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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 22 09:51:01 2024
@author: jarin.ritu
"""
## Python standard libraries
from __future__ import print_function
from __future__ import division
import numpy as np
import argparse
import random
## PyTorch dependencies
import torch
import torch.nn as nn
## Local external libraries
from Utils.Network_functions import initialize_model, train_model, test_model
from Utils.RBFHistogramPooling import HistogramLayer
from Utils.Save_Results import save_results
from Utils.Get_Optimizer import get_optimizer
from Demo_Parameters import Parameters
from Prepare_Data import Prepare_DataLoaders
def main(Params):
# Name of dataset
Dataset = Params['Dataset']
# Model(s) to be used
model_name = Params['Model_name']
# Number of classes in dataset
num_classes = Params['num_classes'][Dataset]
# Number of runs and/or splits for dataset
numRuns = Params['Splits'][Dataset]
# Number of bins and input convolution feature maps after channel-wise pooling
numBins = Params['numBins']
num_feature_maps = Params['out_channels'][model_name]
# Local area of feature map after histogram layer
feat_map_size = Params['feat_map_size']
# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Using", torch.cuda.device_count(), "GPUs!")
print('Starting Experiments...')
for split in range(0, numRuns):
#Set random state for reproducibility
torch.manual_seed(split)
np.random.seed(split)
random.seed(split)
torch.cuda.manual_seed(split)
torch.cuda.manual_seed_all(split)
# Keep track of the bins and widths as these values are updated each
# epoch
saved_bins = np.zeros((Params['num_epochs'] + 1,
numBins * int(num_feature_maps / (feat_map_size * numBins))))
saved_widths = np.zeros((Params['num_epochs'] + 1,
numBins * int(num_feature_maps / (feat_map_size * numBins))))
histogram_layer = HistogramLayer(int(num_feature_maps / (feat_map_size * numBins)),
Params['kernel_size'][model_name],
num_bins=numBins, stride=Params['stride'],
normalize_count=Params['normalize_count'],
normalize_bins=Params['normalize_bins'])
# Initialize the histogram model for this run
model_ft, input_size, feature_extraction_layer = initialize_model(model_name, num_classes,
Params['in_channels'][model_name],
# len(Params['feature']),
num_feature_maps,
feature_extract=Params['feature_extraction'],
histogram=Params['histogram'],
histogram_layer=histogram_layer,
parallel=Params['parallel'],
use_pretrained=Params['use_pretrained'],
add_bn=Params['add_bn'],
scale=Params['scale'],
feat_map_size=feat_map_size,
TDNN_feats=(Params['TDNN_feats'][Dataset]),
input_feature = Params['feature'],sample_rate=Params['sample_rate'],
window_length=Params['window_length'],hop_length=Params['hop_length'],
checkpoint_path='Best_Weights.pt')
# Send the model to GPU if available, use multiple if available
if torch.cuda.device_count() > 1:
print("Using", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model_ft = nn.DataParallel(model_ft)
model_ft = model_ft.to(device)
feature_extraction_layer = feature_extraction_layer.to(device)
# Print number of trainable parameters
num_params = sum(p.numel() for p in model_ft.parameters() if p.requires_grad)
print("Initializing Datasets and Dataloaders...")
# Create training and validation dataloaders
dataloaders_dict = Prepare_DataLoaders(Params)
# Save the initial values for bins and widths of histogram layer
# Set optimizer for model
if (Params['histogram']):
reduced_dim = int((num_feature_maps / feat_map_size) / (numBins))
if (Params['in_channels'][model_name] == reduced_dim):
dim_reduced = False
saved_bins[0, :] = model_ft.module.histogram_layer.centers.detach().cpu().numpy()
saved_widths[0, :] = model_ft.module.histogram_layer.widths.reshape(
-1).detach().cpu().numpy()
else:
dim_reduced = True
saved_bins[0, :] = model_ft.module.histogram_layer[
-1].centers.detach().cpu().numpy()
saved_widths[0, :] = model_ft.module.histogram_layer[-1].widths.reshape(
-1).detach().cpu().numpy()
else:
saved_bins = None
saved_widths = None
dim_reduced = None
# Setup the loss fxn and scheduler
criterion = nn.CrossEntropyLoss()
scheduler = None
# Define optimizer for updating weights
params = model_ft.parameters()
optimizer_ft = get_optimizer(params, Params['optimizer'], lr=Params['lr'])
# Train and evaluate
train_dict = train_model(model_ft, dataloaders_dict, criterion,
optimizer_ft,
device,
feature_extraction_layer,
saved_bins=saved_bins, saved_widths=saved_widths,
histogram=Params['histogram'],
num_epochs=Params['num_epochs'],
scheduler=scheduler,
dim_reduced=dim_reduced)
test_dict = test_model(dataloaders_dict['test'], model_ft, feature_extraction_layer,criterion,
device)
# Save results
if (Params['save_results']):
save_results(train_dict, test_dict, split, Params, num_params)
del train_dict, test_dict
torch.cuda.empty_cache()
if (Params['histogram']):
print('**********Run ' + str(split + 1) + ' For '
+ Params['hist_model'] + ' Finished**********')
else:
print('**********Run ' + str(split + 1) + ' For GAP_' +
model_name + ' Finished**********')
def parse_args():
parser = argparse.ArgumentParser(description='Run histogram experiments for dataset')
parser.add_argument('--save_results', default=True, action=argparse.BooleanOptionalAction,
help='Save results of experiments (default: True)')
parser.add_argument('--folder', type=str, default='Saved_Models/test',
help='Location to save models')
parser.add_argument('--model', type=str, default='TDNN',
help='Select baseline model architecture')
parser.add_argument('--histogram', default=False, action=argparse.BooleanOptionalAction,
help='Flag to use histogram model or baseline global average pooling (GAP), --no-histogram (GAP) or --histogram')
parser.add_argument('--data_selection', type=int, default=0,
help='Dataset selection: See Demo_Parameters for full list of datasets')
parser.add_argument('-numBins', type=int, default=16,
help='Number of bins for histogram layer. Recommended values are 4, 8 and 16. (default: 16)')
parser.add_argument('--feature_extraction', default=False, action=argparse.BooleanOptionalAction,
help='Flag for feature extraction. False, train whole model. True, only update fully connected and histogram layers parameters (default: True)')
parser.add_argument('--use_pretrained', default=True, action=argparse.BooleanOptionalAction,
help='Flag to use pretrained model from ImageNet or train from scratch (default: True)')
parser.add_argument('--train_batch_size', type=int, default=32,
help='input batch size for training (default: 128)')
parser.add_argument('--val_batch_size', type=int, default=32,
help='input batch size for validation (default: 512)')
parser.add_argument('--test_batch_size', type=int, default=32,
help='input batch size for testing (default: 256)')
parser.add_argument('--num_epochs', type=int, default=1,
help='Number of epochs to train each model for (default: 50)')
parser.add_argument('--resize_size', type=int, default=256,
help='Resize the image before center crop. (default: 256)')
parser.add_argument('--lr', type=float, default=.001,
help='learning rate (default: 0.001)')
parser.add_argument('--use-cuda', default=True, action=argparse.BooleanOptionalAction,
help='enables CUDA training')
parser.add_argument('--audio_feature', type=str, default='Log_Mel_Spectrogram',
help='Audio feature for extraction')
parser.add_argument('--optimizer', type = str, default = 'Adagrad',
help = 'Select optimizer')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
use_cuda = args.use_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
params = Parameters(args)
main(params)