CODEWITHSUNDEEP
pythonnumpytensorflow
mariya
mariya

Reputation: 127

How to convert numpy code into tensorflow?

I am trying to convert numpy code into tensorflow graph format. But somewhere I am missing an understanding of dimensionality.

Here is numpy code:

def delta_to_boxes3d(deltas, anchors, coordinate='lidar'):
    # Input:
    #   deltas: (N, w, l, 14)(200,240,14)
    #   feature_map_shape: (w, l)
    #   anchors: (w, l, 2, 7)(200,240,2,7)

    # Ouput:
    #   boxes3d: (N, w*l*2, 7)
    #anchros shape 9200,240,2,7)
    anchors_reshaped = anchors.reshape(-1, 7)  #(96000,7) 
    deltas = deltas.reshape(-1, 7) #(96000,7)
    anchors_d = np.sqrt(anchors_reshaped[:, 4]**2 + anchors_reshaped[:, 5]**2)
    boxes3d = np.zeros_like(deltas)
    boxes3d[..., [0, 1]] = deltas[..., [0, 1]] * \
        anchors_d[:, np.newaxis] + anchors_reshaped[..., [0, 1]] #in this line I have the problem
    boxes3d[..., [2]] = deltas[..., [2]] * \
        1.73 + anchors_reshaped[..., [2]]     #ANCHOR_H = 1.73
    boxes3d[..., [3, 4, 5]] = np.exp(
        deltas[..., [3, 4, 5]]) * anchors_reshaped[..., [3, 4, 5]]
    boxes3d[..., 6] = deltas[..., 6] + anchors_reshaped[..., 6]

    return boxes3d

Here is the code which I have been trying:

def delta_boxes3d():

    anchors = tf.placeholder(tf.float32,shape=[None,None,2,7],name="anchor")  #check the anchor type later
    anchors_reshaped = tf.reshape(anchors,shape=[96000,7])
    delta = tf.placeholder(tf.float32,shape=[None,None,14],name="delta")
    anchors_d = tf.sqrt(tf.add(tf.pow(anchors_reshaped[:,4],2),tf.pow(anchors_reshaped[:,5],2)))  #96000
    deltas = tf.reshape(delta,[96000,7]) 
    x_shape = tf.shape(deltas)
    boxes3d_ = tf.multiply(deltas[:,0:2],tf.add(tf.expand_dims(anchors_d,-1),anchors_reshaped[:,0:2]))
    boxes3d = tf.ones(x_shape[:-1]) + boxes3d_
    
    elta_ = np.random.rand(200,240,14)
    anchor_ = np.random.rand(200,240,2,7)
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    result = sess.run(boxes3d1,feed_dict={anchors:anchor_,delta:delta_}) #(96000,7)  #need to get boxes3d
print(result.shape)

I am getting below error:

ValueError: Dimensions must be equal, but are 96000 and 2 for '{{node add_2}} = AddV2[T=DT_FLOAT](ones, Mul)' with input shapes: [96000], [96000,2].

Could someone help me with this?

Thanks in advance

Upvotes: 0

Views: 151

Answers (1)

Ladislav Ondris
Ladislav Ondris

Reputation: 1293

The error comes from the line boxes3d = tf.ones(x_shape[:-1]) + boxes3d_. You are trying to add shapes (96000,) and (96000,2), which you can't without expanding dims. If you want to add a scalar, you can do boxes3d = 1 + boxes3d. In the example above, you want to do multiplication by scalar followed by addition.

Note that in the NumPy example in the highlighted line, you do the multiplication first and then addition. In TensorFlow, you made it the other way around (possibly by mistake).

I rewrote your NumPy example to Tensorflow 2 so that both functions return the same output.

def delta_boxes3d(deltas, anchors):
    deltas = tf.constant(deltas)
    anchors = tf.constant(anchors)

    anchors_reshaped = tf.reshape(anchors, shape=[96000, 7])
    anchors_d = tf.sqrt(tf.add(tf.pow(anchors_reshaped[:, 4], 2), tf.pow(anchors_reshaped[:, 5], 2)))  # 96000
    deltas = tf.reshape(deltas, [96000, 7])

    boxes3d_01 = tf.add(tf.multiply(deltas[:, 0:2], tf.expand_dims(anchors_d, -1)), anchors_reshaped[:, 0:2])
    boxes3d_2 = deltas[..., 2:3] * 1.73 + anchors_reshaped[..., 2:3]
    boxes3d_345 = tf.exp(deltas[..., 3:6]) * anchors_reshaped[..., 3:6]
    boxes3d_6 = deltas[..., 6:7] + anchors_reshaped[..., 6:7]
    boxes3d = tf.concat([boxes3d_01, boxes3d_2, boxes3d_345, boxes3d_6], axis=-1)
    return boxes3d


deltas = np.random.rand(200, 240, 14)
anchors = np.random.rand(200, 240, 2, 7)
print(delta_to_boxes3d(deltas, anchors))
print(delta_boxes3d(deltas, anchors))

You can notice I created smaller arrays first, and then I concatenated them. This is because Tensorflow won't allow me to modify EagerTensors.

Notice the difference between deltas[..., 2] and deltas[..., 2:3]. The second one doesn't reduce the last dimension. They return shapes (96000,), and (96000,1) respectively.

Upvotes: 1

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