This repository is written to simplify the common neural network pipeline
In this repo, you will find: 1) code for training and evaluation of neural networks; 2) code for layerwise analysis of computational complexity
Installation:
pip install nn-pipelineExample for analyzing complexity:
from nnutils.cnn_complexity_analyzer.profile import profile_compute_layers
# model: torch.nn.Module
# inputs: dictionary type decribing input to the network
# Example: most CNN models on cifar10: inputs={'x': torch.randn(1,3,32,32)}
# Keys in inputs needs to be the same as the parameter names for model's forward function
# profile results will look like:
# {
# layer1.weight: ComputeProfile
# ...
# }
# where the keys are the same as keys in model.state_dict()
# Attributes of ComputeProfile is found in nnutils.cnn_complexity_analyzer.utils.NNComputeModuleProfile
# Number of keys in profile results is the sum of CONV layers and FC layers in the network
profile_results, model_sparsity = profile_compute_layers(model, inputs=inputs, verbose=True)Example for evaluating accuracy:
from nnutils.training_pipeline import accuracy_evaluator
# model: torch.nn.Module
# device: 'cpu' or 'cuda' device to evalaute the network
# data_loader: dataset for the model
# criterion: loss function for the model
top1_acc, loss = accuracy_evaluator.eval(model, device, data_loader, criterion, print_acc=True)Example for evaluating the latency:
from nnutils.cnn_complexity_analyzer.profile import profile_compute_layers
from nnutils.training_pipeline import latency_evaluator
target_kernel = 'mkl'
target_device = 'cpu'
num_trials = 5
profile_results, _ = profile_compute_layers(model, inputs=inputs, verbose=True)
net_state = model.state_dict() # state dict for model
total_latency, layerwise_latency = latency_evaluator.evaluate_latency(
model_state=net_state,
inputs=profile_results,
target_kernel=target_kernel,
target_device=target_device,
num_trials=num_trials,
verbose=True
) # average latency measured over num_trials runs
