chore: import upstream snapshot with attribution

tutorials/04-utils/tensorboard/README.md

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+# TensorBoard in PyTorch
+
+In this tutorial, we implement a MNIST classifier using a simple neural network and visualize the training process using [TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard). In training phase, we plot the loss and accuracy functions through `scalar_summary` and visualize the training images through `image_summary`. In addition, we visualize the weight and gradient values of the parameters of the neural network using `histogram_summary`. PyTorch code for handling these summary functions can be found [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/main.py#L81-L97).
+
+![alt text](gif/tensorboard.gif)
+
+<br>
+
+## Usage 
+
+#### 1. Install the dependencies
+```bash
+$ pip install -r requirements.txt
+```
+
+#### 2. Train the model
+```bash
+$ python main.py
+```
+
+#### 3. Open the TensorBoard
+To run the TensorBoard, open a new terminal and run the command below. Then, open http://localhost:6006/ on your web browser.
+```bash
+$ tensorboard --logdir='./logs' --port=6006
+```

tutorials/04-utils/tensorboard/logger.py

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+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+import tensorflow as tf
+import numpy as np
+import scipy.misc 
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+
+
+class Logger(object):
+    
+    def __init__(self, log_dir):
+        """Create a summary writer logging to log_dir."""
+        self.writer = tf.summary.FileWriter(log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        """Log a scalar variable."""
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
+        self.writer.add_summary(summary, step)
+
+    def image_summary(self, tag, images, step):
+        """Log a list of images."""
+
+        img_summaries = []
+        for i, img in enumerate(images):
+            # Write the image to a string
+            try:
+                s = StringIO()
+            except:
+                s = BytesIO()
+            scipy.misc.toimage(img).save(s, format="png")
+
+            # Create an Image object
+            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
+                                       height=img.shape[0],
+                                       width=img.shape[1])
+            # Create a Summary value
+            img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))
+
+        # Create and write Summary
+        summary = tf.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+        
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+
+        # Create a histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill the fields of the histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values**2))
+
+        # Drop the start of the first bin
+        bin_edges = bin_edges[1:]
+
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()

tutorials/04-utils/tensorboard/main.py

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+import torch
+import torch.nn as nn
+import torchvision
+from torchvision import transforms
+from logger import Logger
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# MNIST dataset 
+dataset = torchvision.datasets.MNIST(root='../../data', 
+                                     train=True, 
+                                     transform=transforms.ToTensor(),  
+                                     download=True)
+
+# Data loader
+data_loader = torch.utils.data.DataLoader(dataset=dataset, 
+                                          batch_size=100, 
+                                          shuffle=True)
+
+
+# Fully connected neural network with one hidden layer
+class NeuralNet(nn.Module):
+    def __init__(self, input_size=784, hidden_size=500, num_classes=10):
+        super(NeuralNet, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size) 
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, num_classes)  
+    
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+model = NeuralNet().to(device)
+
+logger = Logger('./logs')
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.Adam(model.parameters(), lr=0.00001)  
+
+data_iter = iter(data_loader)
+iter_per_epoch = len(data_loader)
+total_step = 50000
+
+# Start training
+for step in range(total_step):
+    
+    # Reset the data_iter
+    if (step+1) % iter_per_epoch == 0:
+        data_iter = iter(data_loader)
+
+    # Fetch images and labels
+    images, labels = next(data_iter)
+    images, labels = images.view(images.size(0), -1).to(device), labels.to(device)
+    
+    # Forward pass
+    outputs = model(images)
+    loss = criterion(outputs, labels)
+    
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    # Compute accuracy
+    _, argmax = torch.max(outputs, 1)
+    accuracy = (labels == argmax.squeeze()).float().mean()
+
+    if (step+1) % 100 == 0:
+        print ('Step [{}/{}], Loss: {:.4f}, Acc: {:.2f}' 
+               .format(step+1, total_step, loss.item(), accuracy.item()))
+
+        # ================================================================== #
+        #                        Tensorboard Logging                         #
+        # ================================================================== #
+
+        # 1. Log scalar values (scalar summary)
+        info = { 'loss': loss.item(), 'accuracy': accuracy.item() }
+
+        for tag, value in info.items():
+            logger.scalar_summary(tag, value, step+1)
+
+        # 2. Log values and gradients of the parameters (histogram summary)
+        for tag, value in model.named_parameters():
+            tag = tag.replace('.', '/')
+            logger.histo_summary(tag, value.data.cpu().numpy(), step+1)
+            logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), step+1)
+
+        # 3. Log training images (image summary)
+        info = { 'images': images.view(-1, 28, 28)[:10].cpu().numpy() }
+
+        for tag, images in info.items():
+            logger.image_summary(tag, images, step+1)

tutorials/04-utils/tensorboard/requirements.txt

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+tensorflow
+torch
+torchvision
+scipy
+numpy