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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.
import math
from dataclasses import dataclass, field
from typing import Optional
from PIL import Image
from torchvision import transforms
import torch
import numpy as np
from fairseq import metrics
from fairseq.data import data_utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from fairseq import utils
from omegaconf import II
from models import clip
def custom_to_pil(x):
x = x.detach().cpu()
x = torch.clamp(x, -1., 1.)
x = (x + 1.) / 2.
x = x.permute(1, 2, 0).numpy()
x = (255 * x).astype(np.uint8)
x = Image.fromarray(x)
if not x.mode == "RGB":
x = x.convert("RGB")
return x
def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
if ignore_index is not None:
pad_mask = target.eq(ignore_index)
loss.masked_fill_(pad_mask, 0.0)
ntokens = (~pad_mask).sum()
else:
loss = loss.squeeze(-1)
ntokens = target.numel()
if reduce:
loss = loss.sum()
return loss, ntokens
@dataclass
class ClipScstRewardCriterionConfig(FairseqDataclass):
ignore_prefix_size: int = field(
default=0,
metadata={"help": "Ignore first N tokens"},
)
sentence_avg: bool = II("optimization.sentence_avg")
constraint_range: Optional[str] = field(
default=None,
metadata={"help": "constraint range"}
)
@register_criterion(
"clip_scst_reward_criterion", dataclass=ClipScstRewardCriterionConfig
)
class ClipScstRewardCriterion(FairseqCriterion):
CLIP_REWARD_WEIGHT = 2.5
def __init__(
self,
task,
sentence_avg,
ignore_prefix_size=0,
constraint_range=None
):
super().__init__(task)
self.sentence_avg = sentence_avg
self.ignore_prefix_size = ignore_prefix_size
self.constraint_start = None
self.constraint_end = None
if constraint_range is not None:
constraint_start, constraint_end = constraint_range.split(',')
self.constraint_start = int(constraint_start)
self.constraint_end = int(constraint_end)
def forward(self, model, sample, update_num=0, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
sample_size = (
nsentences if self.sentence_avg else ntokens
)
logging_output = {
"loss": loss.data,
"score": score,
"ntokens": ntokens,
"nsentences": nsentences,
"sample_size": sample_size,
}
return loss, sample_size, logging_output
def _calculate_clip_scores(self, gen_res, gt_text, device):
'''
gen_res: generated images, list of Image
gt_text: input captions.
device: device for clip model
'''
batch_size = len(gt_text)
gen_res_size = len(gen_res)
img_per_seq = gen_res_size // batch_size
hyp_images = torch.stack(
[self.task.clip_preprocess(gen_image) for gen_image in gen_res], dim=0
).to(device)
clip_input = clip.tokenize([text for text in gt_text]).to(device)
with torch.no_grad():
image_features = self.task.clip_model.encode_image(hyp_images)
text_features = self.task.clip_model.encode_text(clip_input)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
image_features = image_features.view(batch_size, img_per_seq, -1)
text_features = text_features.view(batch_size, 1, -1)
ti_similarity = image_features @ text_features.transpose(1, 2)
ti_similarity = ti_similarity.view(-1)
scores = self.CLIP_REWARD_WEIGHT * ti_similarity
return scores
def get_generator_out(self, model, sample):
model.eval()
with torch.no_grad():
self.task.scst_generator.model.eval()
gen_out = self.task.scst_generator.generate([model], sample)
gen_target = []
gen_res = []
gt_text = []
for i in range(len(gen_out)):
with torch.no_grad():
tokens = torch.stack([item['tokens'][:-1] for item in gen_out[i]], dim=0)
tokens += -len(self.task.src_dict) + self.task.cfg.code_dict_size + self.task.cfg.num_bins
images = self.task.image_tokenizer.decode_code(
tokens.view(-1, self.task.cfg.code_image_size // 8, self.task.cfg.code_image_size // 8)
)
images = [custom_to_pil(image) for image in images]
gen_target += [item['tokens'] for item in gen_out[i]]
gen_res += images
gt_text.append(
self.task.bpe.decode(
self.task.tgt_dict.string(
utils.strip_pad(sample['net_input']['src_tokens'][i], self.padding_idx).cpu().int()
)
)[38:] # remove task instruction.
)
return gen_target, gen_res, gt_text
def get_reward_and_scores(self, gen_res, gt_text, device):
batch_size = len(gt_text)
gen_res_size = len(gen_res)
img_per_sample = gen_res_size // batch_size
scores = self._calculate_clip_scores(gen_res, gt_text, device)
sc_ = scores.reshape(batch_size, img_per_sample)
baseline = (sc_.sum(1, keepdim=True) - sc_) / (sc_.shape[1] - 1)
# sample - baseline
reward = scores.reshape(batch_size, img_per_sample)
reward = reward - baseline
reward = reward.view(-1)
return reward, scores
def get_net_output(self, model, sample, gen_target):
def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
return data_utils.collate_tokens(
sample_list,
pad_idx=self.padding_idx,
eos_idx=eos,
left_pad=False,
move_eos_to_beginning=move_eos_to_beginning,
)
batch_size = len(sample["target"])
gen_target_size = len(gen_target)
img_per_sample = gen_target_size // batch_size
model.train()
sample_src_tokens = torch.repeat_interleave(
sample['net_input']['src_tokens'], img_per_sample, dim=0
)
sample_src_lengths = torch.repeat_interleave(
sample['net_input']['src_lengths'], img_per_sample, dim=0
)
sample_code_masks = torch.repeat_interleave(
sample['net_input']['code_masks'], img_per_sample, dim=0
)
gen_prev_output_tokens = torch.as_tensor(
merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
device=sample["target"].device, dtype=torch.int64
)
gen_target_tokens = torch.as_tensor(
merge(gen_target), device=sample["target"].device, dtype=torch.int64
)
net_output = model(
src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
code_masks=sample_code_masks, prev_output_tokens=gen_prev_output_tokens
)
return net_output, gen_target_tokens
def get_lprobs_and_target(self, model, net_output, gen_target):
if self.constraint_start is not None and self.constraint_end is not None:
net_output[0][:, :, 4:self.constraint_start] = -math.inf
net_output[0][:, :, self.constraint_end:] = -math.inf
lprobs = model.get_normalized_probs(net_output, log_probs=True)
if self.ignore_prefix_size > 0:
if getattr(lprobs, "batch_first", False):
lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
else:
lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
return lprobs, gen_target
def compute_loss(self, model, sample, reduce=True):
gen_target, gen_res, gt_text = self.get_generator_out(model, sample)
reward, scores = self.get_reward_and_scores(gen_res, gt_text, device=sample["target"].device)
net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
nsentences = gen_target_tokens.size(0)
return loss, scores.sum(), ntokens, nsentences
@classmethod
def reduce_metrics(cls, logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
score_sum = sum(log.get("score", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
metrics.log_scalar(
"loss", loss_sum / sample_size, sample_size, round=3
)
metrics.log_scalar(
"score", score_sum / nsentences, nsentences, round=3
)
metrics.log_scalar(
"ntokens", ntokens, 1, round=3
)
metrics.log_scalar(
"nsentences", nsentences, 1, round=3
)
metrics.log_scalar(
"sample_size", sample_size, 1, round=3
)
@staticmethod
def logging_outputs_can_be_summed() -> bool:
"""
Whether the logging outputs returned by `forward` can be summed
across workers prior to calling `reduce_metrics`. Setting this
to True will improves distributed training speed.
"""
return True
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