Spaces:
Sleeping
Sleeping
File size: 1,213 Bytes
cb70b4c 249653c 3eeeced 48b3a1c 249653c 3eeeced fffc3a2 8503f33 3eeeced 115b639 d52eb29 115b639 fffc3a2 3eeeced fffc3a2 115b639 fffc3a2 5264799 cb70b4c 1f780c8 fffc3a2 cb70b4c |
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 |
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):
print_bn()
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() |