darknet.py 文件源码

def forward(self, x):
        if self.has_mean:
            batch_size = x.data.size(0)
            x = x - torch.autograd.Variable(self.mean_img.repeat(batch_size, 1, 1, 1))

        ind = -2
        self.loss = None
        outputs = dict()
        for block in self.blocks:
            ind = ind + 1
            #if ind > 14:
            #    return x

            if block['type'] == 'net':
                continue
            elif block['type'] == 'convolutional' or block['type'] == 'maxpool' or block['type'] == 'reorg' or block['type'] == 'avgpool' or block['type'] == 'softmax' or block['type'] == 'connected' or block['type'] == 'dropout':
                x = self.models[ind](x)
                outputs[ind] = x
            elif block['type'] == 'route':
                layers = block['layers'].split(',')
                layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers]
                if len(layers) == 1:
                    x = outputs[layers[0]]
                    outputs[ind] = x
                elif len(layers) == 2:
                    x1 = outputs[layers[0]]
                    x2 = outputs[layers[1]]
                    x = torch.cat((x1,x2),1)
                    outputs[ind] = x
            elif block['type'] == 'shortcut':
                from_layer = int(block['from'])
                activation = block['activation']
                from_layer = from_layer if from_layer > 0 else from_layer + ind
                x1 = outputs[from_layer]
                x2 = outputs[ind-1]
                x  = x1 + x2
                if activation == 'leaky':
                    x = F.leaky_relu(x, 0.1, inplace=True)
                elif activation == 'relu':
                    x = F.relu(x, inplace=True)
                outputs[ind] = x
            elif block['type'] == 'cost':
                continue
            elif block['type'] == 'region':
                continue
            else:
                print('unknown type %s' % (block['type']))
        return x