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# from transformers import AutoModel
import argparse
import logging
import os
import glob
import tqdm
import torch, re
import PIL
import cv2
import numpy as np
import torch.nn.functional as F
from torchvision import transforms
from utils import Config, Logger, CharsetMapper
import gradio as gr
import gdown
gdown.download(id='16PF_b4dURVkBt4OT7E-a-vq-SRxi0uDl', output='lol.pth')
gdown.download(id='19rGjfo73P25O_keQv30snfe3IHrK0uV2', output='config.yaml')
gdown.download(id='1qyNV80qmYHx_r4KsG3_8PXQ6ff1a1dov', output='modules.zip')
gdown.download(id='1UMZ7i8SpfuNw0N2JvVY8euaNx9gu3x6N', output='configs.zip')
gdown.download(id='1yHD7_4DD_keUwGs2nenAYDaQ2CNEA5IU', output='data.zip')
os.system('unzip data.zip && unzip configs.zip && unzip modules.zip')
def get_model(config):
import importlib
names = config.model_name.split('.')
module_name, class_name = '.'.join(names[:-1]), names[-1]
cls = getattr(importlib.import_module(module_name), class_name)
model = cls(config)
logging.info(model)
model = model.eval()
return model
def load(model, file, device=None, strict=True):
if device is None: device = 'cpu'
elif isinstance(device, int): device = torch.device('cuda', device)
assert os.path.isfile(file)
state = torch.load(file, map_location=device)
if set(state.keys()) == {'model', 'opt'}:
state = state['model']
model.load_state_dict(state, strict=strict)
return model
config = Config('config.yaml')
config.model_vision_checkpoint = None
model = get_model(config)
model = load(model, 'lol.pth')
def postprocess(output, charset, model_eval):
def _get_output(last_output, model_eval):
if isinstance(last_output, (tuple, list)):
for res in last_output:
if res['name'] == model_eval: output = res
else: output = last_output
return output
def _decode(logit):
""" Greed decode """
out = F.softmax(logit, dim=2)
pt_text, pt_scores, pt_lengths = [], [], []
for o in out:
text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
text = text.split(charset.null_char)[0] # end at end-token
pt_text.append(text)
pt_scores.append(o.max(dim=1)[0])
pt_lengths.append(min(len(text) + 1, charset.max_length)) # one for end-token
return pt_text, pt_scores, pt_lengths
output = _get_output(output, model_eval)
logits, pt_lengths = output['logits'], output['pt_lengths']
pt_text, pt_scores, pt_lengths_ = _decode(logits)
return pt_text, pt_scores, pt_lengths_
def preprocess(img, width, height):
img = cv2.resize(np.array(img), (width, height))
img = transforms.ToTensor()(img).unsqueeze(0)
mean = torch.tensor([0.485, 0.456, 0.406])
std = torch.tensor([0.229, 0.224, 0.225])
return (img-mean[...,None,None]) / std[...,None,None]
def process_image(image):
charset = CharsetMapper(filename=config.dataset_charset_path, max_length=config.dataset_max_length + 1)
img = image.convert('RGB')
img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
res = model(img)
return postprocess(res, charset, 'alignment')[0][0]
iface = gr.Interface(fn=process_image,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Textbox(),
title="8kun kek",
description="Making Jim Watkins sheete because he is a techlet pedo",
# article=article,
# examples=glob.glob('figs/test/*.png')
)
iface.launch(debug=True) |