0db94786a7a463fed49825811fac903f1f1fc3c8,python/caffe/classifier.py,Classifier,predict,#Classifier#,49

Before Change

                     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])

        if oversample:
            // Generate center, corner, and mirrored crops.
            inputs = caffe.io.oversample(inputs, self.crop_dims)

After Change

        
        // Scale to standardize input dimensions.
        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)

Instances