Faster RCNN on custom dataset with 3 classes, output layer check fails.

[Mozie2341]

Hey all,

I'm trying to train faster RCNN on a custom dataset with 3 classes, but for some reason I always get the following error:

F0412 15:16:54.629330 5021 smooth_L1_loss_layer.cpp:28] Check failed: bottom[0]->channels() == bottom[1]->channels() (16 vs. 84)

In which 84 is the expected output blob for the old pascal VOC dataset. But I have no idea why it's expecting the old reference instead of my (3+1)*4 output blob. The input layer has been modified to accept the 3 classes of my dataset instead of the 21 pascal VOC classes.

I'm getting the distinct feeling I'm overlooking something stupid.

Thanks in advance

For reference, heres the train.prototxt (Using ZF)

name: "ZF"
layer {
name: 'input-data'
type: 'Python'
top: 'data'
top: 'im_info'
top: 'gt_boxes'
python_param {
module: 'roi_data_layer.layer'
layer: 'RoIDataLayer'
param_str: "'num_classes': 4"
}
}

layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 96
kernel_size: 7
pad: 3
stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 3
alpha: 0.00005
beta: 0.75
norm_region: WITHIN_CHANNEL
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
kernel_size: 3
stride: 2
pad: 1
pool: MAX
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 256
kernel_size: 5
pad: 2
stride: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 3
alpha: 0.00005
beta: 0.75
norm_region: WITHIN_CHANNEL
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
kernel_size: 3
stride: 2
pad: 1
pool: MAX
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 384
kernel_size: 3
pad: 1
stride: 1
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 384
kernel_size: 3
pad: 1
stride: 1
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}

layer {
name: "rpn_conv/3x3"
type: "Convolution"
bottom: "conv5"
top: "rpn/output"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 256
kernel_size: 3 pad: 1 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "rpn_relu/3x3"
type: "ReLU"
bottom: "rpn/output"
top: "rpn/output"
}

layer {
name: "rpn_cls_score"
type: "Convolution"
bottom: "rpn/output"
top: "rpn_cls_score"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 18 # 2(bg/fg) * 9(anchors)
kernel_size: 1 pad: 0 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "rpn_bbox_pred"
type: "Convolution"
bottom: "rpn/output"
top: "rpn_bbox_pred"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 36 # 4 * 9(anchors)
kernel_size: 1 pad: 0 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
bottom: "rpn_cls_score"
top: "rpn_cls_score_reshape"
name: "rpn_cls_score_reshape"
type: "Reshape"
reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
}
layer {
name: 'rpn-data'
type: 'Python'
bottom: 'rpn_cls_score'
bottom: 'gt_boxes'
bottom: 'im_info'
bottom: 'data'
top: 'rpn_labels'
top: 'rpn_bbox_targets'
top: 'rpn_bbox_inside_weights'
top: 'rpn_bbox_outside_weights'
python_param {
module: 'rpn.anchor_target_layer'
layer: 'AnchorTargetLayer'
param_str: "'feat_stride': 16"
}
}
layer {
name: "rpn_loss_cls"
type: "SoftmaxWithLoss"
bottom: "rpn_cls_score_reshape"
bottom: "rpn_labels"
propagate_down: 1
propagate_down: 0
top: "rpn_cls_loss"
loss_weight: 1
loss_param {
ignore_label: -1
normalize: true
}
}
layer {
name: "rpn_loss_bbox"
type: "SmoothL1Loss"
bottom: "rpn_bbox_pred"
bottom: "rpn_bbox_targets"
bottom: 'rpn_bbox_inside_weights'
bottom: 'rpn_bbox_outside_weights'
top: "rpn_loss_bbox"
loss_weight: 1
smooth_l1_loss_param { sigma: 3.0 }
}

