#mse = []
#ssim = []
############################################################################
## Load the model
ofmodel = loadOpticalFlowModel(opticalflow_ckpt_file)
ofSample = torch.FloatTensor(2, FRA, numOfPixels)
# set test list name:
testFolderFile = 'testlist01.txt'
# set test data directory:
rootDir = '/data/Abhishek/frames/'
# for UCF dataset:
testFoldeList = getListOfFolders(testFolderFile)[::10]
## if Kitti: use folderList instead of testFoldeList
## folderList = [name for name in os.listdir(rootDir) if os.path.isdir(os.path.join(rootDir))]
## folderList.sort()
flowDir = '/home/abhishek/Workspace/UCF_Flows/Flows_ByName/'
for numfo,folder in enumerate(testFoldeList):
print("Started testing for - "+ folder)
if not os.path.exists(os.path.join("Results", str(10*numfo+1))):
os.makedirs(os.path.join("Results", str(10*numfo+1)))
frames = [each for each in os.listdir(os.path.join(rootDir, folder)) if each.endswith(('.jpeg'))]
frames.sort()
trained_encoder = '../preTrainedModel/UCFModel.pth' # for Kitti Dataset: 'KittiModel.pth'
# pick up training
# trained_encoder = checkptname
checkptname = "DYAN-ResNet50-RGB"
ckpt_file = './DYAN-ResNet50-RGB60.pth'
## Load input data
# set train list name:
trainFolderFile = 'trainlist01.txt'
# set training data directory:
rootDir = '/data/Abhishek/frames/' # RGB
# rootDir = '/storage/truppr/UCF-FLOWS-FULL' # PGOF
trainFoldeList = getListOfFolders(trainFolderFile)[::5]
trainingData = videoDatasetRawFrames(folderList=trainFoldeList,
rootDir=rootDir,
N_FRAME=N_FRAME) # for raw RGB frames
# trainingData = videoDatasetPreGenOF(folderList=trainFoldeList, rootDir=rootDir, N_FRAME=N_FRAME) # for pre-generated OF
dataloader = DataLoader(trainingData,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=1)
## Initializing r, theta
stateDict = torch.load(trained_encoder)['state_dict']
Dtheta = stateDict['l1.theta']
Drr = stateDict['l1.rr']
