def main(epochs):
Task.init(project_name="examples", task_name="fastai v2")
path = untar_data(URLs.PETS)
files = get_image_files(path / "images")
dls = ImageDataLoaders.from_name_func(path,
files,
label_func,
item_tfms=Resize(224),
num_workers=0)
dls.show_batch()
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(epochs)
learn.show_results()
def create_dataloaders() -> DataLoaders:
"""
Create the dataloaders for the cats vs. dogs dataset.
"""
path = untar_data(URLs.PETS) / "images"
dls = ImageDataLoaders.from_name_func(
path,
get_image_files(path),
valid_pct=0.2,
batch_size=8,
seed=42,
label_func=is_cat,
item_tfms=Resize(224),
)
return dls
from fastai.vision.all import untar_data, URLs, ImageDataLoaders, get_image_files, Resize, error_rate, resnet34, \
cnn_learner
from labml import lab, experiment
from labml.utils.fastai import LabMLFastAICallback
path = untar_data(
URLs.PETS,
dest=lab.get_data_path(),
fname=lab.get_data_path() / URLs.path(URLs.PETS).name) / 'images'
def is_cat(x):
return x[0].isupper()
dls = ImageDataLoaders.from_name_func(path,
get_image_files(path),
valid_pct=0.2,
seed=42,
label_func=is_cat,
item_tfms=Resize(224))
# Train the model ⚡
learn = cnn_learner(dls,
resnet34,
metrics=error_rate,
cbs=LabMLFastAICallback())
with experiment.record(name='pets', exp_conf=learn.labml_configs()):
learn.fine_tune(5)
def remove_failed_images(path: Path) -> L:
image_paths = V.get_image_files(path)
failed_images = V.verify_images(image_paths)
return failed_images.map(Path.unlink)