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import argparse
import time
import os
import numpy as np
import torch.nn.parallel
import torch.optim
# from sklearn.metrics import confusion_matrix
from lib.dataset import VideoDataSet, ShortVideoDataSet
from lib.models import VideoModule, TSN
from lib.transforms import *
from lib.utils.tools import AverageMeter, accuracy
import pdb
import logging
def set_logger(debug_mode=False):
import time
from time import gmtime, strftime
logdir = os.path.join(args.experiment_root, 'log')
if not os.path.exists(logdir):
os.makedirs(logdir)
log_file = "logfile_" + time.strftime("%d_%b_%Y_%H:%M:%S", time.localtime())
log_file = os.path.join(logdir, log_file)
handlers = [logging.FileHandler(log_file), logging.StreamHandler()]
""" add '%(filename)s:%(lineno)d %(levelname)s:' to format show source file """
logging.basicConfig(level=logging.DEBUG if debug_mode else logging.INFO,
format='%(asctime)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
handlers = handlers)
# options
parser = argparse.ArgumentParser(
description="Standard video-level testing")
parser.add_argument('dataset', type=str)
parser.add_argument('test_list', type=str)
parser.add_argument('weights', type=str)
parser.add_argument('--arch', type=str, default="resnet50_3d_v1")
parser.add_argument('--mode', type=str, default="TSN+3D")
# parser.add_argument('--save_scores', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=2)
parser.add_argument('--num_segments', type=int, default=10)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--resize', type=int, default=256)
parser.add_argument('--t_length', type=int, default=16)
parser.add_argument('--t_stride', type=int, default=4)
# parser.add_argument('--crop_fusion_type', type=str, default='avg',
# choices=['avg', 'max', 'topk'])
parser.add_argument('--image_tmpl', type=str)
parser.add_argument('--dropout', type=float, default=0.2)
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 4)')
args = parser.parse_args()
experiment_id = '_'.join(map(str, ['test', args.dataset, args.arch, args.mode,
'length'+str(args.t_length), 'stride'+str(args.t_stride),
'seg'+str(args.num_segments)]))
args.experiment_root = os.path.join('./output', experiment_id)
set_logger()
logging.info(args)
if not os.path.exists(args.experiment_root):
os.makedirs(args.experiment_root)
def main():
if args.dataset == 'ucf101':
num_class = 101
elif args.dataset == 'hmdb51':
num_class = 51
elif args.dataset == 'kinetics400':
num_class = 400
elif args.dataset == 'kinetics200':
num_class = 200
elif args.dataset == 'sthsth_v1':
num_class = 174
else:
raise ValueError('Unknown dataset '+args.dataset)
data_root = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"data/{}/access".format(args.dataset))
net = VideoModule(num_class=num_class,
base_model_name=args.arch,
dropout=args.dropout,
pretrained=False)
# compute params number of a model
num_params = 0
for param in net.parameters():
num_params += param.reshape((-1, 1)).shape[0]
logging.info("Model Size is {:.3f}M".format(num_params / 1000000))
net = torch.nn.DataParallel(net).cuda()
net.eval()
# load weights
model_state = torch.load(args.weights)
state_dict = model_state['state_dict']
test_epoch = model_state['epoch']
best_metric = model_state['best_metric']
arch = model_state['arch']
logging.info("Model Epoch: {}; Best_Top1: {}".format(test_epoch, best_metric))
assert arch == args.arch
net.load_state_dict(state_dict)
tsn = TSN(args.batch_size, net,
args.num_segments, args.t_length,
mode=args.mode).cuda()
## test data
test_transform = torchvision.transforms.Compose([
GroupScale(256),
GroupOverSampleKaiming(args.input_size),
Stack(mode=args.mode),
ToTorchFormatTensor(),
GroupNormalize(),
])
test_dataset = VideoDataSet(
root_path=data_root,
list_file=args.test_list,
t_length=args.t_length,
t_stride=args.t_stride,
num_segments=args.num_segments,
image_tmpl=args.image_tmpl,
transform=test_transform,
phase="Test")
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# Test
batch_timer = AverageMeter()
top1_m = AverageMeter()
top5_m = AverageMeter()
top1_a = AverageMeter()
top5_a = AverageMeter()
results_m = None
results_a = None
# set eval mode
tsn.eval()
end = time.time()
for ind, (data, label) in enumerate(test_loader):
label = label.cuda(non_blocking=True)
with torch.no_grad():
output_m, pred_m, output_a, pred_a = tsn(data)
prec1_m, prec5_m = accuracy(pred_m, label, topk=(1, 5))
prec1_a, prec5_a = accuracy(pred_a, label, topk=(1, 5))
top1_m.update(prec1_m.item(), data.shape[0])
top5_m.update(prec5_m.item(), data.shape[0])
top1_a.update(prec1_a.item(), data.shape[0])
top5_a.update(prec5_a.item(), data.shape[0])
# pdb.set_trace()
batch_timer.update(time.time() - end)
end = time.time()
if results_m is not None:
np.concatenate((results_m, output_m.cpu().numpy()), axis=0)
else:
results_m = output_m.cpu().numpy()
if results_a is not None:
np.concatenate((results_a, output_a.cpu().numpy()), axis=0)
else:
results_a = output_a.cpu().numpy()
logging.info("{0}/{1} done, Batch: {batch_timer.val:.3f}({batch_timer.avg:.3f}), maxTop1: {top1_m.val:>6.3f}({top1_m.avg:>6.3f}), maxTop5: {top5_m.val:>6.3f}({top5_m.avg:>6.3f}), avgTop1: {top1_a.val:>6.3f}({top1_a.avg:>6.3f}), avgTop5: {top5_a.val:>6.3f}({top5_a.avg:>6.3f})".
format(ind + 1, len(test_loader),
batch_timer=batch_timer,
top1_m=top1_m, top5_m=top5_m, top1_a=top1_a, top5_a=top5_a))
max_target_file = os.path.join(args.experiment_root, "arch_{0}-epoch_{1}-top1_{2}-top5_{3}_max.npz".format(arch, test_epoch, top1_m.avg, top5_m.avg))
avg_target_file = os.path.join(args.experiment_root, "arch_{0}-epoch_{1}-top1_{2}-top5_{3}_avg.npz".format(arch, test_epoch, top1_a.avg, top5_a.avg))
print("saving {}".format(max_target_file))
np.savez(max_target_file, results_m)
print("saving {}".format(avg_target_file))
np.savez(avg_target_file, results_a)
if __name__ == "__main__":
main()