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# Copyright 2022 The OFA-Sys Team.
# All rights reserved.
# This source code is licensed under the Apache 2.0 license
# found in the LICENSE file in the root directory.
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__)
from mapcalc import calculate_map, calculate_map_range
from functools import partial
@dataclass
class RefcocoConfig(OFAConfig):
eval_acc: bool = field(
default=False, metadata={"help": "evaluation with accuracy"}
)
eval_args: Optional[str] = field(
default='{}',
metadata={
"help": 'generation args, 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'
},
)
acc_thresh: Optional[str] = field(
default=None, metadata={"help": "acc thresh for refcoco"}
)
metric: Optional[str] = field(
default='acc',
metadata={"help": "metric"}
)
max_area_size: Optional[float] = field(
default=None, metadata={"help": "max_area_size"}
)
min_area_size: Optional[float] = field(
default=None, metadata={"help": "min_area_size"}
)
@register_task("refcoco", dataclass=RefcocoConfig)
class RefcocoTask(OFATask):
def __init__(self, cfg: RefcocoConfig, src_dict, tgt_dict):
super().__init__(cfg, src_dict, tgt_dict)
self.metric = cfg.metric
self.min_area_size = cfg.min_area_size
self.max_area_size = cfg.max_area_size
try:
self.acc_thresh = float(cfg.acc_thresh)
except:
self.acc_thresh = cfg.acc_thresh
print(self.acc_thresh, self.metric, self.min_area_size, self.max_area_size)
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, min_area_size=None, max_area_size=None):
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)
if max_area_size is not None and min_area_size is not None:
ious = ious * ((area_targets < max_area_size).float() + (area_targets > min_area_size).float())/2
elif min_area_size is not None:
ious = ious * (area_targets > min_area_size).float()
elif max_area_size is not None:
ious = ious * (area_targets < max_area_size).float()
if thresh is None:
return ious
else:
return ((ious >= thresh) & (interacts_w > 0) & (interacts_h > 0)).float()
def _calculate_map_score(self, hyps, refs, thresh=0.5):
ground_truth = {
'boxes': refs.cpu().numpy().tolist(),
'labels': [1 for i in range(refs.shape[0])]
}
result_dict = {
'boxes': hyps.cpu().numpy().tolist(),
'labels': [1 for i in range(hyps.shape[0])],
}
score = calculate_map(ground_truth, result_dict, thresh)
score = torch.tensor(score).unsqueeze(0).repeat(refs.shape[0]).to(hyps.device)
return score
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())
# scores = self._calculate_ap_score(hyps, refs)
scores = self._calculate_ap_score(hyps, sample['region_coords'].float(), thresh=self.acc_thresh)
if self.min_area_size is not None:
large_scores = self._calculate_ap_score(hyps, sample['region_coords'].float(), thresh=self.acc_thresh,
min_area_size=self.min_area_size)
logging_output["_large_score_sum"] = large_scores.sum().item()
logging_output["_large_score_cnt"] = large_scores.size(0)
if self.max_area_size is not None:
small_scores = self._calculate_ap_score(hyps, sample['region_coords'].float(), thresh=self.acc_thresh,
max_area_size=self.max_area_size)
logging_output["_small_score_sum"] = small_scores.sum().item()
logging_output["_small_score_cnt"] = small_scores.size(0)
if self.metric == 'map':
map_scores = self._calculate_map_score(hyps, sample['region_coords'].float(), thresh=self.acc_thresh)
logging_output["_map_score_sum"] = map_scores.sum().item()
logging_output["_map_score_cnt"] = map_scores.size(0)
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, prefix='_score'):
score = meters[prefix+"_sum"].sum / meters[prefix+"_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)
if self.metric == 'map':
metrics.log_scalar("_map_score_sum", sum_logs("_map_score_sum"))
metrics.log_scalar("_map_score_cnt", sum_logs("_map_score_cnt"))
metrics.log_derived("score", partial(compute_score, prefix='_map_score'))
if self.min_area_size is not None:
metrics.log_scalar("_large_score_sum", sum_logs("_large_score_sum"))
metrics.log_scalar("_large_score_cnt", sum_logs("_large_score_cnt"))
metrics.log_derived("score", partial(compute_score, prefix='_large_score'))
if self.max_area_size is not None:
metrics.log_scalar("_small_score_sum", sum_logs("_small_score_sum"))
metrics.log_scalar("_small_score_cnt", sum_logs("_small_score_cnt"))
metrics.log_derived("score", partial(compute_score, prefix='_small_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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