Created
June 21, 2017 10:37
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Deploy prototxt file for FlowNetS, FlyingChairs dataset
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| # Enter your network definition here. | |
| # Use Shift+Enter to update the visualization. | |
| layer { | |
| name: "CustomData1" | |
| type: "CustomData" | |
| top: "blob0" | |
| top: "blob1" | |
| top: "blob2" | |
| top: "blob3" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| source: "../../../data/FlyingChairs_release_lmdb" | |
| batch_size: 8 | |
| backend: LMDB | |
| preselection_file: "../../../data/FlyingChairs_release_test_train_split.list" | |
| preselection_label: 1 | |
| rand_permute: true | |
| rand_permute_seed: 77 | |
| slice_point: 3 | |
| slice_point: 6 | |
| slice_point: 8 | |
| encoding: UINT8 # image1 | |
| encoding: UINT8 # image2 | |
| encoding: UINT16FLOW # flow ground truth | |
| encoding: BOOL1 # validation | |
| verbose: true | |
| } | |
| } | |
| layer { | |
| name: "CustomData2" | |
| type: "CustomData" | |
| top: "blob0" | |
| top: "blob1" | |
| top: "blob2" | |
| top: "blob3" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| source: "../../../data/FlyingChairs_release_lmdb" | |
| batch_size: 8 | |
| backend: LMDB | |
| preselection_file: "../../../data/FlyingChairs_release_test_train_split.list" | |
| preselection_label: 2 | |
| rand_permute: true | |
| rand_permute_seed: 77 | |
| slice_point: 3 | |
| slice_point: 6 | |
| slice_point: 8 | |
| encoding: UINT8 | |
| encoding: UINT8 | |
| encoding: UINT16FLOW | |
| encoding: BOOL1 | |
| verbose: true | |
| } | |
| } | |
| layer { | |
| name: "Eltwise1" | |
| type: "Eltwise" | |
| bottom: "blob0" | |
| top: "blob4" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.00392156862745 | |
| } | |
| } | |
| layer { | |
| name: "Eltwise2" | |
| type: "Eltwise" | |
| bottom: "blob1" | |
| top: "blob5" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.00392156862745 | |
| } | |
| } | |
| layer { | |
| name: "img0s_aug" | |
| type: "DataAugmentation" | |
| bottom: "blob4" | |
| top: "img0_aug" | |
| top: "blob7" | |
| propagate_down: false | |
| augmentation_param { | |
| max_multiplier: 1 | |
| augment_during_test: false | |
| recompute_mean: 1000 | |
| mean_per_pixel: false | |
| translate { | |
| rand_type: "uniform_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| rotate { | |
| rand_type: "uniform_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| zoom { | |
| rand_type: "uniform_bernoulli" | |
| exp: true | |
| mean: 0.2 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| squeeze { | |
| rand_type: "uniform_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.3 | |
| prob: 1.0 | |
| } | |
| lmult_pow { | |
| rand_type: "uniform_bernoulli" | |
| exp: true | |
| mean: -0.2 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| lmult_mult { | |
| rand_type: "uniform_bernoulli" | |
| exp: true | |
| mean: 0.0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| lmult_add { | |
| rand_type: "uniform_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.03 | |
| prob: 1.0 | |
| } | |
| sat_pow { | |
| rand_type: "uniform_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| sat_mult { | |
| rand_type: "uniform_bernoulli" | |
| exp: true | |
| mean: -0.3 | |
| spread: 0.5 | |
| prob: 1.0 | |
| } | |
| sat_add { | |
| rand_type: "uniform_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.03 | |
| prob: 1.0 | |
| } | |
| col_pow { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| col_mult { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.2 | |
| prob: 1.0 | |
| } | |
| col_add { | |
| rand_type: "gaussian_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.02 | |
| prob: 1.0 | |
| } | |
| ladd_pow { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| ladd_mult { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0.0 | |
| spread: 0.4 | |
| prob: 1.0 | |
| } | |
| ladd_add { | |
| rand_type: "gaussian_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.04 | |
| prob: 1.0 | |
| } | |
| col_rotate { | |
| rand_type: "uniform_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 1 | |
| prob: 1.0 | |
| } | |
| crop_width: 448 | |
| crop_height: 320 | |
| chromatic_eigvec: 0.51 | |
| chromatic_eigvec: 0.56 | |
| chromatic_eigvec: 0.65 | |
| chromatic_eigvec: 0.79 | |
| chromatic_eigvec: 0.01 | |
| chromatic_eigvec: -0.62 | |
