def get_dataset(opts): """ Dataset And Augmentation """ if opts.dataset == 'voc': train_transform = et.ExtCompose([ #et.ExtResize(size=opts.crop_size), et.ExtRandomScale((0.5, 2.0)), et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True), et.ExtRandomHorizontalFlip(), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) if opts.crop_val: val_transform = et.ExtCompose([ et.ExtResize(opts.crop_size), et.ExtCenterCrop(opts.crop_size), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) else: val_transform = et.ExtCompose([ et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) train_dst = VOCSegmentation(root=opts.data_root, year=opts.year, image_set='train', download=opts.download, transform=train_transform) val_dst = VOCSegmentation(root=opts.data_root, year=opts.year, image_set='val', download=False, transform=val_transform) if opts.dataset == 'cityscapes': train_transform = et.ExtCompose([ #et.ExtResize( 512 ), et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size)), et.ExtColorJitter( brightness=0.5, contrast=0.5, saturation=0.5 ), et.ExtRandomHorizontalFlip(), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) val_transform = et.ExtCompose([ et.ExtResize( 256 ), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) train_dst = Cityscapes(root=opts.data_root, split='train', transform=train_transform) val_dst = Cityscapes(root=opts.data_root, split='val', transform=val_transform) return train_dst, val_dst
def get_dataset(opts): """ Dataset And Augmentation """ if opts.dataset == 'voc': train_transform = et.ExtCompose([ #et.ExtResize(size=opts.crop_size), et.ExtRandomScale((0.5, 2.0)), et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True), et.ExtRandomHorizontalFlip(), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) if opts.crop_val: val_transform = et.ExtCompose([ et.ExtResize(opts.crop_size), et.ExtCenterCrop(opts.crop_size), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) else: val_transform = et.ExtCompose([ et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) train_dst = VOCSegmentation(root=opts.data_root, year=opts.year, image_set='train', download=opts.download, transform=train_transform) val_dst = VOCSegmentation(root=opts.data_root, year=opts.year, image_set='val', download=False, transform=val_transform) if opts.dataset == 'cityscapes': train_transform = et.ExtCompose([ #et.ExtResize( 512 ), et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size)), et.ExtColorJitter(brightness=0.5, contrast=0.5, saturation=0.5), et.ExtRandomHorizontalFlip(), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) val_transform = et.ExtCompose([ #et.ExtResize( 512 ), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) train_dst = Cityscapes(root=opts.data_root, split='train', transform=train_transform) val_dst = Cityscapes(root=opts.data_root, split='val', transform=val_transform) if opts.dataset == 'weedcluster': train_dst = WeedClusterDataset(root=opts.data_root, split='train') val_dst = WeedClusterDataset(root=opts.data_root, split='val') if opts.dataset == 'cloudshadow': train_dst = CloudShadowDataset(root=opts.data_root, split='train') val_dst = CloudShadowDataset(root=opts.data_root, split='val') if opts.dataset == 'doubleplant': train_dst = DoublePlantDataset(root=opts.data_root, split='train') val_dst = DoublePlantDataset(root=opts.data_root, split='val') if opts.dataset == 'planterskip': train_dst = PlanterSkipDataset(root=opts.data_root, split='train') val_dst = PlanterSkipDataset(root=opts.data_root, split='val') if opts.dataset == 'standingwater': train_dst = StandingWaterDataset(root=opts.data_root, split='train') val_dst = StandingWaterDataset(root=opts.data_root, split='val') if opts.dataset == 'waterway': train_dst = WaterwayDataset(root=opts.data_root, split='train') val_dst = WaterwayDataset(root=opts.data_root, split='val') return train_dst, val_dst
from datasets.cityscapes import Cityscapes from utils import ext_transforms as et import torch import torch.nn as nn import torch.nn.functional as F from PIL import Image import matplotlib import matplotlib.pyplot as plt data_root = '/data/kdn/Dataset/Cityscapes' crop_size = 520 train_transform = et.ExtCompose([ et.ExtRandomCrop(size=(crop_size, crop_size)), et.ExtColorJitter(brightness=0.5, contrast=0.5, saturation=0.5), et.ExtRandomHorizontalFlip(), et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) val_transform = et.ExtCompose([ et.ExtToTensor(), et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) train_dst = Cityscapes(root=data_root, split='train', transform=train_transform) val_dst = Cityscapes(root=data_root, split='val', transform=val_transform)