Part Number: TDA4VMXEVM
Hello Friends,
I tried importing a pointnet based caffe model using the TIDL importer.I got the following error:
Name of the Network : pointnet_cls_basic
WARNING: Reshape layer only supports following with avg-pooling/inner product/ssd context.
WARNING: Reshape layer only supports following with avg-pooling/inner product/ssd context.
WARNING: Reshape layer only supports following with avg-pooling/inner product/ssd context.
WARNING: Reshape layer only supports following with avg-pooling/inner product/ssd context.
WARNING: Reshape layer only supports following with avg-pooling/inner product/ssd context.
WARNING: Reshape layer only supports following with avg-pooling/inner product/ssd context.
Segmentation fault
I have hereby attached the corresponding prototxt file for your reference(I have renamed the extension since the tool did not allow to upload the file with .prototxt extension).I request you to assist me in solving the issue.
Best Regards,
Vijay
name: "pointnet_cls_basic"
# ---------------------- data layer ---------------
#layer {
# name: "data"
# type: "HDF5Data"
# top: "data"
# top: "label"
# hdf5_data_param {
# source: "data/modelnet40_ply_hdf5_2048/train_files.txt"
# batch_size: 32
# }
# include: { phase: TRAIN }
#}
#layer {
# name: "data"
# type: "HDF5Data"
# top: "data"
# top: "label"
# hdf5_data_param {
# source: "data/modelnet40_ply_hdf5_2048/test_files.txt"
# batch_size: 32
# }
# include: { phase: TEST }
#}
layer {
name: "reshape"
type: "Reshape"
bottom: "data"
top: "data_reshape"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 1
dim: -1
dim: 3 # infer it from the other dimensions
}
}
}
# -------------------- input tansform net ---------------------
layer {
name: "conv1_tnet1"
type: "Convolution"
bottom: "data_reshape"
top: "conv1_tnet1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 0
kernel_w: 3
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn1_tnet1"
type: "BatchNorm"
bottom: "conv1_tnet1"
top: "conv1_tnet1"
}
layer {
bottom: "conv1_tnet1"
top: "conv1_tnet1"
name: "scale1_tnet1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu1_tnet1"
type: "ReLU"
bottom: "conv1_tnet1"
top: "conv1_tnet1"
}
layer {
name: "conv2_tnet1"
type: "Convolution"
bottom: "conv1_tnet1"
top: "conv2_tnet1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 128
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn2_tnet1"
type: "BatchNorm"
bottom: "conv2_tnet1"
top: "conv2_tnet1"
}
layer {
bottom: "conv2_tnet1"
top: "conv2_tnet1"
name: "scale2_tnet1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_tnet1"
type: "ReLU"
bottom: "conv2_tnet1"
top: "conv2_tnet1"
}
layer {
name: "conv3_tnet1"
type: "Convolution"
bottom: "conv2_tnet1"
top: "conv3_tnet1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 1024
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn3_tnet1"
type: "BatchNorm"
bottom: "conv3_tnet1"
top: "conv3_tnet1"
}
layer {
bottom: "conv3_tnet1"
top: "conv3_tnet1"
name: "scale3_tnet1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_tnet1"
type: "ReLU"
bottom: "conv3_tnet1"
top: "conv3_tnet1"
}
layer {
name: "pool_tnet1"
type: "Pooling"
bottom: "conv3_tnet1"
top: "global_feat_tnet1"
pooling_param {
pool: MAX
pad: 0
kernel_h: 2048
kernel_w: 1
stride: 1
}
}
layer {
name: "fc1_tnet1"
type: "InnerProduct"
bottom: "global_feat_tnet1"
top: "fc1_tnet1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn6_tnet1"
type: "BatchNorm"
bottom: "fc1_tnet1"
top: "fc1_tnet1"
}
layer {
bottom: "fc1_tnet1"
top: "fc1_tnet1"
name: "scale6_tnet1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu6_tnet1"
type: "ReLU"
bottom: "fc1_tnet1"
top: "fc1_tnet1"
}
layer {
name: "fc2_tnet1"
type: "InnerProduct"
bottom: "fc1_tnet1"
top: "fc2_tnet1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn7_tnet1"
type: "BatchNorm"
bottom: "fc2_tnet1"
top: "fc2_tnet1"
}
layer {
bottom: "fc2_tnet1"
top: "fc2_tnet1"
name: "scale7_tnet1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu7_tnet1"
type: "ReLU"
bottom: "fc2_tnet1"
top: "fc2_tnet1"
}
layer {
name: "fc3_tnet1"
type: "InnerProduct"
bottom: "fc2_tnet1"
top: "fc3_tnet1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 9
weight_filler {
type: "constant"
value: 0
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "reshape_tnet1"
type: "Reshape"
bottom: "fc3_tnet1"
top: "fc3_tnet1_reshape"
reshape_param {
shape {
dim: -1
dim: 3
dim: 3
}
}
}
#layer {
# name: "eye_tnet1"
# type: "Python"
# bottom: "fc3_tnet1"
# top: "eye_tnet1"
# python_param {
# module: "eye_matrix_layer"
# layer: "EyeMatrixLayer"
# param_str: "{\'K\': 3}"
# }
#}
layer {
name: "eltwise_sum_tnet1"
type: "Eltwise"
bottom: "fc3_tnet1_reshape"
# bottom: "eye_tnet1"
top: "transform1"
eltwise_param { operation: SUM }
}
# -------------------- input transform ---------------------
#layer {
# name: "matmul_input"
# type: "MatrixMultiplication"
# bottom: "data" # BxNx3
# bottom: "transform1" # 3x3
# top: "data_transform1" # 10x1x1 # BxNx3
#}
layer {
name: "reshape_input_data"
type: "Reshape"
bottom: "data_transform1"
top: "data_transform1_reshape"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 1
dim: -1
dim: 3 # infer it from the other dimensions
}
}
}
# -------------------- mlp ---------------------
layer {
name: "conv1"
type: "Convolution"
bottom: "data_transform1_reshape"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 0
kernel_w: 3
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn1"
type: "BatchNorm"
bottom: "conv1"
top: "conv1"
}
layer {
bottom: "conv1"
top: "conv1"
name: "scale1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "conv2"
type: "Convolution"
bottom: "conv1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn2"
type: "BatchNorm"
bottom: "conv2"
top: "conv2"
}
layer {
bottom: "conv2"
top: "conv2"
name: "scale2"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
# -------------------- feat tansform net ---------------------
layer {
name: "conv1_tnet2"
type: "Convolution"
bottom: "conv2"
top: "conv1_tnet2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn1_tnet2"
type: "BatchNorm"
bottom: "conv1_tnet2"
top: "conv1_tnet2"
}
layer {
bottom: "conv1_tnet2"
top: "conv1_tnet2"
name: "scale1_tnet2"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu1_tnet2"
type: "ReLU"
bottom: "conv1_tnet2"
top: "conv1_tnet2"
}
layer {
name: "conv2_tnet2"
type: "Convolution"
bottom: "conv1_tnet2"
top: "conv2_tnet2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 128
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn2_tnet2"
type: "BatchNorm"
bottom: "conv2_tnet2"
top: "conv2_tnet2"
}
layer {
bottom: "conv2_tnet2"
top: "conv2_tnet2"
name: "scale2_tnet2"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_tnet2"
type: "ReLU"
bottom: "conv2_tnet2"
top: "conv2_tnet2"
}
layer {
name: "conv3_tnet2"
type: "Convolution"
bottom: "conv2_tnet2"
top: "conv3_tnet2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 1024
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn3_tnet2"
type: "BatchNorm"
bottom: "conv3_tnet2"
top: "conv3_tnet2"
}
layer {
bottom: "conv3_tnet2"
top: "conv3_tnet2"
name: "scale3_tnet2"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_tnet2"
type: "ReLU"
bottom: "conv3_tnet2"
top: "conv3_tnet2"
}
layer {
name: "pool_tnet2"
type: "Pooling"
bottom: "conv3_tnet2"
top: "global_feat_tnet2"
pooling_param {
pool: MAX
pad: 0
kernel_h: 2048
kernel_w: 1
stride: 1
}
}
layer {
name: "fc1_tnet2"
type: "InnerProduct"
bottom: "global_feat_tnet2"
top: "fc1_tnet2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn6_tnet2"
type: "BatchNorm"
bottom: "fc1_tnet2"
top: "fc1_tnet2"
}
layer {
bottom: "fc1_tnet2"
top: "fc1_tnet2"
name: "scale6_tnet2"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu6_tnet2"
type: "ReLU"
bottom: "fc1_tnet2"
top: "fc1_tnet2"
}
layer {
name: "fc2_tnet2"
type: "InnerProduct"
bottom: "fc1_tnet2"
top: "fc2_tnet2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn7_tnet2"
type: "BatchNorm"
bottom: "fc2_tnet2"
top: "fc2_tnet2"
}
layer {
bottom: "fc2_tnet2"
top: "fc2_tnet2"
name: "scale7_tnet2"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu7_tnet2"
type: "ReLU"
bottom: "fc2_tnet2"
top: "fc2_tnet2"
}
layer {
name: "fc3_tnet2"
type: "InnerProduct"
bottom: "fc2_tnet2"
top: "fc3_tnet2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 4096 # 64*64
weight_filler {
type: "constant"
value: 0
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "reshape_tnet2"
