There is argument issue about Pooling layer(AveragePooling2D : border_mode) #82
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I also encounter this issue when trying to use Inception_v3 in Keras. I think there is an issue in python2.7/site-packages/keras/backend/mxnet_backend.py when convert the argument from Keras to MXnet. They are not compatible. Is there anyone look into this issue? Thanks |
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@mli @pluskid @superbobry @MaJieCornell another problem comes:Using MXNet backend. Stack trace returned 10 entries: Traceback (most recent call last): |
I want to use keras.applications.inception_v3 model. After I load that model it, there is an error about pooling layer.
Error Message
:----> 1 InceptionV3(include_top=True,input_tensor=None, input_shape=None,weights=None)
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/applications/inception_v3.pyc in InceptionV3(include_top, weights, input_tensor, input_shape, classes)
149
150 branch_pool = AveragePooling2D(
--> 151 (3, 3), strides=(1, 1), border_mode='same')(x)
152 branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
153 x = merge([branch1x1, branch5x5, branch3x3dbl, branch_pool],
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/engine/topology.pyc in call(self, x, mask)
570 if inbound_layers:
571 # This will call layer.build() if necessary.
--> 572 self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
573 # Outputs were already computed when calling self.add_inbound_node.
574 outputs = self.inbound_nodes[-1].output_tensors
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/engine/topology.pyc in add_inbound_node(self, inbound_layers, node_indices, tensor_indices)
633 # creating the node automatically updates self.inbound_nodes
634 # as well as outbound_nodes on inbound layers.
--> 635 Node.create_node(self, inbound_layers, node_indices, tensor_indices)
636
637 def get_output_shape_for(self, input_shape):
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/engine/topology.pyc in create_node(cls, outbound_layer, inbound_layers, node_indices, tensor_indices)
164
165 if len(input_tensors) == 1:
--> 166 output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
167 output_masks = to_list(outbound_layer.compute_mask(input_tensors[0], input_masks[0]))
168 # TODO: try to auto-infer shape
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/layers/pooling.pyc in call(self, x, mask)
158 strides=self.strides,
159 border_mode=self.border_mode,
--> 160 dim_ordering=self.dim_ordering)
161 return output
162
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/layers/pooling.pyc in _pooling_function(self, inputs, pool_size, strides, border_mode, dim_ordering)
249 border_mode, dim_ordering):
250 output = K.pool2d(inputs, pool_size, strides,
--> 251 border_mode, dim_ordering, pool_mode='avg')
252 return output
253
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/backend/mxnet_backend.pyc in func_wrapper(*args, **kwargs)
31 old = learning_phase()
32 set_learning_phase(1)
---> 33 train_ret = func(*args, **kwargs)
34 set_learning_phase(0)
35 test_ret = func(*args, **kwargs)
/pang/linetor/miniconda2/envs/py27/lib/python2.7/site-packages/keras/backend/mxnet_backend.pyc in pool2d(x, pool_size, strides, border_mode, dim_ordering, pool_mode)
2910 x = _preprocess_convnd_input(x, dim_ordering)
2911 s = mx.sym.Pooling(data=x.symbol, kernel=pool_size, pool_type=pool_mode, pooling_convention=border_mode,
-> 2912 stride=strides)
2913 out = _postprocess_convnd_output(KerasSymbol(s), dim_ordering)
2914 return out
/pang/linetor/.local/lib/python2.7/site-packages/mxnet/symbol.pyc in Pooling(data, global_pool, cudnn_off, kernel, pool_type, pooling_convention, stride, pad, name, attr, out, **kwargs)
/pang/linetor/.local/lib/python2.7/site-packages/mxnet/_ctypes/symbol.pyc in _symbol_creator(handle, args, kwargs, keys, vals, name)
125 c_array(ctypes.c_char_p, [c_str(i) for i in keys]),
126 c_array(ctypes.c_char_p, [c_str(str(i)) for i in vals]),
--> 127 ctypes.byref(sym_handle)))
128
129 if args and kwargs:
/pang/linetor/.local/lib/python2.7/site-packages/mxnet/base.pyc in check_call(ret)
127 """
128 if ret != 0:
--> 129 raise MXNetError(py_str(_LIB.MXGetLastError()))
130
131 if sys.version_info[0] < 3:
MXNetError: Invalid Input: 'same', valid values are: {'full', 'valid'}, in operator Pooling(name="", stride="(1, 1)", pooling_convention="same", pool_type="avg", kernel="(3, 3)")
If I read error message, I think argument about border _mode. In keras 1.2.2, Pooling layer 's argument : border_mode need to be in ('valid' or 'same'). But by using mxnet, back-end it says {'full', 'valid'}.
If I use "same', mxnet makes a issue. If I use 'full', keras makes issue. So I can't implement InceptionResNetV2.
Is there any comment about this?
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