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Python refactor and simplification (#12624)

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* Python code cleanup

* Auto-format by Ultralytics actions

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn JocherandUltralyticsAssistant authored and GitHub committed 2024-01-13 22:34:05 +01:00
1 parent de64179cd4
commit 22e9afef74
11 files changed
+155 -172
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utils/loggers/clearml/clearml_utils.py
+23 -21
View File
@@ -31,7 +31,7 @@ def construct_dataset(clearml_info_string):
"More than one yaml file was found in the dataset root, cannot determine which one contains "
"the dataset definition this way."
)
elif len(yaml_filenames) == 0:
elif not yaml_filenames:
raise ValueError(
"No yaml definition found in dataset root path, check that there is a correct yaml file "
"inside the dataset root path."
@@ -45,7 +45,7 @@ def construct_dataset(clearml_info_string):
{"train", "test", "val", "nc", "names"}
), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
data_dict = dict()
data_dict = {}
data_dict["train"] = (
str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None
)
@@ -96,7 +96,7 @@ class ClearmlLogger:
self.data_dict = None
if self.clearml:
self.task = Task.init(
project_name=opt.project if not str(opt.project).startswith("runs/") else "YOLOv5",
project_name="YOLOv5" if str(opt.project).startswith("runs/") else opt.project,
task_name=opt.name if opt.name != "exp" else "Training",
tags=["YOLOv5"],
output_uri=True,
@@ -202,24 +202,26 @@ class ClearmlLogger:
class_names (dict): dict containing mapping of class int to class name
image (Tensor): A torch tensor containing the actual image data
"""
if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0:
# Log every bbox_interval times and deduplicate for any intermittend extra eval runs
if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images:
im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
annotator = Annotator(im=im, pil=True)
for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
color = colors(i)
if (
len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch
and self.current_epoch >= 0
and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images)
):
im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
annotator = Annotator(im=im, pil=True)
for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
color = colors(i)
class_name = class_names[int(class_nr)]
confidence_percentage = round(float(conf) * 100, 2)
label = f"{class_name}: {confidence_percentage}%"
class_name = class_names[int(class_nr)]
confidence_percentage = round(float(conf) * 100, 2)
label = f"{class_name}: {confidence_percentage}%"
if conf > conf_threshold:
annotator.rectangle(box.cpu().numpy(), outline=color)
annotator.box_label(box.cpu().numpy(), label=label, color=color)
if conf > conf_threshold:
annotator.rectangle(box.cpu().numpy(), outline=color)
annotator.box_label(box.cpu().numpy(), label=label, color=color)
annotated_image = annotator.result()
self.task.get_logger().report_image(
title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image
)
self.current_epoch_logged_images.add(image_path)
annotated_image = annotator.result()
self.task.get_logger().report_image(
title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image
)
self.current_epoch_logged_images.add(image_path)