drop np.asarray() in favor of declaration (~1.75x speedup)

2 files changed, 16 insertions, 8 deletions

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 61e916cc..fe471ca1 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -60,12 +60,15 @@ class Classifier(caffe.Net):
                      for N images and C classes.
         """
         # Scale to standardize input dimensions.
-        inputs = np.asarray([caffe.io.resize_image(im, self.image_dims)
-                             for im in inputs])
+        input_ = np.zeros((len(inputs),
+            self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
+            dtype=np.float32)
+        for ix, in_ in enumerate(inputs):
+            input_[ix] = caffe.io.resize_image(in_, self.image_dims)
 
         if oversample:
             # Generate center, corner, and mirrored crops.
-            inputs = caffe.io.oversample(inputs, self.crop_dims)
+            input_ = caffe.io.oversample(input_, self.crop_dims)
         else:
             # Take center crop.
             center = np.array(self.image_dims) / 2.0
@@ -73,11 +76,13 @@ class Classifier(caffe.Net):
                 -self.crop_dims / 2.0,
                 self.crop_dims / 2.0
             ])
-            inputs = inputs[:, crop[0]:crop[2], crop[1]:crop[3], :]
+            input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
-        caffe_in = np.asarray([self.preprocess(self.inputs[0], in_)
-                    for in_ in inputs])
+        caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+                            dtype=np.float32)
+        for ix, in_ in enumerate(input_):
+            caffe_in[ix] = self.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 2fc23db0..f219b610 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -76,8 +76,11 @@ class Detector(caffe.Net):
                 window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
-        caffe_in = np.asarray([self.preprocess(self.inputs[0], window_in)
-                    for window_in in window_inputs])
+        caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+                            + self.blobs[self.inputs[0]].data.shape[2:],
+                            dtype=np.float32)
+        for ix, window_in in enumerate(window_inputs):
+            caffe_in[ix] = self.preprocess(self.inputs[0], window_in)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))