Implementation of The One Hundred Layers Tiramisu for semantic segmentation in Keras
Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation from https://arxiv.org/abs/1611.09326
Hi, I tried your code and I got this error and I couldn't figure it out.
File "testModel.py", line 41, in <module>
model = Tiramisu()
File "/home/hallab/Github/project/FC-DenseNet-Keras/tiramisu_net.py", line 79, in Tiramisu
skip_connection_list[i], block_to_upsample, n_filters_keep)
File "/home/hallab/Github/project/FC-DenseNet-Keras/layers.py", line 39, in TransitionUp
l = concatenate([l, skip_connection], axis=-1)
File "/home/hallab/.local/lib/python3.5/site-packages/keras/layers/merge.py", line 641, in concatenate
return Concatenate(axis=axis, **kwargs)(inputs)
File "/home/hallab/.local/lib/python3.5/site-packages/keras/engine/base_layer.py", line 431, in __call__
self.build(unpack_singleton(input_shapes))
File "/home/hallab/.local/lib/python3.5/site-packages/keras/layers/merge.py", line 354, in build
'Got inputs shapes: %s' % (input_shape))
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 44, 64, 192), (None, 45, 64, 464)]
the only thing I changed in the code is inputShape (360,512,3).
请问这里的n_filters是不是应该先加上growth_rate（第52行应该拿到第50行前面来），然后作为下一层卷积的输出Channel啊？
然后第50行你用的growth_rate作为n_filters，是不是应该使用已经加了growth_rate的n_filters
我觉得应该是下面这样，还望指正！
n_filters += growth_rate
l = BN_ReLU_Conv(stack, n_filters, dropout_p=dropout_p)
stack = concatenate([stack, l]) 
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