(input, dup=False)
| 14 | |
| 15 | |
| 16 | def FlowNetModule(input, dup=False): |
| 17 | # inout must follow the follow the shape format: (1, H, W, C) |
| 18 | if not dup: |
| 19 | net = TimeDistributed(Convolution2D(64, 7, 7, subsample=(2, 2), border_mode='same'), name='conv1')(input) |
| 20 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU1')(net) |
| 21 | net = TimeDistributed(Convolution2D(128, 5, 5, subsample=(2, 2), border_mode='same'), name='conv2')(net) |
| 22 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU2')(net) |
| 23 | net = TimeDistributed(Convolution2D(256, 5, 5, subsample=(2, 2), border_mode='same'), name='conv3')(net) |
| 24 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU3')(net) |
| 25 | net = TimeDistributed(Convolution2D(256, 3, 3, subsample=(1, 1), border_mode='same'), name='conv3_1')(net) |
| 26 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU4')(net) |
| 27 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(2, 2), border_mode='same'), name='conv4')(net) |
| 28 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU5')(net) |
| 29 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(1, 1), border_mode='same'), name='conv4_1')(net) |
| 30 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU6')(net) |
| 31 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(2, 2), border_mode='same'), name='conv5')(net) |
| 32 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU7')(net) |
| 33 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(1, 1), border_mode='same'), name='conv5_1')(net) |
| 34 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU8')(net) |
| 35 | net = TimeDistributed(Convolution2D(1024, 3, 3, subsample=(2, 2), border_mode='same'), name='conv6')(net) |
| 36 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU9')(net) |
| 37 | else: |
| 38 | net = TimeDistributed(Convolution2D(64, 7, 7, subsample=(2, 2), border_mode='same'), name='conv1'+'_dup')(input) |
| 39 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU1'+'_dup')(net) |
| 40 | net = TimeDistributed(Convolution2D(128, 5, 5, subsample=(2, 2), border_mode='same'), name='conv2'+'_dup')(net) |
| 41 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU2'+'_dup')(net) |
| 42 | net = TimeDistributed(Convolution2D(256, 5, 5, subsample=(2, 2), border_mode='same'), name='conv3'+'_dup')(net) |
| 43 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU3'+'_dup')(net) |
| 44 | net = TimeDistributed(Convolution2D(256, 3, 3, subsample=(1, 1), border_mode='same'), name='conv3_1'+'_dup')(net) |
| 45 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU4'+'_dup')(net) |
| 46 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(2, 2), border_mode='same'), name='conv4'+'_dup')(net) |
| 47 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU5'+'_dup')(net) |
| 48 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(1, 1), border_mode='same'), name='conv4_1'+'_dup')(net) |
| 49 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU6'+'_dup')(net) |
| 50 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(2, 2), border_mode='same'), name='conv5'+'_dup')(net) |
| 51 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU7'+'_dup')(net) |
| 52 | net = TimeDistributed(Convolution2D(512, 3, 3, subsample=(1, 1), border_mode='same'), name='conv5_1'+'_dup')(net) |
| 53 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU8'+'_dup')(net) |
| 54 | net = TimeDistributed(Convolution2D(1024, 3, 3, subsample=(2, 2), border_mode='same'), name='conv6'+'_dup')(net) |
| 55 | net = TimeDistributed(LeakyReLU(alpha=0.1), name='ReLU9'+'_dup')(net) |
| 56 | return net |
| 57 | |
| 58 | ################################################################ |
| 59 | # model building functions for lidar, radar and panoramic inputs. |
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