diff options
Diffstat (limited to 'python/caffe/pycaffe.py')
| -rw-r--r-- | python/caffe/pycaffe.py | 74 |
1 files changed, 34 insertions, 40 deletions
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 8a9e1aab..537e737b 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -238,9 +238,9 @@ def _Net_set_channel_swap(self, input_, order): self.channel_swap[input_] = order -def _Net_preprocess(self, input_name, inputs): +def _Net_preprocess(self, input_name, input_): """ - Format inputs for Caffe: + Format input for Caffe: - convert to single - resize to input dimensions (preserving number of channels) - scale feature @@ -249,51 +249,45 @@ def _Net_preprocess(self, input_name, inputs): - transpose dimensions to K x H x W Take - input_name: name of input blob - inputs: list of (H' x W' x K) ndarray + input_name: name of input blob to preprocess for + input_: (H' x W' x K) ndarray Give caffe_inputs: (K x H x W) ndarray """ - caffe_inputs = [] - for in_ in inputs: - caffe_in = in_.astype(np.float32) - input_scale = self.input_scale.get(input_name) - channel_order = self.channel_swap.get(input_name) - mean = self.mean.get(input_name) - in_size = self.blobs[input_name].data.shape[2:] - if caffe_in.shape[:2] != in_size: - caffe_in = caffe.io.resize_image(caffe_in, in_size) - if input_scale: - caffe_in *= input_scale - if channel_order: - caffe_in = caffe_in[:, :, channel_order] - caffe_in = caffe_in.transpose((2, 0, 1)) - if mean is not None: - caffe_in -= mean - caffe_inputs.append(caffe_in) - return np.asarray(caffe_inputs) - - -def _Net_deprocess(self, input_name, inputs): + caffe_in = input_.astype(np.float32) + input_scale = self.input_scale.get(input_name) + channel_order = self.channel_swap.get(input_name) + mean = self.mean.get(input_name) + in_size = self.blobs[input_name].data.shape[2:] + if caffe_in.shape[:2] != in_size: + caffe_in = caffe.io.resize_image(caffe_in, in_size) + if input_scale: + caffe_in *= input_scale + if channel_order: + caffe_in = caffe_in[:, :, channel_order] + caffe_in = caffe_in.transpose((2, 0, 1)) + if mean is not None: + caffe_in -= mean + return caffe_in + + +def _Net_deprocess(self, input_name, input_): """ Invert Caffe formatting; see Net.preprocess(). """ - decaf_inputs = [] - for in_ in inputs: - decaf_in = in_.squeeze() - input_scale = self.input_scale.get(input_name) - channel_order = self.channel_swap.get(input_name) - mean = self.mean.get(input_name) - if mean is not None: - decaf_in += mean - decaf_in = decaf_in.transpose((1,2,0)) - if channel_order: - decaf_in = decaf_in[:, :, channel_order[::-1]] - if input_scale: - decaf_in /= input_scale - decaf_inputs.append(decaf_in) - return np.asarray(decaf_inputs) + decaf_in = input_.copy().squeeze() + input_scale = self.input_scale.get(input_name) + channel_order = self.channel_swap.get(input_name) + mean = self.mean.get(input_name) + if mean is not None: + decaf_in += mean + decaf_in = decaf_in.transpose((1,2,0)) + if channel_order: + decaf_in = decaf_in[:, :, channel_order[::-1]] + if input_scale: + decaf_in /= input_scale + return decaf_in def _Net_set_input_arrays(self, data, labels): |