paddingStart = 0 ## try it without padding for now. numImgColors = numChannels # create the images images = g.randn((numChannels, imSizeX, imSizeX, numImages)) filters = g.randn((numModulesX, numModulesX, numFilterColors, filterSizeX, filterSizeX, numFilters)) from cudamat_conv import localUp, localDown from cudamat_conv.cudamat_conv_py import localUp as localUp_py, localDown as localDown_py T1 = localUp(images, filters) #, paddingStart=-1) t1 = localDown(T1, filters) #, paddingStart=-1) t1_py = localDown_py(T1, filters)#, paddingStart=-1) assert t1.shape==t1_py.shape print 't1 = ',abs(t1).mean() print 't1_py = ',abs(t1_py).mean() print 't1_diff = ',abs(t1-t1_py).mean() print 't1.shape = ', t1.shape print # T2 = localUp(images, filters, paddingStart=0) # t2 = localDown(T2, filters, paddingStart=0) # t2_py = localDown_py(T2, filters, paddingStart=0) # assert t2.shape==t2_py.shape # print 't2 = ',abs(t2).mean()
### TODO: ask Alex about moduleStride and numGroups. ### But ignoring these I'm good to go. paddingStart = 0 ## try it without padding for now. numImgColors = numChannels # create the images images = g.randn((numChannels, imSizeX, imSizeX, numImages)) filters = g.randn((numModulesX, numModulesX, numFilterColors, filterSizeX, filterSizeX, numFilters)) from cudamat_conv import localUp from cudamat_conv.cudamat_conv_py import localUp as localUp_py t1 = localUp(images, filters) t1_py = localUp_py(images, filters) assert t1.shape == t1_py.shape print 't1 = ', abs(t1).mean() print 't1_py = ', abs(t1_py).mean() print 't1_diff = ', abs(t1 - t1_py).mean() print 't1.shape = ', t1.shape print filters = g.randn((numModulesX + 1, numModulesX + 1, numFilterColors, filterSizeX, filterSizeX, numFilters)) t2 = localUp(images, filters) t2_py = localUp_py(images, filters) assert t2.shape == t2_py.shape
paddingStart = 0 ## try it without padding for now. numImgColors = numChannels # create the images images = g.randn((numChannels, imSizeX, imSizeX, numImages)) filters = g.randn((numModulesX, numModulesX, numFilterColors, filterSizeX, filterSizeX, numFilters)) from cudamat_conv import localUp from cudamat_conv.cudamat_conv_py import localUp as localUp_py t1 = localUp(images, filters) t1_py = localUp_py(images, filters) assert t1.shape==t1_py.shape print 't1 = ',abs(t1).mean() print 't1_py = ',abs(t1_py).mean() print 't1_diff = ',abs(t1-t1_py).mean() print 't1.shape = ', t1.shape print filters = g.randn((numModulesX+1, numModulesX+1, numFilterColors, filterSizeX, filterSizeX, numFilters)) t2 = localUp(images, filters) t2_py = localUp_py(images, filters) assert t2.shape==t2_py.shape print 't2 = ',abs(t2).mean()