def test_tf(img): img = tfs.Resize(256)(img) img, _ = tfs.CenterCrop(224)(img) normalize = tfs.Compose([ tfs.ToTensor(), tfs.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) img = normalize(img) return img
def img_transforms(img, label, crop_size): img, label = random_crop(img, label, crop_size) img_tfs = tfs.Compose([ tfs.ToTensor(), tfs.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) img = img_tfs(img) label = image2label(label) label = torch.from_numpy(label) return img, label
import torch from config import opt from mxtorch import meter from mxtorch import transforms as tfs from mxtorch.trainer import * from mxtorch.vision import model_zoo from torch import nn from torch.autograd import Variable from torch.utils.data import DataLoader from torchvision.datasets import ImageFolder from tqdm import tqdm train_tf = tfs.Compose([ tfs.RandomResizedCrop(224), tfs.RandomHorizontalFlip(), tfs.ToTensor(), tfs.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) def test_tf(img): img = tfs.Resize(256)(img) img, _ = tfs.CenterCrop(224)(img) normalize = tfs.Compose([ tfs.ToTensor(), tfs.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) img = normalize(img) return img