Package: eznet-torch
ezNet-torchPyTorch implementation ofezNet("easy net"), a package containing "easy" implementation of a collection of basic and widely-used deep learning models.This implementation is for PyTorch. Seeherefor an identical Keras (TensorFlow) implementation.Author: Pouya P. Niaz (pniaz20@ku.edu.tr,pouya.p.niaz@gmail.com)Version: 0.0.1Last Update: July 15, 2023Install with:pipinstalleznet-torch1- IntroYou can build, train and evaluate all manner of PyTorch models using the utilities in this package, similar to torch-lightning, but with additional functionality, and easier.
Furthermore, there is a collection of basic and widely-used deep learning models ready to be used immediately.
This package also offersPyTorchSmartModule, a sublass ofnn.Modulethat has built-in functions for manipulating its hyperparameters,
training, evaluation, testing, storing, etc.Note that for all the functions and classes described briefly here, the docstrings provide much more detailed information.Also note that even though most of the functionality here exists in torch-lightning, there is additional functionality here, and it is easy to use.
Furthermore, inspecting the code can be useful for educational purposes.1-1- Implementation notesUnlike, e.g., PyTorch, you may not be able to just import everything together as inimport eznet_torchand then use dot indexing to access everything underneath
(I would love to do that, I haven't yet quite figured out how to. I am a newbie.)Instead, import the specific module, class or function, e.g.,fromeznet_torch.modelsimportANN# orfromeznet_torch.utilsimportcalc_image_size2- Applications2-1- Smart Model for Convenient DL Training and DeploymentThePyTorchSmartModuleclass enables you to write any kind of PyTorch model, and it has built-in functions for training, testing, evaluaiton, saving, etc.fromeznet_torch.modelsimportPyTorchSmartModuleclassMyModel(PyTorchSmartModule):def__init__(self,hparams:dict=None):super(MyModel,self).__init__(hparams)## Some code here#defforward(self,x):# something or anothersample_hparams={'model_name':'PyTorchSmartModule',# Name of the model. Arbitrary.'l2_reg':0.0001,# L2 regularization parameter'batch_size':16,# Mini-batch size'epochs':2,# Maximum training epochs'validation_data':[0.05,'trainset'],# Portion of train set used for validation (if necessary)'validation_tolerance_epochs':10,# Validation patience for early stopping'learning_rate':0.0001,# (Initial) Learning rate'learning_rate_decay_gamma':0.99,# Learning rate exponential decay gamma'loss_function':'CrossEntropyLoss',# Loss function string (name of class)'loss_function_params':None,# Dictionary of parameters to pass to the loss function'optimizer':'Adam',# Optimizer string (name of class)'optimizer_params':None# Dictionary of parameters to pass to the optimizer constructor}model=MyModel(sample_hparams)trainset=...# some torch.utils.data.Datasetvalset=...# some torch.utils.data.Datasettestset=...# some torch.utils.data.Datasetmodel.train_model([trainset,valset],verbose=1,script_before_save=True,saveto="mymodel.pt",num_workers=0)results=model.evaluate_model(testset,verbose=1)A few implementation notes:Whatever hyperparameters you have for the model itself, can be included in the hparams dictionary.The__init__method must always calculate the listsself.batch_input_shapeandbatch_output_shape.The__init__method of thePyTorchSmartModuleclass will create attributes with the same name as the hyperparameters shown above.Tha__init__method of the class may alter some of its attributes if the input hyperparameters don't exist or don't make sense.Theself.historyattribute isNoneat the beginning, but is updated after training, to hold losses, accuracies, and learning rates during training.Thescript_before_saveargument tells whether the model should be converted to torchscript before saving.
Comes in handy when you frequently deploy deep learning models on robotic/mechatronic hardware that use C++ (therefore libtorch),
in which case you MUST convert the model to torchscript before saving it, so it can be imported using libtorch in C++.We have coded thetrain_modelfunction (and some others) so it automatically recognizes if the loss is a classification or regression loss,
in both of which cases metrics are chosen to be eitheraccuracy, orr2_score, respectively. Metrics are reported for every epoch.
(hence why scikit-learn is a dependency. It is not used elsewhere. This is unnecessary dependency just for a calculation, and will be removed in a future version).2-2- Utility Functions for Manipulating PyTorch ModelsIneznet_torch.utils, there are some functions for manipulating PyTorch models, that come in handy if you frequently work with custom DL models.make_path(path)creates a path for a folder or file, if some folders in the path don't exist.
