i try now simply to follow the pytorch - tensorboard tutorial 3 and it’s not working.
more specifically, the tensorboard web page starts but i can’t upload an image.
details:
conda 4.8.5 environment
windows 10
python 3.6.11
pytorch - 1.6
tensorboard - 1.15
pycharm IDE
i tried upgrading tensorboard, tensorboardX.
steps:
i run the program like the tutorial shows, in pycharm.
(not sure it’s critical but i’m new to pycharm and to module based programming. maybe i did something wrong there? full code added below)
in command line i activate the torch environment, change directory to the pycharm project/runs folder and then execute:
 tensorboard --logdir==runs

i get the link to local host web page and see the tensorboard GUI but no image data found. nothing is found for that matter but i’m trying as a first step to upload an image, so nothing works.
pycharm code:
test.py

import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch
from Net import Net
from data_load import classes, trainloader, testloader
from helper_func import matplotlib_imshow
from board import writer

net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

if __name__ == "__main__":
    # default `log_dir` is "runs" - we'll be more specific here
    # get some random training images
    dataiter = iter(trainloader)
    images, labels = dataiter.next()
    print(images.shape)
    # create grid of images
    img_grid = torchvision.utils.make_grid(images)

    # show images
    matplotlib_imshow(img_grid, one_channel=True)

    # write to tensorboard
    writer.add_image('four_fashion_mnist_images', img_grid)

board.py
from torch.utils.tensorboard import SummaryWriter
#from tensorboardX import SummaryWriter

writer = SummaryWriter('runs/fashion_mnist_experiment_1')

data_load.py
import torchvision
import torch
from helper_func import transform


# constant for classes
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
        'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')


# datasets
trainset = torchvision.datasets.FashionMNIST('./data',
    download=True,
    train=True,
    transform=transform)
testset = torchvision.datasets.FashionMNIST('./data',
    download=True,
    train=False,
    transform=transform)

# dataloaders
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                        shuffle=True, num_workers=2)


testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                        shuffle=False, num_workers=2)

helper_func.py
import matplotlib.pyplot as plt
import numpy as np
import torchvision.transforms as transforms

# helper function to show an image
# (used in the `plot_classes_preds` function below)
def matplotlib_imshow(img, one_channel=False):
    if one_channel:
        img = img.mean(dim=0)
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    if one_channel:
        plt.imshow(npimg, cmap="Greys")
    else:
        plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()
# transforms
transform = transforms.Compose(
    [transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))])

Net.py
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


i mention all  this code because so far i did all my programming in notebooks so i’m still confused about the order python interprets all this modules and maybe that caused an error.