========= RoI Proposal ============

layer {
name: "rpn_cls_prob"
type: "Softmax"
bottom: "rpn_cls_score_reshape"
top: "rpn_cls_prob"
}
layer {
name: 'rpn_cls_prob_reshape'
type: 'Reshape'
bottom: 'rpn_cls_prob'
top: 'rpn_cls_prob_reshape'
reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
}
layer {
name: 'proposal'
type: 'Python'
bottom: 'rpn_cls_prob_reshape'
bottom: 'rpn_bbox_pred'
bottom: 'im_info'
top: 'rpn_rois'

python_param {
module: 'rpn.proposal_layer'
layer: 'ProposalLayer'
param_str: "'feat_stride': 16"
}
}

layer {
name: 'roi-data'
type: 'Python'
bottom: 'rpn_rois'
bottom: 'gt_boxes'
top: 'rois'
top: 'labels'
top: 'bbox_targets'
top: 'bbox_inside_weights'
top: 'bbox_outside_weights'
python_param {
module: 'rpn.proposal_target_layer'
layer: 'ProposalTargetLayer'
param_str: "'num_classes': 21"
}
}

========= RCNN ============

layer {
name: "roi_pool_conv5"
type: "ROIPooling"
bottom: "conv5"
bottom: "rois"
top: "roi_pool_conv5"
roi_pooling_param {
pooled_w: 6
pooled_h: 6
spatial_scale: 0.0625 # 1/16
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "roi_pool_conv5"
top: "fc6"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
scale_train: false
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
scale_train: false
}
}
layer {
name: "cls_score
type: "InnerProduct"
bottom: "fc7"
top: "cls_score"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 21
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bbox_pred"
type: "InnerProduct"
bottom: "fc7"
top: "bbox_pred"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 16
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss_cls"
type: "SoftmaxWithLoss"
bottom: "cls_score"
bottom: "labels"
propagate_down: 1
propagate_down: 0
top: "cls_loss"
loss_weight: 1
loss_param {
ignore_label: -1
normalize: true
}
}
layer {
name: "loss_bbox"
type: "SmoothL1Loss"
bottom: "bbox_pred"
bottom: "bbox_targets"
bottom: 'bbox_inside_weights'
bottom: 'bbox_outside_weights'
top: "bbox_loss"
loss_weight: 1
}

[deboc]

IMO the 2 occurences of "num_output: 21" should be replaced by "num_output: 4"

[Mozie2341]

That did it, thanks a ton

[guysoft]

Hey, can you explain why 4? is it "3 classes" + 1?

[deboc]

Correct. In faster-rcnn always add the background class.

[Jilliansea]

Hello, I have the same problem when I train my own faster rcnn in matlab:
F0604 10:27:45.549877 29210 smooth_L1_loss_layer.cpp:32] Check failed: bottom[0]->channels() == bottom[1]->channels() (16 vs. 20)
I have k=3 classes, so (k+1)*4 = 16, and the original code has k=20 classes. I have checked models for many times, but I can't find where I haven't changed the parameters.Could you please help me to have a look.
Uploading models (copy).zip…

[ujsyehao]

@Jilliansea you can view train.prototxt and send it to me

[dylanGeng]

Hi, I have k =2, and change "input-data" layer, "cls_score" layer and "bbox_pred" layer.
But I still have the following error:
F0606 10:57:01.104756 13676 smooth_l1_loss_layer.cpp:23] Check failed: bottom[0]->channels() == bottom[1]->channels() (12 vs. 84)
Could you please help me to have a look.
-----------------------------------train.prototxt-------------------------------
name: "ZF"
layer {
name: 'input-data'
type: 'Python'
top: 'data'
top: 'im_info'
top: 'gt_boxes'
python_param {
module: 'roi_data_layer.layer'
layer: 'RoIDataLayer'
param_str: "'num_classes': 3"
}
}
layer {
name: "cls_score"
type: "InnerProduct"
bottom: "fc7"
top: "cls_score"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bbox_pred"
type: "InnerProduct"
bottom: "fc7"
top: "bbox_pred"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 12
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}

[ishanashastri]

@Jilliansea I'm having the same problem as you. Did you figure it out yet?

[qwertyDvo]

Same here I have same problem how to solve it pleas?