hyperpose.Model.openpose.model package

Submodules

hyperpose.Model.openpose.model.lw_openpose module

class hyperpose.Model.openpose.model.lw_openpose.LightWeightOpenPose(parts=<enum 'CocoPart'>, limbs=[(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13), (1, 2), (2, 3), (3, 4), (2, 16), (1, 5), (5, 6), (6, 7), (5, 17), (1, 0), (0, 14), (0, 15), (14, 16), (15, 17)], colors=None, n_pos=19, n_limbs=19, num_channels=128, hin=368, win=368, hout=46, wout=46, backbone=None, pretraining=False, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

Cpm_stage([n_filter, in_channels, data_format])	Attributes
Dilated_mobilenet([data_format])	Attributes
Init_stage([n_filter, n_confmaps, …])	Attributes
Refinement_stage([n_filter, in_channels, …])	Attributes
__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x[, is_train, stage_num, …])	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self, x)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

cal_loss
count_params
print_params

class Cpm_stage(n_filter=128, in_channels=512, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Dilated_mobilenet(data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Init_stage(n_filter=128, n_confmaps=19, n_pafmaps=38, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Refinement_stage(n_filter=128, in_channels=185, n_confmaps=19, n_pafmaps=38, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

Refinement_block(n_filter, in_channels[, …])	Attributes
__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

class Refinement_block(n_filter, in_channels, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

cal_loss(self, gt_conf, gt_paf, mask, stage_confs, stage_pafs)

forward(self, x, is_train=False, stage_num=1, domainadapt=False)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

infer(self, x)

Set this network in evaluation mode.

hyperpose.Model.openpose.model.lw_openpose.conv_block(n_filter, in_channels, filter_size=(3, 3), strides=(1, 1), dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object at 0x7fa0aa21ecf8>, b_init=<tensorlayer.initializers.TruncatedNormal object at 0x7fa0aa21ecf8>, padding='SAME', data_format='channels_first')

hyperpose.Model.openpose.model.lw_openpose.dw_conv_block(n_filter, in_channels, filter_size=(3, 3), strides=(1, 1), dilation_rate=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object at 0x7fa0aa21ecf8>, b_init=<tensorlayer.initializers.TruncatedNormal object at 0x7fa0aa21ecf8>, data_format='channels_first')

hyperpose.Model.openpose.model.lw_openpose.nobn_dw_conv_block(n_filter, in_channels, filter_size=(3, 3), strides=(1, 1), W_init=<tensorlayer.initializers.TruncatedNormal object at 0x7fa0aa21ecf8>, b_init=<tensorlayer.initializers.TruncatedNormal object at 0x7fa0aa21ecf8>, data_format='channels_first')

hyperpose.Model.openpose.model.mbv2_sm_openpose module

class hyperpose.Model.openpose.model.mbv2_sm_openpose.Mobilenetv2_small_Openpose(parts=<enum 'CocoPart'>, limbs=[(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13), (1, 2), (2, 3), (3, 4), (2, 16), (1, 5), (5, 6), (6, 7), (5, 17), (1, 0), (0, 14), (0, 15), (14, 16), (15, 17)], colors=None, n_pos=19, n_limbs=19, num_channels=128, hin=368, win=368, hout=46, wout=46, backbone=None, pretraining=False, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

Init_stage([n_confmaps, n_pafmaps, …])	Attributes
Mobilenetv2_variant([data_format])	Attributes
Refinement_stage([n_confmaps, n_pafmaps, …])	Attributes
__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x[, mask_conf, mask_paf, …])	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self, x)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

cal_loss
count_params
print_params

class Init_stage(n_confmaps=19, n_pafmaps=38, in_channels=704, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Mobilenetv2_variant(data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Refinement_stage(n_confmaps=19, n_pafmaps=38, in_channels=761, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

cal_loss(self, gt_conf, gt_paf, mask, stage_confs, stage_pafs)

forward(self, x, mask_conf=None, mask_paf=None, is_train=False, stage_num=4, domainadapt=False)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

infer(self, x)

Set this network in evaluation mode.

