Hi,
First,thank you for Tensorrt 4’s comming,so We can write less custom layers, such as “Crop” layer. And thank you for the author of GitHub - dusty-nv/jetson-inference: Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson..
I have used segnet and fcn8s to image segmentation with caffe. I think fcn8s is better according to their result, so I want to use fcn8s with tensorrt.
I inferenced the same image with caffe and tensorrt 4(use the same caffemodel and the same prototxt),but the result of caffe is very good and tensorrt’s is very terible.The attachments are result images. Following is my prototxt and I used the same prototxt with caffe and tensorrt.(I have 16+1 classes and my command is “./segnet-console 1_5_3_90.jpg 1_5_3_90_out.png --prototxt=$NET/jetsoninference/data/networks/fcn8s/deploy_traffic.prototxt --model=$NET/jetson-inference/data/networks/fcn8s/train_iter_400000.caffemodel --labels=$NET/jetson-inference/data/networks/fcn8s/class.txt --colors=$NET/jetson-inference/data/networks/fcn8s/color.txt --input_blob=data --output_blob=score”)
Prototxt:
layer {
name: “input”
type: “Input”
top: “data”
input_param {
shape { dim: 1 dim: 3 dim: 810 dim: 1080 }
}
}
layer {
name: “conv1_1”
type: “Convolution”
bottom: “data”
top: “conv1_1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 100
kernel_size: 3
stride: 1
}
}
layer {
name: “relu1_1”
type: “ReLU”
bottom: “conv1_1”
top: “conv1_1”
}
layer {
name: “conv1_2”
type: “Convolution”
bottom: “conv1_1”
top: “conv1_2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu1_2”
type: “ReLU”
bottom: “conv1_2”
top: “conv1_2”
}
layer {
name: “pool1”
type: “Pooling”
bottom: “conv1_2”
top: “pool1”
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: “conv2_1”
type: “Convolution”
bottom: “pool1”
top: “conv2_1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu2_1”
type: “ReLU”
bottom: “conv2_1”
top: “conv2_1”
}
layer {
name: “conv2_2”
type: “Convolution”
bottom: “conv2_1”
top: “conv2_2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu2_2”
type: “ReLU”
bottom: “conv2_2”
top: “conv2_2”
}
layer {
name: “pool2”
type: “Pooling”
bottom: “conv2_2”
top: “pool2”
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: “conv3_1”
type: “Convolution”
bottom: “pool2”
top: “conv3_1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu3_1”
type: “ReLU”
bottom: “conv3_1”
top: “conv3_1”
}
layer {
name: “conv3_2”
type: “Convolution”
bottom: “conv3_1”
top: “conv3_2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu3_2”
type: “ReLU”
bottom: “conv3_2”
top: “conv3_2”
}
layer {
name: “conv3_3”
type: “Convolution”
bottom: “conv3_2”
top: “conv3_3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu3_3”
type: “ReLU”
bottom: “conv3_3”
top: “conv3_3”
}
layer {
name: “pool3”
type: “Pooling”
bottom: “conv3_3”
top: “pool3”
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: “conv4_1”
type: “Convolution”
bottom: “pool3”
top: “conv4_1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu4_1”
type: “ReLU”
bottom: “conv4_1”
top: “conv4_1”
}
layer {
name: “conv4_2”
type: “Convolution”
bottom: “conv4_1”
top: “conv4_2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu4_2”
type: “ReLU”
bottom: “conv4_2”
top: “conv4_2”
}
layer {
name: “conv4_3”
type: “Convolution”
bottom: “conv4_2”
top: “conv4_3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu4_3”
type: “ReLU”
bottom: “conv4_3”
top: “conv4_3”
}
layer {
name: “pool4”
type: “Pooling”
bottom: “conv4_3”
top: “pool4”
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: “conv5_1”
type: “Convolution”
bottom: “pool4”
top: “conv5_1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu5_1”
type: “ReLU”
bottom: “conv5_1”
top: “conv5_1”
}
layer {
name: “conv5_2”
type: “Convolution”
bottom: “conv5_1”
top: “conv5_2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu5_2”
type: “ReLU”
bottom: “conv5_2”
top: “conv5_2”
}
layer {
name: “conv5_3”
type: “Convolution”
bottom: “conv5_2”
top: “conv5_3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: “relu5_3”
type: “ReLU”
bottom: “conv5_3”
top: “conv5_3”
}
layer {
name: “pool5”
type: “Pooling”
bottom: “conv5_3”
top: “pool5”
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: “fc6”
type: “Convolution”
bottom: “pool5”
top: “fc6”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
pad: 0
kernel_size: 7
stride: 1
}
}
layer {
name: “relu6”
type: “ReLU”
bottom: “fc6”
top: “fc6”
}
layer {
name: “fc7”
type: “Convolution”
bottom: “fc6”
top: “fc7”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: “relu7”
type: “ReLU”
bottom: “fc7”
top: “fc7”
}
layer {
name: “score_fr_traffic”
type: “Convolution”
bottom: “fc7”
top: “score_fr”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 17
pad: 0
kernel_size: 1
}
}
layer {
name: “upscore2_traffic”
type: “Deconvolution”
bottom: “score_fr”
top: “upscore2”
param {
lr_mult: 0
}
convolution_param {
num_output: 17
bias_term: false
kernel_size: 4
stride: 2
}
}
layer {
name: “score_pool4_traffic”
type: “Convolution”
bottom: “pool4”
top: “score_pool4”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 17
pad: 0
kernel_size: 1
}
}
layer {
name: “score_pool4c”
type: “Crop”
bottom: “score_pool4”
bottom: “upscore2”
top: “score_pool4c”
crop_param {
axis: 2
offset: 5
}
}
layer {
name: “fuse_pool4”
type: “Eltwise”
bottom: “upscore2”
bottom: “score_pool4c”
top: “fuse_pool4”
eltwise_param {
operation: SUM
}
}
layer {
name: “upscore_pool4_traffic”
type: “Deconvolution”
bottom: “fuse_pool4”
top: “upscore_pool4”
param {
lr_mult: 0
}
convolution_param {
num_output: 17
bias_term: false
kernel_size: 4
stride: 2
}
}
layer {
name: “score_pool3_traffic”
type: “Convolution”
bottom: “pool3”
top: “score_pool3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 17
pad: 0
kernel_size: 1
}
}
layer {
name: “score_pool3c”
type: “Crop”
bottom: “score_pool3”
bottom: “upscore_pool4”
top: “score_pool3c”
crop_param {
axis: 2
offset: 9
}
}
layer {
name: “fuse_pool3”
type: “Eltwise”
bottom: “upscore_pool4”
bottom: “score_pool3c”
top: “fuse_pool3”
eltwise_param {
operation: SUM
}
}
layer {
name: “upscore8_traffic”
type: “Deconvolution”
bottom: “fuse_pool3”
top: “upscore8”
param {
lr_mult: 0
}
convolution_param {
num_output: 17
bias_term: false
kernel_size: 16
stride: 8
}
}
layer {
name: “score”
type: “Crop”
bottom: “upscore8”
bottom: “data”
top: “score”
crop_param {
axis: 2
offset: 31
}
}
Who can give me some suggestions?
Thanks