Anywhere you want to save something, instead ofpath/to/foo.baryou can just usemake_path("path/to/foo.bar")so ifpathortodirectories
don't exist they will be created.autoname(name)gets a string as a name, and appends the current time stamp to it. Comes in handy when trying to time stamp the multiple training runs you'll do.calc_image_size(size_in, kernel_size, padding, stride, dilation)gets the input image dimension (1D, 2D or 3D), along with the
parameters of a convolution or pooling operation, and returns the output image dimensions. Comes in handy when you want to check to see if your
CNN layers are not shrinking the image too much.generate_geometric_array()gets an initial count and returns an array where the count doubles or halves along the array.
Comes in handy when you want to automatically assign number of filters/channels or hidden sizes in ANNs and CNNs.generate_array_for_hparam()gets the value of a hyperparameter specified by the user (e.g. ANN width), computes whether the hyperparameter needs to be an array
(e.g. the user input an integer but ANN width should be an array with length equal to ANN depth), and then returns an array that properly holds the hyperparameter values.generate_sample_batch(model)getsmodel.batch_input_shapeandmodel.batch_output_shapeand returns random input and output batches.train_pytorch_model(model, dataset=[trainset, valset], **kwargs)trains anynn.Moduleinstance given some parameters, and optionally, scripts and saves it afterward.
The function gets dataset and automatically generates dataloaders from that.save_pytorch_model(model, saveto, **kwargs)scripts the model first if requested, and then saves it.evaluate_pytorch_model(model, dataset, **kwargs)evaluates a model on a dataset, reporting such metrics as accuracy, r2_score, etc.predict_pytorch_model(model, dataset, **kwargs)predicts the model on all batches of a dataset and returns the predictions.2-3- Functions and Classes for Adding and Manipulating Modular Dense Blocks and Conv BlocksThis package also has classes and functions that can be used to create entire Dense blocks and Conv blocks.classeznet_torch.models.DenseBlock(nn.Module)Gets some arguments to the constructor and returns a module that holds
aDenselayer, followed optionally by anormlizationlayer, anactivationlayer, and adropoutlayer. The arguments to the constructor are
sufficient to build any kind of modularDenseBlock, stacking it on top of other layers in your model.
I created this class becasue Dense blocks almost always have a widely-used format: dense, norm, activation, dropout.classeznet_torch.models.ConvBlock(nn.Module)Gets some arguments to the constructor and returns a module that contains aconvolutionlayer, followed optionally
by anormalizationlayer, anactivationlayer, apoolinglayer, and adropoutlayer.
Again, the inputs are fully sufficient to make any kind ofConvBlockand stack it on top of other layers in your CNN.
Similar to the previous case, I created this class becasue it has easy-to-use modular capabilities to build widely-used CNN blocks
that have these kinds of layers in them.2-4- Easy to Use Famous Deep Learning Models for ConvenienceThis package also holds some widely-used and basic deep learning models as in MLP, CNN, RNN, etc. that can get a dictionary of
well-defined hyperparameters, and return aPyTorchSmartModuleinstance that can be easily trained, evaluated, stored and deployed.All of the following models reside in theeznet_torch.modelssubmodule.NOTETo see a list of all hyperparameters that each of the following classes use, simply invoke theclass.sample_hparamsclass attribute.
Also, you can simply call thehelp(class)function to read the docstrings.
ForANN, for instance,fromeznet_torch.modelsimportANNprint(ANN.sample_hparams)help(ANN)ANNis a multi-layer perceptron containing manyDenseBlocks, stacked together. For all hyperparameters such as width,
you can specify an integer to be used for all hidden layers (i.e., all blocks), or an array of different values for each hidden layer (i.e., block).
For every hyperparameter such as normalization layer type, useNonein its place in the array to indicate that the corresponding
Dense block in that place does not have any normalization layers at all. The same goes for many other hyperparameters.Conv_Networkis a CNN where you not only choose the dimensionality (1D, 2D or 3D convolutions) but also all the other hyperparameters of all
Convolution blocks and Dense blocks residing in the network. This network is some Convulution blocks, followed by some Dense blocks.
You get to choose which Conv block has what kind of Conv, Norm (if any), Activation, Pooling (if any), or Dropout (if any) layer.
You also get to choose custom parametrers (**kwargs) for Conv, Pooling, Norm, Activation and Dense layer constructors, so that you can add additional parameters,
or overwrite the ones used by the class itself. You have full freedom.Recurrent_Networkis an RNN containing some RNN layers (RNN, GRU, LSTM, etc.) followed by some Dense blocks. Again, the whole thing is fully modular and you have full freedom.LanguageModelis an RNN model that gets a sequence of characters and predicts the next character. It is a character-level language model.More model varieties with modular and easy-to-use functionality will be added in time.3- LicenseThis package itself has MIT license, but PyTorch has different licenses, which need to be accounted for when using this package.4- CreditsPyTorchhttps://pytorch.org/https://github.com/pytorch/pytorch