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cogview2_text2image.py
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cogview2_text2image.py
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# -*- encoding: utf-8 -*-
'''
@File : inference_coglm.py
@Time : 2021/10/09 19:41:58
@Author : Ming Ding
@Contact : dm18@mails.tsinghua.edu.cn
'''
# here put the import lib
import os
import sys
import math
import random
import torch
import argparse
from functools import partial
import numpy as np
from SwissArmyTransformer import get_args, get_tokenizer
from SwissArmyTransformer.model import CachedAutoregressiveModel
from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
from SwissArmyTransformer.generation.autoregressive_sampling import filling_sequence, evaluate_perplexity
from SwissArmyTransformer.generation.utils import timed_name, save_multiple_images, generate_continually
from coglm_strategy import CoglmStrategy
from icetk import icetk as tokenizer
tokenizer.add_special_tokens(['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
def get_masks_and_position_ids_coglm(seq, context_length):
tokens = seq.unsqueeze(0)
attention_mask = torch.ones((1, len(seq), len(seq)), device=tokens.device)
attention_mask.tril_()
attention_mask[..., :context_length] = 1
attention_mask.unsqueeze_(1)
position_ids = torch.zeros(len(seq), device=tokens.device, dtype=torch.long)
torch.arange(0, context_length, out=position_ids[:context_length])
torch.arange(512, 512 + len(seq) - context_length,
out=position_ids[context_length:]
)
position_ids = position_ids.unsqueeze(0)
return tokens, attention_mask, position_ids
def main(args):
model, args = InferenceModel.from_pretrained(args, 'coglm')
text_model = CachedAutoregressiveModel(args, transformer=model.transformer)
query_template = args.query_template
invalid_slices = [slice(tokenizer.num_image_tokens, None)]
strategy = CoglmStrategy(invalid_slices,
temperature=args.temp_all_gen, top_k=args.topk_gen, top_k_cluster=args.temp_cluster_gen)
from sr_pipeline import SRGroup
if not args.only_first_stage:
srg = SRGroup(args)
def process(raw_text):
if args.with_id:
query_id, raw_text = raw_text.split('\t')
print('raw text: ', raw_text)
text = query_template.format(raw_text)
seq = tokenizer.encode(text)
if len(seq) > 110:
raise ValueError('text too long.')
txt_len = len(seq) - 1
seq = torch.tensor(seq + [-1]*400, device=args.device)
# calibrate text length
log_attention_weights = torch.zeros(len(seq), len(seq),
device=args.device, dtype=torch.half if args.fp16 else torch.float32)
log_attention_weights[:, :txt_len] = args.attn_plus
# generation
mbz = args.max_inference_batch_size
assert args.batch_size < mbz or args.batch_size % mbz == 0
get_func = partial(get_masks_and_position_ids_coglm, context_length=txt_len)
output_list, score_list = [], []
for tim in range(max(args.batch_size // mbz, 1)):
strategy.start_pos = txt_len + 1
coarse_samples = filling_sequence(model, seq.clone(),
batch_size=min(args.batch_size, mbz),
strategy=strategy,
log_attention_weights=log_attention_weights,
get_masks_and_position_ids=get_func
)[0]
# get ppl for inverse prompting
if args.inverse_prompt:
image_text_seq = torch.cat(
(
coarse_samples[:, -400:],
torch.tensor([tokenizer['<start_of_chinese>']]+tokenizer.encode(raw_text), device=args.device).expand(coarse_samples.shape[0], -1)
), dim=1)
seqlen = image_text_seq.shape[1]
attention_mask = torch.zeros(seqlen, seqlen, device=args.device, dtype=torch.long)
attention_mask[:, :400] = 1
attention_mask[400:, 400:] = 1
attention_mask[400:, 400:].tril_()
position_ids = torch.zeros(seqlen, device=args.device, dtype=torch.long)
torch.arange(513, 513+400, out=position_ids[:400])
torch.arange(0, seqlen-400, out=position_ids[400:])
loss_mask = torch.zeros(seqlen, device=args.device, dtype=torch.long)
loss_mask[401:] = 1
