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Colab_Demo

DIS

xuxh@dp.tech

更新于 2024-09-23

推荐镜像 :Basic Image:bohrium-notebook:2023-04-07

推荐机型 :c2_m4_cpu

Clone official repo

Imports

Helpers

Set Parameters

Build Model

Predict Mask

©️ Copyright 2023 @ Authors
作者：xuebinqin
共享协议：本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。
快速开始：点击上方的 开始连接 按钮，选择 bohrium-notebook:2023-04-07镜像及任意CPU节点配置，稍等片刻即可运行。

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Clone official repo

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[1]

! git clone https://github.com/xuebinqin/DIS

%cd ./DIS/IS-Net

!pip install gdown

Cloning into 'DIS'...
remote: Enumerating objects: 151, done.
remote: Counting objects: 100% (116/116), done.
remote: Compressing objects: 100% (83/83), done.
remote: Total 151 (delta 35), reused 108 (delta 30), pack-reused 35
Receiving objects: 100% (151/151), 43.23 MiB | 34.50 MiB/s, done.
Resolving deltas: 100% (37/37), done.
/content/DIS/IS-Net
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (4.4.0)
Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from gdown) (4.6.3)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown) (4.64.0)
Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from gdown) (3.7.1)
Requirement already satisfied: requests[socks] in /usr/local/lib/python3.7/dist-packages (from gdown) (2.23.0)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.24.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (2022.6.15)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (2.10)
Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.7.1)

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Imports

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[2]

import numpy as np

from PIL import Image

import torch

from torch.autograd import Variable

from torchvision import transforms

import torch.nn.functional as F

import gdown

import os

import requests

import matplotlib.pyplot as plt

from io import BytesIO

# project imports

from data_loader_cache import normalize, im_reader, im_preprocess

from models import *

/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead.
  warnings.warn(warning.format(ret))

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Helpers

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[3]

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Download official weights

if not os.path.exists("./saved_models"):

!mkdir ./saved_models

MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"

gdown.download(MODEL_PATH_URL, "./saved_models/isnet.pth", use_cookies=False)

class GOSNormalize(object):

'''

Normalize the Image using torch.transforms

'''

def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):

self.mean = mean

self.std = std

def __call__(self,image):

image = normalize(image,self.mean,self.std)

return image

transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])

def load_image(im_path, hypar):

if im_path.startswith("http"):

im_path = BytesIO(requests.get(im_path).content)

im = im_reader(im_path)

im, im_shp = im_preprocess(im, hypar["cache_size"])

im = torch.divide(im,255.0)

shape = torch.from_numpy(np.array(im_shp))

return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape

def build_model(hypar,device):

net = hypar["model"]#GOSNETINC(3,1)

# convert to half precision

if(hypar["model_digit"]=="half"):

net.half()

for layer in net.modules():

if isinstance(layer, nn.BatchNorm2d):

layer.float()

net.to(device)

if(hypar["restore_model"]!=""):

net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"],map_location=device))

net.to(device)

net.eval()

return net

def predict(net, inputs_val, shapes_val, hypar, device):

'''

Given an Image, predict the mask

'''

net.eval()

if(hypar["model_digit"]=="full"):

inputs_val = inputs_val.type(torch.FloatTensor)

else:

inputs_val = inputs_val.type(torch.HalfTensor)

inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable

ds_val = net(inputs_val_v)[0] # list of 6 results

pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W # we want the first one which is the most accurate prediction

## recover the prediction spatial size to the orignal image size

pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))

ma = torch.max(pred_val)

mi = torch.min(pred_val)

pred_val = (pred_val-mi)/(ma-mi) # max = 1

if device == 'cuda': torch.cuda.empty_cache()

return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need

Downloading...
From: https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn
To: /content/DIS/IS-Net/saved_models/isnet.pth
100%|██████████| 177M/177M [00:03<00:00, 56.6MB/s]

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Set Parameters

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[4]

hypar = {} # paramters for inferencing

hypar["model_path"] ="./saved_models" ## load trained weights from this path

hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights

hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision

## choose floating point accuracy --

hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number

hypar["seed"] = 0

hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size

## data augmentation parameters ---

hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images

hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation

hypar["model"] = ISNetDIS()

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Build Model

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[5]

net = build_model(hypar, device)

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Predict Mask

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[6]

image_path = "https://i5.walmartimages.com/asr/43995148-22bf-4836-b6d3-e8f64a73be54.5398297e6f59fc510e0111bc6ff3a02a.jpeg"

image_bytes = BytesIO(requests.get(image_path).content)

image_tensor, orig_size = load_image(image_path, hypar)

mask = predict(net,image_tensor,orig_size, hypar, device)

f, ax = plt.subplots(1,2, figsize = (35,20))

ax[0].imshow(np.array(Image.open(image_bytes))) # Original image

ax[1].imshow(mask, cmap = 'gray') # retouched image

ax[0].set_title("Original Image")

ax[1].set_title("Mask")

plt.show()

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3722: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
  warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1960: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
  warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")

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DIS

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