Added visualization code using saliency maps

nolearn/lasagne/visualize.py

@@ -2,6 +2,7 @@
 
 from lasagne.layers import get_output
 from lasagne.layers import get_output_shape
+from lasagne.objectives import binary_crossentropy
 import matplotlib.pyplot as plt
 import numpy as np
 import theano
@@ -176,6 +177,37 @@ def occlusion_heatmap(net, x, target, square_length=7):
     return heat_array
 
 
+def _plot_heat_map(net, X, figsize, get_heat_image):
+    if (X.ndim != 4):
+        raise ValueError("This function requires the input data to be of "
+                         "shape (b, c, x, y), instead got {}".format(X.shape))
+
+    num_images = X.shape[0]
+    if figsize[1] is None:
+        figsize = (figsize[0], num_images * figsize[0] / 3)
+    figs, axes = plt.subplots(num_images, 3, figsize=figsize)
+
+    for ax in axes.flatten():
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.axis('off')
+
+    for n in range(num_images):
+        heat_img = get_heat_image(net, X[n:n + 1, :, :, :], n)
+
+        ax = axes if num_images == 1 else axes[n]
+        img = X[n, :, :, :].mean(0)
+        ax[0].imshow(-img, interpolation='nearest', cmap='gray')
+        ax[0].set_title('image')
+        ax[1].imshow(-heat_img, interpolation='nearest', cmap='Reds')
+        ax[1].set_title('critical parts')
+        ax[2].imshow(-img, interpolation='nearest', cmap='gray')
+        ax[2].imshow(-heat_img, interpolation='nearest', cmap='Reds',
+                     alpha=0.6)
+        ax[2].set_title('super-imposed')
+    return plt
+
+
 def plot_occlusion(net, X, target, square_length=7, figsize=(9, None)):
     """Plot which parts of an image are particularly import for the
     net to classify the image correctly.
@@ -210,33 +242,20 @@ def plot_occlusion(net, X, target, square_length=7, figsize=(9, None)):
     and both images super-imposed.
 
     """
-    if (X.ndim != 4):
-        raise ValueError("This function requires the input data to be of "
-                         "shape (b, c, x, y), instead got {}".format(X.shape))
+    return _plot_heat_map(net, X, figsize, lambda net, X, n: occlusion_heatmap(net, X, target[n], square_length))
 
-    num_images = X.shape[0]
-    if figsize[1] is None:
-        figsize = (figsize[0], num_images * figsize[0] / 3)
-    figs, axes = plt.subplots(num_images, 3, figsize=figsize)
 
-    for ax in axes.flatten():
-        ax.set_xticks([])
-        ax.set_yticks([])
-        ax.axis('off')
+def saliency_map(input, output, pred, X):
+    score = -binary_crossentropy(output[:, pred], np.array([1])).sum()
+    return np.abs(T.grad(score, input).eval({input: X}))
 
-    for n in range(num_images):
-        heat_img = occlusion_heatmap(
-            net, X[n:n + 1, :, :, :], target[n], square_length
-        )
 
-        ax = axes if num_images == 1 else axes[n]
-        img = X[n, :, :, :].mean(0)
-        ax[0].imshow(-img, interpolation='nearest', cmap='gray')
-        ax[0].set_title('image')
-        ax[1].imshow(-heat_img, interpolation='nearest', cmap='Reds')
-        ax[1].set_title('critical parts')
-        ax[2].imshow(-img, interpolation='nearest', cmap='gray')
-        ax[2].imshow(-heat_img, interpolation='nearest', cmap='Reds',
-                     alpha=0.6)
-        ax[2].set_title('super-imposed')
-    return plt
+def saliency_map_net(net, X):
+    input = net.layers_[0].input_var
+    output = get_output(net.layers_[-1])
+    pred = output.eval({input: X}).argmax(axis=1)
+    return saliency_map(input, output, pred, X)[0].transpose(1, 2, 0).squeeze()
+
+
+def plot_saliency(net, X, figsize=(9, None)):
+    return _plot_heat_map(net, X, figsize, lambda net, X, n: -saliency_map_net(net, X))