Add training commands

hiroharu-kato · Dec 1, 2019 · 425b2dd · 425b2dd
1 parent 63733ba
commit 425b2dd
Show file tree

Hide file tree

Showing 4 changed files with 64 additions and 4 deletions.
diff --git a/README.md b/README.md
@@ -76,6 +76,24 @@ output the following images.
 ![](https://raw.githubusercontent.com/hiroharu-kato/view_prior_learning/master/data/examples/4231883e92a3c1a21c62d11641ffbd35_00.png)
 ![](https://raw.githubusercontent.com/hiroharu-kato/view_prior_learning/master/data/examples/4231883e92a3c1a21c62d11641ffbd35_00_shapenet_multi_color_nv20_uvr_cc.gif)
 
+## Training
+
+Training requires pre-trained AlexNet model.
+
+```shell script
+cd data
+mkdir caffemodel
+cd caffemodel
+wget http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
+cd ../../
+```
+
+Training of the provided pre-trained ShapeNet models is done by
+
+```shell script
+bash train_shapenet.sh
+```
+
 ## Citation
 
 ```

diff --git a/mesh_reconstruction/encoders.py b/mesh_reconstruction/encoders.py
@@ -143,7 +143,6 @@ def get_encoder(encoder_name, dim_in, dim_out):
     if encoder_name == 'resnet18':
         return ResNet18(dim_in, dim_out)
     elif encoder_name == 'resnet18pt':
-        return PreTrainedResNet18(dim_in, dim_out, '/data/unagi0/kato/large_data/caffemodel/resnet-18.caffemodel')
-        # return PreTrainedResNet18(dim_in, dim_out, '/media/disk2/lab/caffemodel/resnet-18.caffemodel')
+        return PreTrainedResNet18(dim_in, dim_out, './data/caffemodel/resnet-18.caffemodel')
     elif encoder_name == 'dummy':
         return DummyEncoder()
diff --git a/mesh_reconstruction/perceptual_loss.py b/mesh_reconstruction/perceptual_loss.py
@@ -13,8 +13,7 @@ def __init__(self):
         self.conv4 = cl.Convolution2D(None, 384, 3, pad=1)
         self.conv5 = cl.Convolution2D(None, 256, 3, pad=1)
 
-        # caffe_model = chainer.links.caffe.CaffeFunction('/media/disk2/lab/caffemodel/bvlc_alexnet.caffemodel')
-        caffe_model = chainer.links.caffe.CaffeFunction('/home/mil/kato/large_data/caffemodel/bvlc_alexnet.caffemodel')
+        caffe_model = chainer.links.caffe.CaffeFunction('./data/caffemodel/bvlc_alexnet.caffemodel')
         self.conv1.W.data = caffe_model.conv1.W.data.copy()
         self.conv1.b.data = caffe_model.conv1.b.data.copy()
         self.conv2.W.data = caffe_model.conv2.W.data.copy()

diff --git a/train_shapenet.sh b/train_shapenet.sh
@@ -0,0 +1,44 @@
+####################################################################################################
+# Single-view
+
+# silhouettes, w/o regularization
+python mesh_reconstruction/train.py -eid shapenet_single_sil_nv1 -dd $DD -ni 50000 -ds shapenet -svt 1 -nv 1 -sm 1 
+
+# silhouettes, w/ regularization
+python mesh_reconstruction/train.py -eid shapenet_single_sil_nv1_uvr -dd $DD -ni 100000 -ds shapenet -svt 1 -nv 1 -ld 0.1 -sm 1  
+python mesh_reconstruction/train.py -eid shapenet_single_sil_nv1_uvr_cc -dd $DD -ni 100000 -ds shapenet -svt 1 -nv 1 -ld 0.2 -cc 1 -sm 1 
+
+# color, w/o regularization
+python mesh_reconstruction/train.py -eid shapenet_single_color_nv1 -dd $DD -ni 50000 -ds shapenet -svt 1 -nv 1 -lp 0.5 -sm 1 
+
+# color, w/ regularization
+python mesh_reconstruction/train.py -eid shapenet_single_color_nv1_uvr -dd $DD -ni 100000 -ds shapenet -svt 1 -nv 1 -lp 0.5 -ld 2 -sm 1 
+python mesh_reconstruction/train.py -eid shapenet_single_color_nv1_uvr_cc -dd $DD -ni 100000 -ds shapenet -svt 1 -nv 1 -lp 0.5 -ld 2 -cc 1 -sm 1  
+
+####################################################################################################
+# Multi-view
+
+# silhouettes
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv20 -dd $DD -ni 500000 -ds shapenet -sm 1  
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv10 -dd $DD -ni 250000 -ds shapenet -nv 10 -sm 1 
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv5 -dd $DD -ni 125000 -ds shapenet -nv 5 -sm 1 
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv3 -dd $DD -ni 75000 -ds shapenet -nv 3 -sm 1 
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv2 -dd $DD -ni 50000 -ds shapenet -nv 2 -sm 1 
+
+# silhouettes + regularization
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv20_uvr_cc_long -dd $DD -ni 1000000 -ds shapenet -ld 0.03 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv20_uvr_cc -dd $DD -ni 500000 -ds shapenet -ld 0.03 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv10_uvr_cc -dd $DD -ni 500000 -ds shapenet -nv 10 -ld 0.03 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv5_uvr_cc -dd $DD -ni 250000 -ds shapenet -nv 5 -ld 0.03 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv3_uvr_cc -dd $DD -ni 150000 -ds shapenet -nv 3 -ld 0.03 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_sil_nv2_uvr_cc -dd $DD -ni 100000 -ds shapenet -nv 2 -ld 0.03 -cc 1 -sm 1
+
+# color
+python mesh_reconstruction/train.py -eid shapenet_multi_color_nv20 -dd $DD -ni 500000 -ds shapenet -lp 0.1 -sm 1 
+python mesh_reconstruction/train.py -eid shapenet_multi_color_nv2 -dd $DD -ni 50000 -ds shapenet -lp 0.1 -nv 2 -sm 1  
+
+# color + regularization
+python mesh_reconstruction/train.py -eid shapenet_multi_color_nv20_uvr_cc_long -dd $DD -ni 1000000 -ds shapenet -lp 0.1 -nv 20 -ld 0.3 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_color_nv20_uvr_cc -dd $DD -ni 500000 -ds shapenet -lp 0.1 -nv 20 -ld 0.3 -cc 1 -sm 1
+python mesh_reconstruction/train.py -eid shapenet_multi_color_nv2_uvr_cc -dd $DD -ni 100000 -ds shapenet -lp 0.1 -nv 2 -ld 0.3 -cc 1 -sm 1
+