scores = evaluate_perplexity(
text_model, image_text_seq, attention_mask,
position_ids, loss_mask#, invalid_slices=[slice(0, 20000)], reduction='mean'
)
score_list.extend(scores.tolist())
# ---------------------
output_list.append(
coarse_samples
)
output_tokens = torch.cat(output_list, dim=0)
if args.inverse_prompt:
order_list = np.argsort(score_list)[::-1]
print(sorted(score_list))
else:
order_list = range(output_tokens.shape[0])
imgs, txts = [], []
if args.only_first_stage:
for i in order_list:
seq = output_tokens[i]
decoded_img = tokenizer.decode(image_ids=seq[-400:])
decoded_img = torch.nn.functional.interpolate(decoded_img, size=(480, 480))
imgs.append(decoded_img) # only the last image (target)
if not args.only_first_stage: # sr
iter_tokens = srg.sr_base(output_tokens[:, -400:], seq[:txt_len])
for seq in iter_tokens:
decoded_img = tokenizer.decode(image_ids=seq[-3600:])
decoded_img = torch.nn.functional.interpolate(decoded_img, size=(480, 480))
imgs.append(decoded_img) # only the last image (target)
# save
if args.with_id:
full_path = os.path.join(args.output_path, query_id)
os.makedirs(full_path, exist_ok=True)
save_multiple_images(imgs, full_path, False)
else:
prefix = raw_text.replace('/', '')[:20]
full_path = timed_name(prefix, '.jpeg', args.output_path)
imgs = torch.cat(imgs, dim=0)
print("\nSave to: ", full_path, flush=True)
from PIL import Image
from torchvision.utils import make_grid
grid = make_grid(imgs, nrow=3, padding=0)
# Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
im = Image.fromarray(ndarr)
im.save(full_path, quality=100, subsampling=0)
os.makedirs(args.output_path, exist_ok=True)
generate_continually(process, args.input_source)
class InferenceModel(CachedAutoregressiveModel):
def final_forward(self, logits, **kwargs):
logits_parallel = logits
logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float())
return logits_parallel
def get_recipe(name):
r = {
'attn_plus': 1.4,
'temp_all_gen': 1.15,
'topk_gen': 16,
'temp_cluster_gen': 1.,
'temp_all_dsr': 1.5,
'topk_dsr': 100,
'temp_cluster_dsr': 0.89,
'temp_all_itersr': 1.3,
'topk_itersr': 16,
'query_template': '{}<start_of_image>'
}
if name == 'none':
pass
elif name == 'mainbody':
r['query_template'] = '{} 高清摄影 隔绝<start_of_image>'
elif name == 'photo':
r['query_template'] = '{} 高清摄影<start_of_image>'
elif name == 'flat':
r['query_template'] = '{} 平面风格<start_of_image>'
# r['attn_plus'] = 1.8
# r['temp_cluster_gen'] = 0.75
r['temp_all_gen'] = 1.1
r['topk_dsr'] = 5
r['temp_cluster_dsr'] = 0.4
r['temp_all_itersr'] = 1
r['topk_itersr'] = 5
elif name == 'comics':
r['query_template'] = '{} 漫画 隔绝<start_of_image>'
r['topk_dsr'] = 5
r['temp_cluster_dsr'] = 0.4
r['temp_all_gen'] = 1.1
r['temp_all_itersr'] = 1
r['topk_itersr'] = 5
elif name == 'oil':
r['query_template'] = '{} 油画风格<start_of_image>'
pass
elif name == 'sketch':
r['query_template'] = '{} 素描风格<start_of_image>'
r['temp_all_gen'] = 1.1
elif name == 'isometric':
r['query_template'] = '{} 等距矢量图<start_of_image>'
r['temp_all_gen'] = 1.1
elif name == 'chinese':
r['query_template'] = '{} 水墨国画<start_of_image>'
r['temp_all_gen'] = 1.12
elif name == 'watercolor':
r['query_template'] = '{} 水彩画风格<start_of_image>'
return r
if __name__ == "__main__":
py_parser = argparse.ArgumentParser(add_help=False)
py_parser.add_argument('--img-size', type=int, default=160)
py_parser.add_argument('--only-first-stage', action='store_true')
py_parser.add_argument('--inverse-prompt', action='store_true')
py_parser.add_argument('--style', type=str, default='mainbody',
choices=['none', 'mainbody', 'photo', 'flat', 'comics', 'oil', 'sketch', 'isometric', 'chinese', 'watercolor'])
known, args_list = py_parser.parse_known_args()
args = get_args(args_list)
args = argparse.Namespace(**vars(args), **vars(known), **get_recipe(known.style))
with torch.no_grad():
main(args)