I have compiled the following model from keras (h5) to tensorflow binary version (.pb) [ I have tried the compilation function previously with different models and it worked well ]
Then using intel modivus supplied kit compiled the (.pb) to xml file that can be used in runtime.
However resulted in the following runtime error:
what(): Cannot convert layer "up_sampling2d_1/ResizeNearestNeighbor" due to unsupported layer type "Resample"
inputs = Input((img_rows, img_cols,img_channels))
inputs_norm = Lambda(lambda x: x/127.5 - 1.)
conv1 = Conv2D(8, (3, 3), padding='same')(inputs)
conv1 = Activation('relu')(conv1)
conv1 = Conv2D(8, (3, 3), padding='same')(conv1)
conv1 = Activation('relu')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(16, (3, 3), padding='same')(pool1)
conv2 = Activation('relu')(conv2)
conv2 = Conv2D(16, (3, 3), padding='same')(conv2)
conv2 = Activation('relu')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(32, (3, 3), padding='same')(pool2)
conv3 = Activation('relu')(conv3)
conv3 = Conv2D(32, (3, 3), padding='same')(conv3)
conv3 = Activation('relu')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(64, (3, 3), padding='same')(pool3)
conv4 = Activation('relu')(conv4)
conv4 = Conv2D(64, (3, 3), padding='same')(conv4)
conv4 = Activation('relu')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(128, (3, 3), padding='same')(pool4)
conv5 = Activation('relu')(conv5)
conv5 = Conv2D(128, (3, 3), padding='same')(conv5)
conv5 = Activation('relu')(conv5)
pool5 = MaxPooling2D(pool_size=(2, 2))(conv5)
#
#
#up6
up6 = UpSampling2D()(conv5)
up6 = concatenate([up6, conv4], axis=3)
conv6 = Conv2D(64, (3, 3), padding='same')(up6)
conv6 = Activation('relu')(conv6)
conv6 = Conv2D(64, (3, 3), padding='same')(conv6)
conv6 = Activation('relu')(conv6)
#up 7
up7 = UpSampling2D()(conv6)
up7 = concatenate([up7, conv3], axis=3)
conv7 = Conv2D(32, (3, 3), padding='same')(up7)
conv7 = Activation('relu')(conv7)
conv7 = Conv2D(32, (3, 3), padding='same')(conv7)
conv7 = Activation('relu')(conv7)
# up 8
up8 = UpSampling2D()(conv7)
up8 = concatenate([up8, conv2], axis=3)
conv8 = Conv2D(16, (3, 3), padding='same')(up8)
conv8 = Activation('relu')(conv8)
conv8 = Conv2D(16, (3, 3), padding='same')(conv8)
conv8 = Activation('relu')(conv8)
# up 9
up9 = UpSampling2D()(conv8)
up9 = concatenate([up9, conv1], axis=3)
conv9 = Conv2D(8, (3, 3), padding='same')(up9)
conv9 = Activation('relu')(conv9)
conv9 = Conv2D(8, (3, 3), padding='same')(conv9)
conv9 = Activation('relu')(conv9)
conv10 = Conv2D(1, (1, 1))(conv9)
conv10 = Activation('sigmoid')(conv10)
model = Model(inputs=[inputs], outputs=[conv10])
What could be the reason, what and how i can tackle it.
