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# ------------------------------------------------------------------------
# Modified from OFA (https://github.com/OFA-Sys/OFA)
# 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.
# ------------------------------------------------------------------------
# Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
from dataclasses import dataclass, field
import os
import logging
from typing import Optional
import math
import numpy as np
import torch
from fairseq import metrics
from fairseq.tasks import register_task
from tasks.base_task import BaseTask, BaseConfig
from data.refcoco_pretrain_dataset import RefcocoPretrainDataset
from data.file_dataset import FileDataset
from tasks.base_task import BaseTask, BaseConfig, load_bert_pretrained_weights
logger = logging.getLogger(__name__)
@dataclass
class RefcocoPretrainConfig(BaseConfig):
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'
},
)
uses_ema: Optional[bool] = field(
default=False,
metadata={"help": "whether to use ema"},
)
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_pretrain", dataclass=RefcocoPretrainConfig)
class RefcocoPretrainTask(BaseTask):
def __init__(self, cfg: RefcocoPretrainConfig, 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] = RefcocoPretrainDataset(
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)
bert_path = "../../pretrained_weights/bert-base-uncased-pytorch_model.bin"
if os.path.exists(bert_path):
load_bert_pretrained_weights(model.encoder.bert, bert_path)
if cfg._name == 'polyformer_b':
swin_path = "../../pretrained_weights/swin_base_patch4_window12_384_22k.pth"
else:
swin_path = "../../pretrained_weights/swin_large_patch4_window12_384_22k.pth"
if os.path.exists(swin_path):
model.encoder.embed_images.init_weights(pretrained=swin_path)
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(sample, model)
scores = self._calculate_ap_score(hyps.float(), refs.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, sample, model):
hyps = self.inference_step(model, sample)
refs = sample['region_coords'].float()
hyps = hyps * self.cfg.max_image_size
hyps[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1)
hyps[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1)
return hyps, refs
def inference_step(self, model, sample):
with torch.no_grad():
if isinstance(model, list):
model = model[0]
total_len = 2
model.eval()
img = sample["net_input"]["patch_images"]
b = img.shape[0]
prev_output_token_11 = [[0] for _ in range(b)]
prev_output_token_12 = [[0] for _ in range(b)]
prev_output_token_21 = [[0] for _ in range(b)]
prev_output_token_22 = [[0] for _ in range(b)]
delta_x1 = [[0] for _ in range(b)]
delta_y1 = [[0] for _ in range(b)]
delta_x2 = [[1] for _ in range(b)]
delta_y2 = [[1] for _ in range(b)]
gen_out = [[] for _ in range(b)]
n_bins = self.cfg.num_bins
encoder_out = model.encoder(
sample['net_input']['src_tokens'],
src_lengths=sample['net_input']['src_lengths'],
att_masks=sample['net_input']['att_masks'],
patch_images=sample['net_input']['patch_images'],
patch_masks=sample['net_input']['patch_masks'],
token_embeddings=None,
return_all_hiddens=False,
sample_patch_num=None
)
for i in range(total_len):
prev_output_tokens_11_tensor = torch.tensor(np.array(prev_output_token_11)).to(img.device).long()
prev_output_tokens_12_tensor = torch.tensor(np.array(prev_output_token_12)).to(img.device).long()
prev_output_tokens_21_tensor = torch.tensor(np.array(prev_output_token_21)).to(img.device).long()
prev_output_tokens_22_tensor = torch.tensor(np.array(prev_output_token_22)).to(img.device).long()
delta_x1_tensor = torch.tensor(np.array(delta_x1)).to(img.device)
delta_x2_tensor = torch.tensor(np.array(delta_x2)).to(img.device)
delta_y1_tensor = torch.tensor(np.array(delta_y1)).to(img.device)
delta_y2_tensor = torch.tensor(np.array(delta_y2)).to(img.device)
net_output = model.decoder(
prev_output_tokens_11_tensor,
prev_output_tokens_12_tensor,
prev_output_tokens_21_tensor,
prev_output_tokens_22_tensor,
delta_x1_tensor,
delta_y1_tensor,
delta_x2_tensor,
delta_y2_tensor,
code_masks=None,
encoder_out=encoder_out,
features_only=False,
alignment_layer=None,
alignment_heads=None,
src_lengths=sample['net_input']['src_lengths'],
return_all_hiddens=False
)
net_output = net_output[1]
for j in range(b):
output_j_x, output_j_y = net_output[j, i].cpu().numpy()
gen_out[j].extend([output_j_x, output_j_y])
output_j_x = output_j_x * (n_bins - 1)
output_j_y = output_j_y * (n_bins - 1)
output_j_x_floor = math.floor(output_j_x)
output_j_y_floor = math.floor(output_j_y)
output_j_x_ceil = math.ceil(output_j_x)
output_j_y_ceil = math.ceil(output_j_y)
# convert to token
prev_output_token_11[j].append(output_j_x_floor * n_bins + output_j_y_floor + 4)
prev_output_token_12[j].append(output_j_x_floor * n_bins + output_j_y_ceil + 4)
prev_output_token_21[j].append(output_j_x_ceil * n_bins + output_j_y_floor + 4)
prev_output_token_22[j].append(output_j_x_ceil * n_bins + output_j_y_ceil + 4)
delta_x = output_j_x - output_j_x_floor
delta_y = output_j_y - output_j_y_floor
delta_x1[j].append(delta_x)
delta_y1[j].append(delta_y)
delta_x2[j].append(1-delta_x)
delta_y2[j].append(1-delta_y)
return torch.tensor(gen_out).to(img.device)
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