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app.py

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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')
+os.system('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)