hyperpose.Model.openpose.model.mbv2_sm_openpose.conv_block(n_filter=32, in_channels=3, filter_size=3, 3, strides=1, 1, act=tensorflow.nn.relu, padding='SAME', data_format='channels_first')

hyperpose.Model.openpose.model.mbv2_sm_openpose.separable_block(n_filter=32, in_channels=3, filter_size=3, 3, strides=1, 1, act=tensorflow.nn.relu, padding='SAME', data_format='channels_first')

hyperpose.Model.openpose.model.mbv2_th_openpose module

class hyperpose.Model.openpose.model.mbv2_th_openpose.MobilenetThinOpenpose(parts=<enum 'CocoPart'>, limbs=[(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13), (1, 2), (2, 3), (3, 4), (2, 16), (1, 5), (5, 6), (6, 7), (5, 17), (1, 0), (0, 14), (0, 15), (14, 16), (15, 17)], colors=None, n_pos=19, n_limbs=19, num_channels=128, hin=368, win=368, hout=46, wout=46, backbone=None, pretraining=False, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

Init_stage([n_confmaps, n_pafmaps, …])	Attributes
Mobilenetv2_variant([data_format])	Attributes
Refinement_stage([n_confmaps, n_pafmaps, …])	Attributes
__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x[, is_train, stage_num, …])	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self, x)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

cal_loss
count_params
print_params

class Init_stage(n_confmaps=19, n_pafmaps=38, in_channels=1152, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Mobilenetv2_variant(data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Refinement_stage(n_confmaps=19, n_pafmaps=38, in_channels=1209, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

cal_loss(self, gt_conf, gt_paf, mask, stage_confs, stage_pafs)

forward(self, x, is_train=False, stage_num=5, domainadapt=False)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

infer(self, x)

Set this network in evaluation mode.

hyperpose.Model.openpose.model.mbv2_th_openpose.conv_block(n_filter=32, in_channels=3, filter_size=3, 3, strides=1, 1, act=tensorflow.nn.relu, padding='SAME', data_format='channels_first')

hyperpose.Model.openpose.model.mbv2_th_openpose.separable_block(n_filter=32, in_channels=3, filter_size=3, 3, strides=1, 1, dilation_rate=1, 1, act=tensorflow.nn.relu, data_format='channels_first')

hyperpose.Model.openpose.model.openpose module

class hyperpose.Model.openpose.model.openpose.OpenPose(parts=<enum 'CocoPart'>, limbs=[(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13), (1, 2), (2, 3), (3, 4), (2, 16), (1, 5), (5, 6), (6, 7), (5, 17), (1, 0), (0, 14), (0, 15), (14, 16), (15, 17)], colors=None, n_pos=19, n_limbs=19, num_channels=128, hin=368, win=368, hout=46, wout=46, backbone=None, pretraining=False, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

Init_stage([n_confmaps, n_pafmaps, …])	Attributes
Refinement_stage([n_confmaps, n_pafmaps, …])	Attributes
__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x[, is_train, stage_num, …])	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self, x[, stage_num])	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

cal_loss
count_params
print_params

class Init_stage(n_confmaps=19, n_pafmaps=38, in_channels=128, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

class Refinement_stage(n_confmaps=19, n_pafmaps=38, in_channels=185, data_format='channels_first')

Bases: tensorlayer.models.core.Model

Attributes

all_drop

all_layers

Return all layers of this network in a list.

all_params

all_weights

Return all weights of this network in a list.

config

inputs

n_weights

Return the number of weights (parameters) in this network.

nontrainable_weights

Return nontrainable weights of this network in a list.

outputs

trainable_weights

Return trainable weights of this network in a list.

Methods

__call__(self, inputs[, is_train])	Forward input tensors through this network by calling.
as_layer(self)	Return this network as a ModelLayer so that it can be integrated into another Model.
eval(self)	Set this network in evaluation mode.
forward(self, x)	Network forwarding given input tensors
get_layer(self[, name, index])	Network forwarding given input tensors
infer(self)	Set this network in evaluation mode.
load(filepath[, load_weights])	Load model from a given file, which should be previously saved by Model.save().
load_weights(self, filepath[, format, …])	Load model weights from a given file, which should be previously saved by self.save_weights().
print_all_layers(self)
release_memory(self)	WARNING: This function should be called with great caution.
save(self, filepath[, save_weights, …])	Save model into a given file.
save_weights(self, filepath[, format])	Input filepath, save model weights into a file of given format.
test(self)	Set this network in evaluation mode.
train(self)	Set this network in training mode.

count_params
print_params

forward(self, x)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

cal_loss(self, gt_conf, gt_paf, mask, stage_confs, stage_pafs)

forward(self, x, is_train=False, stage_num=5, domainadapt=False)

Network forwarding given input tensors

Parameters

inputsTensor or list of Tensors

input tensor(s)

kwargs :

For other keyword-only arguments.

Returns

output tensor(s)Tensor or list of Tensor(s)

infer(self, x, stage_num=5)

Set this network in evaluation mode.

Module contents