def main():
# Parse command-line arguments
args = parse_args()
# Split data between training and testing
splitter = GrandparentSplitter(train_name="training", valid_name="testing")
# Prepare DataBlock which is a generic container to quickly build Datasets and DataLoaders
mnist = DataBlock(
blocks=(ImageBlock(PILImage), CategoryBlock),
get_items=get_image_files,
splitter=splitter,
get_y=parent_label,
)
# Download, untar the MNIST data set and create DataLoader from DataBlock
data = mnist.dataloaders(untar_data(URLs.MNIST), bs=256, num_workers=0)
# Enable auto logging
mlflow.fastai.autolog()
# Create Learner model
learn = cnn_learner(data, resnet18)
# Start MLflow session
with mlflow.start_run():
# Train and fit with default or supplied command line arguments
learn.fit_one_cycle(args.epochs, args.lr)
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()
# ## TensorBoardCallback
# +
path = untar_data(URLs.PETS)
db = DataBlock(blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
item_tfms=Resize(128),
splitter=RandomSubsetSplitter(train_sz=0.1, valid_sz=0.01),
batch_tfms=aug_transforms(size=64),
get_y=using_attr(RegexLabeller(r'(.+)_\d+.*$'), 'name'))
dls = db.dataloaders(path / 'images')
# -
learn = cnn_learner(dls, resnet18, metrics=accuracy)
learn.unfreeze()
learn.fit_one_cycle(3, cbs=TensorBoardCallback(Path.home() / 'tmp' / 'runs' / 'tb', trace_model=True))
# ## Projector
# ### Projector in TensorBoardCallback
path = untar_data(URLs.PETS)
# +
db = DataBlock(blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
item_tfms=Resize(128),
splitter=RandomSubsetSplitter(train_sz=0.05, valid_sz=0.01),
def __init__(self, verbose=True, base_dir='.', test_only=False, **kwargs):
"""Initialize substructure-predicting model
Args:
verbose: if True, print messages during initialization
base_dir: Path to directory containing datasets (each in their
own folder). Defaults to current directory.
test_only: if True, initialize with a dummy dataset
(2 blank images with meaningless metadata).
**kwargs: Configuration options
"""
self.print = print = builtins.print if verbose else lambda x: x
self.train_config = tc = dict(val_galaxies=dds.metadata.val_galaxies,
bad_galaxies=dds.load_bad_galaxies())
tc.update(**kwargs)
self.fit_parameters = tc['fit_parameters']
self.n_params = len(self.fit_parameters)
self.short_names = [
dds.short_names.get(pname, pname) for pname in self.fit_parameters
]
if test_only:
print(f"Setting up model with meaningless toy data. You won't be "
"able to train or use predict_all, but you can run predict "
"on new images.")
self.data_dir = dds.make_dummy_dataset()
else:
self.data_dir = Path(base_dir) / tc['dataset_name']
if self.data_dir.exists():
print(f"Setting up model for dataset {tc['dataset_name']}")
else:
raise FileNotFoundError(
f"{self.data_dir} not found! Check base dir, or "
"pass test_only = True to setup model for evaluation only."
)
self.metadata, self.galaxy_indices = dds.load_metadata(
self.data_dir,
val_galaxies=tc['val_galaxies'],
bad_galaxies=tc['bad_galaxies'],
remove_bad=True,
verbose=False if test_only else verbose)
if 'normalizer_means' in tc:
self.normalizer = dds.Normalizer(
fit_parameters=self.fit_parameters,
means=tc['normalizer_means'],
scales=tc['normalizer_scales'])
else:
self.normalizer = dds.Normalizer(self.metadata,
self.fit_parameters)
self.has_cuda = torch.cuda.is_available()
print(f"Cuda available: {self.has_cuda}")
if self.has_cuda:
print("CUDA device: " +
torch.cuda.get_device_name(torch.cuda.current_device()))
# Setting these up will take a while; looks like it's loading the entire
# dataset in RAM? I'm probably butchering the fastai dataloader API...
print("Setting up data block and data loaders, could take a while")
self.data_block = dds.data_block(
self.metadata,
fit_parameters=tc['fit_parameters'],
data_dir=self.data_dir,
uncertainty=tc['uncertainty'],
augment_rotation=tc['augment_rotation'])
self.data_loaders = self.data_block.dataloaders(None,
bs=tc['batch_size'])
print("Dataloaders initialized")
self.metrics = dds.all_metrics(self.fit_parameters, self.normalizer,
self.short_names,
self.train_config['uncertainty'])
arch = getattr(fv, tc['architecture'])
if 'architecture_options' in tc:
arch = partial(arch, **tc['architecture_options'])
self.dropout_switch = dds.TestTimeDropout()
if not tc.get('truncate_final'):
truncate_final_to = None
else:
# Truncate final parameter to physical value = 0;
# find encoded value of zero
final_p = list(self.fit_parameters)[-1]
truncate_final_to = self.normalizer.norm(0, param_name=final_p)
print(f"Truncating {final_p} to 0, encoded as {truncate_final_to}")
self.learner = fv.cnn_learner(
dls=self.data_loaders,
arch=arch,
n_in=1,
n_out=dds.n_out(self.n_params, tc['uncertainty']),
loss_func=dds.loss_for(
self.fit_parameters,
tc['uncertainty'],
truncate_final_to=truncate_final_to,
parameter_weights=tc.get('parameter_weights')),
metrics=self.metrics,
pretrained=False,
ps=tc.get('dropout_p', 0.5),
cbs=[self.dropout_switch],
bn_final=tc['bn_final'])
def _create_learner(self):
dls = self._instantiate_empty_dataloader()
self.learn = cnn_learner(dls, resnet34, metrics=error_rate)
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)
products = products.new(item_tfms=RandomResizedCrop(168, min_scale=0.8),
batch_tfms=aug_transforms())
project_path = Path("/home/yaro/Workspace/fastai/")
dataset_path = project_path.joinpath("for_test")
dls = products.dataloaders(dataset_path)
gpu = None
if torch.cuda.is_available():
if gpu is not None: torch.cuda.set_device(gpu)
n_gpu = torch.cuda.device_count()
else:
n_gpu = None
learn = cnn_learner(dls, resnet18, metrics=error_rate).to_fp16()
# The context manager way of dp/ddp, both can handle single GPU base case.
if gpu is None and n_gpu is not None:
ctx = learn.parallel_ctx
with partial(ctx, gpu)():
print(
f"Training in {ctx.__name__} context on GPU {list(range(n_gpu))}")
learn.fine_tune(2)
else:
learn.fine_tune(2)
# In[]:
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix()
plt.show()