type: "Reshape"
bottom: "fc3_tnet2"
top: "fc3_tnet2_reshape"
reshape_param {
shape {
dim: -1
dim: 64
dim: 64
}
}
}
#layer {
# name: "eye_tnet2"
# type: "Python"
# bottom: "fc3_tnet2"
# top: "eye_tnet2"
# python_param {
# module: "eye_matrix_layer"
# layer: "EyeMatrixLayer"
# param_str: "{\'K\': 64}"
# }
#}
layer {
name: "eltwise_sum_tnet2"
type: "Eltwise"
bottom: "fc3_tnet2_reshape"
# bottom: "eye_tnet2"
top: "transform2"
eltwise_param { operation: SUM }
}
# -------------------- feat transform ---------------------
layer {
name: "perm_scale2"
type: "Permute"
bottom: "conv2" # Bx64xNx1
top: "scale2_permute" # BxNx64x1
permute_param {
order: 0
order: 2
order: 1
order: 3
}
}
layer {
name: "reshape_scale2"
type: "Reshape"
bottom: "scale2_permute" # BxNx64x1
top: "scale2_reshape" # BxNx64
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: -1
dim: 64 # infer it from the other dimensions
}
}
}
#layer {
# name: "matmul_feat"
# type: "MatrixMultiplication"
# bottom: "scale2_reshape" # BxNx64
# bottom: "transform2" # 64x64
# top: "scale2_transform2" # BxNx64
#}
layer {
name: "perm_scale2_transform2"
type: "Permute"
bottom: "scale2_transform2" # BxNx64
top: "scale2_permute_transform2" # Bx64xN
permute_param {
order: 0
order: 2
order: 1
}
}
layer {
name: "reshape_scale2_transform2"
type: "Reshape"
bottom: "scale2_permute_transform2" # Bx64xN
top: "scale2_transform2_reshape" # Bx64xNx1
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 0
dim: 0
dim: 1
}
}
}
# -------------------- mlp ---------------------
layer {
name: "conv3"
type: "Convolution"
bottom: "scale2_transform2_reshape"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn3"
type: "BatchNorm"
bottom: "conv3"
top: "conv3"
}
layer {
bottom: "conv3"
top: "conv3"
name: "scale3"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 128
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn4"
type: "BatchNorm"
bottom: "conv4"
top: "conv4"
}
layer {
bottom: "conv4"
top: "conv4"
name: "scale4"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 1024
pad: 0
kernel_w: 1
kernel_h: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn5"
type: "BatchNorm"
bottom: "conv5"
top: "conv5"
}
layer {
bottom: "conv5"
top: "conv5"
name: "scale5"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
# -------------------- max pool ---------------------
layer {
name: "pool"
type: "Pooling"
bottom: "conv5"
top: "global_feat"
pooling_param {
pool: MAX
pad: 0
kernel_h: 2048
kernel_w: 1
stride: 1
}
}
# -------------------- fc ---------------------
layer {
name: "fc1"
type: "InnerProduct"
bottom: "global_feat"
top: "fc1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn6"
type: "BatchNorm"
bottom: "fc1"
top: "fc1"
}
layer {
bottom: "fc1"
top: "fc1"
name: "scale6"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc1"
top: "fc1"
}
layer {
name: "fc2"
type: "InnerProduct"
bottom: "fc1"
top: "fc2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bn7"
type: "BatchNorm"
bottom: "fc2"
top: "fc2"
}
layer {
bottom: "fc2"
top: "fc2"
name: "scale7"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc2"
top: "fc2"
}
layer {
name: "drop1"
type: "Dropout"
bottom: "fc2"
top: "drop1"
dropout_param {
dropout_ratio: 0.3
}
}
layer {
name: "fc3"
type: "InnerProduct"
bottom: "drop1"
top: "fc3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 1
}
inner_product_param {
num_output: 40
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
#layer {
# name: "accuracy"
# type: "Accuracy"
# bottom: "fc3"
# bottom: "label"
# top: "accuracy"
# include {
# phase: TEST
# }
#}
layer {
name: "loss"
#type: "SoftmaxWithLoss"
type:"Softmax"
bottom: "fc3"
bottom: "label"
top: "loss"
}
# Enforce the transformation as orthogonal matrix
layer {
name: "perm_loss_transform2"
type: "Permute"
bottom: "transform2" # Bx64x64
top: "transform2_transpose" # Bx64x64
permute_param {
order: 0
order: 2
order: 1
}
}
#layer {
# name: "matmul_feat_loss"
# type: "MatrixMultiplication"
# bottom: "transform2"
# bottom: "transform2_transpose"
# top: "transform2_mul" # Bx64x64
#}
#layer {
# name: "loss_feat"
# type: "EuclideanLoss"
# bottom: "transform2_mul"
# bottom: "eye_tnet2"
# top: "loss_feat"
# loss_weight: 0.001
#}
