hyperpose.Model package¶
Subpackages¶
Submodules¶
hyperpose.Model.backbones module¶
hyperpose.Model.common module¶
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class
hyperpose.Model.common.
MPIIPart
(value)¶ Bases:
enum.Enum
An enumeration.
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Head
= 13¶
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LAnkle
= 5¶
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LElbow
= 10¶
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LHip
= 3¶
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LKnee
= 4¶
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LShoulder
= 9¶
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LWrist
= 11¶
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Neck
= 12¶
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RAnkle
= 0¶
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RElbow
= 7¶
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RHip
= 2¶
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RKnee
= 1¶
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RShoulder
= 8¶
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RWrist
= 6¶
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static
from_coco
(human)¶
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class
hyperpose.Model.common.
Profiler
¶ Bases:
object
Methods
__call__
(self, name, duration)Call self as a function.
report
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report
(self)¶
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hyperpose.Model.common.
draw_humans
(npimg, humans)¶
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hyperpose.Model.common.
get_op
(graph, name)¶
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hyperpose.Model.common.
get_optim
(optim_type)¶
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hyperpose.Model.common.
get_sample_images
(w, h)¶
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hyperpose.Model.common.
init_log
(config)¶
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hyperpose.Model.common.
load_graph
(model_file)¶ Load a freezed graph from file.
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hyperpose.Model.common.
log
(msg)¶
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hyperpose.Model.common.
measure
(f, name=None)¶
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hyperpose.Model.common.
pad_image
(img, stride, pad_value=0.0)¶
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hyperpose.Model.common.
pad_image_shape
(img, shape, pad_value=0.0)¶
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hyperpose.Model.common.
plot_humans
(image, heatMat, pafMat, humans, name)¶
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hyperpose.Model.common.
read_imgfile
(path, width, height, data_format='channels_last')¶ Read image file and resize to network input size.
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hyperpose.Model.common.
regulize_loss
(target_model, weight_decay_factor)¶
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hyperpose.Model.common.
rename_tensor
(x, name)¶
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hyperpose.Model.common.
scale_image
(image, hin, win, scale_rate=0.95)¶
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hyperpose.Model.common.
tf_repeat
(tensor, repeats)¶ Args:
input: A Tensor. 1-D or higher. repeats: A list. Number of repeat for each dimension, length must be the same as the number of dimensions in input
Returns:
A Tensor. Has the same type as input. Has the shape of tensor.shape * repeats
hyperpose.Model.human module¶
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class
hyperpose.Model.human.
BodyPart
(parts, u_idx, part_idx, x, y, score, w=- 1, h=- 1)¶ Bases:
object
part_idx : part index(eg. 0 for nose) x, y: coordinate of body part score : confidence score
Methods
get_part_name
get_x
get_y
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get_part_name
(self)¶
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get_x
(self)¶
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get_y
(self)¶
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class
hyperpose.Model.human.
Human
(parts, limbs, colors)¶ Bases:
object
body_parts: list of BodyPart
Methods
bias
draw_human
get_area
get_bbx
get_global_id
get_partnum
get_score
print
scale
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bias
(self, bias_w, bias_h)¶
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draw_human
(self, img)¶
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get_area
(self)¶
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get_bbx
(self)¶
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get_global_id
(self)¶
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get_partnum
(self)¶
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get_score
(self)¶
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print
(self)¶
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scale
(self, scale_w, scale_h)¶
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Module contents¶
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hyperpose.Model.
get_evaluate
(config)¶ get evaluate pipeline based on config object
construct evaluate pipeline based on the chosen model_type and dataset_type, the evaluation metric fellows the official metrics of the chosen dataset.
the returned evaluate pipeline can be easily used by evaluate(model,dataset), where model is obtained by Model.get_model(), dataset is obtained by Dataset.get_dataset()
the evaluate pipeline will: 1.loading newest model at path ./save_dir/model_name/model_dir/newest_model.npz 2.perform inference and parsing over the chosen evaluate dataset 3.visualize model output in evaluation in directory ./save_dir/model_name/eval_vis_dir 4.output model metrics by calling dataset.official_eval()
- Parameters
- arg1config object
the config object return by Config.get_config() function, which includes all the configuration information.
