## Load saved model load_ckpt = False ckpt_file = '' # for Kitti Dataset: 'KittiModel.pth' # checkptname = "UCFModel" checkptname = "Kitti_simple-ST_lam0.1_" ## Load input data rootDir = '/home/armandcomas/datasets/Kitti_Flows/' listOfFolders = [name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir, name))] trainingData = videoDataset(folderList=listOfFolders, rootDir=rootDir) dataloader = DataLoader(trainingData, batch_size=BATCH_SIZE , shuffle=True, num_workers=1) ## Initializing r, theta P,Pall = gridRing(N) Drr = abs(P) Drr = torch.from_numpy(Drr).float() Dtheta = np.angle(P) Dtheta = torch.from_numpy(Dtheta).float() # ## Create the time model # model_ti = OFModel(Drr, Dtheta, T, PRE, gpu_id)
checkpt1 = 'Kitti_DCT_lam01_Frames-mean_FRA5_Caltech_loss-weighted_inputin01' checkptname1 = os.path.join(rootCkpt, checkpt1) checkpt2 = 'Kitti_DCT_lam01_Frames-meanstd_FRA5_Caltech_loss-weighted_inputin01' checkptname2 = os.path.join(rootCkpt, checkpt2) ## Load input data # rootDir = '/home/armandcomas/datasets/Kitti_Flows/' rootDir = '/home/armandcomas/datasets/Caltech/images/' listOfFolders = [ name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir, name)) ] trainingData = videoDataset(folderList=listOfFolders, rootDir=rootDir, blockSize=blockSize, nfra=N_FRAME) dataloader = DataLoader(trainingData, batch_size=BATCH_SIZE, shuffle=True, num_workers=1) ## Initializing r, theta P, Pall = gridRing(N) Drr = abs(P) Drr = torch.from_numpy(Drr).float() Dtheta = np.angle(P) Dtheta = torch.from_numpy(Dtheta).float() # What and where is gamma ## Create the time model
rootDir = '/home/armandcomas/DYAN/Code/datasets/Kitti_Flows/' # rootDir = '/home/armandcomas/DYAN/Code/datasets/DisentanglingMotion/importing_data/moving_symbols/output/MovingSymbols2_same_4px-OF/train' listOfFolders = [name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir, name))] # listOfFolders_1[0] = os.path.join(rootDir,listOfFolders_1[0]) # listOfFolders_1[1] = os.path.join(rootDir,listOfFolders_1[1]) # listOfFolders_H = [name for name in os.listdir(listOfFolders_1[0]) if os.path.isdir(os.path.join(listOfFolders_1[0], name))] # listOfFolders_V = [name for name in os.listdir(listOfFolders_1[1]) if os.path.isdir(os.path.join(listOfFolders_1[1], name))] # listFolderFile = '/home/armandcomas/DYAN/Code/datasets/DisentanglingMotion/importing_data/moving_symbols/MovingSymbols2_trainlist.txt' # listOfFolders = getListOfFolders(listFolderFile)[::10] # Function for the PyTorch Dataloader trainingData = videoDataset(listOfFolders=listOfFolders, rootDir=rootDir) dataloader = DataLoader(trainingData, batch_size=BATCH_SIZE , shuffle=True, num_workers=1) ## Create the model model = SC2(Drr, Dtheta, Gamma, T, PRE) model.cuda(gpu_id) optimizer = torch.optim.Adam(model.parameters(), lr=LR) exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[100,150], gamma=0.1) loss_l1 = nn.L1Loss() loss_mse = nn.MSELoss() start_epoch = 1 #ckpt_file = 'NormDict154.pth'
# set train list name: trainFolderFile = '/home/armandcomas/datasets/DisentanglingMotion/importing_data/moving_symbols/MovingSymbols2_trainlist.txt' # trainFolderFile = 'trainlist01.txt' # set training data directory: rootDir = '/home/armandcomas/datasets/DisentanglingMotion/importing_data/moving_symbols/output/MovingSymbols2_same_4px-OF/train' # rootDir = './datasets/UCF-101-Frames' trainFoldeList = getListOfFolders(trainFolderFile)[::10] # if Kitti dataset: use listOfFolders instead of trainFoldeList # listOfFolders = [name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir, name))] trainingData = videoDataset(folderList=trainFoldeList, rootDir=rootDir, N_FRAME=N_FRAME) dataloader = DataLoader(trainingData, batch_size=BATCH_SIZE , shuffle=True, num_workers=1) ## Initializing r, theta P,Pall = gridRing(N) Drr = abs(P) Drr = torch.from_numpy(Drr).float() Dtheta = np.angle(P) Dtheta = torch.from_numpy(Dtheta).float() # What and where is gamma ## Create the model
## Load saved model load_ckpt = False ckpt_file = 'MS_Model_4px_22.pth' # for Kitti Dataset: 'KittiModel.pth' # checkptname = "UCFModel" checkptname = "Kitti_GL_" # set training data directory: rootDir = '/home/armandcomas/DYAN/Code/datasets/Kitti_Flows/' # rootDir = './datasets/UCF-101-Frames' #trainFoldeList = getListOfFolders(trainFolderFile)[::10] # if Kitti dataset: use listOfFolders instead of trainFoldeList trainFoldeList = [name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir, name))] trainingData = videoDataset(listOfFolders=trainFoldeList, rootDir=rootDir) dataloader = DataLoader(trainingData, batch_size=BATCH_SIZE , shuffle=True, num_workers=1) ## Initializing r, theta P,Pall = gridRing(N) Drr = abs(P) Drr = torch.from_numpy(Drr).float() Dtheta = np.angle(P) Dtheta = torch.from_numpy(Dtheta).float() # What and where is gamma ## Create the model model = OFModel(Drr, Dtheta, T, PRE, gpu_id)
# trainFolderFile = './datasets/DisentanglingMotion/importing_data/moving_symbols/MovingSymbols2_trainlist.txt' # trainFolderFile = 'trainlist01.txt' # set training data directory: # rootDir = './datasets/DisentanglingMotion/importing_data/moving_symbols/output/MovingSymbols2_same_4px-OF/train' rootDir = '/home/armandcomas/datasets/Kitti_Flows/' # trainFoldeList = getListOfFolders(trainFolderFile)[::10] # if Kitti dataset: use listOfFolders instead of trainFoldeList listOfFolders = [ name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir, name)) ] trainingData = videoDataset(folderList=listOfFolders, rootDir=rootDir, N_FRAME=N_FRAME, N_FRAME_FOLDER=N_FRAME_FOLDER) dataloader = DataLoader(trainingData, batch_size=BATCH_SIZE, shuffle=True, num_workers=1) ## Initializing r, theta P, Pall = gridRing(N) Drr = abs(P) Drr = torch.from_numpy(Drr).float() Dtheta = np.angle(P) Dtheta = torch.from_numpy(Dtheta).float() # What and where is gamma