Add feature map visualization (#3804)

* Add feature map visualization

Add a feature_visualization function to visualize the mid feature map of the model.

* Update yolo.py

* remove boolean from forward and reorder if statement

* remove print from forward

* General cleanup

* Indent

* Update plots.py

Co-authored-by: Glenn Jocher <[email protected]>

models/yolo.py

@@ -17,6 +17,7 @@ from models.common import *
 from models.experimental import *
 from utils.autoanchor import check_anchor_order
 from utils.general import make_divisible, check_file, set_logging
+from utils.plots import feature_visualization
 from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
     select_device, copy_attr
 
@@ -135,7 +136,7 @@ class Model(nn.Module):
             y.append(yi)
         return torch.cat(y, 1), None  # augmented inference, train
 
-    def forward_once(self, x, profile=False):
+    def forward_once(self, x, profile=False, feature_vis=False):
         y, dt = [], []  # outputs
         for m in self.model:
             if m.f != -1:  # if not from previous layer
@@ -153,6 +154,9 @@ class Model(nn.Module):
 
             x = m(x)  # run
             y.append(x if m.i in self.save else None)  # save output
+            
+            if feature_vis and m.type == 'models.common.SPP':
+                feature_visualization(x, m.type, m.i)
 
         if profile:
             logger.info('%.1fms total' % sum(dt))

utils/plots.py

@@ -15,8 +15,9 @@ import seaborn as sn
 import torch
 import yaml
 from PIL import Image, ImageDraw, ImageFont
+from torchvision import transforms
 
-from utils.general import xywh2xyxy, xyxy2xywh
+from utils.general import increment_path, xywh2xyxy, xyxy2xywh
 from utils.metrics import fitness
 
 # Settings
@@ -299,7 +300,7 @@ def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
     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)
-    # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195 
+    # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195
     ax[0].set_ylabel('instances')
     if 0 < len(names) < 30:
         ax[0].set_xticks(range(len(names)))
@@ -445,3 +446,28 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
 
     ax[1].legend()
     fig.savefig(Path(save_dir) / 'results.png', dpi=200)
+
+
+def feature_visualization(features, module_type, module_idx, n=64):
+    """
+    features:       Features to be visualized
+    module_type:    Module type
+    module_idx:     Module layer index within model
+    n:              Maximum number of feature maps to plot
+    """
+    project, name = 'runs/features', 'exp'
+    save_dir = increment_path(Path(project) / name)  # increment run
+    save_dir.mkdir(parents=True, exist_ok=True)  # make dir
+
+    plt.figure(tight_layout=True)
+    blocks = torch.chunk(features, features.shape[1], dim=1)  # block by channel dimension
+    n = min(n, len(blocks))
+    for i in range(n):
+        feature = transforms.ToPILImage()(blocks[i].squeeze())
+        ax = plt.subplot(int(math.sqrt(n)), int(math.sqrt(n)), i + 1)
+        ax.axis('off')
+        plt.imshow(feature)  # cmap='gray'
+
+    f = f"layer_{module_idx}_{module_type.split('.')[-1]}_features.png"
+    print(f'Saving {save_dir / f}...')
+    plt.savefig(save_dir / f, dpi=300)