| chromatic_eigvec: 0.35 | |
| chromatic_eigvec: -0.83 | |
| chromatic_eigvec: 0.44 | |
| noise { | |
| rand_type: "uniform_bernoulli" | |
| exp: false | |
| mean: 0.03 | |
| spread: 0.03 | |
| prob: 1.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "aug_params1" | |
| type: "GenerateAugmentationParameters" | |
| bottom: "blob7" | |
| bottom: "blob4" | |
| bottom: "img0_aug" | |
| top: "blob8" | |
| augmentation_param { | |
| augment_during_test: false | |
| translate { | |
| rand_type: "gaussian_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.03 | |
| prob: 1.0 | |
| } | |
| rotate { | |
| rand_type: "gaussian_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.03 | |
| prob: 1.0 | |
| } | |
| zoom { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.03 | |
| prob: 1.0 | |
| } | |
| gamma { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.02 | |
| prob: 1.0 | |
| } | |
| brightness { | |
| rand_type: "gaussian_bernoulli" | |
| exp: false | |
| mean: 0 | |
| spread: 0.02 | |
| prob: 1.0 | |
| } | |
| contrast { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.02 | |
| prob: 1.0 | |
| } | |
| color { | |
| rand_type: "gaussian_bernoulli" | |
| exp: true | |
| mean: 0 | |
| spread: 0.02 | |
| prob: 1.0 | |
| } | |
| } | |
| coeff_schedule_param { | |
| half_life: 50000 | |
| initial_coeff: 0.5 | |
| final_coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "img1s_aug" | |
| type: "DataAugmentation" | |
| bottom: "blob5" | |
| bottom: "blob8" | |
| top: "img1_aug" | |
| propagate_down: false | |
| propagate_down: false | |
| augmentation_param { | |
| max_multiplier: 1 | |
| augment_during_test: false | |
| recompute_mean: 1000 | |
| mean_per_pixel: false | |
| crop_width: 448 | |
| crop_height: 320 | |
| chromatic_eigvec: 0.51 | |
| chromatic_eigvec: 0.56 | |
| chromatic_eigvec: 0.65 | |
| chromatic_eigvec: 0.79 | |
| chromatic_eigvec: 0.01 | |
| chromatic_eigvec: -0.62 | |
| chromatic_eigvec: 0.35 | |
| chromatic_eigvec: -0.83 | |
| chromatic_eigvec: 0.44 | |
| } | |
| } | |
| layer { | |
| name: "FlowAugmentation1" | |
| type: "FlowAugmentation" | |
| bottom: "blob2" | |
| bottom: "blob7" | |
| bottom: "blob8" | |
| top: "flow_gt_aug" | |
| augmentation_param { | |
| crop_width: 448 | |
| crop_height: 320 | |
| } | |
| } | |
| layer { | |
| name: "Eltwise3" | |
| type: "Eltwise" | |
| bottom: "flow_gt_aug" | |
| top: "scaled_flow_gt" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.05 | |
| } | |
| } | |
| layer { | |
| name: "Concat1" | |
| type: "Concat" | |
| bottom: "img0_aug" | |
| bottom: "img1_aug" | |
| top: "input" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "input" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU4" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU5" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU6" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU7" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "conv5" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU8" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv6" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU9" | |
| type: "ReLU" | |
| bottom: "conv6" | |
| top: "conv6" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv6_1" | |
| type: "Convolution" | |
| bottom: "conv6" | |
| top: "conv6_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU10" | |
| type: "ReLU" | |
| bottom: "conv6_1" | |
| top: "conv6_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Convolution1" | |
| type: "Convolution" | |
| bottom: "conv6_1" | |
| top: "predict_flow6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Downsample1" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow6" | |
| top: "blob24" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "flow_loss6" | |
| type: "L1Loss" | |
| bottom: "predict_flow6" | |
| bottom: "blob24" | |
| top: "flow_loss6" | |
| loss_weight: 0.32 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "deconv5" | |
| type: "Deconvolution" | |
| bottom: "conv6_1" | |
| top: "deconv5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU11" | |
| type: "ReLU" | |
| bottom: "deconv5" | |
| top: "deconv5" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "upsample_flow6to5" | |
| type: "Deconvolution" | |
| bottom: "predict_flow6" | |
| top: "upsampled_flow6_to_5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Concat2" | |
| type: "Concat" | |
| bottom: "conv5_1" | |
| bottom: "deconv5" | |
| bottom: "upsampled_flow6_to_5" | |
| top: "concat5" | |
| } | |
| layer { | |
| name: "Convolution2" | |
| type: "Convolution" | |
| bottom: "concat5" | |
| top: "predict_flow5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Downsample2" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow5" | |
| top: "blob29" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "flow_loss5" | |
| type: "L1Loss" | |
| bottom: "predict_flow5" | |
| bottom: "blob29" | |
| top: "flow_loss5" | |
| loss_weight: 0.08 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "deconv4" | |
| type: "Deconvolution" | |
| bottom: "concat5" | |
| top: "deconv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU12" | |
| type: "ReLU" | |
| bottom: "deconv4" | |
| top: "deconv4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "upsample_flow5to4" | |
| type: "Deconvolution" | |
| bottom: "predict_flow5" | |
| top: "upsampled_flow5_to_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Concat3" | |
| type: "Concat" | |
| bottom: "conv4_1" | |
| bottom: "deconv4" | |
| bottom: "upsampled_flow5_to_4" | |
| top: "concat4" | |
| } | |
| layer { | |
| name: "Convolution3" | |
| type: "Convolution" | |
| bottom: "concat4" | |
| top: "predict_flow4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Downsample3" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow4" | |
| top: "blob34" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "flow_loss4" | |
| type: "L1Loss" | |
| bottom: "predict_flow4" | |
| bottom: "blob34" | |
| top: "flow_loss4" | |
| loss_weight: 0.02 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "deconv3" | |
| type: "Deconvolution" | |
| bottom: "concat4" | |
| top: "deconv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU13" | |
| type: "ReLU" | |
| bottom: "deconv3" | |
| top: "deconv3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "upsample_flow4to3" | |
| type: "Deconvolution" | |
| bottom: "predict_flow4" | |
| top: "upsampled_flow4_to_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Concat4" | |
| type: "Concat" | |
| bottom: "conv3_1" | |
| bottom: "deconv3" | |
| bottom: "upsampled_flow4_to_3" | |
| top: "concat3" | |
| } | |
| layer { | |
| name: "Convolution4" | |
| type: "Convolution" | |
| bottom: "concat3" | |
| top: "predict_flow3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Downsample4" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow3" | |
| top: "blob39" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "flow_loss3" | |
| type: "L1Loss" | |
| bottom: "predict_flow3" | |
| bottom: "blob39" | |
| top: "flow_loss3" | |
| loss_weight: 0.01 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "deconv2" | |
| type: "Deconvolution" | |
| bottom: "concat3" | |
| top: "deconv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "ReLU14" | |
| type: "ReLU" | |
| bottom: "deconv2" | |
| top: "deconv2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "upsample_flow3to2" | |
| type: "Deconvolution" | |
| bottom: "predict_flow3" | |
| top: "upsampled_flow3_to_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Concat5" | |
| type: "Concat" | |
| bottom: "conv2" | |
| bottom: "deconv2" | |
| bottom: "upsampled_flow3_to_2" | |
| top: "concat2" | |
| } | |
| layer { | |
| name: "Convolution5" | |
| type: "Convolution" | |
| bottom: "concat2" | |
| top: "predict_flow2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Downsample5" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow2" | |
| top: "blob44" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "flow_loss2" | |
| type: "L1Loss" | |
| bottom: "predict_flow2" | |
| bottom: "blob44" | |
| top: "flow_loss2" | |
| loss_weight: 0.005 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "Eltwise4" | |
| type: "Eltwise" | |
| bottom: "predict_flow2" | |
| top: "blob45" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 20.0 | |
| } | |
| } | |
| layer { | |
| name: "Silence1" | |
| type: "Silence" | |
| bottom: "blob0" | |
| } | |
| layer { | |
| name: "Silence2" | |
| type: "Silence" | |
| bottom: "blob1" | |
| } | |
| layer { | |
| name: "Silence3" | |
| type: "Silence" | |
| bottom: "blob2" | |
| } | |
| layer { | |
| name: "Silence4" | |
| type: "Silence" | |
| bottom: "blob3" | |
| } | |
| layer { | |
| name: "Silence5" | |
| type: "Silence" | |
| bottom: "blob45" | |
| } |
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