Config or collab problem

[pnzr00t]

Hello, try run this model on Collab, but after loading config, a always get this settings

    # test mode
    config.MODE = 2
    config.MODEL = args.model if args.model is not None else 3
    config.OBJECTS = args.remove if args.remove is not None else [3,15]
    config.SEG_DEVICE = 'cpu' if args.cpu is not None else 'cuda'
    config.INPUT_SIZE = 256
    if args.input is not None:
        config.TEST_FLIST = args.input

    if args.edge is not None:
        config.TEST_EDGE_FLIST = args.edge
    if args.output is not None:
        config.RESULTS = args.output
    else: 
        if not os.path.exists('./results_images'):
            os.makedirs('./results_images')
        config.RESULTS = './results_images'

    config.MODE = 2
    config.print()
    print("config.print()")

Model configurations:

MODE: 1 # 1: train, 2: test, 3: eval MODEL: 1 # 1: edge model, 2: inpaint model, 3: edge-inpaint model, 4: joint model MASK: 3 # 1: random block, 2: half, 3: external, 4: (external, random block), 5: (external, random block, half) EDGE: 1 # 1: canny, 2: external NMS: 1 # 0: no non-max-suppression, 1: applies non-max-suppression on the external edges by multiplying by Canny SEED: 10 # random seed GPU: [0] # list of gpu ids DEBUG: 0 # turns on debugging mode VERBOSE: 0 # turns on verbose mode in the output console

TRAIN_FLIST: ./datasets/places2_train.flist VAL_FLIST: ./datasets/places2_val.flist TEST_FLIST: ./datasets/places2_test.flist

TRAIN_EDGE_FLIST: ./datasets/places2_edges_train.flist VAL_EDGE_FLIST: ./datasets/places2_edges_val.flist TEST_EDGE_FLIST: ./datasets/places2_edges_test.flist

TRAIN_MASK_FLIST: ./datasets/masks_train.flist VAL_MASK_FLIST: ./datasets/masks_val.flist TEST_MASK_FLIST: ./datasets/masks_test.flist

LR: 0.0001 # learning rate D2G_LR: 0.1 # discriminator/generator learning rate ratio BETA1: 0.0 # adam optimizer beta1 BETA2: 0.9 # adam optimizer beta2 BATCH_SIZE: 8 # input batch size for training INPUT_SIZE: 256 # input image size for training 0 for original size SIGMA: 2 # standard deviation of the Gaussian filter used in Canny edge detector (0: random, -1: no edge) MAX_ITERS: 2e6 # maximum number of iterations to train the model

EDGE_THRESHOLD: 0.5 # edge detection threshold L1_LOSS_WEIGHT: 1 # l1 loss weight FM_LOSS_WEIGHT: 10 # feature-matching loss weight STYLE_LOSS_WEIGHT: 250 # style loss weight CONTENT_LOSS_WEIGHT: 0.1 # perceptual loss weight INPAINT_ADV_LOSS_WEIGHT: 0.1 # adversarial loss weight

GAN_LOSS: nsgan # nsgan | lsgan | hinge GAN_POOL_SIZE: 0 # fake images pool size

SAVE_INTERVAL: 1000 # how many iterations to wait before saving model (0: never) SAMPLE_INTERVAL: 1000 # how many iterations to wait before sampling (0: never) SAMPLE_SIZE: 12 # number of images to sample EVAL_INTERVAL: 0 # how many iterations to wait before model evaluation (0: never) LOG_INTERVAL: 10 # how many iterations to wait before logging training status (0: never) SEG_NETWORK: 0 # 0:DeepLabV3 resnet 101 segmentation , 1: FCN resnet 101 segmentation

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config.print()