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, 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 __call__(self,
input_var=None,
use_from=None,
use_up_to='detection',
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)
nnp_input_size = self.get_nnp_input_size()
callback = NnpNetworkPass(verbose)
callback.set_variable('x', input_var)
callback.set_batch_normalization_batch_stat_all(training)
self.use_up_to(use_up_to, callback)
if use_up_to != 'detection':
self.use_up_to('Arange', callback)
self.use_up_to('Arange2', callback)
funcs_to_drop = ('Reshape_3', 'Arange', 'Arange_2')
callback.drop_function(*funcs_to_drop)
if use_up_to == 'lastconv':
callback.drop_function('Convolution_23')
# Output dimension of reshape, arange, slice etc functions are taken from .nnp file.
# These dimensions depend on the input image size with which the nnp file was created.
# When different input image size is given to the model, these dimensions will change and therefore
# shape of output from these functions need to be generalized whenevr they are generated.
# The same has been done in below callbacks.
# Reshape operation for simulating darknet reorg bug
@callback.on_generate_function_by_name('Reshape')
def reshape_for_darknet_reorg_bug(f):
s = f.inputs[0].variable.shape
stride = 2
r = f.proto.reshape_param
r.shape.dim[:] = [
s[0],
int(s[1] / stride / stride), s[2], stride, s[3], stride
]
return f
# Reshape operation for simulating darknet reorg bug
@callback.on_generate_function_by_name('Reshape_2')
def reshape_for_darknet_reorg_bug(f):
s = f.inputs[0].variable.shape
r = f.proto.reshape_param
r.shape.dim[:] = [
s[0], s[1] * s[2] * s[3] * s[1] * s[2], s[4] // s[1],
s[5] // s[2]
]
return f
# Reshape operation for output variable of yolov2 function in yolov2_activate.
@callback.on_generate_function_by_name('Reshape_3')
def reshape_yolov2_activate(f):
s = f.inputs[0].variable.shape
anchors = 5
r = f.proto.reshape_param
num_class = r.shape.dim[2] - 5
s_add = (s[0], anchors, num_class + 5) + (s[2:])
r.shape.dim[:] = s_add
return f
# Slicing the variable y in yolov2_activate to get t_xy
@callback.on_generate_function_by_name('Slice')
def slicing_t_xy(f):
s = (f.inputs[0].variable.shape)
s = list(s)
s[2] = 2
r = f.proto.slice_param
r.stop[:] = [s[0], s[1], s[2], s[3], s[4]]
return f
# Arange operation in range of zero to width of input variable
@callback.on_generate_function_by_name('Arange')
def arange__yolov2_image_coordinate_xs(f):
s = input_var.shape
r = f.proto.arange_param
r.stop = s[3] // 32
return f
# Arange operation in range of zero to height of input variable
@callback.on_generate_function_by_name('Arange_2')
def arange_yolov2_image_coordinate_ys(f):
s = input_var.shape
r = f.proto.arange_param
r.stop = s[2] // 32
return f
# Slicing the variable y in yolov2_activate to get t_wh
@callback.on_generate_function_by_name('Slice_2')
def slicing_t_wh(f):
s = list(f.inputs[0].variable.shape)
s[2] = 4
r = f.proto.slice_param
r.stop[:] = [s[0], s[1], s[2], s[3], s[4]]
return f
# Slicing the variable y in yolov2_activate to get t_o
@callback.on_generate_function_by_name('Slice_3')
def slicing_t_o(f):
s = list(f.inputs[0].variable.shape)
s[2] = 5
r = f.proto.slice_param
r.stop[:] = [s[0], s[1], s[2], s[3], s[4]]
return f
# Slicing the variable y in yolov2_activate to get t_p
@callback.on_generate_function_by_name('Slice_4')
def slicing_t_p(f):
s = list(f.inputs[0].variable.shape)
r = f.proto.slice_param
r.stop[:] = [s[0], s[1], s[2], s[3], s[4]]
return f
# Reshape the output of Arange to get xs
@callback.on_generate_function_by_name('Reshape_4')
def reshape_yolov2_image_coordinate_xs(f):
s = f.inputs[0].variable.shape
r = f.proto.reshape_param
r.shape.dim[3] = s[0]
return f
# Reshape operation to get t_x
@callback.on_generate_function_by_name('Reshape_5')
def reshape__yolov2_image_coordinate_t_x(f):
s = f.inputs[0].variable.shape
r = f.proto.reshape_param
r.shape.dim[:] = [s[0], s[1], s[0] // s[0], s[2], s[3]]
return f
