def load_dataset(self,
csv_file,
root_dir=None,
augment=False,
shuffle=True,
batch_size=1):
"""Create a tree dataset for inference
Csv file format is .csv file with the columns "image_path", "xmin","ymin","xmax","ymax" for the image name and bounding box position.
Image_path is the relative filename, not absolute path, which is in the root_dir directory. One bounding box per line.
Args:
csv_file: path to csv file
root_dir: directory of images. If none, uses "image_dir" in config
augment: Whether to create a training dataset, this activates data augmentations
Returns:
ds: a pytorch dataset
"""
ds = dataset.TreeDataset(
csv_file=csv_file,
root_dir=root_dir,
transforms=dataset.get_transform(augment=augment),
label_dict=self.label_dict)
data_loader = torch.utils.data.DataLoader(
ds,
batch_size=batch_size,
shuffle=shuffle,
collate_fn=utilities.collate_fn,
num_workers=self.config["workers"],
)
return data_loader
def run():
csv_file = get_data("OSBS_029.csv")
root_dir = os.path.dirname(csv_file)
ds = dataset.TreeDataset(csv_file=csv_file,
root_dir=root_dir,
transforms=dataset.get_transform(augment=True))
for x in range(1000):
next(iter(ds))
def test_collate():
"""Due to data augmentations the dataset class may yield empty bounding box annotations"""
csv_file = get_data("example.csv")
root_dir = os.path.dirname(csv_file)
ds = dataset.TreeDataset(csv_file=csv_file,
root_dir=root_dir,
transforms=dataset.get_transform(augment=False))
for i in range(len(ds)):
#Between 0 and 1
batch = ds[i]
collated_batch = utilities.collate_fn(batch)
assert len(collated_batch) == 2
def test_TreeDataset_transform(augment):
csv_file = get_data("example.csv")
root_dir = os.path.dirname(csv_file)
ds = dataset.TreeDataset(csv_file=csv_file,
root_dir=root_dir,
transforms=dataset.get_transform(augment=augment))
for i in range(len(ds)):
#Between 0 and 1
path, image, targets = ds[i]
assert image.max() <= 1
assert image.min() >= 0
assert targets["boxes"].shape == (79, 4)
assert targets["labels"].shape == (79, )
def log_images(self, pl_module):
ds = dataset.TreeDataset(
csv_file=self.csv_file,
root_dir=self.root_dir,
transforms=dataset.get_transform(augment=False),
label_dict=pl_module.label_dict)
if self.n > len(ds):
self.n = len(ds)
ds = torch.utils.data.Subset(ds, np.arange(0, self.n, 1))
data_loader = torch.utils.data.DataLoader(
ds, batch_size=1, shuffle=False, collate_fn=utilities.collate_fn)
pl_module.model.eval()
for batch in data_loader:
paths, images, targets = batch
if not pl_module.device.type == "cpu":
images = [x.to(pl_module.device) for x in images]
predictions = pl_module.model(images)
for path, image, prediction, target in zip(paths, images,
predictions, targets):
image = image.permute(1, 2, 0)
image = image.cpu()
visualize.plot_prediction_and_targets(image=image,
predictions=prediction,
targets=target,
image_name=path,
savedir=self.savedir)
plt.close()
try:
saved_plots = glob.glob("{}/*.png".format(self.savedir))
for x in saved_plots:
pl_module.logger.experiment.log_image(x)
except Exception as e:
print(
"Could not find logger in ligthning module, skipping upload, images were saved to {}, error was rasied {}"
.format(self.savedir, e))
def test_multi_image_warning():
tmpdir = tempfile.gettempdir()
csv_file1 = get_data("example.csv")
csv_file2 = get_data("OSBS_029.csv")
df1 = pd.read_csv(csv_file1)
df2 = pd.read_csv(csv_file2)
df = pd.concat([df1, df2])
csv_file = "{}/multiple.csv".format(tmpdir)
df.to_csv(csv_file)
root_dir = os.path.dirname(csv_file1)
ds = dataset.TreeDataset(csv_file=csv_file,
root_dir=root_dir,
transforms=dataset.get_transform(augment=False))
for i in range(len(ds)):
#Between 0 and 1
batch = ds[i]
collated_batch = utilities.collate_fn([None, batch, batch])
len(collated_batch[0]) == 2