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import string
import math
import torch
from data import data_utils
def get_symbols_to_strip_from_output(generator):
if hasattr(generator, "symbols_to_strip_from_output"):
return generator.symbols_to_strip_from_output
else:
return {generator.bos, generator.eos}
def decode_fn(x, tgt_dict, bpe, generator, tokenizer=None):
x = tgt_dict.string(x.int().cpu(), extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator))
if bpe is not None:
x = bpe.decode(x)
if tokenizer is not None:
x = tokenizer.decode(x)
return x
def eval_caption(task, generator, models, sample):
transtab = str.maketrans({key: None for key in string.punctuation})
hypos = task.inference_step(generator, models, sample)
results = []
for i, sample_id in enumerate(sample["id"].tolist()):
detok_hypo_str = decode_fn(hypos[i][0]["tokens"], task.tgt_dict, task.bpe, generator)
results.append({"image_id": str(sample_id), "caption": detok_hypo_str.translate(transtab).strip()})
return results, None
def eval_refcoco(task, generator, models, sample):
def _calculate_ap_score(hyps, refs, thresh=0.5):
interacts = torch.cat(
[torch.where(hyps[:, :2] < refs[:, :2], refs[:, :2], hyps[:, :2]),
torch.where(hyps[:, 2:] < refs[:, 2:], hyps[:, 2:], refs[:, 2:])],
dim=1
)
area_predictions = (hyps[:, 2] - hyps[:, 0]) * (hyps[:, 3] - hyps[:, 1])
area_targets = (refs[:, 2] - refs[:, 0]) * (refs[:, 3] - refs[:, 1])
interacts_w = interacts[:, 2] - interacts[:, 0]
interacts_h = interacts[:, 3] - interacts[:, 1]
area_interacts = interacts_w * interacts_h
ious = area_interacts / (area_predictions + area_targets - area_interacts + 1e-6)
return ((ious >= thresh) & (interacts_w > 0) & (interacts_h > 0)).float()
gen_out = task.inference_step(generator, models, sample)
hyps = []
for i in range(len(gen_out)):
hyps.append(gen_out[i][0]["tokens"][:-1] - len(task.src_dict) + task.cfg.num_bins)
hyps = torch.stack(hyps, dim=0)
hyps = hyps / (task.cfg.num_bins - 1) * task.cfg.max_image_size
hyps[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1)
hyps[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1)
results = [
{"uniq_id": sample_id,
"box": [hyps[i][0].item(), hyps[i][1].item(), hyps[i][2].item(), hyps[i][3].item()]}
for i, sample_id in enumerate(sample["id"].tolist())
]
scores = _calculate_ap_score(hyps, sample['region_coords'].float())
return results, scores
def eval_step(task, generator, models, sample):
if task.cfg._name == 'caption':
return eval_caption(task, generator, models, sample)
elif task.cfg._name == 'refcoco':
return eval_refcoco(task, generator, models, sample)
else:
raise NotImplementedError
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