mirror of
https://github.com/clearml/clearml
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215 lines
7.0 KiB
Python
215 lines
7.0 KiB
Python
# Using ClearML's Dataset class to register data
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# Make sure to execute dataset_creation.py first
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import matplotlib.pyplot as plt
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision.datasets as datasets
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import torchvision.transforms as transforms
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from ignite.contrib.handlers import TensorboardLogger
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from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
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from ignite.handlers import global_step_from_engine
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from ignite.metrics import Accuracy, Loss, Recall
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from ignite.utils import setup_logger
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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from clearml import Dataset, Task
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dataset_name = "cifar_dataset"
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dataset_project = "dataset_examples"
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task = Task.init(project_name="Image Example", task_name="Image classification with clearml-data")
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params = {
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"number_of_epochs": 20,
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"batch_size": 64,
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"dropout": 0.25,
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"base_lr": 0.001,
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"momentum": 0.9,
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"loss_report": 100,
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}
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params = task.connect(params) # enabling configuration override by clearml/
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print(params) # printing actual configuration (after override in remote mode)
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# The below gets the dataset and stores in the cache. If you want to download the dataset regardless if it's in the
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# cache, use the Dataset.get(dataset_name, dataset_project).get_mutable_local_copy(path to download)
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dataset_path = Dataset.get(
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dataset_name=dataset_name, dataset_project=dataset_project
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).get_local_copy()
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# Dataset and Dataloader initializations
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transform = transforms.Compose([transforms.ToTensor()])
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trainset = datasets.CIFAR10(
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root=dataset_path, train=True, download=False, transform=transform
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)
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trainloader = torch.utils.data.DataLoader(
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trainset, batch_size=params.get("batch_size", 4), shuffle=True, num_workers=10
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)
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testset = datasets.CIFAR10(
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root=dataset_path, train=False, download=False, transform=transform
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)
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testloader = torch.utils.data.DataLoader(
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testset, batch_size=params.get("batch_size", 4), shuffle=False, num_workers=10
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)
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classes = (
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"plane",
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"car",
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"bird",
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"cat",
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"deer",
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"dog",
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"frog",
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"horse",
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"ship",
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"truck",
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)
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tb_logger = TensorboardLogger(log_dir="cifar-output")
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# Helper function to store predictions and scores using matplotlib
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def predictions_gt_images_handler(engine, logger, *args, **kwargs):
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x, _ = engine.state.batch
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y_pred, y = engine.state.output
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num_x = num_y = 4
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le = num_x * num_y
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fig = plt.figure(figsize=(20, 20))
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trans = transforms.ToPILImage()
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for idx in range(le):
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preds = torch.argmax(F.softmax(y_pred[idx], dim=0))
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probs = torch.max(F.softmax(y_pred[idx], dim=0))
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ax = fig.add_subplot(num_x, num_y, idx + 1, xticks=[], yticks=[])
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ax.imshow(trans(x[idx]))
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ax.set_title(
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"{0} {1:.1f}% (label: {2})".format(
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classes[preds], probs * 100, classes[y[idx]]
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),
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color=("green" if preds == y[idx] else "red"),
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)
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logger.writer.add_figure(
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"predictions vs actuals", figure=fig, global_step=engine.state.epoch
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)
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(3, 6, 3)
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self.conv2 = nn.Conv2d(6, 16, 3)
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self.pool = nn.MaxPool2d(2, 2)
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self.fc1 = nn.Linear(16 * 6 * 6, 120)
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self.fc2 = nn.Linear(120, 84)
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self.dorpout = nn.Dropout(p=params.get("dropout", 0.25))
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self.fc3 = nn.Linear(84, 10)
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def forward(self, x):
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x = self.pool(F.relu(self.conv1(x)))
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x = self.pool(F.relu(self.conv2(x)))
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x = x.view(-1, 16 * 6 * 6)
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.fc3(self.dorpout(x))
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return x
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# Training
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def run(epochs, lr, momentum, log_interval):
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device = "cuda" if torch.cuda.is_available() else "cpu"
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net = Net().to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(net.parameters(), lr=lr, momentum=momentum)
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trainer = create_supervised_trainer(net, optimizer, criterion, device=device)
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trainer.logger = setup_logger("trainer")
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val_metrics = {"accuracy": Accuracy(), "loss": Loss(criterion), "recall": Recall()}
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evaluator = create_supervised_evaluator(net, metrics=val_metrics, device=device)
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evaluator.logger = setup_logger("evaluator")
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# Attach handler to plot trainer's loss every 100 iterations
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tb_logger.attach_output_handler(
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trainer,
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event_name=Events.ITERATION_COMPLETED(every=params.get("loss_report")),
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tag="training",
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output_transform=lambda loss: {"loss": loss},
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)
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# Attach handler to dump evaluator's metrics every epoch completed
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for tag, evaluator in [("training", trainer), ("validation", evaluator)]:
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tb_logger.attach_output_handler(
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evaluator,
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event_name=Events.EPOCH_COMPLETED,
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tag=tag,
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metric_names="all",
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global_step_transform=global_step_from_engine(trainer),
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)
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# Attach function to build debug images and report every epoch end
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tb_logger.attach(
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evaluator,
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log_handler=predictions_gt_images_handler,
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event_name=Events.EPOCH_COMPLETED(once=1),
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)
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desc = "ITERATION - loss: {:.2f}"
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pbar = tqdm(initial=0, leave=False, total=len(trainloader), desc=desc.format(0))
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@trainer.on(Events.ITERATION_COMPLETED(every=log_interval))
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def log_training_loss(engine):
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pbar.desc = desc.format(engine.state.output)
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pbar.update(log_interval)
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@trainer.on(Events.EPOCH_COMPLETED)
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def log_training_results(engine):
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pbar.refresh()
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evaluator.run(trainloader)
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metrics = evaluator.state.metrics
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avg_accuracy = metrics["accuracy"]
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avg_nll = metrics["loss"]
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tqdm.write(
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"Training Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}".format(
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engine.state.epoch, avg_accuracy, avg_nll
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)
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)
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@trainer.on(Events.EPOCH_COMPLETED)
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def log_validation_results(engine):
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evaluator.run(testloader)
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metrics = evaluator.state.metrics
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avg_accuracy = metrics["accuracy"]
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avg_nll = metrics["loss"]
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tqdm.write(
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"Validation Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}".format(
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engine.state.epoch, avg_accuracy, avg_nll
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)
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)
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pbar.n = pbar.last_print_n = 0
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@trainer.on(Events.EPOCH_COMPLETED | Events.COMPLETED)
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def log_time():
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tqdm.write(
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"{} took {} seconds".format(
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trainer.last_event_name.name,
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trainer.state.times[trainer.last_event_name.name],
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)
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)
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trainer.run(trainloader, max_epochs=epochs)
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pbar.close()
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PATH = "./cifar_net.pth"
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torch.save(net.state_dict(), PATH)
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print("Finished Training")
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print("Task ID number is: {}".format(task.id))
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run(params.get("number_of_epochs"), params.get("base_lr"), params.get("momentum"), 10)
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