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PWC-Net train.prototxt
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| # use the same data augmentation scheme as the FlowNet2 paper | |
| layer { | |
| name: "img0" | |
| type: "CustomData" | |
| top: "img0" | |
| top: "img1" | |
| top: "flow_gt" | |
| top: "aux" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| source: "/mnt/zfs/projects/ml_flow/v1.2/dispflownet-release/data/FlyingChairs_release_lmdb" # PLEASE MODIFY TO YOUR LOCAL DIRECTORY | |
| batch_size: 8 | |
| backend: LMDB | |
| preselection_file: "/mnt/zfs/projects/ml_flow/v1.2/dispflownet-release/data/FlyingChairs_release_test_train_split.list" # PLEASE MODIFY TO YOUR LOCAL DIRECTORY | |
| preselection_label: 1 | |
| 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: "img0_subtract" | |
| type: "Eltwise" | |
| bottom: "img0" | |
| top: "img0_subtract" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.00392156862745 | |
| } | |
| } | |
| layer { | |
| name: "img1_subtract" | |
| type: "Eltwise" | |
| bottom: "img1" | |
| top: "img1_subtract" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.00392156862745 | |
| } | |
| } | |
| layer { | |
| name: "img0_aug" | |
| type: "DataAugmentation" | |
| bottom: "img0_subtract" | |
| top: "img0_aug" | |
| top: "img0_aug_params" | |
| 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: "GenerateAugmentationParameters1" | |
| type: "GenerateAugmentationParameters" | |
| bottom: "img0_aug_params" | |
| bottom: "img0_subtract" | |
| bottom: "img0_aug" | |
| top: "GenerateAugmentationParameters1" | |
| coeff_schedule_param { | |
| half_life: 50000 | |
| initial_coeff: 0.5 | |
| final_coeff: 1 | |
| } | |
| 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 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "img1_aug" | |
| type: "DataAugmentation" | |
| bottom: "img1_subtract" | |
| bottom: "GenerateAugmentationParameters1" | |
| 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: "flow_gt_aug" | |
| type: "FlowAugmentation" | |
| bottom: "flow_gt" | |
| bottom: "img0_aug_params" | |
| bottom: "GenerateAugmentationParameters1" | |
| top: "flow_gt_aug" | |
| augmentation_param { | |
| crop_width: 448 | |
| crop_height: 320 | |
| } | |
| } | |
| layer { | |
| name: "scaled_flow_gt" | |
| type: "Eltwise" | |
| bottom: "flow_gt_aug" | |
| top: "scaled_flow_gt" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.05 | |
| } | |
| } | |
| layer { | |
| name: "conv0_1_a" | |
| type: "Convolution" | |
| bottom: "img0_aug" | |
| bottom: "img1_aug" | |
| top: "conv0_1_a" | |
| top: "conv1_1_a" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_1_a" | |
| type: "ReLU" | |
| bottom: "conv0_1_a" | |
| top: "conv0_1_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_1_a" | |
| type: "ReLU" | |
| bottom: "conv1_1_a" | |
| top: "conv1_1_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_1_aa" | |
| type: "Convolution" | |
| bottom: "conv0_1_a" | |
| bottom: "conv1_1_a" | |
| top: "conv0_1_aa" | |
| top: "conv1_1_aa" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_1_aa" | |
| type: "ReLU" | |
| bottom: "conv0_1_aa" | |
| top: "conv0_1_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_1_aa" | |
| type: "ReLU" | |
| bottom: "conv1_1_aa" | |
| top: "conv1_1_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_1_b" | |
| type: "Convolution" | |
| bottom: "conv0_1_aa" | |
| bottom: "conv1_1_aa" | |
| top: "conv0_1_b" | |
| top: "conv1_1_b" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_1_b" | |
| type: "ReLU" | |
| bottom: "conv0_1_b" | |
| top: "conv0_1_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_1_b" | |
| type: "ReLU" | |
| bottom: "conv1_1_b" | |
| top: "conv1_1_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_2_a" | |
| type: "Convolution" | |
| bottom: "conv0_1_b" | |
| bottom: "conv1_1_b" | |
| top: "conv0_2_a" | |
| top: "conv1_2_a" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_2_a" | |
| type: "ReLU" | |
| bottom: "conv0_2_a" | |
| top: "conv0_2_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_2_a" | |
| type: "ReLU" | |
| bottom: "conv1_2_a" | |
| top: "conv1_2_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_2_aa" | |
| type: "Convolution" | |
