Image classification task¶

• Image classification on OpenML Task (362128), tiniest ImageNet dataset.

In [ ]:

import torch.nn
import torch.optim

import openml_pytorch.config
import openml
import logging
import warnings

# Suppress FutureWarning messages
warnings.simplefilter(action='ignore')

############################################################################
# Enable logging in order to observe the progress while running the example.
openml.config.logger.setLevel(logging.DEBUG)
openml_pytorch.config.logger.setLevel(logging.DEBUG)
############################################################################

############################################################################
from openml_pytorch.trainer import OpenMLTrainerModule
from openml_pytorch.trainer import OpenMLDataModule
from torchvision.transforms import Compose, Resize, ToPILImage, ToTensor, Lambda
import torchvision

from openml_pytorch.trainer import convert_to_rgb

import torch.nn import torch.optim import openml_pytorch.config import openml import logging import warnings # Suppress FutureWarning messages warnings.simplefilter(action='ignore') ############################################################################ # Enable logging in order to observe the progress while running the example. openml.config.logger.setLevel(logging.DEBUG) openml_pytorch.config.logger.setLevel(logging.DEBUG) ############################################################################ ############################################################################ from openml_pytorch.trainer import OpenMLTrainerModule from openml_pytorch.trainer import OpenMLDataModule from torchvision.transforms import Compose, Resize, ToPILImage, ToTensor, Lambda import torchvision from openml_pytorch.trainer import convert_to_rgb

Define the Model¶

In [ ]:

model = torchvision.models.efficientnet_b0(num_classes=200)

model = torchvision.models.efficientnet_b0(num_classes=200)

Configure the Data Module¶

• Make sure the data is present in the file_dir directory, and the filename_col is correctly set along with this column correctly pointing to where your data is stored.

In [ ]:

transform = Compose(
    [
        ToPILImage(),  # Convert tensor to PIL Image to ensure PIL Image operations can be applied.
        Lambda(
            convert_to_rgb
        ),  # Convert PIL Image to RGB if it's not already.
        Resize(
            (64, 64)
        ),  # Resize the image.
        ToTensor(),  # Convert the PIL Image back to a tensor.
    ]
)
data_module = OpenMLDataModule(
    type_of_data="image",
    file_dir="datasets",
    filename_col="image_path",
    target_mode="categorical",
    target_column="label",
    batch_size = 64,
    transform=transform
)

transform = Compose( [ ToPILImage(), # Convert tensor to PIL Image to ensure PIL Image operations can be applied. Lambda( convert_to_rgb ), # Convert PIL Image to RGB if it's not already. Resize( (64, 64) ), # Resize the image. ToTensor(), # Convert the PIL Image back to a tensor. ] ) data_module = OpenMLDataModule( type_of_data="image", file_dir="datasets", filename_col="image_path", target_mode="categorical", target_column="label", batch_size = 64, transform=transform )

Configure the Trainer Module¶

In [ ]:

trainer = OpenMLTrainerModule(
    data_module=data_module,
    verbose = True,
    epoch_count = 1,
    callbacks=[],
)
openml_pytorch.config.trainer = trainer

trainer = OpenMLTrainerModule( data_module=data_module, verbose = True, epoch_count = 1, callbacks=[], ) openml_pytorch.config.trainer = trainer

Download the task¶

In [ ]:

# Download the OpenML task for tiniest imagenet
task = openml.tasks.get_task(362128)

# Download the OpenML task for tiniest imagenet task = openml.tasks.get_task(362128)

Run the model on the task¶

In [ ]:

run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

In [ ]:

run.publish()

run.publish()

View loss¶

In [ ]:

trainer.runner.cbs[1].plot_loss()

trainer.runner.cbs[1].plot_loss()

View learning rate¶

In [ ]:

trainer.runner.cbs[1].plot_lr()

trainer.runner.cbs[1].plot_lr()

View the classes in the model¶

In [ ]:

trainer.learn.model_classes

trainer.learn.model_classes

Publish the run to OpenML¶

In [ ]:

run.publish()

run.publish()