Add functions rescale input and use a rotation matrix

theano/gpuarray/tests/test_dnn.py

@@ -2306,25 +2306,39 @@ def test_dnn_spatialtf_grid_generator():
     utt.seed_rng()
 
     # shape: (num_images, channels, height, width), equivalent to NCHW
-    grid_dims = (10, 3, 64, 128)
+    grid_dims = (3, 3, 128, 128)
 
-    identity = [[1, 0, 0],
-                [0, 1, 0]]
+    identity = [[-1, 0, 0],
+                [0, -1, 0]]
 
-    theta = np.asarray(grid_dims[0] * [identity], dtype=theano.config.floatX)
+    float_type = theano.config.floatX
+
+    theta = np.asarray(grid_dims[0] * [identity], dtype=float_type)
     theta_gpu = gpuarray_shared_constructor(theta)
 
-    from scipy import misc
-    f = misc.face().astype(np.float32)
-    f = np.transpose(f, axes=(2, 0, 1))
+    def normalize_input(input):
         # Scale input from [0, 255] to [0, 2]
-    sc = 1. / 128.
-    f *= sc
+        scale_factor = 1. / 128.
+        input *= scale_factor
         # Re-scale input from [0, 2] to [-1, 1] (normalized)
-    f -= 1
+        input -= 1
+        return input
+
+    def rescale_input(input):
+        # Re-scale output to range [0, 2]
+        input += 1
+        # Re-scale output to range [0, 255]
+        input *= 128
+        return input
+
+    from scipy import misc
+    f = misc.face().astype(float_type)
+    # Convert from HWC to CHW
+    f = np.transpose(f, axes=(2, 0, 1))
+    f = normalize_input(f)
 
     # Create array of images
-    img = np.asarray(grid_dims[0] * [f], dtype=theano.config.floatX)
+    img = np.asarray(grid_dims[0] * [f], dtype=float_type)
     # Create GPU variable for the images
     img_gpu = gpuarray_shared_constructor(img)
 
@@ -2334,23 +2348,14 @@ def test_dnn_spatialtf_grid_generator():
 
     result, = spatialtf_fn()
 
-    img_out = np.asarray(result, dtype=np.float32)
-
-    print(img_out.shape)
-
-    for i in range(len(img_out)):
-        # Re-scale output to range [0, 2]
-        img_out[i] += 1
-        # Re-scale output to range [0, 255]
-        img_out[i] *= 128
+    img_out = np.asarray(result, dtype=float_type)
+    # Re-scale image to range [0, 255]
+    img_out = rescale_input(img_out)
+    # Convert to uint8 (byte)
     img_out = img_out.astype(dtype=np.uint8)
+    # Transpose back to NHWC
     img_out = np.transpose(img_out, axes=(0, 2, 3, 1))
 
-    for i in range(len(img_out)):
-        print("[sampled image #{0}]".format(i))
-        print("Min/Max: {0}/{1}".format(img_out[i].min(), img_out[i].max()))
-        print(img_out[i])
-
     import matplotlib.pyplot as plt
     for img_idx in range(len(img_out)):
         plt.imshow(img_out[img_idx])