(x, s_factor=2), mode="bilinear", align_corners=True
)
# H/2, W/2 -> H/4, W/4
# x_sub2 = self.convbnrelu1_1(x_sub2)
# x_sub2 = self.convbnrelu1_2(x_sub2)
# x_sub2 = self.convbnrelu1_3(x_sub2)
# H/4, W/4 -> H/8, W/8
# x_sub2 = F.max_pool2d(x_sub2, 3, 2, 1)
x_sub2 = self.layer0(x_sub2) # resnet conv+maxpool
# H/8, W/8 -> H/16, W/16
# x_sub2 = self.res_block2(x_sub2)
x_sub2 = self.layer1(x_sub2)
x_sub2 = self.res_block3_conv(x_sub2)
# H/16, W/16 -> H/32, W/32
x_sub4 = F.interpolate(
x_sub2, size=get_interp_size(x_sub2, s_factor=2), mode="bilinear", align_corners=True
)
x_sub4 = self.res_block3_identity(x_sub4)
# x_sub4 = self.res_block4(x_sub4)
# x_sub4 = self.res_block5(x_sub4)
x_sub4 = self.layer3(x_sub4)
x_sub4 = self.layer4(x_sub4)
x_sub4 = self.pyramid_pooling(x_sub4)
x_sub4 = self.conv5_4_k1(x_sub4)
x_sub1 = self.convbnrelu1_sub1(x)
x_sub1 = self.convbnrelu2_sub1(x_sub1)
x_sub1 = self.convbnrelu3_sub1(x_sub1)
x_sub24, sub4_cls = self.cff_sub24(x_sub4, x_sub2)
x_sub12, sub24_cls = self.cff_sub12(x_sub24, x_sub1)
x_sub12 = F.interpolate(
x_sub12, size=get_interp_size(x_sub12, z_factor=2), mode="bilinear", align_corners=True
)
x_sub4 = self.res_block3_identity(x_sub4)
sub124_cls = self.classification(x_sub12)
if self.training:
return (sub124_cls, sub24_cls, sub4_cls)
else:
sub124_cls = F.interpolate(
sub124_cls,
size=get_interp_size(sub124_cls, z_factor=4),
mode="bilinear",
align_corners=True,
)
return sub124_cls
def forward(self, x):
h, w = x.shape[2:]
# H, W -> H/2, W/2
x_sub2 = F.interpolate(
x, size=get_interp_size(x, s_factor=2), mode="bilinear", align_corners=True
)
# H/2, W/2 -> H/4, W/4
x_sub2 = self.convbnrelu1_1(x_sub2)
x_sub2 = self.convbnrelu1_2(x_sub2)
x_sub2 = self.convbnrelu1_3(x_sub2)
# H/4, W/4 -> H/8, W/8
x_sub2 = F.max_pool2d(x_sub2, 3, 2, 1)
# H/8, W/8 -> H/16, W/16
x_sub2_ = self.res_block2(x_sub2)
x_sub2 = self.res_block3_conv(x_sub2_)
x_sub2_inst = self.res_block3_conv_inst(x_sub2_)
# H/16, W/16 -> H/32, W/32
x_sub4 = F.interpolate(
x_sub2, size=get_interp_size(x_sub2, s_factor=2), mode="bilinear", align_corners=True
)
x_sub4 = self.res_block3_identity(x_sub4)
x_sub4 = self.res_block4(x_sub4)
x_sub4 = self.res_block5(x_sub4)
x_sub4_ = self.pyramid_pooling(x_sub4)
x_sub4 = self.conv5_4_k1(x_sub4_)
x_sub4_inst = self.conv5_4_k1_inst(x_sub4_)
x_sub1 = self.convbnrelu1_sub1(x)
x_sub1_ = self.convbnrelu2_sub1(x_sub1)
x_sub1 = self.convbnrelu3_sub1(x_sub1_)
x_sub1_inst = self.convbnrelu3_sub1_inst(x_sub1_)
x_sub24, sub4_cls = self.cff_sub24(x_sub4, x_sub2)
x_sub12, sub24_cls = self.cff_sub12(x_sub24, x_sub1)
x_sub24_inst, sub4_inst_fe = self.cff_sub24_inst(x_sub4_inst, x_sub2_inst)
x_sub12_inst, sub24_inst_fe = self.cff_sub12_inst(x_sub24_inst, x_sub1_inst)
x_sub12 = F.interpolate(
x_sub12, size=get_interp_size(x_sub12, z_factor=2), mode="bilinear", align_corners=True
)
x_sub12_inst = F.interpolate(
x_sub12_inst, size=get_interp_size(x_sub12_inst, z_factor=2), mode="bilinear", align_corners=True
)
# x_sub4 = self.res_block3_identity(x_sub4)
sub124_cls = self.classification(x_sub12)
sub124_inst_fe = self.inst_feature_embedding(x_sub12_inst)
if self.training:
return (sub124_cls, sub24_cls, sub4_cls, sub124_inst_fe, sub24_inst_fe, sub4_inst_fe)
else:
sub124_cls = F.interpolate(
sub124_cls,
size=get_interp_size(sub124_cls, z_factor=4),
mode="bilinear",
align_corners=True,
)
sub124_inst_fe = F.interpolate(
sub124_inst_fe,
size=get_interp_size(sub124_inst_fe, z_factor=4),
mode="bilinear",
align_corners=True,
)
return sub124_cls, sub124_inst_fe