- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Permalink
- Report Inappropriate Content
Hi everybody, i have changed all rectangular convolution in my model to square ones(padding with zeros) and i have padded my inputs properly.
After this i have cropped some of the garbage and finally the networks have the same performance… but i used DummyData to pass the reference blob to the Crop-Layer, which is not supported, so i can i do something similar without using DummyData?
name: "UNIPINET"
input:"data"
input_shape{
dim: 1
dim: 1
dim: 63
dim: 13
}
layer {
name: "crop1"
type: "DummyData"
top: "crop1"
dummy_data_param{
shape: { dim: 1 dim: 1 dim:49 dim:11 }
data_filler { type: "constant" value: 0 }
}
}
layer {
name: "crop2"
type: "DummyData"
top: "crop2"
dummy_data_param{
shape: { dim: 1 dim: 16 dim: 40 dim: 9 }
data_filler { type: "constant" value: 0 }
}
}
layer {
name: "crop3"
type: "DummyData"
top: "crop3"
dummy_data_param{
shape: { dim: 1 dim: 64 dim: 36 dim: 7 }
data_filler { type: "constant" value: 0 }
}
}
layer {
name: "conv1/dw"
type: "Convolution"
bottom: "data"
top: "conv1/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 1
bias_term: false
kernel_h: 15
kernel_w: 15
pad_w: 12
group: 1
#engine: CAFFE
stride: 1
weight_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "cropL1"
type: "Crop"
bottom: "conv1/dw"
bottom: "crop1"
top: "conv1/dw/cropped"
crop_param {
axis: -1
offset: 12
}
}
layer {
name: "conv1/sep"
type: "Convolution"
bottom: "conv1/dw/cropped"
top: "conv1/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 16
bias_term: true
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
Link Copied
0 Replies
Reply
Topic Options
- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page