def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=0, path='../bold_data/BOLD_ijcv/BOLD_public/frames/', ucf_list='../bold_data/BOLD_ijcv/BOLD_public/annotations/', ucf_split='04') train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training model.run()
def main(): global arg arg = parser.parse_args() print arg #Preparing DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=12, # path='/home/ubuntu/data/UCF101/spatial_no_sampled/' path= '/media/semanticslab11/hdd1/data/two-stream-action/data/backup/ucf101/', #path to your ucf 101 data # ucf_list ='/home/ubuntu/lab/pytorch/ucf101_two_stream/github/UCF_list/', ucf_list='UCF_list/', #here use ucf_list path ucf_split='01', ) train_loader, test_loader, test_video = data_loader.run() #initiating our model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video, pretrain_finetune=arg.fine_tune_flag, pretrain_last_layer=arg.last_layer_flag) #Calling run function to run the model model.run()
def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=1, num_workers=1, path= '/home/yifu/Documents/Mycode/python/two-stream-pytorch/datasets/jpegs_256/', ucf_list= '/home/yifu/Documents/Mycode/python/two-stream-pytorch/twostream/UCF_list/', ucf_split='01') train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training model.run()
def main(ds_path, trainfile, testfile, prefix): global arg arg = parser.parse_args() print arg method = EXPERIMENTS.MAIN_AUTHOR #Prepare DataLoader data_loader = dataloader.spatial_dataloader(BATCH_SIZE=arg.batch_size, num_workers=12, path=ds_path, trainfile=trainfile, testfile=testfile, step_size=arg.step, experiment=method) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video, num_classes=data_loader.max_cls_ind, prefix=prefix) #Training model.run()
def main(): global arg arg = parser.parse_args() print arg #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=8, path='/home/ubuntu/data/UCF101/spatial_no_sampled/', ucf_list ='/home/ubuntu/cvlab/pytorch/ucf101_two_stream/github/UCF_list/', ucf_split ='01', ) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN( nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video ) #Training model.run()
def main(): global arg arg = parser.parse_args() print arg #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=8, path='/home/lenovo/xuzeshan/data/UCF101/', ucf_list ='/home/lenovo/xuzeshan/two-stream-action-recognition/UCF_lis/', ucf_split ='01', ) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN( nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video ) #Training model.run()
def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=12, path='/media/lsc/DATA/Data/UCF101/spatial_no_sampled/', ucf_list= '/media/lsc/DATA/github/two-stream-action-recognition/UCF_list/', ucf_split='01', ) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training model.run()
def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=8, path='/media/dataDisk/THUMOS14/THUMOS14_video/thumos14_preprocess/', train_list= './thumos14_list/new_thumos_14_20_one_label_temporal_val.txt', test_list='./thumos14_list/new_thumos_14_20_one_label_temporal_test.txt' ) train_loader, test_loader, test_video = data_loader.run() checkpoint_dir = os.path.join("./record/spatial", "pretrain_ucf101") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training model.run(checkpoint_dir)
def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=8, path='/research/dept2/mli/Data/jpegs_256/', ucf_list='./UCF_list/', ucf_split='01', ) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training if arg.evaluate: cudnn.benchmark = True model.build_model() model.resume_and_evaluate() else: model.run()
def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=0, path='/home/lb/video_action_recognition/data/jpegs_256', ucf_list='/home/lb/video_action_recognition/data/datalists', ucf_split='01', ) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training model.run()
def main(): global arg arg = parser.parse_args() print(arg) #Prepare DataLoader data_loader = dataloader.spatial_dataloader( BATCH_SIZE=arg.batch_size, num_workers=0, path='D:\\Data\\UCF101RGB\\UCF101\\ucf101\\jpegs_256\\', ucf_list= 'D:\\Radha\\Downloads\\two-stream-action-recognition-master\\UCF_list\\', ucf_split='01', ) train_loader, test_loader, test_video = data_loader.run() #Model model = Spatial_CNN(nb_epochs=arg.epochs, lr=arg.lr, batch_size=arg.batch_size, resume=arg.resume, start_epoch=arg.start_epoch, evaluate=arg.evaluate, train_loader=train_loader, test_loader=test_loader, test_video=test_video) #Training model.run()
arg = parser.parse_args() print(arg) rgb_preds = arg.rgbPred opf_preds = arg.optPred with open(rgb_preds, 'rb') as f: rgb = pickle.load(f) f.close() with open(opf_preds, 'rb') as f: opf = pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, path=arg.jpeg, nda_list=arg.NDAlist, nda_split='01', crop_size=arg.imgCropSize) train_loader, val_loader, test_video = dataloader.run() rgb_video_level_preds = np.zeros((len(rgb.keys()), arg.numClass)) opt_video_level_preds = np.zeros((len(rgb.keys()), arg.numClass)) fuse_video_level_preds = np.zeros((len(rgb.keys()), arg.numClass)) video_level_labels = np.zeros(len(rgb.keys())) # correct=0 ii = 0 for name in sorted(rgb.keys()): r = rgb[name] o = opf[name] label = int(test_video[name]) - 1
