Python inference_roi_s_filterの例

コード例 #1

ファイル: autorun_infer.py プロジェクト: jingxiachen/ROICNN

def inference_roi_s_cnn(images, keep_prob, layer=2, feat=[2, 4]):

    _print_tensor_size(images, 'inference_roi_s_cnn')
    assert isinstance(keep_prob, object)

    if not layer == len(feat):
        print(
            'Make sure you have defined the feature map size for each layer.')
        return

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_s_filter(images,
                                                              'conv0',
                                                              out_feat=feat[0])
    # pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)
    # for l in range(1, layer):
    #     conv_tensor = rsvp_quick_inference.inference_roi_s_filter\
    #         (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
    #     pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)

    logits = rsvp_quick_inference.inference_fully_connected_1layer(
        conv_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits

コード例 #2

ファイル: autorun_infer_pooling_1layer.py プロジェクト: AliMiraftab/ROICNN

def inference_roi_s_cnn_1layer(images, keep_prob, feat=[2]):

    _print_tensor_size(images, 'inference_roi_s_cnn')
    assert isinstance(keep_prob, object)

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_s_filter(images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)
    logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits

コード例 #3

ファイル: autorun_infer_pooling_1layer.py プロジェクト: jingxiachen/ROICNN

def inference_roi_s_cnn_1layer(images, keep_prob, feat=[2]):

    _print_tensor_size(images, 'inference_roi_s_cnn')
    assert isinstance(keep_prob, object)

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_s_filter(images,
                                                              'conv0',
                                                              out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)
    logits = rsvp_quick_inference.inference_fully_connected_1layer(
        pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits

コード例 #4

ファイル: autorun_infer.py プロジェクト: ZijingMao/ROICNN

def inference_roi_s_cnn(images, keep_prob, layer=2, feat=[2, 4]):

    _print_tensor_size(images, 'inference_roi_s_cnn')
    assert isinstance(keep_prob, object)

    if not layer == len(feat):
        print('Make sure you have defined the feature map size for each layer.')
        return

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_s_filter(images, 'conv0', out_feat=feat[0])
    # pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)
    # for l in range(1, layer):
    #     conv_tensor = rsvp_quick_inference.inference_roi_s_filter\
    #         (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
    #     pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)

    logits = rsvp_quick_inference.inference_fully_connected_1layer(conv_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits