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test.py
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test.py
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import argparse
import time
import numpy as np
import torch.nn.parallel
import torch.optim
import torchvision
from sklearn.metrics import confusion_matrix
from dataset import I3DDataSet
from i3d import I3DModel
from transforms import *
# options
parser = argparse.ArgumentParser(
description="Standard video-level testing")
parser.add_argument('dataset', type=str, choices=['ucf101', 'hmdb51', 'kinetics', 'ucf-crime','meitu'])
parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff'])
parser.add_argument('test_list', type=str)
parser.add_argument('weights', type=str)
parser.add_argument('--arch', type=str, default="resnet101")
parser.add_argument('--save_scores', type=str, default=None)
parser.add_argument('--test_clips', type=int, default=10)
parser.add_argument('--sample_frames', type=int, default=32)
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--k', type=int, default=3)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
parser.add_argument('--flow_prefix', type=str, default='')
args = parser.parse_args()
if args.dataset == 'ucf101':
num_class = 101
elif args.dataset == 'hmdb51':
num_class = 51
elif args.dataset == 'kinetics':
num_class = 400
elif args.dataset == 'meitu':
num_class = 400
else:
raise ValueError('Unknown dataset '+args.dataset)
i3d_model = I3DModel(num_class, args.sample_frames, args.modality,
base_model=args.arch, dropout=args.dropout)
checkpoint = torch.load(args.weights)
print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1']))
base_dict = {'.'.join(k.split('.')[1:]): v for k,v in list(checkpoint['state_dict'].items())}
i3d_model.load_state_dict(base_dict)
exit()
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(i3d_model.scale_size),
GroupCenterCrop(i3d_model.input_size),
])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(i3d_model.input_size, i3d_model.scale_size)
])
else:
raise ValueError("Only 1 and 10 crops are supported while we got {}".format(args.test_crops))
data_loader = torch.utils.data.DataLoader(
I3DDataSet("", args.test_list, sample_frames=args.sample_frames,
modality=args.modality,
image_tmpl="image_{:05d}.jpg" if args.modality in ['RGB', 'RGBDiff'] else args.flow_prefix+"{}_{:05d}.jpg",
train_mode=False, test_clips=args.test_clips,
transform=torchvision.transforms.Compose([
cropping,
Stack(),
ToTorchFormatTensor(),
GroupNormalize(i3d_model.input_mean, i3d_model.input_std),
])),
batch_size=1, shuffle=False,
num_workers=args.workers * 2, pin_memory=True)
if args.gpus is not None:
devices = [args.gpus[i] for i in range(args.workers)]
else:
devices = list(range(args.workers))
i3d_model = torch.nn.DataParallel(i3d_model.cuda(devices[0]), device_ids=devices)
i3d_model.eval()
data_gen = enumerate(data_loader)
total_num = len(data_loader.dataset)
output = []
def eval_video(video_data):
i, data, label = video_data
if args.modality == 'RGB':
num_channel = 3
num_depth = 32
else:
raise ValueError("Unknown modality "+args.modality)
data = data.squeeze(0)
data = data.view(num_channel,-1,num_depth,data.size(2),data.size(3)).contiguous()
data = data.permute(1,0,2,3,4).contiguous()
#data = data.view(data.size(0),num_channel,-1,num_depth,data.size(3),data.size(4))
#data = data.squeeze(0).permute(1,0,2,3,4).contiguous()
input_var = torch.autograd.Variable(data, volatile=True)
rst = i3d_model(input_var).data.cpu().numpy().copy()
return i, rst.reshape((args.test_clips*args.test_crops, num_class)).mean(axis=0).reshape((1, num_class)), \
label[0]
proc_start_time = time.time()
for i, (data, label) in data_gen:
rst = eval_video((i, data, label))
output.append(rst[1:])
cnt_time = time.time() - proc_start_time
print('video {} done, total {}/{}, average {} sec/video'.format(i, i+1,
total_num,
float(cnt_time) / (i+1)))
video_pred = [np.argmax(x[0]) for x in output]
video_labels = [x[1] for x in output]
cf = confusion_matrix(video_labels, video_pred).astype(float)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt
print(cls_acc)
print('Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
if args.save_scores is not None:
# reorder before saving
name_list = [x.strip().split()[0] for x in open(args.test_list)]
order_dict = {e:i for i, e in enumerate(sorted(name_list))}
reorder_output = [None] * len(output)
reorder_label = [None] * len(output)
for i in range(len(output)):
idx = order_dict[name_list[i]]
reorder_output[idx] = output[i]
reorder_label[idx] = video_labels[i]
np.savez(args.save_scores, scores=reorder_output, labels=reorder_label)