Modification to the eigen smoothing

pytorch_grad_cam/utils/svd_on_activations.py

@@ -1,19 +1,29 @@
-import numpy as np
 
 
-def get_2d_projection(activation_batch):
-    # TBD: use pytorch batch svd implementation
-    activation_batch[np.isnan(activation_batch)] = 0
-    projections = []
-    for activations in activation_batch:
-        reshaped_activations = (activations).reshape(
-            activations.shape[0], -1).transpose()
-        # Centering before the SVD seems to be important here,
-        # Otherwise the image returned is negative
-        reshaped_activations = reshaped_activations - \
-            reshaped_activations.mean(axis=0)
-        U, S, VT = np.linalg.svd(reshaped_activations, full_matrices=True)
-        projection = reshaped_activations @ VT[0, :]
-        projection = projection.reshape(activations.shape[1:])
-        projections.append(projection)
-    return np.float32(projections)
+def get_2d_projection( batch_activations): 
+
+    batch_activations = torch.from_numpy( batch_activations)
+
+    b, c, h, w = batch_activations.shape
+
+    #x = rearrange(batch_activations, "b c h w -> b (h w) c")
+    x = batch_activations.reshape( b, c, h * w).permute( 0, 2, 1)
+
+    x_mean = x.mean(1, keepdim=True)
+
+    x = x - x_mean
+
+    U, S, VT = torch.linalg.svd( x )
+
+    #transpose 
+
+    #V = rearrange(VT, 'a b c -> a c b')
+    V = VT.permute( 0, 2, 1)
+    V = V[ :, :, 0 : 1 ]
+
+    projection = torch.bmm(x, V).squeeze( -1 )
+
+    #projection = rearrange( projection, 'b (h w) -> b h w', h = h, w = w)
+    projection = projection.reshape( b, h, w)
+
+    return projection.detach().numpy( )