Refactor for simplification (#9054)

* Refactor for simplification

* cleanup

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/downloads.py

@@ -46,7 +46,7 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
     except Exception as e:  # url2
         file.unlink(missing_ok=True)  # remove partial downloads
         LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
-        os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
+        os.system(f"curl -# -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
     finally:
         if not file.exists() or file.stat().st_size < min_bytes:  # check
             file.unlink(missing_ok=True)  # remove partial downloads

utils/general.py

@@ -582,7 +582,7 @@ def url2file(url):
 
 
 def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3):
-    # Multi-threaded file download and unzip function, used in data.yaml for autodownload
+    # Multithreaded file download and unzip function, used in data.yaml for autodownload
     def download_one(url, dir):
         # Download 1 file
         success = True
@@ -594,7 +594,8 @@ def download_one(url, dir):
             for i in range(retry + 1):
                 if curl:
                     s = 'sS' if threads > 1 else ''  # silent
-                    r = os.system(f'curl -{s}L "{url}" -o "{f}" --retry 9 -C -')  # curl download with retry, continue
+                    r = os.system(
+                        f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -')  # curl download with retry, continue
                     success = r == 0
                 else:
                     torch.hub.download_url_to_file(url, f, progress=threads == 1)  # torch download

utils/metrics.py

@@ -141,7 +141,7 @@ def process_batch(self, detections, labels):
         """
         if detections is None:
             gt_classes = labels.int()
-            for i, gc in enumerate(gt_classes):
+            for gc in gt_classes:
                 self.matrix[self.nc, gc] += 1  # background FN
             return
 

utils/plots.py

@@ -3,6 +3,7 @@
 Plotting utils
 """
 
+import contextlib
 import math
 import os
 from copy import copy
@@ -180,8 +181,7 @@ def output_to_target(output):
     # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
     targets = []
     for i, o in enumerate(output):
-        for *box, conf, cls in o.cpu().numpy():
-            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
+        targets.extend([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf] for *box, conf, cls in o.cpu().numpy())
     return np.array(targets)
 
 
@@ -357,10 +357,8 @@ def plot_labels(labels, names=(), save_dir=Path('')):
     matplotlib.use('svg')  # faster
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    try:  # color histogram bars by class
+    with contextlib.suppress(Exception):  # color histogram bars by class
         [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # known issue #3195
-    except Exception:
-        pass
     ax[0].set_ylabel('instances')
     if 0 < len(names) < 30:
         ax[0].set_xticks(range(len(names)))

utils/torch_utils.py

@@ -45,11 +45,10 @@ def decorate(fn):
 def smartCrossEntropyLoss(label_smoothing=0.0):
     # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0
     if check_version(torch.__version__, '1.10.0'):
-        return nn.CrossEntropyLoss(label_smoothing=label_smoothing)  # loss function
-    else:
-        if label_smoothing > 0:
-            LOGGER.warning(f'WARNING: label smoothing {label_smoothing} requires torch>=1.10.0')
-        return nn.CrossEntropyLoss()  # loss function
+        return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
+    if label_smoothing > 0:
+        LOGGER.warning(f'WARNING: label smoothing {label_smoothing} requires torch>=1.10.0')
+    return nn.CrossEntropyLoss()
 
 
 def smart_DDP(model):
@@ -118,7 +117,7 @@ def select_device(device='', batch_size=0, newline=True):
         assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
             f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
 
-    if not (cpu or mps) and torch.cuda.is_available():  # prefer GPU if available
+    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
         devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
         n = len(devices)  # device count
         if n > 1 and batch_size > 0:  # check batch_size is divisible by device_count