Python refactor and simplification (#2174)

* Python code cleanup

* Auto-format by Ultralytics actions

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

export.py

@@ -546,7 +546,7 @@ def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")):
         "--quantize_uint8" if int8 else "",
         "--output_node_names=Identity,Identity_1,Identity_2,Identity_3",
         str(f_pb),
-        str(f),
+        f,
     ]
     subprocess.run([arg for arg in args if arg], check=True)
 

models/common.py

@@ -847,11 +847,17 @@ def pandas(self):
     def tolist(self):
         # return a list of Detections objects, i.e. 'for result in results.tolist():'
         r = range(self.n)  # iterable
-        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
-        # for d in x:
-        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
+        return [
+            Detections(
+                [self.ims[i]],
+                [self.pred[i]],
+                [self.files[i]],
+                self.times,
+                self.names,
+                self.s,
+            )
+            for i in r
+        ]
 
     def print(self):
         LOGGER.info(self.__str__())

utils/downloads.py

@@ -24,9 +24,7 @@ def is_url(url, check=True):
 def gsutil_getsize(url=""):
     # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
     output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8")
-    if output:
-        return int(output.split()[0])
-    return 0
+    return int(output.split()[0]) if output else 0
 
 
 def url_getsize(url="https://ultralytics.com/images/bus.jpg"):

utils/loggers/clearml/README.md

@@ -34,15 +34,15 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t
 
 1. Install the `clearml` python package:
 
-   ```bash
-   pip install clearml
-   ```
+    ```bash
+    pip install clearml
+    ```
 
 2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
 
-   ```bash
-   clearml-init
-   ```
+    ```bash
+    clearml-init
+    ```
 
 That's it! You're done 😎
 

utils/loggers/clearml/clearml_utils.py

@@ -29,7 +29,7 @@ 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."
@@ -43,7 +43,7 @@ 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
     )
@@ -148,24 +148,26 @@ def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_thres
         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)
-
-                    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)
-
-                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)
+        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}%"
+
+                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)

utils/loggers/comet/__init__.py

@@ -166,34 +166,33 @@ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwar
 
     def _get_experiment(self, mode, experiment_id=None):
         if mode == "offline":
+            return (
+                comet_ml.ExistingOfflineExperiment(
+                    previous_experiment=experiment_id,
+                    **self.default_experiment_kwargs,
+                )
+                if experiment_id is not None
+                else comet_ml.OfflineExperiment(
+                    **self.default_experiment_kwargs,
+                )
+            )
+        try:
             if experiment_id is not None:
-                return comet_ml.ExistingOfflineExperiment(
+                return comet_ml.ExistingExperiment(
                     previous_experiment=experiment_id,
                     **self.default_experiment_kwargs,
                 )
 
-            return comet_ml.OfflineExperiment(
-                **self.default_experiment_kwargs,
-            )
+            return comet_ml.Experiment(**self.default_experiment_kwargs)
 
-        else:
-            try:
-                if experiment_id is not None:
-                    return comet_ml.ExistingExperiment(
-                        previous_experiment=experiment_id,
-                        **self.default_experiment_kwargs,
-                    )
-
-                return comet_ml.Experiment(**self.default_experiment_kwargs)
-
-            except ValueError:
-                logger.warning(
-                    "COMET WARNING: "
-                    "Comet credentials have not been set. "
-                    "Comet will default to offline logging. "
-                    "Please set your credentials to enable online logging."
-                )
-                return self._get_experiment("offline", experiment_id)
+        except ValueError:
+            logger.warning(
+                "COMET WARNING: "
+                "Comet credentials have not been set. "
+                "Comet will default to offline logging. "
+                "Please set your credentials to enable online logging."
+            )
+            return self._get_experiment("offline", experiment_id)
 
         return
 
@@ -242,10 +241,7 @@ def check_dataset(self, data_file):
         path = data_config.get("path")
         if path and path.startswith(COMET_PREFIX):
             path = data_config["path"].replace(COMET_PREFIX, "")
-            data_dict = self.download_dataset_artifact(path)
-
-            return data_dict
-
+            return self.download_dataset_artifact(path)
         self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
 
         return check_dataset(data_file)
@@ -269,24 +265,22 @@ def log_predictions(self, image, labelsn, path, shape, predn):
             self.log_image(native_scale_image, name=image_name)
             self.logged_image_names.append(image_name)
 
