from utils import progress_bar File "C:\Users\Debadri\pytorch-cifar\utils.py", line 45, in _, term_width = os.popen('stty size', 'r').read().split() ValueError: not enough values to unpack (expected 2, got 0) I'm getting this error. I'm writing this command: "python main.py"

Is there any argument I need to pass?

I run the Vgg16 model on Cifar-10 ,but the acc in test image only has 89%. I set epoch =350, and I automatically change learning rate as you mentioned. And I want to know why do you set classifier=nn.Linear(512,10)? 512 ,the number is how to determine? Thanks!

Hi --

I was looking at the implementation of the preactivation ResNet and had a question -- in the paper, they show the preactivation block below (on the right):

which AFAICT should look like:

    def forward(self, x):
        shortcut = self.shortcut(x)
        out = self.bn1(x)
        out = F.relu(out)
        out = self.conv1(out)
        
        out = self.bn2(out)
        out = F.relu(out)
        out = self.conv2(out)
        
        out += shortcut
        return out

which is slightly different from your implementation:

    def forward(self, x):
        out = self.bn1(x)
        out = F.relu(out)
        shortcut = self.shortcut(out)
        out = self.conv1(out)
        
        out = self.bn2(out)
        out = F.relu(out)
        out = self.conv2(out)
        
        out += shortcut
        return out

In the picture, I think your implementation would look like shifting the first bn and relu into the grey band, then splitting into two branches.

Any thoughts? Am I misinterpreting, or is there a reason for the difference?

Hi, I'm totally a newbie in machine learning and I am trying out the transfer learning tutorial on the Pytorch website https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html. When I run the file, this error pops up : AttributeError: 'ResNet' object has no attribute 'to'. Can any help me?

The first time I run mobilenet v2 experiment, it gives me an error in forward function. So I change the the kernel size of avg pooling layer from 2 --> 4. I train this model using batch size 128, rl 0.1 (x0.1 when epoch in [150, 250]). It gives me around 91.0% . What could be the problem? Thanks!

Hey,

How did you calculate the standard deviation values for transforms.Normalize? I am getting the same means, but different standard deviations:

import numpy as np

from torchvision import datasets
from torchvision import transforms

transform_train = transforms.Compose([
#     transforms.RandomCrop(32, padding=4),
#     transforms.RandomHorizontalFlip(),
    transforms.ToTensor()
])

trainset = datasets.CIFAR10(root='data', train=True, download=True, transform=transform_train)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=50_000, shuffle=True)

train = train_loader.__iter__().next()[0]

print('Mean: {}'.format(np.mean(train.numpy(), axis=(0, 2, 3))))
# Mean: [ 0.49139765  0.48215759  0.44653141]
print('STD: {}'.format(np.std(train.numpy(), axis=(0, 2, 3))))
# STD: [ 0.24703199  0.24348481  0.26158789]

Since these networks work well on cifar10 and cifar100. .Now I want to work on VGG16 with other dataset i.e STL10. image shape is 96 X 96 X 3.. But it is giving me below error. How to fix?

File "/home/kumar/kumar/ResNet18 CIFAR10/models/vgg.py", line 48, in test y = net(x) File "/home/kumar/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call result = self.forward(*input, **kwargs) File "/home/kumar/kumar/ResNet18 CIFAR10/models/vgg.py", line 26, in forward out = self.classifier(out) File "/home/kumar/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call result = self.forward(*input, **kwargs) File "/home/kumar/.local/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 87, in forward return F.linear(input, self.weight, self.bias) File "/home/kumar/.local/lib/python3.6/site-packages/torch/nn/functional.py", line 1369, in linear ret = torch.addmm(bias, input, weight.t()) RuntimeError: size mismatch, m1: [2 x 4608], m2: [512 x 10] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:752

ubuntu@ip-172-31-35-111:~/pytorch-cifar$ python3 main.py Traceback (most recent call last): File "main.py", line 16, in from models import * File "/home/ubuntu/pytorch-cifar/models/init.py", line 1, in from vgg import * ModuleNotFoundError: No module named 'vgg'

File "main.py", line 69, in net = net.to(device) AttributeError: 'ResNet' object has no attribute 'to'

Is there anybody who got the same error?

Forgive my ignorance, I am wondering about the the following code transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010) Why are these number for (mean, std)? Are they for some mini-batch (how large the batch size) or total dataset?

Thanks?

In this line: https://github.com/kuangliu/pytorch-cifar/blob/master/models/vgg.py#L37 There is a 1x1 average pooling layer with stride 1. According to my knowledge of pooling, this layer basically does nothing?

layer_name: <class 'torch.nn.modules.conv.Conv2d'>, total_params: 15121584, total_traina_params: 15121584, n_layers: 39
device:  cpu
Traceback (most recent call last):
  File "main.py", line 208, in <module>
    test(epoch)
  File "main.py", line 189, in test
    outputs = net(inputs)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/brcao/Repos/pytorch-cifar/models/dla_simple.py", line 106, in forward
    out = self.base(x)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/container.py", line 117, in forward
    input = module(input)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 423, in forward
    return self._conv_forward(input, self.weight)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 419, in _conv_forward
    return F.conv2d(input, weight, self.bias, self.stride,
RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _thnn_conv2d_forward

I ran this repo with ResNet18 using 4 GPUs and with the latest Pytorch version and I got 95.67% instead of 93.02% reported in the ReadMe table. I'm wondering whether anything improved in Pytorch that could explain this major improvement?

My requirements list: numpy==1.22.3 torch==1.11.0+cu115 torchvision==0.12.0+cu115 -f https://download.pytorch.org/whl/torch_stable.html