Pytorch NetFramework

Pytorch framework for quick implementation

Framework overview

Dataset

1-Implementation of dataset.py in module [customdataset]

FILE:

dataloaders/[customdataset]/dataset.py

IMPLEMENT:

class cdataset(data.Dataset)

def warp_Variable(sample,use_cuda=False,grad=True)

CONSTRAINT:

cdataset.__getitem__(...) must return a dictionary with a key 'image'

2-Implementation of ctransforms.py in module [customdataset]

FILE:

dataloaders/[customdataset]/ctransforms.py

IMPLEMENT:

class ToTensor(object)

problem specific transformation class

CONSTRAINT:

Training transformations receive and return a dictionary with same keys as cdataset.__getitem__(...)

3-Default train/test paramenters

FILE:

defaults/dataconfig_train.json

defaults/dataconfig_test.json

DEFINE:

use a json for cdataset.__init__(...) parameters initialization where keys are __init__ parameters names

CONSTRAINT:

in dataconfig_[train/test].json must be defined "module" key where the value is the name of cdataset module (i.e. dataloaders.customdataset)

in dataconfig_[train/test].json must be defined "transform_param" key where the value is a string with a sequence of transformations in ctransforms

NOTE:

default behaivor could be overwritten for a specific experiment with --datasetparam argument, (i.e. --datasetparam="{'transform_param':'RandomCrop((100,100)),ToTensor()'}" ).

Model

4-Implementation of arch/[model].py

FILE:

models/arch/[model].py

IMPLEMENT:

class [myarch](nn.Module):

CONSTRAINT:

[myarch].__init__(...) return a nn.Module

5-Default model paramenters

FILE:

defaults/modelconfig.json

DEFINE:

use a json for [myarch].__init__(...) parameters initialization where keys are __init__ parameters names

CONSTRAINT:

in modelconfig.json must be defined "module" key where the value is the name of [myarch] package (i.e. models.arch.model)

in modelconfig.json must be defined "arch" key where the value is a string with architecture class name (i.e. myarch)

NOTE:

default behaivor could be overwritten for a specific experiment with --modelparam argument, (i.e. --modelparam="{'num_class':'2'}" ).

Metrics definitions

6- Implement custom metrics criterion

FILE:

loss/lossfunc.py

IMPLEMENT:

class [metric](nn.Module)

CONSTRAINT:

[metric].__init__(...) return a nn.Module

7- Problem specific metrics definition

FILE:

defaults/loss_definition.json

DEFINE:

use a json for criterion module reference and call parameters dictionary specification

CONSTRAINT:

in loss_definition.json must be defined "module" key where the value is the name of loss packages (i.e. loss.lossfunc)

in loss_definition.json must be defined a "criterion" key which is a reference to criterion class of step 6. Its [metric].__init__(...) parameters will be initialized with --lossparam argument for loss function and defaults/metrics.json for other metrics.

in loss_definition.json must be defined a "criterionparam" key where are specified criterion call parameters [metric].forward(criterionparam). Variables names used must match with outputs for network forward and sample[...] for custom dataset keys

8- Metrics to be used in network analysis

FILE:

defaults/metrics.json

DEFINE:

use a json to specify the list of metrics to be computed

CONSTRAINT:

in metrics.json must be defined a "metric" key which is one of the criterions defined in loss_definition.json

in metrics.json must be defined a "param" key with initialization [metric].__init__(param)

Custom Visualization

9- Implement custom behavior of NetFramework using python inherit

FILE:

netutil/[customnet].py

IMPLEMENT:

class [CustomNet](NetFramework):
	def __init__(self,args):
	        NetFramework.__init__(self,args)

	def valid_visualization(self,current_epoch,index=0,save=False)
		#do visualization

CONSTRAINT:

[CustomNet] must inherit from NetFramework

valid_visualization function must use NetFramework attributes