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test.py
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from dataset.dataset import TSNDataSet
from model.model import TSN
from dataset.transforms import *
from config import parser
from utils import *
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def main():
args = parser.parse_args()
torch.backends.cudnn.benchmark = True
if args.dataset == 'ucf101':
num_class = 101
elif args.dataset == 'hmdb51':
num_class = 51
elif args.dataset == 'kinetics':
num_class = 400
elif args.dataset =='sthsth':
num_class=174
else:
raise ValueError('Unknown dataset' + args.dataset)
RGBmodel = TSN(num_class, args.num_segments, 'RGB',
base_model=args.arch, consensus_type=args.consensus_type,
dropout=args.dropout, partial_bn=not args.nopartial_bn).to(device)
RGBDiffmodel = TSN(num_class, args.num_segments, 'RGBDiff',
base_model=args.arch, consensus_type=args.consensus_type,
dropout=args.dropout, partial_bn=not args.nopartial_bn).to(device)
# RGBDiffmodel = TSN(num_class, args.num_segments, 'Flow',
# base_model=args.arch, consensus_type=args.consensus_type,
# dropout=args.dropout, partial_bn=not args.nopartial_bn).to(device)
checkpoint = torch.load('./record/RGBbest.pth')
RGBmodel.load_state_dict(checkpoint['state_dict'])
checkpoint = torch.load('./record/RGBDiffbest.pth')
RGBDiffmodel.load_state_dict(checkpoint['state_dict'])
# checkpoint = torch.load('./record/Flow/Flowbest.pth')
# RGBDiffmodel.load_state_dict(checkpoint['state_dict'])
crop_size = RGBmodel.crop_size
scale_size = RGBmodel.scale_size
input_mean = RGBmodel.input_mean
input_std = RGBmodel.input_std
RGB_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=1,
modality='RGB',
image_tmpl="{:05d}.jpg" ,
random_shift=False,
test_mode=True,
On_Video=True,
interval=1,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception'),
GroupNormalize(input_mean, input_std)
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
RGBDiff_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=5,
modality='RGBDiff',
image_tmpl="{:05d}.jpg" if args.modality in ["RGB","RGBDiff"] else "{}/{}/frame{:06d}.jpg",
test_mode=True,
random_shift=False,
On_Video=True,
interval=2,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception')
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
#actually this is the Flow loader.i am lazy to change the name.
# RGBDiff_loader = torch.utils.data.DataLoader(
# TSNDataSet('/home/qx/project/data/UCF101/tvl1_flow/', args.val_list, num_segments=args.num_segments,
# new_length=5,
# modality='Flow',
# image_tmpl="{}/{}/frame{:06d}.jpg",
# random_shift=False,
# test_mode=True,
# On_Video=False,
# interval=2,
# transform=torchvision.transforms.Compose([
# GroupScale(int(scale_size)),
# GroupCenterCrop(crop_size),
# Stack(roll=args.arch == 'BNInception'),
# ToTorchFormatTensor(div=args.arch != 'BNInception'),
# GroupNormalize(input_mean, input_std)
# ])),
# batch_size=args.batch_size, shuffle=False,
# num_workers=args.workers, pin_memory=True)
RGBmodel.eval()
RGBDiffmodel.eval()
epoch_prec1 = 0
epoch_prec5 = 0
class_num = np.array([0] * num_class)
class_prec1 = np.array([0] * num_class)
class_prec5 = np.array([0] * num_class)
with torch.no_grad():
for (RGBdata, target), (RGBDiffdata, _) in zip(RGB_loader, RGBDiff_loader):
#print(RGBdata.shape,RGBDiffdata.shape)
target = target.to(device)
RGBDiffdata = RGBDiffdata.to(device)
RGBdata = RGBdata.to(device)
RGBoutput = RGBmodel(RGBdata)
RGBDiffoutput = RGBDiffmodel(RGBDiffdata)
#print(RGBoutput.shape,RGBDiffoutput.shape)
output = RGBoutput+RGBDiffoutput
# _,pred = output.topk(5, 1, True, True)
# pred=pred.cpu().numpy()
# target=target.cpu().numpy()
# for i in range(args.batch_size):
# pred_name=index2name(pred[i],'./raw/classInd.txt')
# true_name=index2name(target[i:i+1],'./raw/classInd.txt')
# print('pred name:',pred_name,'true name:',true_name)
(prec1, prec5), (class_prec1_t, class_prec5_t), class_num_t \
= class_accuracy(output.data, target, num_class, topk=(1, 5))
class_num += class_num_t
class_prec1 += class_prec1_t
class_prec5 += class_prec5_t
#prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
epoch_prec1 += prec1.item() * target.size(0)
epoch_prec5 += prec5.item() * target.size(0)
epoch_prec1 = 1.0 * epoch_prec1 / len(RGBDiff_loader.dataset)
epoch_prec5 = 1.0 * epoch_prec5 / len(RGBDiff_loader.dataset)
print("Accuracy top1: {} top5:{}".format(epoch_prec1, epoch_prec5))
sorted_name=index2name(np.argsort(class_prec1 / class_num),'./raw/classInd.txt')
sorted_score=np.sort(class_prec1 / class_num)
for i in range(num_class):
print(sorted_name[i],sorted_score[i])
print()
sorted_name = index2name(np.argsort(class_prec5 / class_num), './raw/classInd.txt')
sorted_score = np.sort(class_prec5 / class_num)
for i in range(num_class):
print(sorted_name[i], sorted_score[i])
if __name__ == '__main__':
main()