## 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
