Spaces:
Runtime error
Runtime error
File size: 7,040 Bytes
0bf81ba 5a61cb9 0bf81ba 5a61cb9 0bf81ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
import os
import math
import platform
import logging
from pathlib import Path
import torch
from transformers import AutoModelForCausalLM
from PIL import Image
import numpy as np
from numpy import asarray
import gradio as gr
import clip
from arguments import get_args
from load import load_model_args, load_model
from utils import get_first_sentence
logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO"))
log = logging.getLogger(__name__)
def prepare(args):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
log.info(f'Device: {device}')
args = load_model_args(args)
def load_style(args, checkpoint):
model = AutoModelForCausalLM.from_pretrained(args.init_model)
if checkpoint is not None and Path(checkpoint).is_file():
log.info("joint model: loading pretrained style generator")
state = torch.load(checkpoint, map_location=torch.device('cpu'))
if 'global_step' in state:
step = state['global_step']
log.info(f'trained for {step} steps')
weights = state['state_dict']
key = 'model.'
weights = {k[len(key):]: v for k, v in weights.items() if k.startswith(key)}
model.load_state_dict(weights)
else:
log.info("joint model: loading vanila gpt")
return model
log.info(f'loading models')
joint_model = load_style(args, checkpoint=getattr(args, 'demo_joint_model_weight', 'None'))
joint_model = joint_model.to(device)
model = load_model(args, device)
tokenizer = model.tokenizer
log.info(f'loaded models ')
class Inferer:
def __init__(self, args, model, joint_model, tokenizer, device):
self.args = args
self.model = model
self.joint_model = joint_model
self.tokenizer = tokenizer
self.device = device
self.clip_model, self.clip_preprocess = clip.load(args.clip_model_type, device=device, jit=False)
def infer_joint(self, batch, window_size=10, vanilla_length=20, sample=False, temperature=0.7, **kwargs):
with torch.no_grad():
rollouts = self.model.sample(input_ids=batch['input_ids'], attention_mask=batch['attention_mask'],
features=batch['features'], labels=None,
max_len=self.args.response_length, sample=sample,
no_repeat_ngram_size=self.args.infer_no_repeat_size,
invalidate_eos=False)
'''
query = rollouts['query/input_ids']
res = rollouts['response/input_ids']
gen1 = torch.cat([query, res], dim=1)
mask1 = torch.cat([rollouts['query/mask'], rollouts['response/mask']], dim=1)
'''
res = rollouts['response/text']
query = rollouts['query/text']
generations = [f'{q} {v.strip()}' for q, v in zip(query, res)]
cur_length = self.args.response_length
if vanilla_length > 0:
for i in range(math.ceil(vanilla_length / window_size)):
cur_length += window_size
generations = self.tokenizer(generations, padding=True, return_tensors='pt').to(self.device)
context = generations['input_ids'][:, :-window_size]
inputs = generations['input_ids'][:, -window_size:]
out = self.joint_model.generate(input_ids=inputs,
max_length=cur_length, sample=sample,
no_repeat_ngram_size=self.args.infer_no_repeat_size,
pad_token_id=self.tokenizer.eos_token_id)
out = torch.cat([context, out], dim=1)
text = [self.tokenizer.decode(v, skip_special_tokens=True, clean_up_tokenization_spaces=True)
for v in out]
# generations = [get_first_sentence(v) for v in generations]
generations = text
query = rollouts['query/text']
del rollouts
torch.cuda.empty_cache()
return query, generations
def get_feature(self, image):
image = self.clip_preprocess(image).unsqueeze(0).to(self.device)
feature = self.clip_model.encode_image(image)
return feature
def __call__(self, image, prompt, length=20, window_size=20, **kwargs):
window_size = min(window_size, length)
vanilla_length = max(0, length - self.args.response_length)
if not prompt:
prompt = 'The'
feature = self.get_feature(image)
feature = feature.unsqueeze(0).to(self.device)
batch = self.tokenizer(prompt, padding=True, return_tensors='pt').to(self.device)
batch['features'] = feature
query, generations = self.infer_joint(batch, window_size=window_size,
vanilla_length=vanilla_length, **kwargs)
# text = f'{query[0].strip()} {generations[0].strip()}'
text = generations[0].strip()
return text
inferer = Inferer(args, model, joint_model, tokenizer, device)
return inferer
class Runner:
def __init__(self, inferer):
self.inferer = inferer
def __call__(self, inp, prompt, length, window_size, sample):
# inp = inp.reshape((224, 224, 3))
img = Image.fromarray(np.uint8(inp))
text = self.inferer(img, prompt, length, window_size, sample=sample)
return prompt, text
# return inp, prompt, text
'''
# test_run
sample_img = asarray(Image.open('../data/coco/images/sample.jpg'))
img, _, text = run(sample_img, 'There lies', 50, 20, sample=False)
print('test_run:', text)
'''
def launch(examples=None):
args = get_args()
inferer = prepare(args)
runner = Runner(inferer)
iface = gr.Interface(
title="Demo for ESPER",
fn=runner.__call__,
inputs=[gr.components.Image(shape=(224, 224)),
gr.components.Textbox(label='prompt'),
gr.components.Slider(20, 120, step=1, label='length'),
gr.components.Slider(10, 100, step=1, label='window_size'),
gr.components.Checkbox(label='do sample')],
outputs=[gr.components.Textbox(label='prompt'),
gr.components.Textbox(label='generation')],
examples=examples
)
if args.port is not None:
print(f"running from {platform.node()}")
iface.launch(
server_name="0.0.0.0",
server_port=args.port
)
else:
iface.launch()
if __name__ == "__main__":
print(f"running from {platform.node()}")
launch()
|