def main(args, _=None):
"""Run ``catalyst-data project-embeddings`` script."""
df = pd.read_csv(args.in_csv)
os.makedirs(args.out_dir, exist_ok=True)
if args.meta_cols is not None:
meta_header = args.meta_cols.split(",")
else:
raise ValueError("meta-cols must not be None")
features = np.load(args.in_npy, mmap_mode="r")
assert len(df) == len(features)
if args.num_rows is not None:
indices = np.random.choice(len(df), args.num_rows)
features = features[indices, :]
df = df.iloc[indices]
if args.img_col is not None:
img_data = _load_image_data(
rootpath=args.img_rootpath, paths=df[args.img_col].values
)
else:
img_data = None
summary_writer = SummaryWriter(args.out_dir)
metadata = df[meta_header].values.tolist()
metadata = [
[
str(text)
.replace("\n", " ")
.replace(r"\s", " ")
.replace(r"\s\s+", " ")
.strip()
for text in texts
]
for texts in metadata
]
assert len(metadata) == len(features)
summary_writer.add_embedding(
features,
metadata=metadata,
label_img=img_data,
metadata_header=meta_header,
)
summary_writer.close()
print(
f"Done. Run `tensorboard --logdir={args.out_dir}` "
+ "to view in Tensorboard"
)
def main(args, _=None):
"""Run ``catalyst-data project-embeddings`` script."""
df = pd.read_csv(args.in_csv)
os.makedirs(args.out_dir, exist_ok=True)
if args.meta_cols is not None:
meta_header = args.meta_cols.split(",")
else:
raise ValueError("meta-cols must not be None")
features = np.load(args.in_npy, mmap_mode="r")
if args.num_rows is not None:
df = df.sample(n=args.num_rows)
if args.img_col is not None:
image_names = [
path.join(args.img_rootpath, name)
for name in df[args.img_col].values
]
img_data = np.stack(
[load_image(name, args.img_size) for name in image_names], axis=0)
img_data = (
img_data.transpose((0, 3, 1, 2)) / 255.0 # noqa: WPS432
).astype(np.float32)
img_data = torch.from_numpy(img_data)
else:
img_data = None
summary_writer = SummaryWriter(args.out_dir)
summary_writer.add_embedding(
features,
metadata=df[meta_header].astype(str).values,
label_img=img_data,
metadata_header=meta_header,
)
summary_writer.close()
print(f"Done. Run `tensorboard --logdir={args.out_dir}` " +
"to view in Tensorboard")