如何编写从输入层获取调整大小值的自定义调整大小层？

Question

我正在尝试添加一个没有固定调整大小值的自定义调整大小层，而是从输入层获取一个比例值。

我找到了这个，但它有一个固定的调整大小值：Add a resizing layer to a keras sequential model

import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
import tensorflow.keras.backend as K

class Resize(Layer):
    def init(self):
        super(Resize,self).__init__()

    def build(self,input_shape):
        super(Resize,self).build(input_shape)

    def call(self, x, size):
        out = tf.image.resize(x,size=size) 
        return out
    def get_output_shape_for(self, input_shape):
        return (None,None,3)


inp = Input((10,10,3))
size = Input((1,), dtype='int32')

out = Resize()(inp, size=(100,100)) #(inp, size=(size,size))

model = Model([inp,size], out)
model.summary()

当我尝试这个时：

inp = Input((10,10,3))
size = Input((1,), dtype='int32')

out = Resize()(inp, size=(size,size))

model = Model([inp,size], out)
model.summary()

错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/api/_v1/keras/models/__init__.py in <module>()
      2 size = Input((1,), dtype='int32')
      3 
----> 4 out = Resize()(inp, size=(size,size)) #(inp, size=(size,size))
      5 
      6 model = Model([inp,size], out)

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    634                     outputs = base_layer_utils.mark_as_return(outputs, acd)
    635                 else:
--> 636                   outputs = call_fn(inputs, *args, **kwargs)
    637 
    638             except TypeError as e:

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    147       except Exception as e:  # pylint:disable=broad-except
    148         if hasattr(e, 'ag_error_metadata'):
--> 149           raise e.ag_error_metadata.to_exception(type(e))
    150         else:
    151           raise

ValueError: in converted code:

    <ipython-input-1-ab7021ffbc7d>:14 call *
        out = tf.image.resize(x,size=size)
    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/image_ops_impl.py:1182 resize_images
        skip_resize_if_same=True)
    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/image_ops_impl.py:1045 _resize_images_common
        raise ValueError('\'size\' must be a 1-D Tensor of 2 elements: '

    ValueError: 'size' must be a 1-D Tensor of 2 elements: new_height, new_width


​

Answer 1

一种解决方法是设置size=Input(tensor=K.variable([2,2], dtype=tf.int32))。

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
import tensorflow.keras.backend as K

class Resize(Layer):
    def init(self):
        super(Resize,self).__init__()

    def build(self,input_shape):
        super(Resize,self).build(input_shape)

    def call(self, inputs):
        x = inputs[0]
        size = inputs[1]
        out = tf.image.resize(x,size=size) 
        return out
    def get_output_shape_for(self, input_shape):
        return (None,None,3)


inp = Input((10,10,3))
var_size = K.variable([2,2], dtype=tf.int32)
size = Input(tensor=var_size, name='size')

out = Resize()([inp, size])

model = Model([inp,size], out)
model.summary()

# Model: "model"
# __________________________________________________________________________________________________
# Layer (type)                    Output Shape         Param #     Connected to                     
# ==================================================================================================
# input_1 (InputLayer)            [(None, 10, 10, 3)]  0                                            
# __________________________________________________________________________________________________
# input_2 (InputLayer)            [(2,)]               0                                            
# __________________________________________________________________________________________________
# resize (Resize)                 (None, None, None, 3 0           input_1[0][0]                    
#                                                                  input_2[0][0]                    
# ==================================================================================================
# Total params: 0
# Trainable params: 0
# Non-trainable params: 0

input_mat = np.random.randn(100,10,10,3)
K.set_value(var_size, [5,5])
res = model.predict({'x': input_mat})
# res.shape (100,5,5,3)

K.set_value(var_size, [3,3])
res = model.predict({'x': input_mat})
# res.shape (100,3,3,3)