def __call__(self,
input_var=None,
use_from=None,
use_up_to='classifier',
training=False,
force_global_pooling=False,
check_global_pooling=True,
returns_net=False,
verbose=0):
input_var = self.get_input_var(input_var)
callback = NnpNetworkPass(verbose)
callback.remove_and_rewire('ImageAugmentationX')
callback.set_variable('TrainingInput', input_var)
self.configure_global_average_pooling(callback,
force_global_pooling,
check_global_pooling,
'NIN/AveragePooling',
by_type=False)
callback.set_batch_normalization_batch_stat_all(training)
self.use_up_to(use_up_to, callback)
if not training:
callback.remove_and_rewire('NIN/Dropout')
callback.fix_parameters()
batch_size = input_var.shape[0]
net = self.nnp.get_network('Training',
batch_size=batch_size,
callback=callback)
if returns_net:
return net
else:
return list(net.outputs.values())[0]
def __call__(self,
input_var=None,
use_from=None,
use_up_to='classifier',
training=False,
force_global_pooling=False,
check_global_pooling=True,
returns_net=False,
verbose=0):
assert use_from is None, 'This should not be set because it is for forward compatibility.'
input_var = self.get_input_var(input_var)
callback = NnpNetworkPass(verbose)
callback.remove_and_rewire('ImageAugmentationX')
callback.set_variable('InputX', input_var)
self.configure_global_average_pooling(callback, force_global_pooling,
check_global_pooling,
'AveragePooling')
callback.set_batch_normalization_batch_stat_all(training)
index = 0 if self.num_layers == 18 else 1
self.use_up_to(use_up_to, callback, index=index)
if not training:
callback.fix_parameters()
batch_size = input_var.shape[0]
net = self.nnp.get_network('Training',
batch_size=batch_size,
callback=callback)
if returns_net:
return net
return list(net.outputs.values())[0]
def __call__(self,
input_var=None,
use_from=None,
use_up_to='classifier',
training=False,
force_global_pooling=False,
check_global_pooling=True,
returns_net=False,
verbose=0,
with_aux_tower=False):
if not training:
assert not with_aux_tower, "Aux Tower should be disabled when inference process."
input_var = self.get_input_var(input_var)
callback = NnpNetworkPass(verbose)
callback.remove_and_rewire('ImageAugmentationX')
callback.set_variable('InputX', input_var)
self.configure_global_average_pooling(callback, force_global_pooling,
check_global_pooling,
'AveragePooling_3')
callback.set_batch_normalization_batch_stat_all(training)
if with_aux_tower:
self.use_up_to('_aux_classifier_1', callback)
funcs_to_drop1 = ("Affine_2", "SoftmaxCrossEntropy",
"MulScalarLoss1")
self.use_up_to('_aux_classifier_2', callback)
funcs_to_drop2 = ("Affine_4", "SoftmaxCrossEntropy_2",
"MulScalarLoss2")
else:
self.use_up_to('_branching_point_1', callback)
funcs_to_drop1 = ("AveragePooling", "Convolution_22", "ReLU_22",
"Affine", "ReLU_23", "Dropout", "Affine_2",
"SoftmaxCrossEntropy", "MulScalarLoss1")
self.use_up_to('_branching_point_2', callback)
funcs_to_drop2 = ("AveragePooling_2", "Convolution_41", "ReLU_42",
"Affine_3", "ReLU_43", "Dropout_2", "Affine_4",
"SoftmaxCrossEntropy_2", "MulScalarLoss2")
callback.drop_function(*funcs_to_drop1)
callback.drop_function(*funcs_to_drop2)
if not training:
callback.remove_and_rewire('Dropout_3')
callback.fix_parameters()
self.use_up_to(use_up_to, callback)
batch_size = input_var.shape[0]
net = self.nnp.get_network('Train',
batch_size=batch_size,
callback=callback)
if returns_net:
return net
elif with_aux_tower:
return list(net.outputs.values())
else:
return list(net.outputs.values())[0]
def __call__(self,
input_var=None,
use_from=None,
use_up_to='classifier',
training=False,
force_global_pooling=False,
check_global_pooling=True,
returns_net=False,
verbose=0,
with_aux_tower=False):
input_var = self.get_input_var(input_var)
callback = NnpNetworkPass(verbose)
callback.remove_and_rewire('ImageAugmentationX')
callback.set_variable('TrainingInput', input_var)
self.use_up_to(use_up_to, callback)
if not training:
callback.remove_and_rewire('TrainNet/Dropout')
callback.remove_and_rewire('TrainNet/Dropout_2')
callback.fix_parameters()
batch_size = input_var.shape[0]
net = self.nnp.get_network('Training',
batch_size=batch_size,
callback=callback)
if returns_net:
return net
else:
return list(net.outputs.values())[0]
def __call__(self, input_var=None, use_from=None, use_up_to='classifier', training=False, returns_net=False, verbose=0):
assert use_from is None, 'This should not be set because it is for forward compatibility.'
