How to implement custom output layer with dynamic shape in Keras?


I want to implement YOLO-tiny in Keras with Tensorflow 2.0 backend. I want to make a new custom YoloLayer that performs non-maximum suppression to outputs of the previous layer and makes tensor with shape (batch_size, num, 6), where num is a number of found predictions, and each prediction presented as [x, y, w, h, prob, class]. I also set self.trainable = False in __init__() method. Here is my call method:

def call(self, inputs, **kwargs):
        predictions = inputs[...,:5]
        x = tf.math.add(self.cols, tf.nn.sigmoid(predictions[...,0])) / self.grid_size # x
        y = tf.math.add(self.rows, tf.nn.sigmoid(predictions[...,1])) / self.grid_size # y
        w = tf.multiply(self.anchors_w, tf.math.exp(predictions[...,2])) / self.grid_size # w
        h = tf.multiply(self.anchors_h, tf.math.exp(predictions[...,3])) / self.grid_size # h
        c = tf.nn.sigmoid(predictions[...,4]) # confidence
        bounds = tf.stack([x, y, w, h], -1)
        classes = inputs[...,5:]
        probs = tf.multiply(tf.nn.softmax(classes), tf.expand_dims(c, axis=-1))
        prob_mask = tf.greater(probs, self.threshold)
        suppressed_indices = tf.where(prob_mask)
        suppressed_probs = tf.gather_nd(probs, suppressed_indices[...,:3])
        suppressed_boxes = tf.gather_nd(bounds, suppressed_indices[...,:3])
        box_coords = tf.stack([
            suppressed_boxes[...,1] - suppressed_boxes[...,3] / 2., #y1
            suppressed_boxes[...,0] - suppressed_boxes[...,2] / 2., #x1
            suppressed_boxes[...,1] + suppressed_boxes[...,3] / 2., #y2
            suppressed_boxes[...,0] + suppressed_boxes[...,2] / 2., #x2
        ], axis=-1)

        out = tf.TensorArray(tf.float32, size=0, dynamic_size=True)

        for i in range(tf.shape(inputs)[0]):
            image_out = tf.TensorArray(tf.float32, size=self.classes)
            for c in range(self.classes):
                class_probs = suppressed_probs[i,:,c]
                indices = tf.image.non_max_suppression(box_coords[i], class_probs, 10,
                if tf.size(indices) > 0:
                    final_probs = tf.expand_dims(tf.gather(class_probs, indices), axis=-1)
                    final_boxes = tf.gather(suppressed_boxes[i], indices)
                    class_vec = tf.ones((tf.shape(final_probs)[0], 1)) * c
                    image_out.write(c, tf.concat([final_boxes, final_probs, class_vec], axis=1))
            image_out = image_out.concat()
            out.write(i, image_out)
        out = out.stack()
        return out

Then, model.summary() returns:

Model: "sequential_1"
Layer (type)                 Output Shape              Param #   
yolo_layer (YoloLayer)       (None, None, 6)           0         

I loaded pre-trained weights for this model and ran model.predict, but the output gave me an error:

InvalidArgumentError:  Tried to stack elements of an empty list with non-fully-defined element_shape: [?,6]
     [[node sequential_1/yolo_layer/TensorArrayV2Stack/TensorListStack (defined at <ipython-input-2-fbae137dd1a2>:96) ]] [Op:__inference_predict_function_4604]

I also ran this model without YoloLayer and modified its outputs with the same function but separate, and it works correct, but it isn’t with placeholders. What should I do to acheive this?


Ok, I found it out by myself. All had to be done is:

outputs = outputs.write(out_idx, image_out)

Answered By – alextheloafer

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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