# Reshape the output of Arange_2 to get ys
@callback.on_generate_function_by_name('Reshape_6')
def reshape_yolov2_image_coordinate_ys(f):
s = f.inputs[0].variable.shape
r = f.proto.reshape_param
r.shape.dim[2] = s[0]
return f
# Reshape the output of Arange to get t_y
@callback.on_generate_function_by_name('Reshape_7')
def reshape_yolov2_image_coordinate_t_y(f):
s = f.inputs[0].variable.shape
r = f.proto.reshape_param
r.shape.dim[:] = [s[0], s[1], s[0] // s[0], s[2], s[3]]
return f
# Reshape the final variable y
@callback.on_generate_function_by_name('Reshape_8')
def reshape_output_variable_y(f):
s = f.inputs[0].variable.shape
r = f.proto.reshape_param
r.shape.dim[:] = [s[0], s[1] * s[2] * s[3], s[4]]
return f
# Scaler division by width in function Reshape_4 in yolov2_image_coordinate
@callback.on_generate_function_by_name('MulScalar_2')
def mul__yolov2_image_coordinate_t_x(f):
input_arr_shape = list(input_var.shape[2:])
r = f.proto.mul_scalar_param
s = f.proto.mul_scalar_param.val
r.val = s * (nnp_input_size[1] / input_arr_shape[1])
return f
# Scaler division by height in function Reshape_6 in yolov2_image_coordinate
@callback.on_generate_function_by_name('MulScalar_3')
def mul_yolov2_image_coordinate_t_y(f):
input_arr_shape = list(input_var.shape[2:])
r = f.proto.mul_scalar_param
s = f.proto.mul_scalar_param.val
r.val = s * (nnp_input_size[0] / input_arr_shape[0])
return f
# Reshape biases and multiply with t_wh to rescale it.
@callback.on_function_pass_by_name('Mul2')
def reshape_biases(f, variables, param_scope):
bias_param_name = f.inputs[1].proto.name
with nn.parameter_scope('', param_scope):
biases = nn.parameter.get_parameter(bias_param_name)
s = list(input_var.shape)
k = (np.array(
[nnp_input_size[1] // 32,
nnp_input_size[0] // 32]).reshape(1, 1, 2, 1, 1))
m = (np.array([s[3] // 32, s[2] // 32]).reshape(1, 1, 2, 1, 1))
biases = (biases.d) * k
biases = (biases) / m
biases = nn.Variable.from_numpy_array(biases)
nn.parameter.set_parameter('biases', biases)
if not training:
callback.fix_parameters()
batch_size = input_var.shape[0]
net = self.nnp.get_network('runtime',
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='segmentation',
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.set_variable('x', input_var)
'''
Shape of output dimension for Interpolate and AveragePooling function depends on input image size
and if these dimensions are taken from .nnp file, shape mis-matching will be encountered. So
these dimensions has been set according to input image shape in below callbacks.
'''
# changing kernel and stride dimension for AveragePoling
@callback.on_generate_function_by_name('AveragePooling')
def average_pooling_shape(f):
s = f.inputs[0].variable.shape
kernel_dim = f.proto.average_pooling_param
kernel_dim.kernel.dim[:] = [s[2], s[3]]
pool_stride = f.proto.average_pooling_param
pool_stride.stride.dim[:] = [s[2], s[3]]
return f
# changing output size for all Interpolate functions, using the below callbacks.
@callback.on_generate_function_by_name('Interpolate')
def interpolate_output_shape(f):
s = input_var.shape
s1 = f.inputs[0].variable.shape
w, h = s[2], s[3]
for i in range(4):
w = (w - 1) // 2 + 1
h = (h - 1) // 2 + 1
op_shape = f.proto.interpolate_param
op_shape.output_size[:] = [w, h]
return f
@callback.on_generate_function_by_name('Interpolate_2')
def interpolate_output_shape(f):
s = input_var.shape
w, h = s[2], s[3]
for i in range(2):
w = (w - 1) // 2 + 1
h = (h - 1) // 2 + 1
op_shape = f.proto.interpolate_param
op_shape.output_size[:] = [w, h]
return f
@callback.on_generate_function_by_name('Interpolate_3')
def interpolate_output_shape(f):
s = input_var.shape
op_shape = f.proto.interpolate_param
op_shape.output_size[:] = [s[2], s[3]]
return f
callback.set_batch_normalization_batch_stat_all(training)
self.use_up_to(use_up_to, callback)
if not training:
callback.fix_parameters()
batch_size = input_var.shape[0]
net = self.nnp.get_network('runtime',
batch_size=batch_size,
callback=callback)
if returns_net:
return net
return list(net.outputs.values())[0]