Method forward

model.py:185–249

(self, x1, x2, modal=0)

        self.gm_pool = gm_pool

    def forward(self, x1, x2, modal=0):
        if modal == 0:
            x1 = self.visible_module(x1)
            x2 = self.thermal_module(x2)
            x = torch.cat((x1, x2), 0)
        elif modal == 1:
            x = self.visible_module(x1)
        elif modal == 2:
            x = self.thermal_module(x2)

        # shared block
        if self.non_local == 'on':
            NL1_counter = 0
            if len(self.NL_1_idx) == 0: self.NL_1_idx = [-1]
            for i in range(len(self.base_resnet.base.layer1)):
                x = self.base_resnet.base.layer1[i](x)
                if i == self.NL_1_idx[NL1_counter]:
                    _, C, H, W = x.shape
                    x = self.NL_1[NL1_counter](x)
                    NL1_counter += 1
            # Layer 2
            NL2_counter = 0
            if len(self.NL_2_idx) == 0: self.NL_2_idx = [-1]
            for i in range(len(self.base_resnet.base.layer2)):
                x = self.base_resnet.base.layer2[i](x)
                if i == self.NL_2_idx[NL2_counter]:
                    _, C, H, W = x.shape
                    x = self.NL_2[NL2_counter](x)
                    NL2_counter += 1
            # Layer 3
            NL3_counter = 0
            if len(self.NL_3_idx) == 0: self.NL_3_idx = [-1]
            for i in range(len(self.base_resnet.base.layer3)):
                x = self.base_resnet.base.layer3[i](x)
                if i == self.NL_3_idx[NL3_counter]:
                    _, C, H, W = x.shape
                    x = self.NL_3[NL3_counter](x)
                    NL3_counter += 1
            # Layer 4
            NL4_counter = 0
            if len(self.NL_4_idx) == 0: self.NL_4_idx = [-1]
            for i in range(len(self.base_resnet.base.layer4)):
                x = self.base_resnet.base.layer4[i](x)
                if i == self.NL_4_idx[NL4_counter]:
                    _, C, H, W = x.shape
                    x = self.NL_4[NL4_counter](x)
                    NL4_counter += 1
        else:
            x = self.base_resnet(x)
        if self.gm_pool  == 'on':
            b, c, h, w = x.shape
            x = x.view(b, c, -1)
            p = 3.0
            x_pool = (torch.mean(x**p, dim=-1) + 1e-12)**(1/p)
        else:
            x_pool = self.avgpool(x)
            x_pool = x_pool.view(x_pool.size(0), x_pool.size(1))