As per the screenshot, is it normal that anomaly_score is super large? I was using the following config.py (just change to cpu and meta_epochs = 2) to train the dummy_dataset. Have the same huge anomaly_score in the second epochs.
'''This file configures the training procedure because handling arguments in every single function is so exhaustive for
research purposes. Don't try this code if you are a software engineer.'''
# device settings
device = 'cpu' #'cuda' # or 'cpu'
import torch
torch.cuda.set_device(0)
# data settings
dataset_path = "dummy_dataset"
class_name = "dummy_class"
modelname = "dummy_test"
img_size = (448, 448)
img_dims = [3] + list(img_size)
add_img_noise = 0.01
# transformation settings
transf_rotations = True
transf_brightness = 0.0
transf_contrast = 0.0
transf_saturation = 0.0
norm_mean, norm_std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
# network hyperparameters
n_scales = 3 # number of scales at which features are extracted, img_size is the highest - others are //2, //4,...
clamp_alpha = 3 # see paper equation 2 for explanation
n_coupling_blocks = 8
fc_internal = 2048 # number of neurons in hidden layers of s-t-networks
dropout = 0.0 # dropout in s-t-networks
lr_init = 2e-4
n_feat = 256 * n_scales # do not change except you change the feature extractor
# dataloader parameters
n_transforms = 4 # number of transformations per sample in training
n_transforms_test = 64 # number of transformations per sample in testing
batch_size = 24 # actual batch size is this value multiplied by n_transforms(_test)
batch_size_test = batch_size * n_transforms // n_transforms_test
# total epochs = meta_epochs * sub_epochs
# evaluation after <sub_epochs> epochs
meta_epochs = 2
sub_epochs = 8
# output settings
verbose = True
grad_map_viz = True
hide_tqdm_bar = True
save_model = True