def iterate_minibatches(inputs, targets, batchsize, shuffle=False, augment=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
if augment:
# as in paper :
# pad feature arrays with 4 pixels on each side
# and do random cropping of 32x32
padded = np.pad(inputs[excerpt],((0,0),(0,0),(4,4),(4,4)),mode='constant')
random_cropped = np.zeros(inputs[excerpt].shape, dtype=np.float32)
crops = np.random.random_integers(0,high=8,size=(batchsize,2))
for r in range(batchsize):
random_cropped[r,:,:,:] = padded[r,:,crops[r,0]:(crops[r,0]+32),crops[r,1]:(crops[r,1]+32)]
inp_exc = random_cropped
else:
inp_exc = inputs[excerpt]
yield inp_exc, targets[excerpt]
# ############################## Main program ################################
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