| bottom: "conv0_2_a" | |
| bottom: "conv1_2_a" | |
| top: "conv0_2_aa" | |
| top: "conv1_2_aa" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_2_aa" | |
| type: "ReLU" | |
| bottom: "conv0_2_aa" | |
| top: "conv0_2_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_2_aa" | |
| type: "ReLU" | |
| bottom: "conv1_2_aa" | |
| top: "conv1_2_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_2_b" | |
| type: "Convolution" | |
| bottom: "conv0_2_aa" | |
| bottom: "conv1_2_aa" | |
| top: "conv0_2_b" | |
| top: "conv1_2_b" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_2_b" | |
| type: "ReLU" | |
| bottom: "conv0_2_b" | |
| top: "conv0_2_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_2_b" | |
| type: "ReLU" | |
| bottom: "conv1_2_b" | |
| top: "conv1_2_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_3_a" | |
| type: "Convolution" | |
| bottom: "conv0_2_b" | |
| bottom: "conv1_2_b" | |
| top: "conv0_3_a" | |
| top: "conv1_3_a" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_3_a" | |
| type: "ReLU" | |
| bottom: "conv0_3_a" | |
| top: "conv0_3_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_3_a" | |
| type: "ReLU" | |
| bottom: "conv1_3_a" | |
| top: "conv1_3_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_3_aa" | |
| type: "Convolution" | |
| bottom: "conv0_3_a" | |
| bottom: "conv1_3_a" | |
| top: "conv0_3_aa" | |
| top: "conv1_3_aa" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_3_aa" | |
| type: "ReLU" | |
| bottom: "conv0_3_aa" | |
| top: "conv0_3_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_3_aa" | |
| type: "ReLU" | |
| bottom: "conv1_3_aa" | |
| top: "conv1_3_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_3_b" | |
| type: "Convolution" | |
| bottom: "conv0_3_aa" | |
| bottom: "conv1_3_aa" | |
| top: "conv0_3_b" | |
| top: "conv1_3_b" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_3_b" | |
| type: "ReLU" | |
| bottom: "conv0_3_b" | |
| top: "conv0_3_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_3_b" | |
| type: "ReLU" | |
| bottom: "conv1_3_b" | |
| top: "conv1_3_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_4_a" | |
| type: "Convolution" | |
| bottom: "conv0_3_b" | |
| bottom: "conv1_3_b" | |
| top: "conv0_4_a" | |
| top: "conv1_4_a" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_4_a" | |
| type: "ReLU" | |
| bottom: "conv0_4_a" | |
| top: "conv0_4_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_4_a" | |
| type: "ReLU" | |
| bottom: "conv1_4_a" | |
| top: "conv1_4_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_4_aa" | |
| type: "Convolution" | |
| bottom: "conv0_4_a" | |
| bottom: "conv1_4_a" | |
| top: "conv0_4_aa" | |
| top: "conv1_4_aa" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_4_aa" | |
| type: "ReLU" | |
| bottom: "conv0_4_aa" | |
| top: "conv0_4_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_4_aa" | |
| type: "ReLU" | |
| bottom: "conv1_4_aa" | |
| top: "conv1_4_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_4_b" | |
| type: "Convolution" | |
| bottom: "conv0_4_aa" | |
| bottom: "conv1_4_aa" | |
| top: "conv0_4_b" | |
| top: "conv1_4_b" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_4_b" | |
| type: "ReLU" | |
| bottom: "conv0_4_b" | |
| top: "conv0_4_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_4_b" | |
| type: "ReLU" | |
| bottom: "conv1_4_b" | |
| top: "conv1_4_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_5_a" | |
| type: "Convolution" | |
| bottom: "conv0_4_b" | |
| bottom: "conv1_4_b" | |
| top: "conv0_5_a" | |
| top: "conv1_5_a" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_5_a" | |
| type: "ReLU" | |
| bottom: "conv0_5_a" | |
| top: "conv0_5_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_5_a" | |
| type: "ReLU" | |
| bottom: "conv1_5_a" | |
| top: "conv1_5_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_5_aa" | |
| type: "Convolution" | |
| bottom: "conv0_5_a" | |
| bottom: "conv1_5_a" | |
| top: "conv0_5_aa" | |
| top: "conv1_5_aa" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_5_aa" | |
| type: "ReLU" | |
| bottom: "conv0_5_aa" | |
| top: "conv0_5_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_5_aa" | |
| type: "ReLU" | |
| bottom: "conv1_5_aa" | |
