dataset.split = 'train' dataloader = DataLoader(dataset, batch_size=params['batchSize'], shuffle=False, num_workers=params['numWorkers'], drop_last=True, collate_fn=dataset.collate_fn, pin_memory=False) # Initializing visdom environment for plotting data viz = VisdomVisualize(enable=bool(params['enableVisdom']), env_name=params['visdomEnv'], server=params['visdomServer'], port=params['visdomServerPort']) pprint.pprint(params) viz.addText(pprint.pformat(params, indent=4)) # Setup optimizer if params['continue']: # Continuing from a loaded checkpoint restores the following startIterID = params['ckpt_iterid'] + 1 # Iteration ID lRate = params['ckpt_lRate'] # Learning rate print("Continuing training from iterId[%d]" % startIterID) else: # Beginning training normally, without any checkpoint lRate = params['learningRate'] startIterID = 0 optimizer = optim.Adam(parameters, lr=lRate) ############# ##changed
if params['qaCategory'] and params['categoryMap']: category_mapping = json.load(open(params['categoryMap'], 'r')) val_split_name = split_names['val'] test_split_name = split_names['test'] category_mapping_splits = { 'val': category_mapping[val_split_name][params['qaCategory']], 'test': category_mapping[test_split_name][params['qaCategory']] } # Plotting on vizdom viz = VisdomVisualize(enable=bool(params['enableVisdom']), env_name=params['visdomEnv'], server=params['visdomServer'], port=params['visdomServerPort']) pprint.pprint(params) viz.addText(pprint.pformat(params, indent=4)) logging.info("Running evaluation!") numRounds = params['numRounds'] if 'ckpt_iterid' in params: iterId = params['ckpt_iterid'] + 1 else: iterId = -1 for split in splits: if split == 'train': splitName = 'full train - {}'.format(params['evalTitle']) if split == 'val': splitName = 'full Val - {}'.format(params['evalTitle']) if split == 'test': splitName = 'test - {}'.format(params['evalTitle']) logging.info("Using split %s" % split)