def forward(self, x):
W = x[1]
b = None
if len(x) == 3:
b = x[2]
func = conv2d_module.Convolution2D(
W.shape[1], W.shape[0], W.shape[2:],
stride=self.stride, pad=self.pad, use_cudnn=self.use_cudnn,
initialW=W, initial_bias=b)
self.func = func
if any(isinstance(i, cuda.ndarray) for i in x):
func.to_gpu()
return func.forward(x[:1])
def __init__(self, in_channels, out1, proj3, out3, proj5, out5, proj_pool):
self.f = function_set.FunctionSet(
conv1=convolution_2d.Convolution2D(in_channels, out1, 1),
proj3=convolution_2d.Convolution2D(in_channels, proj3, 1),
conv3=convolution_2d.Convolution2D(proj3, out3, 3, pad=1),
proj5=convolution_2d.Convolution2D(in_channels, proj5, 1),
conv5=convolution_2d.Convolution2D(proj5, out5, 5, pad=2),
projp=convolution_2d.Convolution2D(in_channels, proj_pool, 1),
)
def __init__(self, in_channels, out1, proj3, out3, proj33, out33,
pooltype, proj_pool=None, stride=1):
if out1 > 0:
assert stride == 1
assert proj_pool is not None
self.f = function_set.FunctionSet(
proj3=convolution_2d.Convolution2D(
in_channels, proj3, 1, nobias=True),
conv3=convolution_2d.Convolution2D(
proj3, out3, 3, pad=1, stride=stride, nobias=True),
proj33=convolution_2d.Convolution2D(
in_channels, proj33, 1, nobias=True),
conv33a=convolution_2d.Convolution2D(
proj33, out33, 3, pad=1, nobias=True),
conv33b=convolution_2d.Convolution2D(
out33, out33, 3, pad=1, stride=stride, nobias=True),
proj3n=batch_normalization.BatchNormalization(proj3),
conv3n=batch_normalization.BatchNormalization(out3),
proj33n=batch_normalization.BatchNormalization(proj33),
conv33an=batch_normalization.BatchNormalization(out33),
conv33bn=batch_normalization.BatchNormalization(out33),
)
if out1 > 0:
self.f.conv1 = convolution_2d.Convolution2D(
in_channels, out1, 1, stride=stride, nobias=True)
self.f.conv1n = batch_normalization.BatchNormalization(out1)
self.out1 = out1
if proj_pool is not None:
self.f.poolp = convolution_2d.Convolution2D(
in_channels, proj_pool, 1, nobias=True)
self.f.poolpn = batch_normalization.BatchNormalization(proj_pool)
self.proj_pool = proj_pool
if pooltype == 'max':
self.f.pool = pooling_2d.MaxPooling2D(3, stride=stride, pad=1)
elif pooltype == 'avg':
self.f.pool = pooling_2d.AveragePooling2D(3, stride=stride, pad=1)
else:
raise NotImplementedError()