| top: "conv1_5_aa" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_5_b" | |
| type: "Convolution" | |
| bottom: "conv0_5_aa" | |
| bottom: "conv1_5_aa" | |
| top: "conv0_5_b" | |
| top: "conv1_5_b" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_5_b" | |
| type: "ReLU" | |
| bottom: "conv0_5_b" | |
| top: "conv0_5_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_5_b" | |
| type: "ReLU" | |
| bottom: "conv1_5_b" | |
| top: "conv1_5_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Convolution1" | |
| type: "Convolution" | |
| bottom: "conv0_5_b" | |
| bottom: "conv1_5_b" | |
| top: "Convolution1" | |
| top: "Convolution2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 196 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "conv0_6_a" | |
| type: "Convolution" | |
| bottom: "Convolution1" | |
| bottom: "Convolution2" | |
| top: "conv0_6_a" | |
| top: "conv1_6_a" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 196 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_6_a" | |
| type: "ReLU" | |
| bottom: "conv0_6_a" | |
| top: "conv0_6_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_6_a" | |
| type: "ReLU" | |
| bottom: "conv1_6_a" | |
| top: "conv1_6_a" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv0_6_b" | |
| type: "Convolution" | |
| bottom: "conv0_6_a" | |
| bottom: "conv1_6_a" | |
| top: "conv0_6_b" | |
| top: "conv1_6_b" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 196 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "Relu0_6_b" | |
| type: "ReLU" | |
| bottom: "conv0_6_b" | |
| top: "conv0_6_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Relu1_6_b" | |
| type: "ReLU" | |
| bottom: "conv1_6_b" | |
| top: "conv1_6_b" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "Correlation1" | |
| type: "Correlation" | |
| bottom: "conv0_6_b" | |
| bottom: "conv1_6_b" | |
| top: "Correlation1" | |
| correlation_param { | |
| pad: 4 | |
| kernel_size: 1 | |
| max_displacement: 4 | |
| stride_1: 1 | |
| stride_2: 1 | |
| } | |
| } | |
| layer { | |
| name: "corr6" | |
| type: "ReLU" | |
| bottom: "Correlation1" | |
| top: "Correlation1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "conv6_0" | |
| type: "Convolution" | |
| bottom: "Correlation1" | |
| top: "conv6_0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu6_0" | |
| type: "ReLU" | |
| bottom: "conv6_0" | |
| top: "conv6_0" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat6_0" | |
| type: "Concat" | |
| bottom: "conv6_0" | |
| bottom: "Correlation1" | |
| top: "denseconcat6_0" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv6_1" | |
| type: "Convolution" | |
| bottom: "denseconcat6_0" | |
| top: "conv6_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu6_1" | |
| type: "ReLU" | |
| bottom: "conv6_1" | |
| top: "conv6_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat6_1" | |
| type: "Concat" | |
| bottom: "conv6_1" | |
| bottom: "denseconcat6_0" | |
| top: "denseconcat6_1" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv6_2" | |
| type: "Convolution" | |
| bottom: "denseconcat6_1" | |
| top: "conv6_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu6_2" | |
| type: "ReLU" | |
| bottom: "conv6_2" | |
| top: "conv6_2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat6_2" | |
| type: "Concat" | |
| bottom: "conv6_2" | |
| bottom: "denseconcat6_1" | |
| top: "denseconcat6_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv6_3" | |
| type: "Convolution" | |
| bottom: "denseconcat6_2" | |
| top: "conv6_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu6_3" | |
| type: "ReLU" | |
| bottom: "conv6_3" | |
| top: "conv6_3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat6_3" | |
| type: "Concat" | |
| bottom: "conv6_3" | |
| bottom: "denseconcat6_2" | |
| top: "denseconcat6_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv6_4" | |
| type: "Convolution" | |
| bottom: "denseconcat6_3" | |
| top: "conv6_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu6_4" | |
| type: "ReLU" | |
| bottom: "conv6_4" | |
| top: "conv6_4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat6_4" | |
| type: "Concat" | |
| bottom: "conv6_4" | |
| bottom: "denseconcat6_3" | |
| top: "denseconcat6_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "predict_flow6" | |
