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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
import json
import logging
from typing import Optional
from argparse import Namespace
import torch
from fairseq import metrics
from fairseq.tasks import register_task
from tasks.ofa_task import OFATask, OFAConfig
from data.mm_data.refcoco_dataset import RefcocoDataset
from data.file_dataset import FileDataset
logger = logging.getLogger(__name__)
@dataclass
class RefcocoConfig(OFAConfig):
# options for reporting BLEU during validation
eval_acc: bool = field(
default=False, metadata={"help": "evaluation with BLEU scores"}
)
eval_args: Optional[str] = field(
default='{}',
metadata={
"help": 'generation args for BLUE or CIDEr scoring, e.g., \'{"beam": 4, "lenpen": 0.6}\', as JSON string'
},
)
eval_print_samples: bool = field(
default=False, metadata={"help": "print sample generations during validation"}
)
max_image_size: int = field(
default=512, metadata={"help": "max image size for normalization"}
)
scst: bool = field(
default=False, metadata={"help": "Self-critical sequence training"}
)
scst_args: str = field(
default='{}',
metadata={
"help": 'generation args for Self-critical sequence training, as JSON string'
},
)
@register_task("refcoco", dataclass=RefcocoConfig)
class RefcocoTask(OFATask):
def __init__(self, cfg: RefcocoConfig, src_dict, tgt_dict):
super().__init__(cfg, src_dict, tgt_dict)
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
paths = self.cfg.data.split(',')
assert len(paths) > 0
if split == 'train':
file_path = paths[(epoch - 1) % (len(paths) - 1)]
else:
file_path = paths[-1]
dataset = FileDataset(file_path, self.cfg.selected_cols)
self.datasets[split] = RefcocoDataset(
split,
dataset,
self.bpe,
self.src_dict,
self.tgt_dict,
max_src_length=self.cfg.max_src_length,
max_tgt_length=self.cfg.max_tgt_length,
patch_image_size=self.cfg.patch_image_size,
imagenet_default_mean_and_std=self.cfg.imagenet_default_mean_and_std,
num_bins=self.cfg.num_bins,
max_image_size=self.cfg.max_image_size
)
def build_model(self, cfg):
model = super().build_model(cfg)
if self.cfg.eval_acc:
gen_args = json.loads(self.cfg.eval_args)
self.sequence_generator = self.build_generator(
[model], Namespace(**gen_args)
)
if self.cfg.scst:
scst_args = json.loads(self.cfg.scst_args)
self.scst_generator = self.build_generator(
[model], Namespace(**scst_args)
)
return model
def _calculate_ap_score(self, 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()
def valid_step(self, sample, model, criterion):
loss, sample_size, logging_output = criterion(model, sample)
model.eval()
if self.cfg.eval_acc:
hyps, refs = self._inference(self.sequence_generator, sample, model)
hyps = hyps / (self.cfg.num_bins - 1) * self.cfg.max_image_size
refs = refs / (self.cfg.num_bins - 1) * self.cfg.max_image_size
hyps[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1)
hyps[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1)
refs[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1)
refs[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1)
# scores = self._calculate_ap_score(hyps, refs)
scores = self._calculate_ap_score(hyps, sample['region_coords'].float())
logging_output["_score_sum"] = scores.sum().item()
logging_output["_score_cnt"] = scores.size(0)
return loss, sample_size, logging_output
def reduce_metrics(self, logging_outputs, criterion):
super().reduce_metrics(logging_outputs, criterion)
def sum_logs(key):
import torch
result = sum(log.get(key, 0) for log in logging_outputs)
if torch.is_tensor(result):
result = result.cpu()
return result
def compute_score(meters):
score = meters["_score_sum"].sum / meters["_score_cnt"].sum
score = score if isinstance(score, float) else score.item()
return round(score, 4)
if sum_logs("_score_cnt") > 0:
metrics.log_scalar("_score_sum", sum_logs("_score_sum"))
metrics.log_scalar("_score_cnt", sum_logs("_score_cnt"))
metrics.log_derived("score", compute_score)
def _inference(self, generator, sample, model):
gen_out = self.inference_step(generator, [model], sample)
hyps, refs = [], []
for i in range(len(gen_out)):
hyps.append(gen_out[i][0]["tokens"][:-1] - len(self.src_dict) + self.cfg.num_bins)
refs.append(sample["target"][i][:-1] - len(self.src_dict) + self.cfg.num_bins)
if self.cfg.eval_print_samples:
logger.info("example hypothesis: ", hyps[0])
logger.info("example reference: ", refs[0])
return torch.stack(hyps, dim=0), torch.stack(refs, dim=0)
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