Feature visualization improvements 32 (#3947)

Author: glenn-jocher

detect.py

@@ -103,7 +103,7 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
         t1 = time_synchronized()
         pred = model(img,
                      augment=augment,
-                     visualize=increment_path(save_dir / 'features', mkdir=True) if visualize else False)[0]
+                     visualize=increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False)[0]
 
         # Apply NMS
         pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

utils/plots.py

@@ -16,7 +16,7 @@
 import yaml
 from PIL import Image, ImageDraw, ImageFont
 
-from utils.general import increment_path, xywh2xyxy, xyxy2xywh
+from utils.general import xywh2xyxy, xyxy2xywh
 from utils.metrics import fitness
 
 # Settings
@@ -447,7 +447,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
     fig.savefig(Path(save_dir) / 'results.png', dpi=200)
 
 
-def feature_visualization(x, module_type, stage, n=64, save_dir=Path('runs/detect/exp')):
+def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
     """
     x:              Features to be visualized
     module_type:    Module type
@@ -460,13 +460,14 @@ def feature_visualization(x, module_type, stage, n=64, save_dir=Path('runs/detec
         if height > 1 and width > 1:
             f = f"stage{stage}_{module_type.split('.')[-1]}_features.png"  # filename
 
-            plt.figure(tight_layout=True)
             blocks = torch.chunk(x[0].cpu(), channels, dim=0)  # select batch index 0, block by channels
             n = min(n, channels)  # number of plots
-            ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)[1].ravel()  # 8 rows x n/8 cols
+            fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)  # 8 rows x n/8 cols
+            ax = ax.ravel()
+            plt.subplots_adjust(wspace=0.05, hspace=0.05)
             for i in range(n):
                 ax[i].imshow(blocks[i].squeeze())  # cmap='gray'
                 ax[i].axis('off')
 
             print(f'Saving {save_dir / f}... ({n}/{channels})')
-            plt.savefig(save_dir / f, dpi=300)
+            plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')