| type: "Convolution" | |
| bottom: "denseconcat6_4" | |
| 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: "upsample_flow_6to5" | |
| type: "Deconvolution" | |
| bottom: "predict_flow6" | |
| top: "upsample_flow_6to5" | |
| 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: "upsample_feature_6to5" | |
| type: "Deconvolution" | |
| bottom: "denseconcat6_4" | |
| top: "upsample_feature_6to5" | |
| 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: "scale_flow_6to5" | |
| type: "Eltwise" | |
| bottom: "upsample_flow_6to5" | |
| top: "scale_flow_6to5" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 0.625 | |
| } | |
| } | |
| layer { | |
| name: "warped_image5" | |
| type: "Warp" | |
| bottom: "conv1_5_b" | |
| bottom: "scale_flow_6to5" | |
| top: "warped_image5" | |
| } | |
| layer { | |
| name: "Correlation2" | |
| type: "Correlation" | |
| bottom: "conv0_5_b" | |
| bottom: "warped_image5" | |
| top: "Correlation2" | |
| correlation_param { | |
| pad: 4 | |
| kernel_size: 1 | |
| max_displacement: 4 | |
| stride_1: 1 | |
| stride_2: 1 | |
| } | |
| } | |
| layer { | |
| name: "corr5" | |
| type: "ReLU" | |
| bottom: "Correlation2" | |
| top: "Correlation2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "concat5" | |
| type: "Concat" | |
| bottom: "Correlation2" | |
| bottom: "conv0_5_b" | |
| bottom: "upsample_flow_6to5" | |
| bottom: "upsample_feature_6to5" | |
| top: "concat5" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_0" | |
| type: "Convolution" | |
| bottom: "concat5" | |
| top: "conv5_0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_0" | |
| type: "ReLU" | |
| bottom: "conv5_0" | |
| top: "conv5_0" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat5_0" | |
| type: "Concat" | |
| bottom: "conv5_0" | |
| bottom: "concat5" | |
| top: "denseconcat5_0" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "denseconcat5_0" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_1" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat5_1" | |
| type: "Concat" | |
| bottom: "conv5_1" | |
| bottom: "denseconcat5_0" | |
| top: "denseconcat5_1" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Convolution" | |
| bottom: "denseconcat5_1" | |
| top: "conv5_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_2" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat5_2" | |
| type: "Concat" | |
| bottom: "conv5_2" | |
| bottom: "denseconcat5_1" | |
| top: "denseconcat5_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Convolution" | |
| bottom: "denseconcat5_2" | |
| top: "conv5_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_3" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat5_3" | |
| type: "Concat" | |
| bottom: "conv5_3" | |
| bottom: "denseconcat5_2" | |
| top: "denseconcat5_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4" | |
| type: "Convolution" | |
| bottom: "denseconcat5_3" | |
| top: "conv5_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_4" | |
| type: "ReLU" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat5_4" | |
| type: "Concat" | |
| bottom: "conv5_4" | |
| bottom: "denseconcat5_3" | |
| top: "denseconcat5_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "predict_flow5" | |
| type: "Convolution" | |
| bottom: "denseconcat5_4" | |
| 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: "upsample_flow_5to4" | |
| type: "Deconvolution" | |
| bottom: "predict_flow5" | |
| top: "upsample_flow_5to4" | |
| 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: "upsample_feature_5to4" | |
| type: "Deconvolution" | |
| bottom: "denseconcat5_4" | |
| top: "upsample_feature_5to4" | |
| 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: "scale_flow_5to4" | |
| type: "Eltwise" | |
| bottom: "upsample_flow_5to4" | |
| top: "scale_flow_5to4" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1.25 | |
| } | |
| } | |
| layer { | |
| name: "warped_image4" | |
| type: "Warp" | |
| bottom: "conv1_4_b" | |
| bottom: "scale_flow_5to4" | |
| top: "warped_image4" | |
| } | |
| layer { | |
| name: "Correlation3" | |
| type: "Correlation" | |
| bottom: "conv0_4_b" | |
| bottom: "warped_image4" | |
| top: "Correlation3" | |
| correlation_param { | |
| pad: 4 | |
| kernel_size: 1 | |
| max_displacement: 4 | |
