fast_rcnn_utils.py 文件源码

def extract_hypercolumns(net, im, boxes): 
    blobs, unused_im_scale_factors = _get_blobs(im, boxes)

    # # When mapping from image ROIs to feature map ROIs, there's some aliasing
    # # (some distinct image ROIs get mapped to the same feature ROI).
    # # Here, we identify duplicate feature ROIs, so we only compute features
    # # on the unique subset.
    # if cfg.DEDUP_BOXES > 0:
    #     v = np.array([1, 1e3, 1e6, 1e9, 1e12])
    #     hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
    #     _, index, inv_index = np.unique(hashes, return_index=True,
    #                                     return_inverse=True)
    #     blobs['rois'] = blobs['rois'][index, :]
    #     boxes = boxes[index, :]

    # reshape network inputs
    net.blobs['data'].reshape(*(blobs['data'].shape))
    net.blobs['rois'].reshape(*(blobs['rois'].shape))
    blobs_out = net.forward(data=blobs['data'].astype(np.float32, copy=False),
                            rois=blobs['rois'].astype(np.float32, copy=False))

    print dir(net.blobs), net.blobs.keys(), net.blobs['conv1'].data.shape

    hypercolumns = []
    # layers = ['conv2', 'conv3', 'conv4', 'conv5']
    layers = ['norm1', 'norm2']
    layers = ['pool1', 'pool2', 'pool5']
    # layers = ['fc6', 'fc7']
    for layer in layers: 
        print layer, net.blobs[layer].data.shape
        convmap = net.blobs[layer].data
        for fmap in convmap[0]:
            # print 'fmap', fmap.shape
            upscaled = sp.misc.imresize(fmap, size=(im.shape[0], im.shape[1]),
                                        mode="F", interp='bilinear')
            hypercolumns.append(upscaled)
    return np.asarray(hypercolumns)

    # data = net.blobs['fc7'].data
    # return data[inv_index, :]