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June 6, 2019 02:16
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tx2 load keras model with custom layer
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import tensorflow as tf | |
from tensorflow.keras import backend as K | |
# This was copied from the training code's CustomNets.py | |
class NetVLADLayer( tf.keras.layers.Layer ): | |
def __init__( self, num_clusters, **kwargs ): | |
self.num_clusters = num_clusters | |
super(NetVLADLayer, self).__init__(**kwargs) | |
def build( self, input_shape ): | |
self.K = self.num_clusters | |
self.D = input_shape[-1] | |
self.kernel = self.add_weight( name='kernel', | |
shape=(1,1,self.D,self.K), | |
initializer='uniform', | |
trainable=True ) | |
self.bias = self.add_weight( name='bias', | |
shape=(1,1,self.K), | |
initializer='uniform', | |
trainable=True ) | |
self.C = self.add_weight( name='cluster_centers', | |
shape=[1,1,1,self.D,self.K], | |
initializer='uniform', | |
trainable=True) | |
def call( self, x ): | |
# soft-assignment. | |
s = K.conv2d( x, self.kernel, padding='same' ) + self.bias | |
print( 's.shape=', s.shape) | |
a = K.softmax( s ) | |
self.amap = K.argmax( a, -1 ) | |
# print 'amap.shape', self.amap.shape | |
# Dims used hereafter: batch, H, W, desc_coeff, cluster | |
a = K.expand_dims( a, -2 ) | |
# print 'a.shape=',a.shape | |
# Core | |
v = K.expand_dims(x, -1) + self.C | |
# print 'v.shape', v.shape | |
v = a * v | |
# print 'v.shape', v.shape | |
v = K.sum(v, axis=[1, 2]) | |
# print 'v.shape', v.shape | |
v = K.permute_dimensions(v, pattern=[0, 2, 1]) | |
# print 'v.shape', v.shape | |
#v.shape = None x K x D | |
# Normalize v (Intra Normalization) | |
v = K.l2_normalize( v, axis=-1 ) | |
v = K.batch_flatten( v ) | |
v = K.l2_normalize( v, axis=-1 ) | |
# return [v, self.amap] | |
return v | |
def compute_output_shape( self, input_shape ): | |
# return [(input_shape[0], self.K*self.D ), (input_shape[0], input_shape[1], input_shape[2]) ] | |
return (input_shape[0], self.K*self.D ) | |
def get_config( self ): | |
pass | |
# base_config = super(NetVLADLayer, self).get_config() | |
# return dict(list(base_config.items())) | |
# As suggested by: https://github.com/keras-team/keras/issues/4871#issuecomment-269731817 | |
config = {'num_clusters': self.num_clusters} | |
base_config = super(NetVLADLayer, self).get_config() | |
return dict(list(base_config.items()) + list(config.items())) | |
# This .h5 file (with custom_objects is available here: https://github.com/mpkuse/cerebro/tree/master/scripts/keras.models/June2019/centeredinput-m1to1-240x320x3__mobilenetv2-block_9_add__K16__allpairloss ) | |
# model = tf.keras.models.load_model( 'modelarch_and_weights.800.h5', custom_objects={'NetVLADLayer': NetVLADLayer} ) | |
input_img = tf.keras.layers.Input( batch_shape=(1,60,80,256) ) | |
out = NetVLADLayer( num_clusters=16 )(input_img) | |
# out = tf.keras.layers.Conv2D(10, (2,2))( input_img ) | |
model = tf.keras.models.Model( inputs=input_img, outputs=out ) | |
# model.summary() |
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