| stride_1: 1 | |
| stride_2: 1 | |
| } | |
| } | |
| layer { | |
| name: "corr4" | |
| type: "ReLU" | |
| bottom: "Correlation3" | |
| top: "Correlation3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "concat4" | |
| type: "Concat" | |
| bottom: "Correlation3" | |
| bottom: "conv0_4_b" | |
| bottom: "upsample_flow_5to4" | |
| bottom: "upsample_feature_5to4" | |
| top: "concat4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_0" | |
| type: "Convolution" | |
| bottom: "concat4" | |
| top: "conv4_0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_0" | |
| type: "ReLU" | |
| bottom: "conv4_0" | |
| top: "conv4_0" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat4_0" | |
| type: "Concat" | |
| bottom: "conv4_0" | |
| bottom: "concat4" | |
| top: "denseconcat4_0" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "denseconcat4_0" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_1" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat4_1" | |
| type: "Concat" | |
| bottom: "conv4_1" | |
| bottom: "denseconcat4_0" | |
| top: "denseconcat4_1" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Convolution" | |
| bottom: "denseconcat4_1" | |
| top: "conv4_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat4_2" | |
| type: "Concat" | |
| bottom: "conv4_2" | |
| bottom: "denseconcat4_1" | |
| top: "denseconcat4_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Convolution" | |
| bottom: "denseconcat4_2" | |
| top: "conv4_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_3" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat4_3" | |
| type: "Concat" | |
| bottom: "conv4_3" | |
| bottom: "denseconcat4_2" | |
| top: "denseconcat4_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4" | |
| type: "Convolution" | |
| bottom: "denseconcat4_3" | |
| top: "conv4_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_4" | |
| type: "ReLU" | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat4_4" | |
| type: "Concat" | |
| bottom: "conv4_4" | |
| bottom: "denseconcat4_3" | |
| top: "denseconcat4_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "predict_flow4" | |
| type: "Convolution" | |
| bottom: "denseconcat4_4" | |
| 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: "upsample_flow_4to3" | |
| type: "Deconvolution" | |
| bottom: "predict_flow4" | |
| top: "upsample_flow_4to3" | |
| 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: "upsample_feature_4to3" | |
| type: "Deconvolution" | |
| bottom: "denseconcat4_4" | |
| top: "upsample_feature_4to3" | |
| 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: "scale_flow_4to3" | |
| type: "Eltwise" | |
| bottom: "upsample_flow_4to3" | |
| top: "scale_flow_4to3" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 2.5 | |
| } | |
| } | |
| layer { | |
| name: "warped_image3" | |
| type: "Warp" | |
| bottom: "conv1_3_b" | |
| bottom: "scale_flow_4to3" | |
| top: "warped_image3" | |
| } | |
| layer { | |
| name: "Correlation4" | |
| type: "Correlation" | |
| bottom: "conv0_3_b" | |
| bottom: "warped_image3" | |
| top: "Correlation4" | |
| correlation_param { | |
| pad: 4 | |
| kernel_size: 1 | |
| max_displacement: 4 | |
| stride_1: 1 | |
| stride_2: 1 | |
| } | |
| } | |
| layer { | |
| name: "corr3" | |
| type: "ReLU" | |
| bottom: "Correlation4" | |
| top: "Correlation4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "concat3" | |
| type: "Concat" | |
| bottom: "Correlation4" | |
| bottom: "conv0_3_b" | |
| bottom: "upsample_flow_4to3" | |
| bottom: "upsample_feature_4to3" | |
| top: "concat3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_0" | |
| type: "Convolution" | |
| bottom: "concat3" | |
| top: "conv3_0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_0" | |
| type: "ReLU" | |
| bottom: "conv3_0" | |
| top: "conv3_0" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat3_0" | |
| type: "Concat" | |
| bottom: "conv3_0" | |
| bottom: "concat3" | |
| top: "denseconcat3_0" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "denseconcat3_0" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_1" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat3_1" | |
| type: "Concat" | |
| bottom: "conv3_1" | |
| bottom: "denseconcat3_0" | |
