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trainval_net.py
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trainval_net.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data.sampler import Sampler
from model.utils.config import conbined_roidb
from model.utils.net_utils import weights_normal_init, save_net, load_net, \
adjust_learning_rate, save_checkpoint, clip_gradient
from model.faster_rcnn.vgg16 import vgg16
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Train a Faster R-CNN network')
parser.add_argument('--dataset', dest='dataset',
help='training dataset',
default='pascal_voc', type=str)
parser.add_argument('--net', dest='net',
help='training dataset',
default='vgg16', type=str)
parser.add_argument('--start_epoch', dest='start_epoch'
help='number of epochs to train'
default = 1, type=int)
parser.add_argument('--epochs', dest='max_epochs',
help='number of epochs to train',
default= 100, type=int)
parser.add_argument('--disp_interval', dest='disp_interval',
help='number of iterations to display',
default=100, type=int)
parser.add_argument('--checkpoint_interval', dest='checkpoint_interval',
help='number of iterations to display',
default=10000, type=int)
parser.add_argument('--save_dir', dest='save_dir',
help='directory to save models', default="models",
type=str)
parser.add_argument('--nw', dest='num_workers',
help='number of worker to load data',
default=0, type=int)
parser.add_argument('--cuda', dest='cuda',
help='whether use CUDA',
action='store_true')
parser.add_argument('--ls', dest='large_scale',
help='whether use large imag scale',
action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs',
help='whether use multiple GPUs',
action='store_true')
parser.add_argument('--bs', dest='batch_size',
help='batch_size',
default=1, type=int)
parser.add_argument('--cag', dest='class_agnostic',
help='whether perform class_agnostic bbox regression',
action='store_true')
# config optimization
parser.add_argument('--o', dest='optimizer',
help='training optimizer',
default="sgd", type=str)
parser.add_argument('--lr', dest='lr',
help='starting learning rate',
default=0.001, type=float)
parser.add_argument('--lr_decay_step', dest='lr_decay_step',
help='step to do learning rate decay, unit is epoch',
default=5, type=int)
parser.add_argument('--lr_decay_gamma', dest='lr_decay_gamma',
help='learning rate decay ratio',
default=0.1, type=float)
# set training session
parser.add_argument('--s', dest='session',
help='training session',
default=1, type=int)
# resume trained model
parser.add_argument('--r', dest='resume',
help='resume checkpoint or not',
default=False, type=bool)
parser.add_argument('--checksession', dest='checksession',
help='checksession to load model',
default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch',
help='checkepoch to load model',
default=1, type=int)
parser.add_argument('--checkpoint', dest='checkpoint',
help='checkpoint to load model',
default=0, type=int)
# log and diaplay
parser.add_argument('--use_tfb', dest='use_tfboard',
help='whether use tensorboard',
action='store_true')
args = parser.parse_args()
return args
class sampler(Sampler):
def __init__(self, train_size, batch_size):
self.num_data = train_size
self.num_per_batch = int(train_size / batch_size)
self.batch_size = batch_size
self.range = torch.arange(0, batch_size).view(1, batch_size).long()
self.leftover_flag = False
if train_size % batch_size:
self.leftover = torch.arange(self.num_per_batch * batch_size, train_size).long()
self.leftover_flag = False
def __iter__(self):
"""Shuffle the batch"""
rand_num = torch.randperm(self.num_per_batch).view(-1, 1) * self.batch_size
self.rand_num = rand_num.expand(self.num_per_batch, self.batch_size) + self.range
self.rand_num_view = self.rand_num.view(-1)
def __len__(self):
return self.num_data
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.dataset == "pascal_voc":
args.imdb_name = "voc_2007_trainval"
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']
elif args.dataset == "pascal_voc_0712":
args.imdb_name = "voc_2007_trainval+voc_2012_trainval"
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']
elif args.dataset == "coco":
args.imdb_name = "coco_2014_train+coco_2014_valminusminival"
args.imdbval_name = "coco_2014_minival"
args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']
elif args.dataset == "imagenet":
args.imdb_name = "imagenet_train"
args.imdbval_name = "imagenet_val"
args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '30']
elif args.dataset == "vg":
# train sizes: train, smalltrain, minitrain
# train scale: ['150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450', '1600-400-20']
args.imdb_name = "vg_150-50-50_minitrain"
args.imdbval_name = "vg_150-50-50_minival"
args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']