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| import gradio as gr | |
| import requests | |
| import torch | |
| import torch.nn as nn | |
| import torchvision | |
| import torchvision.transforms as T | |
| import timm | |
| model = timm.create_model("hf_hub:nateraw/resnet18-random", pretrained=True) | |
| model.eval() | |
| import os | |
| def print_bn(): | |
| bn_data = [] | |
| for m in model.modules(): | |
| if(type(m) is nn.BatchNorm2d): | |
| # print(m.momentum) | |
| bn_data.extend(m.running_mean.data.numpy().tolist()) | |
| bn_data.extend(m.running_var.data.numpy().tolist()) | |
| bn_data.append(m.momentum) | |
| return bn_data | |
| def update_bn(image): | |
| cursor_im = 0 | |
| image = image.reshape(-1) | |
| image = T.Resize((40,40))(image) | |
| for m in model.modules(): | |
| if(type(m) is nn.BatchNorm2d): | |
| if(cursor_im < image.shape[0]): | |
| M = m.running_mean.data.shape[0] | |
| if(cursor_im+M < image.shape[0]): | |
| m.running_mean.data = image[cursor_im:cursor_im+M] | |
| cursor_im += M # next | |
| else: | |
| m.running_mean.data[:image.shape[0]-cursor_im] = image[cursor_im:] | |
| break # finish | |
| return | |
| def greet(image): | |
| # url = f'https://huggingface.co/spaces?p=1&sort=modified&search=GPT' | |
| # html = request_url(url) | |
| # key = os.getenv("OPENAI_API_KEY") | |
| # x = torch.ones([1,3,224,224]) | |
| if(image is None): | |
| bn_data = print_bn() | |
| return ','.join([f'{x:.10f}' for x in bn_data]) | |
| else: | |
| print(type(image)) | |
| image = torch.tensor(image).float() | |
| print(image.min(), image.max()) | |
| image = image/255.0 | |
| image = image.unsqueeze(0) | |
| print(image.shape) | |
| image = torch.permute(image, [0,3,1,2]) | |
| out = model(image) | |
| update_bn(image) | |
| # model.train() | |
| return "Hello world!" | |
| image = gr.inputs.Image(label="Upload a photo for beautify", shape=(224,224)) | |
| iface = gr.Interface(fn=greet, inputs=image, outputs="text") | |
| iface.launch() |