v3.0 Release (#725)

* initial commit

* remove yolov3-spp from test.py study

* update study --img range

* update mAP

* cleanup and speed updates

* update README plot

utils/activations.py

@@ -10,12 +10,6 @@ class Swish(nn.Module):  #
         return x * torch.sigmoid(x)
 
 
-class HardSwish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * F.hardtanh(x + 3, 0., 6., True) / 6.
-
-
 class MemoryEfficientSwish(nn.Module):
     class F(torch.autograd.Function):
         @staticmethod

utils/datasets.py

@@ -610,7 +610,7 @@ def load_mosaic(self, index):
 
     labels4 = []
     s = self.img_size
-    yc, xc = s, s  # mosaic center x, y
+    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
     indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)]  # 3 additional image indices
     for i, index in enumerate(indices):
         # Load image
@@ -804,7 +804,7 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea
     return img, targets
 
 
-def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.2):  # box1(4,n), box2(4,n)
+def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1):  # box1(4,n), box2(4,n)
     # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
     w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
     w2, h2 = box2[2] - box2[0], box2[3] - box2[1]

utils/general.py

@@ -1147,7 +1147,7 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_
     ax = ax.ravel()
 
     fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
-    for f in ['coco_study/study_coco_yolov5%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
+    for f in ['study/study_coco_yolov5%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
         y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
         x = np.arange(y.shape[1]) if x is None else np.array(x)
         s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
@@ -1159,7 +1159,7 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_
         ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
                  label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
 
-    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [33.8, 39.6, 43.0, 47.5, 49.4, 50.7],
+    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
              'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
 
     ax2.grid()
@@ -1170,7 +1170,7 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_
     ax2.set_ylabel('COCO AP val')
     ax2.legend(loc='lower right')
     plt.savefig('study_mAP_latency.png', dpi=300)
-    plt.savefig(f.replace('.txt', '.png'), dpi=200)
+    plt.savefig(f.replace('.txt', '.png'), dpi=300)
 
 
 def plot_labels(labels, save_dir=''):
@@ -1247,8 +1247,11 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
     s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
          'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
     if bucket:
-        os.system('rm -rf storage.googleapis.com')
-        files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
+        # os.system('rm -rf storage.googleapis.com')
+        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
+        files = ['results%g.txt' % x for x in id]
+        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
+        os.system(c)
     else:
         files = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt')
     for fi, f in enumerate(files):
@@ -1266,8 +1269,8 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
                 ax[i].set_title(s[i])
                 # if i in [5, 6, 7]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
-        except:
-            print('Warning: Plotting error for %s, skipping file' % f)
+        except Exception as e:
+            print('Warning: Plotting error for %s; %s' % (f, e))
 
     fig.tight_layout()
     ax[1].legend()