import dataloader if __name__ == '__main__': rgb_preds='record/spatial/spatial_video_preds.pickle' opf_preds = 'record/motion/motion_video_preds.pickle' with open(rgb_preds,'rb') as f: rgb =pickle.load(f) f.close() with open(opf_preds,'rb') as f: opf =pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, path='/home/ubuntu/data/UCF101/spatial_no_sampled/', ucf_list='/home/ubuntu/cvlab/pytorch/ucf101_two_stream/github/UCF_list/', ucf_split='01') train_loader,val_loader,test_video = dataloader.run() video_level_preds = np.zeros((len(rgb.keys()),101)) video_level_labels = np.zeros(len(rgb.keys())) correct=0 ii=0 for name in sorted(rgb.keys()): r = rgb[name] o = opf[name] label = int(test_video[name])-1 video_level_preds[ii,:] = (r+o) video_level_labels[ii] = label
if __name__ == '__main__': rgb_preds = 'record/spatial/spatial_video_preds.pickle' opf_preds = 'record/motion/motion_video_preds.pickle' with open(rgb_preds, 'rb') as f: rgb = pickle.load(f) f.close() with open(opf_preds, 'rb') as f: opf = pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader( BATCH_SIZE=1, num_workers=1, path='/home/lb/video_action_recognition/data/jpegs_256', ucf_list='/home/lb/video_action_recognition/data/datalists', ucf_split='01') train_loader, val_loader, test_video = dataloader.run() video_level_preds = np.zeros((len(list(rgb.keys())), 101)) video_level_labels = np.zeros(len(list(rgb.keys()))) correct = 0 ii = 0 final_result = [] for name in sorted(rgb.keys()): r = rgb[name] o = opf[name]
from my_network import * from utils import * import torch.optim as optim import dataloader import torch import os from ResidualNetwork import * os.environ["CUDA_VISIBLE_DEVICES"] = "0" # Prepare DataLoader print '*************************Prepare DataLoader********************************' data_loader = dataloader.spatial_dataloader( BATCH_SIZE=10, num_workers=8, path='/media/ming/DATADRIVE1/Datasets/UCF101 Dataset/jpegs_256/', UCF_list='/media/ming/DATADRIVE1/UCF101 Multi-stream Code/UCF_list/', ) train_loader, test_loader, test_video = data_loader.run() # build the model model = resnet34(pretrained=False, channel=3).cuda() print '------model--------' print model criterion = nn.CrossEntropyLoss().cuda() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) def train_1epoch(epoch):
import torch from utils import * import dataloader rgb_video_pred_collections = 'record/spatial/spatial_video_preds_collections_300.pickle' saliency_video_pred_collections = 'record/saliency/saliency_video_preds_collections_300.pickle' with open(rgb_video_pred_collections, 'rb') as f: rgbs = pickle.load(f) f.close() with open(saliency_video_pred_collections, 'rb') as f: saliencys = pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, path='/media/ming/DATADRIVE1/KTH Dataset 600/KTH1saliency/', KTH_list='/media/ming/DATADRIVE1/KTH Multi-stream Code/KTH_list/' ) train_loader, val_loader, test_video = dataloader.run() nb_epochs = len(rgbs) for epoch in range(nb_epochs): rgb = rgbs[epoch] saliency = saliencys[epoch] video_level_preds = np.zeros((len(rgb.keys()), 101)) video_level_labels = np.zeros(len(rgb.keys())) ii = 0 for name in sorted(rgb.keys()):
if __name__ == '__main__': rgb_preds='record/spatial/spatial_video_preds.pickle' #opf_preds = 'record/motion/motion_video_preds.pickle' opf_preds = 'record/motion_pose/motion_pose_video_preds.pickle' with open(rgb_preds,'rb') as f: rgb =pickle.load(f) f.close() with open(opf_preds,'rb') as f: opf =pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, path='/home/molly/UCF_data/jpegs_256', ucf_list='/home/molly/two-stream-action-recognition/UCF_list/', ucf_split='04') train_loader,val_loader,test_video = dataloader.run() video_level_preds = np.zeros((len(rgb.keys()),15)) video_level_labels = np.zeros(len(rgb.keys())) correct=0 ii=0 for name in sorted(rgb.keys()): r = rgb[name] o = opf[name] label = int(test_video[name])-1 video_level_preds[ii,:] = (r+o) video_level_labels[ii] = label
rgb_preds='record/spatial/spatial_video_preds.pickle' opf_preds = 'record/motion/motion_video_preds.pickle' with open(rgb_preds,'rb') as f: #rgb =pickle.load(f) rgb = pickle.load(f,encoding='latin1') f.close() with open(opf_preds,'rb') as f: #opf =pickle.load(f) opf = pickle.load(f,encoding='latin1') f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, #path='/home/ubuntu/data/UCF101/spatial_no_sampled/', path='/content/jpegs_256/', #ucf_list='/home/ubuntu/cvlab/pytorch/ucf101_two_stream/github/UCF_list/', ucf_list='/content/drive/MyDrive/two_stream/two-stream-action-recognition/UCF_list/', ucf_split='01') train_loader,val_loader,test_video = dataloader.run() video_level_preds = np.zeros((len(rgb.keys()),101)) video_level_labels = np.zeros(len(rgb.keys())) correct=0 ii=0 for name in sorted(rgb.keys()): r = rgb[name] o = opf[name] label = int(test_video[name])-1 video_level_preds[ii,:] = (r+o)