ChannelShuffle(2) >>> input = torch.randn(1, 4, 2, 2) >>> print(input) [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]], [[13, 14], [15, 16]], ]] >>> output = channel_shuffle(input) >>> print(output) [[[1, 2], [3, 4]], [[9, ...
#把train_set导入进来,并分成一个批次一个批次的,shuffle是否将数据集打乱 train_loader = torch.utils.data.DataLoader(train_set, batch_size=36, shuffle=True, num_workers=0) # 10000张验证图片 # 第一次使用时要将download设置为True才会自动去下载数据集 val_set = torchvision.datasets.CIFAR10(root='....
Exception: Could not run 'aten::channel_shuffle' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mob...
🐛 Bug nn.ChannelShuffle module is not accessible in python. This code import torch.nn as nn shuffle = nn.ChannelShuffle(2) returns AttributeError: module 'torch.nn' has no attribute 'ChannelShuffle' I will make PR with fix. Environment P...
train_loader = DataLoader(trainset, batch_size=batch_size,num_workers=4, shuffle=True,drop_last=True) 经验教训 线下模型测试时尽量完备一些,比如加入batch_size=1的测试,就可以在线下测试出这个bug,不然很可能线下测试时模型可以正常训练,但是实际使用时由于样本数量变化导致报错。 参考资料 pytorch论坛 文章转...
return data.DataLoader(dataset, batch_size, shuffle=is_train) # 定义一个类来接收变量 class Accumulator: #@save """在n个变量上累计""" def __init__(self, n): self.data = [0.0] * n def add(self, *args): self.data = [a + float(b) for a, b in zip(self.data, a...
data_loader_train=torch.utils.data.DataLoader(dataset=data_train, batch_size=64, shuffle=True) data_loader_test=torch.utils.data.DataLoader(dataset=data_test, batch_size=64, shuffle=True) images, labels = next(iter(data_loader_train)) print(images.shape) img=torchvision.utils.make_grid(images...