change the default parameters to be same as example written in README

README.md

@@ -1,6 +1,8 @@
-deeppose
+DeepPose
 ========
 
+NOTE: This is not official implementation. Original paper is [DeepPose: Human Pose Estimation via Deep Neural Networks](http://arxiv.org/abs/1312.4659).
+
 # Requirements
 
 - [Chainer](https://github.com/pfnet/chainer) (Neural network framework)
@@ -36,6 +38,14 @@ This script downloads FLIC-full dataset (http://vision.grasp.upenn.edu/cgi-bin/i
 
 ## For FLIC Dataset
 
+Just run:
+
+```
+nohup python scripts/train.py > AlexNet_flic_LCN_AdaGrad_lr-0.0005.log 2>&1 &
+```
+
+It is same as:
+
 ```
 nohup python scripts/train.py \
 --model models/AlexNet_flic.py \
@@ -46,16 +56,18 @@ nohup python scripts/train.py \
 --snapshot 10 \
 --datadir data/FLIC-full \
 --channel 3 \
---flip True \
+--flip 1 \
 --size 220 \
 --crop_pad_inf 1.5 \
 --crop_pad_sup 2.0 \
 --shift 5 \
---lcn True \
+--lcn 1 \
 --joint_num 7 \
 > AlexNet_LCN_AdaGrad_lr-0.0005.log 2>&1 &
 ```
 
+`--flip 1` means it performs LR flip augmentation, and `--flip 0` does nothing. `--lcn 1` means local(should be said "global"?) contrast normalization will be applied.
+
 See the help messages with `--help` option for details.
 
 ### GPU memory requirement

scripts/train.py

@@ -8,12 +8,10 @@
 import logging
 import time
 import os
-import sys
 import imp
 import shutil
 import numpy as np
-from chainer import optimizers, cuda, Variable
-import chainer.functions as F
+from chainer import optimizers, cuda
 from transform import Transform
 import cPickle as pickle
 from draw_loss import draw_loss_curve
@@ -178,22 +176,22 @@ def get_log_msg(stage, epoch, sum_loss, N, args, st):
                         help='model definition file in models dir')
     parser.add_argument('--gpu', type=int, default=0)
     parser.add_argument('--epoch', type=int, default=1000)
-    parser.add_argument('--batchsize', type=int, default=128)
+    parser.add_argument('--batchsize', type=int, default=32)
     parser.add_argument('--prefix', type=str, default='AlexNet_flic')
     parser.add_argument('--snapshot', type=int, default=10)
     parser.add_argument('--datadir', type=str, default='data/FLIC-full')
     parser.add_argument('--channel', type=int, default=3)
-    parser.add_argument('--flip', type=bool, default=True,
+    parser.add_argument('--flip', type=int, default=1,
                         help='flip left and right for data augmentation')
     parser.add_argument('--size', type=int, default=220,
                         help='resizing')
     parser.add_argument('--crop_pad_inf', type=float, default=1.5,
                         help='random number infimum for padding size when cropping')
     parser.add_argument('--crop_pad_sup', type=float, default=2.0,
                         help='random number supremum for padding size when cropping')
-    parser.add_argument('--shift', type=int, default=10,
+    parser.add_argument('--shift', type=int, default=5,
                         help='slide an image when cropping')
-    parser.add_argument('--lcn', type=bool, default=True,
+    parser.add_argument('--lcn', type=int, default=1,
                         help='local contrast normalization for data augmentation')
     parser.add_argument('--joint_num', type=int, default=7)
     parser.add_argument('--fname_index', type=int, default=0,
@@ -220,11 +218,13 @@ def get_log_msg(stage, epoch, sum_loss, N, args, st):
     logging.info('# of test data:{}'.format(N_test))
 
     # augmentation setting
+    _flip = bool(args.flip)
+    _lcn = bool(args.lcn)
     trans = Transform(padding=[args.crop_pad_inf, args.crop_pad_sup],
-                      flip=args.flip,
+                      flip=_flip,
                       size=args.size,
                       shift=args.shift,
-                      lcn=args.lcn)
+                      lcn=_lcn)
 
     logging.info(time.strftime('%Y-%m-%d_%H-%M-%S'))
     logging.info('start training...')