76 lines
1.9 KiB
Python
76 lines
1.9 KiB
Python
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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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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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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
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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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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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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 /= 255.0
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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(backbone, 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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'vgg' : 'https://download.pytorch.org/models/vgg16-397923af.pth',
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'resnet50' : 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth'
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}
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url = download_urls[backbone]
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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)
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