- Returns
- function
a evaluate pipeline function which takes model and dataset as input, and output model metrics
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hyperpose.Model.
get_model
(config)¶ get model based on config object
construct and return a model based on the configured model_type and model_backbone. each preset model architecture has a default backbone, replace it with chosen common model_backbones allow user to change model computation complexity to adapt to application scene.
- Parameters
- arg1config object
the config object return by Config.get_config() function, which includes all the configuration information.
- Returns
- tensorlayer.models.MODEL
a model object inherited from tensorlayer.models.MODEL class, has configured model architecture and chosen model backbone. can be user defined architecture by using Config.set_model_architecture() function.
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hyperpose.Model.
get_postprocessor
(model_type)¶ get a postprocessor class based on the specified model_type
get the postprocessor class of the specified kind of model to help user directly construct their own evaluate pipeline(rather than using the integrated evaluate pipeline) or infer pipeline(to check the model utility) when in need.
the postprocessor is able to parse the model output feature map and output parsed human objects of Human class, which contains all dectected keypoints.
- Parameters
- arg1Config.MODEL
a enum value of enum class Config.MODEL
- Returns
- function
a postprocessor class of the specified kind of model
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hyperpose.Model.
get_preprocessor
(model_type)¶ get a preprocessor class based on the specified model_type
get the preprocessor class of the specified kind of model to help user directly construct their own train pipeline(rather than using the integrated train pipeline) when in need.
the preprocessor class is able to construct a preprocessor object that could convert the image and annotation to the model output format for training.
- Parameters
- arg1Config.MODEL
a enum value of enum class Config.MODEL
- Returns
- class
a preprocessor class of the specified kind of model
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hyperpose.Model.
get_pretrain
(config)¶
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hyperpose.Model.
get_test
(config)¶ get test pipeline based on config object
construct test pipeline based on the chosen model_type and dataset_type, the test metric fellows the official metrics of the chosen dataset.
the returned test pipeline can be easily used by test(model,dataset), where model is obtained by Model.get_model(), dataset is obtained by Dataset.get_dataset()
the test pipeline will: 1.loading newest model at path ./save_dir/model_name/model_dir/newest_model.npz 2.perform inference and parsing over the chosen test dataset 3.visualize model output in test in directory ./save_dir/model_name/test_vis_dir 4.output model test result file at path ./save_dir/model_name/test_vis_dir/pd_ann.json 5.the test dataset ground truth is often preserved by the dataset creator, you may need to upload the test result file to the official server to get model test metrics
- Parameters
- arg1config object
the config object return by Config.get_config() function, which includes all the configuration information.
- Returns
- function
a test pipeline function which takes model and dataset as input, and output model metrics
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hyperpose.Model.
get_train
(config)¶ get train pipeline based on config object
construct train pipeline based on the chosen model_type and dataset_type, default is single train pipeline performed on single GPU, can be parallel train pipeline use function Config.set_train_type()
the returned train pipeline can be easily used by train(model,dataset), where model is obtained by Model.get_model(), dataset is obtained by Dataset.get_dataset()
the train pipeline will: 1.store and restore ckpt in directory ./save_dir/model_name/model_dir 2.log loss information in directory ./save_dir/model_name/log.txt 3.visualize model output periodly during training in directory ./save_dir/model_name/train_vis_dir the newest model is at path ./save_dir/model_name/model_dir/newest_model.npz
- Parameters
- arg1config object
the config object return by Config.get_config() function, which includes all the configuration information.
- Returns
- function
a train pipeline function which takes model and dataset as input, can be either single train or parallel train pipeline.
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hyperpose.Model.
get_visualize
(model_type)¶ get visualize function based model_type
get the visualize function of the specified kind of model to help user construct thier own evaluate pipeline rather than using the integrated train or evaluate pipeline directly when in need
the visualize function is able to visualize model’s output feature map, which is helpful for training and evaluation analysis.
- Parameters
- arg1Config.MODEL
a enum value of enum class Config.MODEL
- Returns
- function
a visualize function of the specified kind of model