Visualize predictions with tables

!pip install wandb -qqq

import wandb
wandb.login()

WANDB_PROJECT = "mnist-viz"

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as T
import torch.nn.functional as F


device = "cuda:0" if torch.cuda.is_available() else "cpu"

# create train and test dataloaders
def get_dataloader(is_train, batch_size, slice=5):
    "Get a training dataloader"
    ds = torchvision.datasets.MNIST(root=".", train=is_train, transform=T.ToTensor(), download=True)
    loader = torch.utils.data.DataLoader(dataset=ds,
                                         batch_size=batch_size,
                                         shuffle=True if is_train else False,
                                         pin_memory=True, num_workers=2)
    return loader

# Number of epochs to run
# Each epoch includes a training step and a test step, so this sets
# the number of tables of test predictions to log
EPOCHS = 1

# Number of batches to log from the test data for each test step
# (default set low to simplify demo)
NUM_BATCHES_TO_LOG = 10 #79

# Number of images to log per test batch
# (default set low to simplify demo)
NUM_IMAGES_PER_BATCH = 32 #128

# training configuration and hyperparameters
NUM_CLASSES = 10
BATCH_SIZE = 32
LEARNING_RATE = 0.001
L1_SIZE = 32
L2_SIZE = 64
# changing this may require changing the shape of adjacent layers
CONV_KERNEL_SIZE = 5

# define a two-layer convolutional neural network
class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, L1_SIZE, CONV_KERNEL_SIZE, stride=1, padding=2),
            nn.BatchNorm2d(L1_SIZE),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(L1_SIZE, L2_SIZE, CONV_KERNEL_SIZE, stride=1, padding=2),
            nn.BatchNorm2d(L2_SIZE),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*L2_SIZE, NUM_CLASSES)
        self.softmax = nn.Softmax(NUM_CLASSES)

    def forward(self, x):
        # uncomment to see the shape of a given layer:
        #print("x: ", x.size())
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out

train_loader = get_dataloader(is_train=True, batch_size=BATCH_SIZE)
test_loader = get_dataloader(is_train=False, batch_size=2*BATCH_SIZE)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# W&B: Initialize a new run to track this model's training
wandb.init(project="table-quickstart")

# W&B: Log hyperparameters using config
cfg = wandb.config
cfg.update({"epochs" : EPOCHS, "batch_size": BATCH_SIZE, "lr" : LEARNING_RATE,
            "l1_size" : L1_SIZE, "l2_size": L2_SIZE,
            "conv_kernel" : CONV_KERNEL_SIZE,
            "img_count" : min(10000, NUM_IMAGES_PER_BATCH*NUM_BATCHES_TO_LOG)})

# define model, loss, and optimizer
model = ConvNet(NUM_CLASSES).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)

# convenience funtion to log predictions for a batch of test images
def log_test_predictions(images, labels, outputs, predicted, test_table, log_counter):
  # obtain confidence scores for all classes
  scores = F.softmax(outputs.data, dim=1)
  log_scores = scores.cpu().numpy()
  log_images = images.cpu().numpy()
  log_labels = labels.cpu().numpy()
  log_preds = predicted.cpu().numpy()
  # adding ids based on the order of the images
  _id = 0
  for i, l, p, s in zip(log_images, log_labels, log_preds, log_scores):
    # add required info to data table:
    # id, image pixels, model's guess, true label, scores for all classes
    img_id = str(_id) + "_" + str(log_counter)
    test_table.add_data(img_id, wandb.Image(i), p, l, *s)
    _id += 1
    if _id == NUM_IMAGES_PER_BATCH:
      break

# train the model
total_step = len(train_loader)
for epoch in range(EPOCHS):
    # training step
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        # forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        # backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # W&B: Log loss over training steps, visualized in the UI live
        wandb.log({"loss" : loss})
        if (i+1) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                .format(epoch+1, EPOCHS, i+1, total_step, loss.item()))


    # W&B: Create a Table to store predictions for each test step
    columns=["id", "image", "guess", "truth"]
    for digit in range(10):
      columns.append("score_" + str(digit))
    test_table = wandb.Table(columns=columns)

    # test the model
    model.eval()
    log_counter = 0
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            if log_counter < NUM_BATCHES_TO_LOG:
              log_test_predictions(images, labels, outputs, predicted, test_table, log_counter)
              log_counter += 1
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        acc = 100 * correct / total
        # W&B: Log accuracy across training epochs, to visualize in the UI
        wandb.log({"epoch" : epoch, "acc" : acc})
        print('Test Accuracy of the model on the 10000 test images: {} %'.format(acc))

    # W&B: Log predictions table to wandb
    wandb.log({"test_predictions" : test_table})

# W&B: Mark the run as complete (useful for multi-cell notebook)
wandb.finish()