from torch.utils.data import dataset
from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, cityscapes
from torchvision import transforms as T
from metrics import StreamSegMetrics
import torch
import torch.nn as nn
from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
from glob import glob
def get_argparser():
parser = argparse.ArgumentParser()
# Datset Options
parser.add_argument("--input", type=str, required=True,
help="path to a single image or image directory")
parser.add_argument("--dataset", type=str, default='voc',
choices=['voc', 'cityscapes'], help='Name of training set')
# Deeplab Options
available_models = sorted(name for name in network.modeling.__dict__ if name.islower() and \
not (name.startswith("__") or name.startswith('_')) and callable(
network.modeling.__dict__[name])
)
parser.add_argument("--model", type=str, default='deeplabv3plus_mobilenet',
choices=available_models, help='model name')
parser.add_argument("--separable_conv", action='store_true', default=False,
help="apply separable conv to decoder and aspp")
parser.add_argument("--output_stride", type=int, default=16, choices=[8, 16])
# Train Options
parser.add_argument("--save_val_results_to", default=None,
help="save segmentation results to the specified dir")
parser.add_argument("--crop_val", action='store_true', default=False,
help='crop validation (default: False)')
parser.add_argument("--val_batch_size", type=int, default=4,
help='batch size for validation (default: 4)')
parser.add_argument("--crop_size", type=int, default=513)
parser.add_argument("--ckpt", default=None, type=str,
help="resume from checkpoint")
parser.add_argument("--gpu_id", type=str, default='0',
help="GPU ID")
return parser
def main():
opts = get_argparser().parse_args()
if opts.dataset.lower() == 'voc':
opts.num_classes = 21
decode_fn = VOCSegmentation.decode_target
elif opts.dataset.lower() == 'cityscapes':
opts.num_classes = 19
decode_fn = Cityscapes.decode_target
os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_id
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Device: %s" % device)
# Setup dataloader
image_files = []
if os.path.isdir(opts.input):
for ext in ['png', 'jpeg', 'jpg', 'JPEG']:
files = glob(os.path.join(opts.input, '**/*.%s'%(ext)), recursive=True)
if len(files)>0:
image_files.extend(files)
elif os.path.isfile(opts.input):
image_files.append(opts.input)
# Set up model (all models are 'constructed at network.modeling)
model = network.modeling.__dict__[opts.model](num_classes=opts.num_classes, output_stride=opts.output_stride)
if opts.separable_conv and 'plus' in opts.model:
network.convert_to_separable_conv(model.classifier)
utils.set_bn_momentum(model.backbone, momentum=0.01)
if opts.ckpt is not None and os.path.isfile(opts.ckpt):
# https://github.com/VainF/DeepLabV3Plus-Pytorch/issues/8#issuecomment-605601402, @PytaichukBohdan
checkpoint = torch.load(opts.ckpt, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint["model_state"])
model = nn.DataParallel(model)
model.to(device)
print("Resume model from %s" % opts.ckpt)
del checkpoint
else:
print("[!] Retrain")
model = nn.DataParallel(model)
model.to(device)
#denorm = utils.Denormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # denormalization for ori images
if opts.crop_val:
transform = T.Compose([
T.Resize(opts.crop_size),
T.CenterCrop(opts.crop_size),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
if opts.save_val_results_to is not None:
os.makedirs(opts.save_val_results_to, exist_ok=True)
with torch.no_grad():
model = model.eval()
for img_path in tqdm(image_files):
ext = os.path.basename(img_path).split('.')[-1]
img_name = os.path.basename(img_path)[:-len(ext)-1]
img = Image.open(img_path).convert('RGB')
img = transform(img).unsqueeze(0) # To tensor of NCHW
img = img.to(device)
pred = model(img).max(1)[1].cpu().numpy()[0] # HW
colorized_preds = decode_fn(pred).astype('uint8')
colorized_preds = Image.fromarray(colorized_preds)
if opts.save_val_results_to:
colorized_preds.save(os.path.join(opts.save_val_results_to, img_name+'.png'))
if __name__ == '__main__':
main()
这个代码运行后的文件存储在哪
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本文介绍了Python中glob模块的基本用法，该模块用于查找符合特定模式的文件路径，并通过实例展示了如何使用glob.glob()函数匹配指定目录下的mp3文件。
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