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)
