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main.py
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main.py
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import argparse
import os
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import time
from dataloaders.kitti_loader import input_options, KittiDepth
from metrics import AverageMeter, Result
import helper
import vis_utils
from model import three_branch_bb
from model import A_CSPN_plus_plus
from torch.optim.lr_scheduler import ReduceLROnPlateau
parser = argparse.ArgumentParser(description='Sparse-to-Dense')
parser.add_argument('-n',
'--network-model',
type=str,
default="bb",
choices=["bb", "sem_att"],
help='choose a model: enet or penet'
)
parser.add_argument('--workers',
default=4,
type=int,
metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs',
default=100,
type=int,
metavar='N',
help='number of total epochs to run (default: 100)')
parser.add_argument('--start-epoch',
default=0,
type=int,
metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--start-epoch-bias',
default=0,
type=int,
metavar='N',
help='manual epoch number bias(useful on restarts)')
parser.add_argument('-b',
'--batch-size',
default=1,
type=int,
help='mini-batch size (default: 1)')
parser.add_argument('--lr',
'--learning-rate',
default=1.2727e-3,
type=float,
metavar='LR',
help='initial learning rate (default 1e-5)')
parser.add_argument('--weight-decay',
'--wd',
default=1e-6,
type=float,
metavar='W',
help='weight decay (default: 0)')
parser.add_argument('--print-freq',
'-p',
default=10,
type=int,
metavar='N',
help='print frequency (default: 10)')
parser.add_argument('--resume',
default="",
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--data-folder',
default="",
type=str,
metavar='PATH',
help='data folder (default: none)')
parser.add_argument('--data-folder-rgb',
default="",
type=str,
metavar='PATH',
help='data folder rgb (default: none)')
parser.add_argument('--data-semantic',
default='',
type=str,
metavar='PATH',
help='data folder test results(default: none)')
#data_folder_save
parser.add_argument('--test-save',
default="test_results/",
type=str,
metavar='PATH',
help='data folder test results(default: none)')
parser.add_argument('-i',
'--input',
type=str,
default='rgbd',
choices=input_options,
help='input: | '.join(input_options))
parser.add_argument('--val',
type=str,
default="select",
choices=["select", "full"],
help='full or select validation set')
parser.add_argument('--jitter',
type=float,
default=0.1,
help='color jitter for images')
parser.add_argument('--rank-metric',
type=str,
default='rmse',
choices=[m for m in dir(Result()) if not m.startswith('_')],
help='metrics for which best result is saved')
#default=''
parser.add_argument('-e', '--evaluate', default='', type=str, metavar='PATH')
parser.add_argument('-f', '--freeze-backbone', action="store_true", default=False,
help='freeze parameters in backbone')
parser.add_argument('--s_p', default="",type=str, metavar='PATH')
parser.add_argument('--val_results', default="val_results/",type=str, metavar='PATH')
#defalut=False
parser.add_argument('--test', action="store_true", default=False,
help='save result kitti test dataset for submission')
parser.add_argument('--cpu', action="store_true", default=False, help='run on cpu')
#random cropping
parser.add_argument('--not-random-crop', action="store_true", default=False,
help='prohibit random cropping')
parser.add_argument('-he', '--random-crop-height', default=320, type=int, metavar='N',
help='random crop height')
parser.add_argument('-w', '--random-crop-width', default=1216, type=int, metavar='N',
help='random crop height')
parser.add_argument('-d', '--dilation-rate', default="2", type=int,
choices=[1, 2, 4],
help='CSPN++ dilation rate')
args = parser.parse_args()
args.result = args.s_p
args.use_rgb = ('rgb' in args.input)
args.use_d = 'd' in args.input
args.use_g = 'g' in args.input
args.val_h = 352
args.val_w = 1216
print(args)
