import gradio as gr import requests import torch import torch.nn as nn import timm model = timm.create_model("hf_hub:nateraw/resnet18-random", pretrained=True) model.train() 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 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:.5f}' 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) image = torch.permute(image, [0,2,3,1]) out = model(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()