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# -*- encoding: utf-8 -*-
'''
@File : iterative_sr.py
@Time : 2022/03/02 15:57:45
@Author : Ming Ding
@Contact : dm18@mails.tsinghua.edu.cn
'''
# here put the import lib
import os
import sys
import math
import random
# here put the import lib
import os
import sys
import math
import random
from PIL import ImageEnhance, Image
import torch
import argparse
from torchvision import transforms
from SwissArmyTransformer.training.model_io import load_checkpoint
from SwissArmyTransformer import get_args
from .itersr_sampling import filling_sequence_itersr, IterativeEntfilterStrategy
from SwissArmyTransformer.generation.utils import timed_name, save_multiple_images, generate_continually
from .itersr_model import ItersrModel
from icetk import icetk as tokenizer
class IterativeSuperResolution:
def __init__(self, args, path, max_bz=4, shared_transformer=None):
args.load = path
args.kernel_size = 5
args.kernel_size2 = 5
args.new_sequence_length = 4624
args.layout = [16,3616]
model = ItersrModel(args, transformer=shared_transformer)
if args.fp16:
model = model.half()
load_checkpoint(model, args) # on cpu
model.eval()
self.model = model.cuda()
# save cpu weights
self.saved_weights = dict((k,v.cpu())
for k, v in model.named_parameters()
if 'transformer' in k
)
invalid_slices = [slice(tokenizer.num_image_tokens, None)]
self.strategy = IterativeEntfilterStrategy(invalid_slices,
temperature=args.temp_all_itersr, topk=args.topk_itersr)
self.max_bz = max_bz
def _restore_transformer_from_cpu(self, non_blocking=False):
for k, v in self.model.named_parameters():
if k in self.saved_weights:
v.copy_(self.saved_weights[k])
def __call__(self, text_tokens, image_tokens, enhance=False, input_mask=None):
if len(text_tokens.shape) == 1:
text_tokens.unsqueeze_(0)
text_tokens = text_tokens.clone()[..., :16]
if len(image_tokens.shape) == 1:
image_tokens.unsqueeze_(0)
if enhance:
new_image_tokens = []
for big_img in image_tokens:
decoded = tokenizer.decode(image_ids=big_img).squeeze(0)
ndarr = decoded.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
image_pil_raw = ImageEnhance.Sharpness(Image.fromarray(ndarr))
big_img2 = tokenizer.encode(image_pil=image_pil_raw.enhance(1.5), image_size=480).view(-1)
new_image_tokens.append(big_img2)
image_tokens = torch.stack(new_image_tokens)
print('Converting Itersr model...')
self._restore_transformer_from_cpu()
model = self.model
print('iterative super-resolution...')
output_list = []
for tim in range(max(text_tokens.shape[0] // self.max_bz, 1)):
big_img = image_tokens[tim*self.max_bz:(tim+1)*self.max_bz]
text_seq = text_tokens[tim*self.max_bz:(tim+1)*self.max_bz]
mask_raw = torch.tensor(
[
-1, 0, 1, 2, 3, 4,
0, -1, 2, -1, -2, 5,
1, -2, 3, 4, 5, 6,
2, 3, 4, 5, -1, 1,
3, -1, -2, 0, -1, 2,
4, 5, 6, 1, 3, -2
]
).view(1, 6, 1, 6).expand(10, 6, 10, 6).reshape(-1).contiguous()
topks = [60, 40, 40, 40, 20, 20, 10]
for mask_ratio in range(1, 7):
self.strategy.topk = topks[mask_ratio]
mask = (mask_raw.to(big_img.device) >= mask_ratio)
if input_mask is not None:
mask = mask & input_mask
big_img.masked_fill_(mask, tokenizer['<start_of_image>'])
seq1 = big_img
output1 = filling_sequence_itersr(model, text_seq, seq1,
warmup_steps=1, block_hw=(1, 0),
strategy=self.strategy
)
big_img = output1
print(f'Iter {mask_ratio} times.')
output_list.append(output1.clone())
return torch.cat(output_list, dim=0) |