input_var = self.get_input_var(input_var)
callback = NnpNetworkPass(verbose)
callback.remove_and_rewire('ImageAugmentationX')
callback.set_variable('TrainingInput', input_var)
callback.set_batch_normalization_batch_stat_all(training)
self.use_up_to(use_up_to, callback)
if not training:
callback.remove_and_rewire(
'VGG{}/Dropout_1'.format(self.num_layers))
callback.remove_and_rewire(
'VGG{}/Dropout_2'.format(self.num_layers))
callback.fix_parameters()
batch_size = input_var.shape[0]
net = self.nnp.get_network(
'Training', batch_size=batch_size, callback=callback)
if returns_net:
return net
return list(net.outputs.values())[0]
def DLAUp(x, test, residual_root=False, channel_last=False):
r, hidden = dla_imagenet(x,
num_classes=1000,
num_layers=34,
test=test,
channel_last=channel_last)
callback = NnpNetworkPass(True)
callback.remove_and_rewire('fc')
ochannels = [256, 128, 64, 32]
with nn.parameter_scope("up16"):
x = upsample(hidden['level5'],
ochannels[0],
test,
kernel_size=4,
channel_last=channel_last)
hidden['up16'] = x
with nn.parameter_scope("up8"):
x = root(x, [hidden['level4']],
ochannels[0],
test,
kernel_size=3,
channel_last=channel_last)
x = upsample(x,
ochannels[1],
test,
kernel_size=4,
channel_last=channel_last)
hidden['up8'] = x
with nn.parameter_scope("up4"):
with nn.parameter_scope("residual_level3"):
level4up = upsample(hidden['level4'],
ochannels[1],
test,
kernel_size=4,
channel_last=channel_last)
with nn.parameter_scope("level3up_root"):
level3up = root(level4up, [hidden['level3']],
ochannels[1],
test,
kernel_size=3,
channel_last=channel_last)
with nn.parameter_scope("x_root"):
x = root(x, [level3up],
ochannels[1],
test,
kernel_size=1,
channel_last=channel_last)
x = upsample(x,
ochannels[2],
test,
kernel_size=4,
channel_last=channel_last)
hidden['up4'] = x
with nn.parameter_scope("up2_b"):
level3up_b = upsample(level3up,
ochannels[2],
test,
kernel_size=4,
channel_last=channel_last)
with nn.parameter_scope("up2_c"):
level3up_c = upsample(hidden['level3'],
ochannels[2],
test,
kernel_size=4,
channel_last=channel_last)
with nn.parameter_scope("level3up_c_root"):
level3up_c = root(hidden['level2'], [level3up_c],
ochannels[2],
test,
kernel_size=3,
channel_last=channel_last)
with nn.parameter_scope("level2up_root"):
level2up = root(level3up_b, [level3up_c],
ochannels[2],
test,
kernel_size=3,
channel_last=channel_last)
with nn.parameter_scope("x_root"):
x = root(x, [level2up],
ochannels[2],
test,
kernel_size=3,
channel_last=channel_last)
return x