def run_eval(model,
model_path,
ds,
batch_size,
num_gpus,
label_key='label',
probs_key='label_probs'):
LOG.info('Loading model state from {}'.format(model_path))
restore_model_state(model, model_path)
ds_loader = VCRLoader.from_dataset(
ds,
batch_size=(batch_size // num_gpus),
num_gpus=num_gpus,
num_workers=(4 * num_gpus),
)
def _to_gpu(td):
if num_gpus > 1:
return td
for k in td:
if k != 'metadata':
td[k] = {k2: v.cuda(async=True)
for k2, v in td[k].items()} if isinstance(
td[k], dict) else td[k].cuda(async=True)
return td
return td
for k in td:
td[k] = {k2: v.cuda(non_blocking=True)
for k2, v in td[k].items()} if isinstance(
td[k], dict) else td[k].cuda(non_blocking=True)
return td
num_workers = (4 * NUM_GPUS if NUM_CPUS == 32 else 2 * NUM_GPUS) - 1
print(f"Using {num_workers} workers out of {NUM_CPUS} possible", flush=True)
loader_params = {
'batch_size': 96 // NUM_GPUS,
'num_gpus': NUM_GPUS,
'num_workers': num_workers
}
train_loader = VCRLoader.from_dataset(train, **loader_params)
val_loader = VCRLoader.from_dataset(val, **loader_params)
test_loader = VCRLoader.from_dataset(test, **loader_params)
ARGS_RESET_EVERY = 100
print("Loading {} for {}".format(params['model'].get('type', 'WTF?'),
'rationales' if args.rationale else 'answer'),
flush=True)
model = Model.from_params(vocab=train.vocab, params=params['model'])
for submodule in model.detector.backbone.modules():
if isinstance(submodule, BatchNorm2d):
submodule.track_running_stats = False
for p in submodule.parameters():
p.requires_grad = False
model = DataParallel(model).cuda() if NUM_GPUS > 1 else model.cuda()
'num_gpus': NUM_GPUS,
'num_workers': num_workers
}
vcr_modes = VCR.eval_splits(
embs_to_load=params['dataset_reader'].get('embs', 'bert_da'),
only_use_relevant_dets=params['dataset_reader'].get(
'only_use_relevant_dets', True))
probs_grp = []
ids_grp = []
for (vcr_dataset,
mode_long) in zip(vcr_modes,
['answer'] + [f'rationale_{i}' for i in range(4)]):
mode = mode_long.split('_')[0]
test_loader = VCRLoader.from_dataset(vcr_dataset, **loader_params)
# Load the params again because allennlp will delete them... ugh.
params = Params.from_file(args.params)
print("Loading {} for {}".format(params['model'].get('type', 'WTF?'),
mode),
flush=True)
model = Model.from_params(vocab=vcr_dataset.vocab, params=params['model'])
for submodule in model.detector.backbone.modules():
if isinstance(submodule, BatchNorm2d):
submodule.track_running_stats = False
model_state = torch.load(getattr(args, f'{mode}_ckpt'),
map_location=device_mapping(-1))
model.load_state_dict(model_state)
