I have naively tried to implement my own pooling and unpooling functions such that the indices are retained while pooling to map the values to the indices while unpooling. Here is the code.
from keras.layers import Input, Layer
from keras.models import Model
from keras.layers.convolutional import Conv2D
import keras.backend as K
class MaxPool2DWithIndices(Layer):
def __init__(self, filter_size=(2,2), strides=(2,2), padding='valid', **kwargs):
super(MaxPool2DWithIndices, self).__init__(**kwargs)
self.filter_size = filter_size
self.strides = strides
self.padding = padding
def __call__(self, inputs):
'''
inputs: (n, H, W, d), ie batch of n inputs of size (H, W, d)
'''
print (inputs.shape)
inputs_shape = inputs.shape #K.array(inputs.shape)
pooled_H = (inputs_shape[1]-self.filter_size[0])//self.strides[0]+1
pooled_W = (inputs_shape[2]-self.filter_size[1])//self.strides[1]+1
#if type(inputs_shape[0]) is not int: inputs_shape[0] = 1
mpooled = np.zeros((inputs_shape[0], pooled_H, pooled_W, inputs_shape[3]))
indices = np.zeros((inputs_shape[0], pooled_H, pooled_W, inputs_shape[3]))
for n in range(0, inputs_shape[0], 2): # for each example
for i in range(0, inputs_shape[1], 2):
for j in range(0, inputs_shape[2], 2):
for k in range(inputs_shape[3]):
mpooled[n, i//2, j//2, k] = np.max(inputs[n, i:i+2, j:j+2, k])
indices[n, i//2, j//2, k] = np.argmax(inputs[n, i:i+2, j:j+2, k])
return [mpooled, indices]
class MaxUnPool2DWithIndices(Layer):
def __init__(self, filter_size=(2,2), strides=(2,2), padding='valid'):
super(MaxUnPool2DWithIndices, self).__init__(**kwargs)
self.indices = indices
self.filter_size = filter_size
self.strides = strides
self.padding = padding
def __call__(self, inputs, indices):
'''
inputs: (n, H, W, d), ie batch of n inputs of size (H, W, d)
'''
inputs_shape = inputs.shape #K.array(inputs.shape)
unpooled_H = (inputs_shape[1]-1)*self.strides[0]+self.filter_size[0]
unpooled_W = (inputs_shape[2]-1)*self.strides[1]+self.filter_size[1]
#if type(inputs_shape[0]) is not int: inputs_shape[0] = 1
max_unpooled = np.zeros((inputs_shape[0], unpooled_H, unpooled_W, inputs_shape[3]))
for n in range(inputs_shape[0]): # for each example
for i in range(0, unpooled_H, 2):
for j in range(0, unpooled_W, 2):
for k in range(inputs_shape[2]):
if self.indices[n, i//2, j//2, k] == 0:
max_unpooled[n, i+0, j+0, k] = inputs[n, i//2, j//2, k]
elif self.indices[n, i//2, j//2, k] == 1:
max_unpooled[n, i+0, j+1, k] = inputs[n, i//2, j//2, k]
elif self.indices[n, i//2, j//2, k] == 2:
max_unpooled[n, i+1, j+0, k] = inputs[n, i//2, j//2, k]
else: # it is 3
max_unpooled[n, i+1, j+1, k] = inputs[n, i//2, j//2, k]
return max_unpooled
Let the following model be an example to replicate the error:
input_layer = Input(shape=(1024, 1024, 1))
encoded_out = Conv2D(4, (3, 3), activation='relu', padding="same")(input_layer)
encoded_out, indices1 = MaxPool2DWithIndices(filter_size=(2,2), strides=(2,2), padding='valid')(encoded_out)
decoded_out = MaxUnPool2DWithIndices(filter_size=(2,2), strides=(2,2), padding='valid')(encoded_out, indices1)
decoded_out = Conv2D(4, (3, 3), activation='relu', padding="same")(decoded_out)
model = Model(input_layer, decoded_out)
The above code outputs the following with the error:
(None, 1024, 1024, 4)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-77-5a2cf359f3ac> in <module>
2
3 encoded_out = Conv2D(4, (3, 3), activation='relu', padding="same")(input_layer)
----> 4 encoded_out, indices1 = MaxPool2DWithIndices(filter_size=(2,2), strides=(2,2), padding='valid')(encoded_out)
5
6 decoded_out = MaxUnPool2DWithIndices(filter_size=(2,2), strides=(2,2), padding='valid')(encoded_out, indices1)
<ipython-input-76-8a54043c249e> in __call__(self, inputs)
20 pooled_W = (inputs_shape[2]-self.filter_size[1])//self.strides[1]+1
21 #if type(inputs_shape[0]) is not int: inputs_shape[0] = 1
---> 22 mpooled = np.zeros((inputs_shape[0], pooled_H, pooled_W, inputs_shape[3]))
23 indices = np.zeros((inputs_shape[0], pooled_H, pooled_W, inputs_shape[3]))
24 for n in range(0, inputs_shape[0], 2): # for each example
TypeError: 'NoneType' object cannot be interpreted as an integer
This is of course due to the batch size not being known beforehand. How do I resolve this?
The __call__ functions required some if-else conditions to deal with cases like input.shape=(None, H, W, d) or input.shape=(None, None, None, d). Solved!