def AD_sigle_perdiction(model_dir,c3d_features,lengths=16,device=None,network=None):
if device==None:
device = torch.device("cuda" if torch.cuda.is_available()
else "cpu")
if network==None:
print("staring the sigle AD networl")
# pediction of AD with pertrain network
network = AnomalyDetector()
network.to(device)
net = static_model(net=network,
criterion=RegularizedLoss(network, custom_objective).cuda(),
model_prefix=model_dir,
)
model_path = net.get_checkpoint_path(20000)
net.load_checkpoint(pretrain_path=model_path, epoch=20000)
net.net.to(device)
else:
net=network
#no need for anatation or batch loading of the C3D featuers
#from annotation_methods import annotatate_file
#annotation_path=annotatate_file(video_parth,dir_list,normal=True,file_name="Demo_anmotation")
# #runing vedio in to network
# data_loader = FeaturesLoaderVal(features_path=features_dir,
# annotation_path=annotation_path)
#
# data_iter = torch.utils.data.DataLoader(data_loader,
# batch_size=1,
# shuffle=False,
# num_workers=1, # 4, # change this part accordingly
# pin_memory=True)
#print("loading of data done")
#for features, start_end_couples, feature_subpaths, lengths in tqdm(data_iter):
# features is a batch where each item is a tensor of 32 4096D features
c3d_features=torch.from_numpy(c3d_features)
#print(c3d_features.shape)
features = c3d_features.to(device)
with torch.no_grad():
input_var = torch.autograd.Variable(features)
outputs = net.predict(input_var)[0] # (batch_size, 32)
outputs = outputs.reshape(1, 32)#outputs.shape[0]
for vid_len, output in zip([lengths], outputs.cpu().numpy()):
y_true = np.zeros(vid_len)
segments_len = vid_len // 32
# for couple in couples:
# if couple[0] != -1:
# y_true[couple[0]: couple[1]] = 1
y_pred = np.zeros(vid_len)
for i in range(32):
segment_start_frame = i * segments_len
segment_end_frame = (i + 1) * segments_len
y_pred[segment_start_frame: segment_end_frame] = output[i]
#print(y_true)
#print(y_pred)
#print("it is over")
return y_pred
def network_setup(ad_model_dir='/home/peter/Documents/actionID/AnomalyDetectionCVPR2018-Pytorch-master/short_60_low_mem/exps/model'):
device = torch.device("cuda" if torch.cuda.is_available()
else "cpu")
c3d_network = C3D(pretrained=pretrained_3d)
c3d_network.to(device)
print("staring the sigle AD networl")
# pediction of AD with pertrain network
AD_network = AnomalyDetector()
AD_network.to(device)
net = static_model(net=AD_network,
criterion=RegularizedLoss(AD_network, custom_objective).cuda(),
model_prefix=ad_model_dir,
)
model_path = net.get_checkpoint_path(20000)
net.load_checkpoint(pretrain_path=model_path, epoch=20000)
net.net.to(device)
return device,c3d_network,net
parser.add_argument('--random-seed', type=int, default=1,
help='random seed (default: 1)')
parser.add_argument('--model-dir', type=str, default="./exps/model",
help="set model dir.")
parser.add_argument('--classid_path', type=str, default="ClassIDs.txt",
help="path to ClassIDs")
if __name__ == "__main__":
args = parser.parse_args()
# load network
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
network = AnomalyDetector()
network.to(device)
net = static_model(net=network,
criterion=RegularizedLoss(network, custom_objective).to(device),
model_prefix=args.model_dir)
model_path = net.get_checkpoint_path(20000)
net.load_checkpoint(pretrain_path=model_path, epoch=20000)
net.net.to(device)
# enable cudnn tune
cudnn.benchmark = True
# load test data
data_loader = FeaturesLoader(features_path=args.features_path,
annotation_path=args.annotation_path_test,
classid_path=args.classid_path)
data_iter = torch.utils.data.DataLoader(data_loader,
def AD_perdiction(model_dir, dir_list, device=None):
if device == None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#pediction of AD with pertrain network
network = AnomalyDetector()
network.to(device)
net = static_model(
net=network,
criterion=RegularizedLoss(network, custom_objective).cuda(),
model_prefix=model_dir,
)
model_path = net.get_checkpoint_path(20000)
net.load_checkpoint(pretrain_path=model_path, epoch=20000)
net.net.to(device)
from annotation_methods import annotatate_file
annotation_path = annotatate_file(video_path,
dir_list,
normal=[True, True],
file_name="Demo_anmotation")
#runing vedio in to network
data_loader = FeaturesLoaderVal(features_path=features_dir,
annotation_path=annotation_path)
data_iter = torch.utils.data.DataLoader(
data_loader,
batch_size=1,
shuffle=False,
num_workers=1, # 4, # change this part accordingly
pin_memory=True)
print("it is over")
for features, start_end_couples, feature_subpaths, lengths in tqdm(
data_iter):
# features is a batch where each item is a tensor of 32 4096D features
features = features.to(device)
with torch.no_grad():
input_var = torch.autograd.Variable(features)
outputs = net.predict(input_var)[0] # (batch_size, 32)
outputs = outputs.reshape(outputs.shape[0], 32)
for vid_len, couples, output in zip(lengths, start_end_couples,
outputs.cpu().numpy()):
y_true = np.zeros(vid_len)
segments_len = vid_len // 32
for couple in couples:
if couple[0] != -1:
y_true[couple[0]:couple[1]] = 1
y_pred = np.zeros(vid_len)
print()
for i in range(32):
segment_start_frame = i * segments_len
segment_end_frame = (i + 1) * segments_len
y_pred[segment_start_frame:segment_end_frame] = output[i]
print(y_true)
print(y_pred)
print("it is over")
return y_pred