def __init__(self,
inputs,
stage_depth,
batch_norm=True,
activation=True,
preprocess=True):
nfms = [
2**(stage + 4) for stage in sorted(list(range(3)) * stage_depth)
]
strides = [
1 if cur == prev else 2 for cur, prev in zip(nfms[1:], nfms[:-1])
]
layers = []
if preprocess:
layers = Preprocess(functor=cifar_mean_subtract)
parallel_axis = inputs['image'].axes.batch_axes()
with ng.metadata(device_id=('1', '2'), parallel=parallel_axis[0]):
layers.append(
Convolution(**conv_params(3, 16, batch_norm=batch_norm)))
layers.append(f_module(nfms[0], first=True))
for nfm, stride in zip(nfms[1:], strides):
layers.append(f_module(nfm, strides=stride))
if batch_norm:
layers.append(BatchNorm())
if activation:
layers.append(Activation(Rectlin()))
layers.append(Pool2D(8, strides=2, op='avg'))
layers.append(
Affine(axes=ax.Y,
weight_init=KaimingInit(),
batch_norm=batch_norm,
activation=Softmax()))
self.layers = layers
def __init__(self,
inputs,
dataset,
stage_depth,
batch_norm=False,
activation=False,
preprocess=False):
nfms = [
2**(stage + 4) for stage in sorted(list(range(3)) * stage_depth)
]
strides = [
1 if cur == prev else 2 for cur, prev in zip(nfms[1:], nfms[:-1])
]
layers = []
if preprocess and dataset == 'cifar10':
layers = Preprocess(functor=cifar_mean_subtract)
layers.append(Convolution(**conv_params(3, 16, batch_norm=batch_norm)))
layers.append(f_module(nfms[0], first=True, batch_norm=batch_norm))
for nfm, stride in zip(nfms[1:], strides):
layers.append(f_module(nfm, strides=stride, batch_norm=batch_norm))
if batch_norm:
layers.append(BatchNorm())
if activation:
layers.append(Activation(Rectlin()))
layers.append(Pool2D(8, strides=2, op='avg'))
if dataset == 'cifar10':
ax.Y.length = 10
layers.append(
Affine(axes=ax.Y,
weight_init=KaimingInit(),
batch_norm=batch_norm,
activation=Softmax()))
elif dataset == 'i1k':
ax.Y.length = 1000
layers.append(
Affine(axes=ax.Y,
weight_init=KaimingInit(),
batch_norm=batch_norm,
activation=Softmax()))
else:
raise ValueError("Incorrect dataset provided")
super(mini_residual_network, self).__init__(layers=layers)