SegNet-Basic with Keras

Hi,

I have few clarifications in SegNet_Basic.py at line 28.

class_weighting= [0.2595, 0.1826, 4.5640, 0.1417, 0.5051, 0.3826, 9.6446, 1.8418, 6.6823, 6.2478, 3.0, 7.3614]

I would like to know how this values are assigned to class weighting.

Regards Mohan

Hello,

I'm facing this error, I also followed your steps, any ideas ?

ValueError: GpuDnnConv images and kernel must have the same stack size

Apply node that caused the error: GpuDnnConv{algo='small', inplace=True}(GpuContiguous.0, GpuContiguous.0, GpuAllocEmpty.0, GpuDnnConvDesc{border_mode='valid', subsample=(1, 1), conv_mode='conv', precision='float32'}.0, Constant{1.0}, Constant{0.0})
Toposort index: 688
Inputs types: [CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D), <theano.gof.type.CDataType object at 0x7f0094d97c50>, Scalar(float32), Scalar(float32)]
Inputs shapes: [(6, 480, 182, 34), (128, 64, 3, 3), (6, 128, 180, 32), 'No shapes', (), ()]
Inputs strides: [(2970240, 6188, 34, 1), (576, 9, 3, 1), (737280, 5760, 32, 1), 'No strides', (), ()]
Inputs values: ['not shown', 'not shown', 'not shown', <PyCObject object at 0x7f0081578c88>, 1.0, 0.0]
Inputs name: ('image', 'kernel', 'output', 'descriptor', 'alpha', 'beta')

Outputs clients: [[GpuElemwise{add,no_inplace}(GpuDnnConv{algo='small', inplace=True}.0, GpuReshape{4}.0)]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

Hello .the Normal Model compiles well but the model_full.py has error on the lower part : because of the reshaping problem . Line 140 segnet_basic.add(Reshape((n_labels, img_h * img_w), input_shape=(12,img_h, img_w)))

They seem to be fine mathematically too . The simplemodel definition(model-basic.py ) is similar like this yet it works .But teh Model-full.py has error in this part .

Any help diagnosing this would be great .

Hello, thank you for your work. I have issues on segnet-basic model. It is confirmed that the paper says that the 7x7 kernel size was used, but I want to know if I am mistaken.

i use the code and train on dataset from https://github.com/yhlleo/DeepCrack. The images changed to binary images. For the code I just changed the path and the label from 12 to 2 which are crack and background for all the py file. But it seems the class weight is not working.The changes i made are all the value 12 to 2 and the path to my dataset. Full code and dataset can be found at https://drive.google.com/open?id=1wK-non5bMkpApMshtCHXIqLXZOJ9jUxU. I have been working on this problem for a long time and didn't found any workable solution online. I am desperate for help. So can you help me find the problem or at least give some possible causes that cause the problem of the class weighting not working on the fit generator. I changed the value for the class weight but still the accuracy the same. Changing the optimizer or learning rate also gives the same accuracy. The accuracy is the same for the first epoch which is 95.67%, changing the class weight did not affect it

I have my Keras version as 2.1.2...

When i tried to run this program i got the below error:

Using TensorFlow backend. model-basic.py:31: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(64, (3, 3), padding="valid") Convolution2D(filter_size, kernel, kernel, border_mode='valid'), model-basic.py:37: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(128, (3, 3), padding="valid") Convolution2D(128, kernel, kernel, border_mode='valid'), model-basic.py:43: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(256, (3, 3), padding="valid") Convolution2D(256, kernel, kernel, border_mode='valid'), model-basic.py:49: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(512, (3, 3), padding="valid") Convolution2D(512, kernel, kernel, border_mode='valid'), Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1628, in _create_c_op c_op = c_api.TF_FinishOperation(op_desc) tensorflow.python.framework.errors_impl.InvalidArgumentError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_2/MaxPool' (op: 'MaxPool') with input shapes: [?,1,180,128].

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "model-basic.py", line 91, in segnet_basic.add(l) File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 489, in add output_tensor = layer(self.outputs[0]) File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 603, in call output = self.call(inputs, **kwargs) File "/usr/local/lib/python3.5/dist-packages/keras/layers/pooling.py", line 154, in call data_format=self.data_format) File "/usr/local/lib/python3.5/dist-packages/keras/layers/pooling.py", line 217, in _pooling_function pool_mode='max') File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 3466, in pool2d data_format=tf_data_format) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 2140, in max_pool name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 4641, in max_pool data_format=data_format, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1792, in init control_input_ops) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1631, in _create_c_op raise ValueError(str(e)) ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_2/MaxPool' (op: 'MaxPool') with input shapes: [?,1,180,128]. *** Error in `python3': free(): invalid pointer: 0x00000000012b2760 *** ======= Backtrace: ========= /lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fa4d1bde7e5] /lib/x86_64-linux-gnu/libc.so.6(+0x8037a)[0x7fa4d1be737a] /lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7fa4d1beb53c] /usr/lib/x86_64-linux-gnu/libprotobuf.so.9(_ZN6google8protobuf8internal28DestroyDefaultRepeatedFieldsEv+0x1f)[0x7fa47b2388af] /usr/lib/x86_64-linux-gnu/libprotobuf.so.9(_ZN6google8protobuf23ShutdownProtobufLibraryEv+0x8b)[0x7fa47b237b3b] /usr/lib/x86_64-linux-gnu/libmirprotobuf.so.3(+0x233b9)[0x7fa42fdce3b9] /lib64/ld-linux-x86-64.so.2(+0x10de7)[0x7fa4d215ede7] /lib/x86_64-linux-gnu/libc.so.6(+0x39ff8)[0x7fa4d1ba0ff8] /lib/x86_64-linux-gnu/libc.so.6(+0x3a045)[0x7fa4d1ba1045] /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf7)[0x7fa4d1b87837] python3(_start+0x29)[0x5d6049]

Please can you specify which version of the Keras this program requires to run???

Also please help me to resolve the issue?

Thanks, Arun