问将roi_pooling图层的输出运行到conv2d

EN

conv5_3 = net.end_points['conv5_3']
#implement ROI Pooling
input_boxes=tf.dtypes.cast(input_boxes,tf.int32)
pooled_features = roi_pooling(conv5_3, input_boxes, pool_height=5, pool_width=30)

(Pdb) p pooled_features
<tf.Tensor 'RoiPooling:0' shape=<unknown> dtype=float32>

def recognitionnet(inputs, fatness = 64, dilation = True):
    """
    backbone net of vgg16
    """
    # End_points collect relevant activations for external use.
    end_points = {}
    # Original VGG-16 blocks.
    with slim.arg_scope([slim.conv2d, slim.max_pool2d], padding='SAME'):
        # Block1
        net = slim.repeat(inputs, 2, slim.conv2d, fatness, [3, 3], scope='conv1')
        end_points['conv1_2'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool1')
        end_points['pool1'] = net
        # Block 2.
        net = slim.repeat(net, 2, slim.conv2d, fatness * 2, [3, 3], scope='conv2')
        end_points['conv2_2'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool2')
        end_points['pool2'] = net
        # fc7 as conv
        net = slim.conv2d(net, fatness * 16, [1, 1], scope='fc7')
        end_points['fc7'] = net
        #model_summary()
        #from keras.utils.visualize_util import plot
        #plot(model, to_file='model.png')
    return net, end_points;  

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