| top: "denseconcat3_1" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Convolution" | |
| bottom: "denseconcat3_1" | |
| top: "conv3_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_2" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat3_2" | |
| type: "Concat" | |
| bottom: "conv3_2" | |
| bottom: "denseconcat3_1" | |
| top: "denseconcat3_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Convolution" | |
| bottom: "denseconcat3_2" | |
| top: "conv3_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat3_3" | |
| type: "Concat" | |
| bottom: "conv3_3" | |
| bottom: "denseconcat3_2" | |
| top: "denseconcat3_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Convolution" | |
| bottom: "denseconcat3_3" | |
| top: "conv3_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_4" | |
| type: "ReLU" | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat3_4" | |
| type: "Concat" | |
| bottom: "conv3_4" | |
| bottom: "denseconcat3_3" | |
| top: "denseconcat3_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "predict_flow3" | |
| type: "Convolution" | |
| bottom: "denseconcat3_4" | |
| 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: "upsample_flow_3to2" | |
| type: "Deconvolution" | |
| bottom: "predict_flow3" | |
| top: "upsample_flow_3to2" | |
| 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: "upsample_feature_3to2" | |
| type: "Deconvolution" | |
| bottom: "denseconcat3_4" | |
| top: "upsample_feature_3to2" | |
| 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: "scale_flow_3to2" | |
| type: "Eltwise" | |
| bottom: "upsample_flow_3to2" | |
| top: "scale_flow_3to2" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 5.0 | |
| } | |
| } | |
| layer { | |
| name: "warped_image2" | |
| type: "Warp" | |
| bottom: "conv1_2_b" | |
| bottom: "scale_flow_3to2" | |
| top: "warped_image2" | |
| } | |
| layer { | |
| name: "Correlation5" | |
| type: "Correlation" | |
| bottom: "conv0_2_b" | |
| bottom: "warped_image2" | |
| top: "Correlation5" | |
| correlation_param { | |
| pad: 4 | |
| kernel_size: 1 | |
| max_displacement: 4 | |
| stride_1: 1 | |
| stride_2: 1 | |
| } | |
| } | |
| layer { | |
| name: "corr2" | |
| type: "ReLU" | |
| bottom: "Correlation5" | |
| top: "Correlation5" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "concat2" | |
| type: "Concat" | |
| bottom: "Correlation5" | |
| bottom: "conv0_2_b" | |
| bottom: "upsample_flow_3to2" | |
| bottom: "upsample_feature_3to2" | |
| top: "concat2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_0" | |
| type: "Convolution" | |
| bottom: "concat2" | |
| top: "conv2_0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_0" | |
| type: "ReLU" | |
| bottom: "conv2_0" | |
| top: "conv2_0" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat2_0" | |
| type: "Concat" | |
| bottom: "conv2_0" | |
| bottom: "concat2" | |
| top: "denseconcat2_0" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "denseconcat2_0" | |
| top: "conv2_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat2_1" | |
| type: "Concat" | |
| bottom: "conv2_1" | |
| bottom: "denseconcat2_0" | |
| top: "denseconcat2_1" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Convolution" | |
| bottom: "denseconcat2_1" | |
| top: "conv2_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_2" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat2_2" | |
| type: "Concat" | |
| bottom: "conv2_2" | |
| bottom: "denseconcat2_1" | |
| top: "denseconcat2_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3" | |
| type: "Convolution" | |
| bottom: "denseconcat2_2" | |
| top: "conv2_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_3" | |
| type: "ReLU" | |
| bottom: "conv2_3" | |
| top: "conv2_3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat2_3" | |
| type: "Concat" | |
| bottom: "conv2_3" | |
| bottom: "denseconcat2_2" | |
| top: "denseconcat2_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_4" | |
| type: "Convolution" | |
| bottom: "denseconcat2_3" | |
| top: "conv2_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_4" | |
| type: "ReLU" | |
| bottom: "conv2_4" | |
| top: "conv2_4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "denseconcat2_4" | |
| type: "Concat" | |
| bottom: "conv2_4" | |
| bottom: "denseconcat2_3" | |
