if category_name == 'n/a':
category_name = category_id
c_it = model_counter[category_id]
if c_it < vis_n_outputs:
data_vis_list.append({'category': category_name, 'it': c_it, 'data': data_vis})
model_counter[category_id] += 1
"""
# Model
model = config.get_model(cfg, device=device, dataset=train_dataset)
# Intialize training
optimizer = optim.Adam(model.parameters(), lr=1e-4)
trainer = config.get_trainer(model, optimizer, cfg, device=device)
checkpoint_io = CheckpointIO(out_dir, model=model, optimizer=optimizer)
try:
load_dict = checkpoint_io.load('model.pt')
except FileExistsError:
load_dict = dict()
epoch_it = load_dict.get('epoch_it', -1)
it = load_dict.get('it', -1)
it0 = load_dict.get('it', -1)
metric_val_best = load_dict.get('loss_val_best',
-model_selection_sign * np.inf)
# Generator
generator = config.get_generator(model, cfg, device=device)
out_file = os.path.join(out_dir, 'eval_full.pkl')
out_file_class = os.path.join(out_dir, 'eval.csv')
# Dataset
dataset = config.get_dataset('test', cfg, return_idx=True)
model = config.get_model(cfg, device=device, dataset=dataset)
checkpoint_io = CheckpointIO(out_dir, model=model)
try:
checkpoint_io.load(cfg['test']['model_file'])
except FileExistsError:
print('Model file does not exist. Exiting.')
exit()
# Trainer
trainer = config.get_trainer(model, None, cfg, device=device)
# Print model
nparameters = sum(p.numel() for p in model.parameters())
print(model)
print('Total number of parameters: %d' % nparameters)
# Evaluate
model.eval()
eval_dicts = []
print('Evaluating networks...')
test_loader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=False,
