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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
import os
import math
import pickle
from typing import Optional
from data.file_dataset import FileDataset
import torch
from fairseq import metrics
from fairseq.tasks import register_task
from data.cv_data.image_classify_dataset import ImageClassifyDataset
from data import data_utils
from tasks.ofa_task import OFAConfig, OFATask
from utils.trie import Trie
logger = logging.getLogger(__name__)
@dataclass
class ImageClassifyConfig(OFAConfig):
ans2label_dict: Optional[str] = field(
default='{"no": 0, "yes":1}',
metadata={"help": 'answer to label dict'},
)
ans2label_file: Optional[str] = field(
default=None,
metadata={"help": "path to load ans2label file"},
)
valid_batch_size: int = field(
default=20,
metadata={"help": "valid batch size per step"},
)
uses_ema: Optional[bool] = field(
default=False,
metadata={"help": "whether to use ema"},
)
@register_task("image_classify", dataclass=ImageClassifyConfig)
class ImageClassifyTask(OFATask):
def __init__(self, cfg: ImageClassifyConfig, src_dict, tgt_dict):
super().__init__(cfg, src_dict, tgt_dict)
self.ans2label_dict = None
if self.cfg.ans2label_file is not None:
self.ans2label_dict = pickle.load(open(self.cfg.ans2label_file, "rb"))
else:
self.ans2label_dict = json.loads(self.cfg.ans2label_dict)
self.uses_ema = self.cfg.uses_ema
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
paths = self.cfg.data.split(',')
assert len(paths) > 0
if split == 'train':
table_path = paths[(epoch - 1) % (len(paths) - 1)]
else:
table_path = paths[-1]
dataset = FileDataset(table_path, self.cfg.selected_cols)
self.datasets[split] = ImageClassifyDataset(
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,
constraint_trie=self.constraint_trie,
imagenet_default_mean_and_std=self.cfg.imagenet_default_mean_and_std
)
def build_model(self, cfg):
model = super().build_model(cfg)
tgt_list = []
prev_output_list = []
self.index2ans = {}
self.ans2index = {}
self.constraint_trie = Trie(self.tgt_dict.eos())
for i, answer in enumerate(self.ans2label_dict.keys()):
answer_item = self.tgt_dict.encode_line(
line=self.bpe.encode(' ' + answer),
add_if_not_exist=False,
append_eos=False
).long()
tgt_list += [torch.cat([answer_item, torch.LongTensor([self.tgt_dict.eos()])])]
prev_output_list += [torch.cat([torch.LongTensor([self.tgt_dict.bos()]), answer_item])]
self.index2ans[i] = answer
self.ans2index[answer] = i
self.constraint_trie.insert([self.tgt_dict.bos()] + answer_item.tolist() + [self.tgt_dict.eos()])
constraint_mask_list = []
for prev_output_item in prev_output_list:
constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
for i in range(len(prev_output_item)):
constraint_prefix_token = prev_output_item[:i+1].tolist()
constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
constraint_mask[i][constraint_nodes] = True
constraint_mask_list.append(constraint_mask)
eos = self.src_dict.eos()
pad = self.src_dict.pad()
self.valid_tgt_list = []
self.valid_prev_output_list = []
self.valid_constraint_masks_list = []
for i in range(0, len(tgt_list), self.cfg.valid_batch_size):
tgt_item = tgt_list[i:i+self.cfg.valid_batch_size]
prev_output_item = prev_output_list[i:i+self.cfg.valid_batch_size]
constrain_mask = constraint_mask_list[i:i+self.cfg.valid_batch_size]
self.valid_tgt_list.append(
data_utils.collate_tokens(tgt_item, pad_idx=pad, eos_idx=eos, left_pad=False)
)
self.valid_prev_output_list.append(
data_utils.collate_tokens(prev_output_item, pad_idx=pad, eos_idx=eos, left_pad=False)
)
self.valid_constraint_masks_list.append(
data_utils.collate_tokens(constrain_mask, pad_idx=pad, left_pad=False)
)
return model
def build_generator(
self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None,
):
seq_generator = super().build_generator(models, args, seq_gen_cls, extra_gen_cls_kwargs, prefix_allowed_tokens_fn)
seq_generator.constraint_trie = self.constraint_trie
return seq_generator
def valid_step(self, sample, model, criterion, **extra_kwargs):
loss, sample_size, logging_output = super().valid_step(sample, model, criterion)
if self.uses_ema:
assert 'ema_model' in extra_kwargs and extra_kwargs['ema_model'] is not None
if self.uses_ema:
eval_model = extra_kwargs['ema_model']
else:
eval_model = model
eval_model.eval()
with torch.no_grad():
batch_size = sample["net_input"]["src_tokens"].size(0)
encoder_out = eval_model.encoder(
sample["net_input"]["src_tokens"],
src_lengths=sample["net_input"]["src_lengths"],
patch_images=sample["net_input"]["patch_images"],
patch_masks=sample["net_input"]["patch_masks"]
)
device = sample["net_input"]["src_tokens"].device
valid_result = []
for valid_tgt, valid_prev_output, valid_constraint_masks in zip(self.valid_tgt_list,
self.valid_prev_output_list,
self.valid_constraint_masks_list):
valid_tgt_size = valid_tgt.size(0)
valid_tgt = valid_tgt.repeat(batch_size, 1).to(device)
valid_prev_output = valid_prev_output.repeat(batch_size, 1).to(device)
valid_constraint_masks = valid_constraint_masks.repeat(batch_size, 1, 1).to(device)
new_encoder_out = {}
new_encoder_out["encoder_out"] = [
encoder_out["encoder_out"][0].repeat_interleave(valid_tgt_size, dim=1)
]
new_encoder_out["encoder_padding_mask"] = [
encoder_out["encoder_padding_mask"][0].repeat_interleave(valid_tgt_size, dim=0)
]
new_encoder_out["position_embeddings"] = [
encoder_out["position_embeddings"][0].repeat_interleave(valid_tgt_size, dim=0)
]
decoder_out = eval_model.decoder(valid_prev_output, encoder_out=new_encoder_out)
decoder_out[0].masked_fill_(~valid_constraint_masks, -math.inf)
lprobs = eval_model.get_normalized_probs(decoder_out, log_probs=True)
scores = lprobs.gather(dim=-1, index=valid_tgt.unsqueeze(-1)).squeeze(-1)
scores = scores.masked_fill(valid_tgt.eq(self.tgt_dict.pad()), 0)
scores = scores.sum(1)
scores = scores.view(-1, valid_tgt_size)
valid_result.append(scores)
valid_result = torch.cat(valid_result, dim=-1)
predicts = valid_result.argmax(1).tolist()
hyps = [self.index2ans[predict_index] for predict_index in predicts]
scores = [ref_dict.get(hyp, 0) for ref_dict, hyp in zip(sample['ref_dict'], hyps)]
logging_output["_score_sum"] = sum(scores)
logging_output["_score_cnt"] = len(scores)
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, 3)
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)
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