93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
import random
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import numpy as np
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import torch
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from PIL import Image
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#---------------------------------------------------------#
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# 将图像转换成RGB图像，防止灰度图在预测时报错。
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# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
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#---------------------------------------------------------#
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def cvtColor(image):
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if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
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return image
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else:
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image = image.convert('RGB')
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return image
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#---------------------------------------------------#
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# 对输入图像进行resize
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#---------------------------------------------------#
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def resize_image(image, size):
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iw, ih = image.size
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w, h = size
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scale = min(w/iw, h/ih)
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nw = int(iw*scale)
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nh = int(ih*scale)
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image = image.resize((nw,nh), Image.BICUBIC)
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new_image = Image.new('RGB', size, (128,128,128))
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new_image.paste(image, ((w-nw)//2, (h-nh)//2))
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return new_image, nw, nh # 颜色会变
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#---------------------------------------------------#
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# 获得学习率
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#---------------------------------------------------#
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def get_lr(optimizer):
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for param_group in optimizer.param_groups:
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return param_group['lr']
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#---------------------------------------------------#
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# 设置种子
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#---------------------------------------------------#
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def seed_everything(seed=11):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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#---------------------------------------------------#
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# 设置Dataloader的种子
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#---------------------------------------------------#
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def worker_init_fn(worker_id, rank, seed):
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worker_seed = rank + seed
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random.seed(worker_seed)
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np.random.seed(worker_seed)
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torch.manual_seed(worker_seed)
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def preprocess_input(image):
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image -= np.array([123.675, 116.28, 103.53], np.float32)
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image /= np.array([58.395, 57.12, 57.375], np.float32)
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return image
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def show_config(**kwargs):
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print('Configurations:')
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print('-' * 70)
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print('|%25s | %40s|' % ('keys', 'values'))
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print('-' * 70)
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for key, value in kwargs.items():
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print('|%25s | %40s|' % (str(key), str(value)))
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print('-' * 70)
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def download_weights(phi, model_dir="./model_data"):
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import os
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from torch.hub import load_state_dict_from_url
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download_urls = {
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'b0' : "https://github.com/bubbliiiing/segformer-pytorch/releases/download/v1.0/segformer_b0_backbone_weights.pth",
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'b1' : "https://github.com/bubbliiiing/segformer-pytorch/releases/download/v1.0/segformer_b1_backbone_weights.pth",
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'b2' : "https://github.com/bubbliiiing/segformer-pytorch/releases/download/v1.0/segformer_b2_backbone_weights.pth",
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'b3' : "https://github.com/bubbliiiing/segformer-pytorch/releases/download/v1.0/segformer_b3_backbone_weights.pth",
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'b4' : "https://github.com/bubbliiiing/segformer-pytorch/releases/download/v1.0/segformer_b4_backbone_weights.pth",
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'b5' : "https://github.com/bubbliiiing/segformer-pytorch/releases/download/v1.0/segformer_b5_backbone_weights.pth",
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}
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url = download_urls[phi]
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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load_state_dict_from_url(url, model_dir) |