lint

nanodet/model/head/gfl_head.py

@@ -5,8 +5,13 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from nanodet.util import (bbox2distance, distance2bbox, images_to_levels,
-                          multi_apply, overlay_bbox_cv)
+from nanodet.util import (
+    bbox2distance,
+    distance2bbox,
+    images_to_levels,
+    multi_apply,
+    overlay_bbox_cv,
+)
 
 from ...data.transform.warp import warp_boxes
 from ..loss.gfocal_loss import DistributionFocalLoss, QualityFocalLoss

nanodet/trainer/trainer.py

@@ -18,8 +18,14 @@
 
 import torch
 
-from nanodet.util import (AverageMeter, DataParallel, MovingAverage,
-                          load_model_weight, mkdir, save_model)
+from nanodet.util import (
+    AverageMeter,
+    DataParallel,
+    MovingAverage,
+    load_model_weight,
+    mkdir,
+    save_model,
+)
 
 
 class Trainer:

nanodet/util/visualization.py

@@ -131,27 +131,6 @@ def rand_cmap(
             "new_map", randRGBcolors, N=nlabels
         )
 
-    # Display colorbar
-    if verbose:
-        from matplotlib import colorbar, colors
-        from matplotlib import pyplot as plt
-
-        fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))
-
-        bounds = np.linspace(0, nlabels, nlabels + 1)
-        norm = colors.BoundaryNorm(bounds, nlabels)
-
-        cb = colorbar.ColorbarBase(
-            ax,
-            cmap=random_colormap,
-            norm=norm,
-            spacing="proportional",
-            ticks=None,
-            boundaries=bounds,
-            format="%1i",
-            orientation=u"horizontal",
-        )
-
     return random_colormap