-        metadata = []
-        for cls, *xyxy in filtered_labels.tolist():
-            metadata.append(
-                {
-                    "label": f"{self.class_names[int(cls)]}-gt",
-                    "score": 100,
-                    "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
-                }
-            )
-        for *xyxy, conf, cls in filtered_detections.tolist():
-            metadata.append(
-                {
-                    "label": f"{self.class_names[int(cls)]}",
-                    "score": conf * 100,
-                    "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
-                }
-            )
-
+        metadata = [
+            {
+                "label": f"{self.class_names[int(cls)]}-gt",
+                "score": 100,
+                "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
+            }
+            for cls, *xyxy in filtered_labels.tolist()
+        ]
+        metadata.extend(
+            {
+                "label": f"{self.class_names[int(cls)]}",
+                "score": conf * 100,
+                "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
+            }
+            for *xyxy, conf, cls in filtered_detections.tolist()
+        )
         self.metadata_dict[image_name] = metadata
         self.logged_images_count += 1
 
@@ -398,9 +392,8 @@ def on_pretrain_routine_end(self, paths):
         for path in paths:
             self.log_asset(str(path))
 
-        if self.upload_dataset:
-            if not self.resume:
-                self.upload_dataset_artifact()
+        if self.upload_dataset and not self.resume:
+            self.upload_dataset_artifact()
 
         return
 
@@ -477,23 +470,22 @@ def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):
         return
 
     def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
-        if self.comet_log_per_class_metrics:
-            if self.num_classes > 1:
-                for i, c in enumerate(ap_class):
-                    class_name = self.class_names[c]
-                    self.experiment.log_metrics(
-                        {
-                            "mAP@.5": ap50[i],
-                            "mAP@.5:.95": ap[i],
-                            "precision": p[i],
-                            "recall": r[i],
-                            "f1": f1[i],
-                            "true_positives": tp[i],
-                            "false_positives": fp[i],
-                            "support": nt[c],
-                        },
-                        prefix=class_name,
-                    )
+        if self.comet_log_per_class_metrics and self.num_classes > 1:
+            for i, c in enumerate(ap_class):
+                class_name = self.class_names[c]
+                self.experiment.log_metrics(
+                    {
+                        "mAP@.5": ap50[i],
+                        "mAP@.5:.95": ap[i],
+                        "precision": p[i],
+                        "recall": r[i],
+                        "f1": f1[i],
+                        "true_positives": tp[i],
+                        "false_positives": fp[i],
+                        "support": nt[c],
+                    },
+                    prefix=class_name,
+                )
 
         if self.comet_log_confusion_matrix:
             epoch = self.experiment.curr_epoch

utils/loggers/comet/comet_utils.py

@@ -4,7 +4,7 @@
 
 try:
     import comet_ml
-except (ModuleNotFoundError, ImportError):
+except ImportError:
     comet_ml = None
 
 import yaml
@@ -109,14 +109,13 @@ def check_comet_weights(opt):
     if comet_ml is None:
         return
 
-    if isinstance(opt.weights, str):
-        if opt.weights.startswith(COMET_PREFIX):
-            api = comet_ml.API()
-            resource = urlparse(opt.weights)
-            experiment_path = f"{resource.netloc}{resource.path}"
-            experiment = api.get(experiment_path)
-            download_model_checkpoint(opt, experiment)
-            return True
+    if isinstance(opt.weights, str) and opt.weights.startswith(COMET_PREFIX):
+        api = comet_ml.API()
+        resource = urlparse(opt.weights)
+        experiment_path = f"{resource.netloc}{resource.path}"
+        experiment = api.get(experiment_path)
+        download_model_checkpoint(opt, experiment)
+        return True
 
     return None
 
@@ -136,15 +135,14 @@ def check_comet_resume(opt):
     if comet_ml is None:
         return
 
-    if isinstance(opt.resume, str):
-        if opt.resume.startswith(COMET_PREFIX):
-            api = comet_ml.API()
-            resource = urlparse(opt.resume)
-            experiment_path = f"{resource.netloc}{resource.path}"
-            experiment = api.get(experiment_path)
-            set_opt_parameters(opt, experiment)
-            download_model_checkpoint(opt, experiment)
+    if isinstance(opt.resume, str) and opt.resume.startswith(COMET_PREFIX):
+        api = comet_ml.API()
+        resource = urlparse(opt.resume)
+        experiment_path = f"{resource.netloc}{resource.path}"
+        experiment = api.get(experiment_path)
+        set_opt_parameters(opt, experiment)
+        download_model_checkpoint(opt, experiment)
 
-            return True
+        return True
 
     return None