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adding 'lane' foler under models.
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Tao Wang committed Dec 1, 2014
1 parent 5c03ce8 commit 8dd7595
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Showing 3 changed files with 108 additions and 270 deletions.
238 changes: 72 additions & 166 deletions models/brody/deploy.prototxt
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Original file line number Diff line number Diff line change
@@ -1,15 +1,13 @@
name: "BrodyNet"
# This is not brody's net.
name: "AlexNet"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 480
input_dim: 640

input_dim: 227
input_dim: 227
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
Expand All @@ -18,49 +16,41 @@ layers {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "data"
top: "conv1"
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
bottom: "conv1"
top: "norm1"
}
layers {
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
bottom: "norm1"
top: "pool1"
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
Expand All @@ -70,49 +60,41 @@ layers {
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "pool1"
top: "conv2"
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
bottom: "conv2"
top: "norm2"
}
layers {
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
bottom: "norm2"
top: "pool2"
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
Expand All @@ -121,15 +103,9 @@ layers {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "pool2"
top: "conv3"
}
layers {
name: "relu3"
Expand All @@ -140,8 +116,6 @@ layers {
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
Expand All @@ -151,15 +125,9 @@ layers {
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "conv3"
top: "conv4"
}
layers {
name: "relu4"
Expand All @@ -170,8 +138,6 @@ layers {
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
Expand All @@ -181,15 +147,9 @@ layers {
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "conv4"
top: "conv5"
}
layers {
name: "relu5"
Expand All @@ -200,140 +160,86 @@ layers {
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
bottom: "conv5"
top: "pool5"
}
layers {
name: "fc6-conv"
type: CONVOLUTION
bottom: "pool5"
top: "fc6-conv"
name: "fc6"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
inner_product_param {
num_output: 4096
kernel_size: 6
pad: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
bottom: "pool5"
top: "fc6"
}
layers {
name: "relu6"
type: RELU
bottom: "fc6-conv"
top: "fc6-conv"
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6-conv"
top: "fc6-conv"
dropout_param {
dropout_ratio: 0.0
dropout_ratio: 0.5
}
bottom: "fc6"
top: "fc6"
}
layers {
name: "fc7-conv"
type: CONVOLUTION
bottom: "fc6-conv"
top: "fc7-conv"
name: "fc7"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
inner_product_param {
num_output: 4096
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
bottom: "fc6"
top: "fc7"
}
layers {
name: "relu7"
type: RELU
bottom: "fc7-conv"
top: "fc7-conv"
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7-conv"
top: "fc7-conv"
dropout_param {
dropout_ratio: 0.0
dropout_ratio: 0.5
}
bottom: "fc7"
top: "fc7"
}

layers {
name: "bb-output"
type: CONVOLUTION
bottom: "fc7-conv"
top: "bb-output"
name: "fc8"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1.0
}
}
}

layers {
name: "pixel-conv"
type: CONVOLUTION
bottom: "fc7-conv"
top: "pixel-conv"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1.0
}
inner_product_param {
num_output: 1000
}
bottom: "fc7"
top: "fc8"
}

# Pixel level logistic prediction.
layers {
name: "pixel-prob"
type: SIGMOID
bottom: "pixel-conv"
top: "pixel-prob"
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}
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