Example #1
0
 def __init__(self):
     self.convs = [
         conv.Convolver(0, 0, 1, 1),
         conv.Convolver(2, 2, 1, 1),
     ]
     self.poolings = [
         conv.Pooler(2, 2, 2, 2, 0, 0, conv.pool_op.max),
         conv.Pooler(3, 3, 3, 3, 0, 0, conv.pool_op.max)
     ]
Example #2
0
 def __init__(self):
     self.convs = [
         co.Convolver(0, 0, 4, 4),  # conv1
         co.Convolver(2, 2, 1, 1),  # conv2
         co.Convolver(1, 1, 1, 1),  # conv3
         co.Convolver(1, 1, 1, 1),  # conv4
         co.Convolver(1, 1, 1, 1)  # conv5
     ]
     self.poolings = [
         co.Pooler(3, 3, 2, 2, co.pool_op.max),  # pool1
         co.Pooler(3, 3, 2, 2, co.pool_op.max),  # pool2
         co.Pooler(3, 3, 2, 2, co.pool_op.max)  # pool5
     ]
Example #3
0
 def __init__(self, params):
     super(ConvConnection, self).__init__(params)
     self.conv_params = params.convolution_param
     self.convolver = co.Convolver(self.conv_params.pad,
                                   self.conv_params.pad,
                                   self.conv_params.stride,
                                   self.conv_params.stride)
Example #4
0
 def __init__(self, params):
     super(ConvConnection, self).__init__(params)
     self.conv_params = params.convolution_param
     self.convolver = co.Convolver(self.conv_params.pad,
                                   self.conv_params.pad,
                                   self.conv_params.stride,
                                   self.conv_params.stride)
     self.convolution_param = params.convolution_param
     self.num_output = params.convolution_param.num_output
     self.group = params.convolution_param.group
     #TODO: hack, we don't want to slice agian to use it into bp as a parameter
     self.group_data = []
     self.group_filter = []
     self.group_bias = []
Example #5
0
import owl
import owl.conv as co
import numpy as np
import demo_common

x = owl.randn([227, 227, 3, 256], 0.0, 1)
w = owl.randn([11, 11, 3, 96], 0.0, 0.1)
b = owl.zeros([96])
conv = co.Convolver(pad_h=0, pad_w=0, stride_v=4, stride_h=4)

y = conv.ff(x, w, b)
print y.to_numpy()
print y.shape

ex = conv.bp(y, w)
print ex.to_numpy()
print ex.shape