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"""
This script provides an exmaple to wrap TencentPretrain for generation.
Given the beginning of a text, language model generates the rest.
"""
import sys
import os
import argparse
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(tencentpretrain_dir)
from tencentpretrain.embeddings import *
from tencentpretrain.layers import *
from tencentpretrain.encoders import *
from tencentpretrain.targets import *
from tencentpretrain.utils.constants import *
from tencentpretrain.utils import *
from tencentpretrain.utils.config import load_hyperparam
from tencentpretrain.model_loader import load_model
from tencentpretrain.opts import model_opts, tokenizer_opts
from scripts.generate_lm import top_k_top_p_filtering
from tencentpretrain.utils.image_tokenizer import *
class GenerateLm(torch.nn.Module):
def __init__(self, args):
super(GenerateLm, self).__init__()
self.embedding = Embedding(args)
for embedding_name in args.embedding:
tmp_emb = str2embedding[embedding_name](args, len(args.tokenizer.vocab))
self.embedding.update(tmp_emb, embedding_name)
self.encoder = str2encoder[args.encoder](args)
self.target = Target()
self.target.update(LmTarget(args, len(args.tokenizer.vocab)), "lm")
def forward(self, src, seg):
emb = self.embedding(src, seg)
output = self.encoder(emb, seg)
output = self.target.lm.output_layer(output)
return output
if __name__ == "__main__":
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Model options.
model_opts(parser)
# Inference options.
parser.add_argument("--load_model_path", default=None, type=str,
help="Path of the input model.")
parser.add_argument("--config_path", type=str, required=True,
help="Path of the config file.")
parser.add_argument("--seq_length", type=int, default=48,
help="Sequence length.")
parser.add_argument("--samples_num", type=int, default=10,
help="Number of iterations for sampling.")
parser.add_argument("--prompt", choices=["to_attributes", "to_caption", "to_image"], default="to_attributes",
help="Prompt that indicates the output format.")
parser.add_argument("--text_prefix_path", type=str, default=None,
help="Text prefix for to_attributes and to_image.")
parser.add_argument("--image_prefix_path", type=str, default=None,
help="Input image path.")
parser.add_argument("--top_k", type=int, default=70)
parser.add_argument("--top_p", type=float, default=0)
parser.add_argument("--temperature", type=float, default=1.0)
tokenizer_opts(parser)
args = parser.parse_args()
args.batch_size = 1
args = load_hyperparam(args)
args.tokenizer = str2tokenizer[args.tokenizer](args)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.text_prefix = None
if args.text_prefix_path is not None:
with open(args.text_prefix_path, "r") as f:
args.text_prefix = f.readline()
def preprocess_vqgan(x):
x = 2.*x - 1.
return x
def convert_color(image, channel):
if channel == 3:
if image.mode == "RGBA":
r, g, b, a = image.split()
image = Image.merge("RGB", (r, g, b))
elif image.mode != "RGB":
image = image.convert("RGBA")
r, g, b, a = image.split()
image = Image.merge("RGB", (r, g, b))
elif channel == 1:
image = image.convert("L")
return image
transform = transforms.Compose([
transforms.Lambda(lambda img: convert_color(img, 3)),
transforms.Resize((args.image_height, args.image_width)),
transforms.ToTensor(),
transforms.Lambda(lambda x: preprocess_vqgan(x)),
])
model = GenerateLm(args)
model = load_model(model, args.load_model_path)
model = model.to(args.device)
model.eval()
vqgan = build_vqgan_model(args)
vqgan = vqgan.to(args.device)
prompt = " ".join(args.prompt.split("_"))
PAD_ID, CLS_ID, SEP_ID, MASK_ID = 0, 101, 102, 103
if args.image_prefix_path is None:
src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(prompt) + [SEP_TOKEN] +
args.tokenizer.tokenize(args.text_prefix) + [SEP_TOKEN])
if len(src) > 64:
src = src[:64]
seg = [1] * len(src)
else:
image = Image.open(args.image_prefix_path)
image = transform(image).to(args.device)
image_token = image_tokenize(vqgan, image)
p_src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(prompt) + [SEP_TOKEN])
src = p_src + [i + args.tokenizer.vocab_bias for i in image_token] + [SEP_ID]
seg = [1] * len(src)
if args.text_prefix is not None:
attr_prompt_src = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(args.text_prefix))
src = src + attr_prompt_src
seg = seg + [2] * len(attr_prompt_src)
beginning_length = len(src)
for r in range(args.samples_num):
src_tensor, seg_tensor = torch.LongTensor([src]), torch.LongTensor([seg])
for i in range(args.seq_length - beginning_length):
src_tensor = src_tensor.to(args.device)
seg_tensor = seg_tensor.to(args.device)
with torch.no_grad():
output = model(src_tensor, seg_tensor)
next_token_logits = output[0][-1] / args.temperature
filtered_logits = top_k_top_p_filtering(next_token_logits, args.top_k, args.top_p)
next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
src_tensor = torch.cat([src_tensor, next_token.view(1, 1)], dim=1)
seg_tensor = torch.cat([seg_tensor, torch.tensor([[2]], device=args.device)], dim=1)
if args.image_prefix_path is not None:
text_id = [str(token_id.item()) for token_id in src_tensor[0][beginning_length:]]
text_id = " ".join(text_id)
text_id = text_id.split(str(SEP_ID))[0].strip().split(" ")
generated_sentence = " ".join(
args.tokenizer.convert_ids_to_tokens([int(i) for i in text_id])
)
print("output " + str(r) + ":" + "\n")
print(generated_sentence + "\n")
else:
image_id = [token_id.item() for token_id in src_tensor[0][beginning_length:]]
img_length = (args.image_height // args.image_tokenizer["frame_size"]) ** 2
img_seg = [i - args.tokenizer.vocab_bias for i in image_id[: img_length]]
image_detokenize(vqgan, img_seg, args.image_tokenizer["image_vocab_size"], False, "output-" + str(r) + ".jpg")
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