cuda = torch.cuda.is_available() and not args.cpu
if cuda:
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
device = torch.device("cuda")
else:
device = torch.device("cpu")
print("=> using '{}' for computation.".format(device))
def iterate(mode, args, loader, model, optimizer, logger, epoch):
actual_epoch = epoch - args.start_epoch + args.start_epoch_bias
block_average_meter = AverageMeter()
block_average_meter.reset(False)
average_meter = AverageMeter()
meters = [block_average_meter, average_meter]
# switch to appropriate mode
assert mode in ["train", "val", "eval", "test_prediction", "test_completion"], \
"unsupported mode: {}".format(mode)
model.eval()
lr = 0
torch.cuda.empty_cache()
for i, batch_data in enumerate(loader):
dstart = time.time()
batch_data = {
key: val.to(device)
for key, val in batch_data.items() if val is not None
}
gt = batch_data[
'gt'] if mode != 'test_prediction' and mode != 'test_completion' else None
data_time = time.time() - dstart
pred = None
start = None
gpu_time = 0
if(args.network_model == 'bb'):
start = time.time()
st1_pred, st2_pred, st3_pred, pred = model(batch_data)
else:
start = time.time()
rgb_conf, semantic_conf, d_conf, rgb_depth, semantic_depth, d_depth,coarse_depth,pred = model(batch_data)
if(args.evaluate):
gpu_time = time.time() - start
if mode == "val":
str_i = str(i)
path_i = str_i.zfill(10) + '.png'
path = os.path.join(args.val_results, path_i)
vis_utils.save_depth_as_uint8colored(batch_data['d'], gt, pred, path)
if mode == "test_completion":
str_i = str(i)
path_i = str_i.zfill(10) + '.png'
path = os.path.join(args.test_save, path_i)
vis_utils.save_depth_as_uint16png_upload(pred, path)
if(not args.evaluate):
gpu_time = time.time() - start
with torch.no_grad():
mini_batch_size = next(iter(batch_data.values())).size(0)
result = Result()
if mode != 'test_prediction' and mode != 'test_completion':
result.evaluate(pred.data, gt.data)
[
m.update(result, gpu_time, data_time, mini_batch_size)
for m in meters
]
logger.conditional_print(mode, i, epoch, lr, len(loader),
block_average_meter, average_meter)
avg = average_meter.average()
is_best = logger.rank_conditional_save_best(mode, avg, epoch)
logger.conditional_summarize(mode, avg, is_best)
return avg, is_best
def main():
global args
checkpoint = None
is_eval = False
if args.evaluate:
args_new = args
if os.path.isfile(args.evaluate):
print("=> loading checkpoint '{}' ... ".format(args.evaluate),
end='')
checkpoint = torch.load(args.evaluate, map_location=device)
args.start_epoch = checkpoint['epoch'] + 1
args.data_folder = args_new.data_folder
args.val = args_new.val
is_eval = True
print("Completed.")
else:
is_eval = True
print("No model found at '{}'".format(args.evaluate))
print("PLEASE PROVIDE CORRECT PATH.")
return
print("=> creating model and optimizer ... ", end='')
model = None
if (args.network_model == 'bb'):
model = three_branch_bb(args).to(device)
else:
model = A_CSPN_plus_plus(args).to(device)
if checkpoint is not None:
if (args.freeze_backbone == True):
model.backbone.load_state_dict(checkpoint['model'])
else:
model.load_state_dict(checkpoint['model'], strict=False)
print("=> checkpoint state loaded.")
logger = helper.logger(args)
if checkpoint is not None:
logger.best_result = checkpoint['best_result']
print("=> logger created.")
test_dataset = None
test_loader = None
if (args.test):
test_dataset = KittiDepth('test_completion', args)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True)
iterate("test_completion", args, test_loader, model, None, logger, 0)
return
val_dataset = KittiDepth('val', args)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1,
shuffle=False,
num_workers=2,
pin_memory=True) # set batch size to be 1 for validation
print("\t==> val_loader size:{}".format(len(val_loader)))
if is_eval == True:
for p in model.parameters():
p.requires_grad = False
result, is_best = iterate("val", args, val_loader, model, None, logger,
args.start_epoch - 1)
return
if __name__ == '__main__':
main()