(self, model_file, pretrained_file, image_dims=None,
mean=None, input_scale=None, raw_scale=None,
channel_swap=None)
| 21 | preprocessing options. |
| 22 | """ |
| 23 | def __init__(self, model_file, pretrained_file, image_dims=None, |
| 24 | mean=None, input_scale=None, raw_scale=None, |
| 25 | channel_swap=None): |
| 26 | caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST) |
| 27 | |
| 28 | # configure pre-processing |
| 29 | in_ = self.inputs[0] |
| 30 | self.transformer = caffe.io.Transformer( |
| 31 | {in_: self.blobs[in_].data.shape}) |
| 32 | self.transformer.set_transpose(in_, (2, 0, 1)) |
| 33 | if mean is not None: |
| 34 | self.transformer.set_mean(in_, mean) |
| 35 | if input_scale is not None: |
| 36 | self.transformer.set_input_scale(in_, input_scale) |
| 37 | if raw_scale is not None: |
| 38 | self.transformer.set_raw_scale(in_, raw_scale) |
| 39 | if channel_swap is not None: |
| 40 | self.transformer.set_channel_swap(in_, channel_swap) |
| 41 | |
| 42 | self.crop_dims = np.array(self.blobs[in_].data.shape[2:]) |
| 43 | if not image_dims: |
| 44 | image_dims = self.crop_dims |
| 45 | self.image_dims = image_dims |
| 46 | |
| 47 | def predict(self, inputs, oversample=True): |
| 48 | """ |
nothing calls this directly
no test coverage detected