def validate(loader: torch.utils.data.DataLoader, model: Parser,
cfg: DictConfig) -> FScore: # type: ignore
"Run validation/testing without beam search"
model.eval()
# testing requires far less GPU memory than training
# so there is no need to split a batch into multiple subbatches
env = Environment(loader, model.encoder, subbatch_max_tokens=9999999)
state = env.reset()
pred_trees = []
gt_trees = []
time_start = time()
with torch.no_grad(): # type: ignore
while True:
with torch.cuda.amp.autocast(cfg.amp): # type: ignore
actions, _ = model(state)
if cfg.decoder == "graph":
# actions for a single step
state, done = env.step(actions)
if not done:
continue
else:
assert cfg.decoder == "sequence"
# actions for all steps
for n_step in itertools.count():
a_t = [
action_seq[n_step] for action_seq in actions
if len(action_seq) > n_step
]
_, done = env.step(a_t)
if done:
break
pred_trees.extend(env.pred_trees)
gt_trees.extend(env.gt_trees)
# load the next batch
try:
with torch.cuda.amp.autocast(cfg.amp): # type: ignore
state = env.reset()
except EpochEnd:
# no next batch available (complete)
f1_score = evalb(
hydra.utils.to_absolute_path("./EVALB"),
gt_trees,
pred_trees # type: ignore
)
log.info("Time elapsed: %f" % (time() - time_start))
return f1_score
def beam_search(
loader: torch.utils.data.DataLoader,
model: Parser,
cfg: DictConfig # type: ignore
) -> FScore:
"Run validation/testing with beam search"
model.eval()
device, _ = get_device()
gt_trees = []
pred_trees = []
bar = ProgressBar(max_value=len(loader))
time_start = time()
with torch.no_grad(): # type: ignore
for i, data_batch in enumerate(loader):
# calculate token embeddings
tokens_emb = model.encoder(
data_batch["tokens_idx"].to(device=device, non_blocking=True),
data_batch["tags_idx"].to(device=device, non_blocking=True),
data_batch["valid_tokens_mask"].to(device=device,
non_blocking=True),
data_batch["word_end_mask"].to(device=device,
non_blocking=True),
)
# initialize the beam
beam = Beam(
data_batch["tokens_word"],
data_batch["tags"],
tokens_emb,
model,
cfg,
)
# keep executing actions and updating the beam until the entire batch is finished
while not beam.grow():
pass
gt_trees.extend(data_batch["trees"])
pred_trees.extend(beam.best_trees())
bar.update(i)
f1_score = evalb(hydra.utils.to_absolute_path("./EVALB"), gt_trees,
pred_trees)
log.info("Time elapsed: %f" % (time() - time_start))
return f1_score
