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Fix tensorboardyt v2 transforms API #3866

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Fix tensorboardyt v2 transforms API #3866

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fe8a6a1
Update introyt tutorial to torchvision v2 APIs
saurabhkthakur May 5, 2026
59c2dc4
Update tensorboardyt tutorial to torchvision v2 APIs
saurabhkthakur May 5, 2026
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25 changes: 9 additions & 16 deletions beginner_source/introyt/tensorboardyt_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@

# Image datasets and image manipulation
import torchvision
import torchvision.transforms as transforms
from torchvision.transforms import v2

# Image display
import matplotlib.pyplot as plt
Expand All @@ -68,14 +68,6 @@
# PyTorch TensorBoard support
from torch.utils.tensorboard import SummaryWriter

# In case you are using an environment that has TensorFlow installed,
# such as Google Colab, uncomment the following code to avoid
# a bug with saving embeddings to your TensorBoard directory

# import tensorflow as tf
# import tensorboard as tb
# tf.io.gfile = tb.compat.tensorflow_stub.io.gfile

######################################################################
# Showing Images in TensorBoard
# -----------------------------
Expand All @@ -84,9 +76,10 @@
#

# Gather datasets and prepare them for consumption
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
transform = v2.Compose(
[v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize((0.5,), (0.5,))])

# Store separate training and validations splits in ./data
training_set = torchvision.datasets.FashionMNIST('./data',
Expand Down Expand Up @@ -171,7 +164,7 @@ def matplotlib_imshow(img, one_channel=False):

class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
super().__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
Expand Down Expand Up @@ -214,18 +207,18 @@ def forward(self, x):

running_loss += loss.item()
if i % 1000 == 999: # Every 1000 mini-batches...
print('Batch {}'.format(i + 1))
print(f'Batch {i+1}')
# Check against the validation set
running_vloss = 0.0

# In evaluation mode some model specific operations can be omitted eg. dropout layer
net.train(False) # Switching to evaluation mode, eg. turning off regularisation
net.eval() # Switching to evaluation mode, eg. turning off regularisation
for j, vdata in enumerate(validation_loader, 0):
vinputs, vlabels = vdata
voutputs = net(vinputs)
vloss = criterion(voutputs, vlabels)
running_vloss += vloss.item()
net.train(True) # Switching back to training mode, eg. turning on regularisation
net.train() # Switching back to training mode, eg. turning on regularisation

avg_loss = running_loss / 1000
avg_vloss = running_vloss / len(validation_loader)
Expand Down
28 changes: 15 additions & 13 deletions beginner_source/introyt/trainingyt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,16 +65,18 @@

import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.transforms import v2

# PyTorch TensorBoard support
from torch.utils.tensorboard import SummaryWriter
from datetime import datetime


transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
transform = v2.Compose([
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize((0.5,), (0.5,))
])

# Create datasets for training & validation, download if necessary
training_set = torchvision.datasets.FashionMNIST('./data', train=True, transform=transform, download=True)
Expand All @@ -89,8 +91,8 @@
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')

# Report split sizes
print('Training set has {} instances'.format(len(training_set)))
print('Validation set has {} instances'.format(len(validation_set)))
print(f'Training set has {len(training_set)} instances')
print(f'Validation set has {len(validation_set)} instances')


######################################################################
Expand Down Expand Up @@ -134,7 +136,7 @@ def matplotlib_imshow(img, one_channel=False):
# PyTorch models inherit from torch.nn.Module
class GarmentClassifier(nn.Module):
def __init__(self):
super(GarmentClassifier, self).__init__()
super().__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
Expand Down Expand Up @@ -176,7 +178,7 @@ def forward(self, x):
print(dummy_labels)

loss = loss_fn(dummy_outputs, dummy_labels)
print('Total loss for this batch: {}'.format(loss.item()))
print(f'Total loss for this batch: {loss.item()}')


#################################################################################
Expand Down Expand Up @@ -251,7 +253,7 @@ def train_one_epoch(epoch_index, tb_writer):
running_loss += loss.item()
if i % 1000 == 999:
last_loss = running_loss / 1000 # loss per batch
print(' batch {} loss: {}'.format(i + 1, last_loss))
print(f' batch {i + 1} loss: {last_loss}')
tb_x = epoch_index * len(training_loader) + i + 1
tb_writer.add_scalar('Loss/train', last_loss, tb_x)
running_loss = 0.
Expand All @@ -276,15 +278,15 @@ def train_one_epoch(epoch_index, tb_writer):

# Initializing in a separate cell so we can easily add more epochs to the same run
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
writer = SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))
writer = SummaryWriter(f'runs/fashion_trainer_{timestamp}')
epoch_number = 0

EPOCHS = 5

best_vloss = 1_000_000.

for epoch in range(EPOCHS):
print('EPOCH {}:'.format(epoch_number + 1))
print(f'EPOCH {epoch_number + 1}:')

# Make sure gradient tracking is on, and do a pass over the data
model.train(True)
Expand All @@ -305,7 +307,7 @@ def train_one_epoch(epoch_index, tb_writer):
running_vloss += vloss

avg_vloss = running_vloss / (i + 1)
print('LOSS train {} valid {}'.format(avg_loss, avg_vloss))
print(f'LOSS train {avg_loss} valid {avg_vloss}')

# Log the running loss averaged per batch
# for both training and validation
Expand All @@ -317,7 +319,7 @@ def train_one_epoch(epoch_index, tb_writer):
# Track best performance, and save the model's state
if avg_vloss < best_vloss:
best_vloss = avg_vloss
model_path = 'model_{}_{}'.format(timestamp, epoch_number)
model_path = f'model_{timestamp}_{epoch_number}'
torch.save(model.state_dict(), model_path)

epoch_number += 1
Expand Down
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