| top: "denseconcat2_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "predict_flow_ini" | |
| type: "Convolution" | |
| bottom: "denseconcat2_4" | |
| top: "predict_flow_ini" | |
| 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: "dc_conv1" | |
| type: "Convolution" | |
| bottom: "denseconcat2_4" | |
| top: "dc_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu_dc1" | |
| type: "ReLU" | |
| bottom: "dc_conv1" | |
| top: "dc_conv1" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "dc_conv2" | |
| type: "Convolution" | |
| bottom: "dc_conv1" | |
| top: "dc_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "relu_dc2" | |
| type: "ReLU" | |
| bottom: "dc_conv2" | |
| top: "dc_conv2" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "dc_conv3" | |
| type: "Convolution" | |
| bottom: "dc_conv2" | |
| top: "dc_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "relu_dc3" | |
| type: "ReLU" | |
| bottom: "dc_conv3" | |
| top: "dc_conv3" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "dc_conv4" | |
| type: "Convolution" | |
| bottom: "dc_conv3" | |
| top: "dc_conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 8 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| dilation: 8 | |
| } | |
| } | |
| layer { | |
| name: "relu_dc4" | |
| type: "ReLU" | |
| bottom: "dc_conv4" | |
| top: "dc_conv4" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "dc_conv5" | |
| type: "Convolution" | |
| bottom: "dc_conv4" | |
| top: "dc_conv5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 16 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| dilation: 16 | |
| } | |
| } | |
| layer { | |
| name: "relu_dc5" | |
| type: "ReLU" | |
| bottom: "dc_conv5" | |
| top: "dc_conv5" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "dc_conv6" | |
| type: "Convolution" | |
| bottom: "dc_conv5" | |
| top: "dc_conv6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu_dc6" | |
| type: "ReLU" | |
| bottom: "dc_conv6" | |
| top: "dc_conv6" | |
| relu_param { | |
| negative_slope: 0.1 | |
| } | |
| } | |
| layer { | |
| name: "predict_flow_inc" | |
| type: "Convolution" | |
| bottom: "dc_conv6" | |
| top: "predict_flow_inc" | |
| 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: "predict_flow2" | |
| type: "Eltwise" | |
| bottom: "predict_flow_ini" | |
| bottom: "predict_flow_inc" | |
| top: "predict_flow2" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "scaled_flow_gt2" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow2" | |
| top: "scaled_flow_gt2" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "loss2" | |
| type: "L1Loss" | |
| bottom: "predict_flow2" | |
| bottom: "scaled_flow_gt2" | |
| top: "loss2" | |
| loss_weight: 0.005 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "scaled_flow_gt3" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow3" | |
| top: "scaled_flow_gt3" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "loss3" | |
| type: "L1Loss" | |
| bottom: "predict_flow3" | |
| bottom: "scaled_flow_gt3" | |
| top: "loss3" | |
| loss_weight: 0.01 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "scaled_flow_gt4" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow4" | |
| top: "scaled_flow_gt4" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "loss4" | |
| type: "L1Loss" | |
| bottom: "predict_flow4" | |
| bottom: "scaled_flow_gt4" | |
| top: "loss4" | |
| loss_weight: 0.02 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "scaled_flow_gt5" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow5" | |
| top: "scaled_flow_gt5" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "loss5" | |
| type: "L1Loss" | |
| bottom: "predict_flow5" | |
| bottom: "scaled_flow_gt5" | |
| top: "loss5" | |
| loss_weight: 0.08 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } | |
| layer { | |
| name: "scaled_flow_gt6" | |
| type: "Downsample" | |
| bottom: "scaled_flow_gt" | |
| bottom: "predict_flow6" | |
| top: "scaled_flow_gt6" | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "loss6" | |
| type: "L1Loss" | |
| bottom: "predict_flow6" | |
| bottom: "scaled_flow_gt6" | |
| top: "loss6" | |
| loss_weight: 0.32 | |
| l1_loss_param { | |
| l2_per_location: true | |
| } | |
| } |
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