stu1130 commented on a change in pull request #13325: [MXNET-1210 ] Gluon Audio - Example URL: https://github.com/apache/incubator-mxnet/pull/13325#discussion_r236926350
########## File path: example/gluon/urban_sounds/train.py ########## @@ -0,0 +1,155 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +"""The module to run training on the Urban sounds dataset +""" +import os +import time +import warnings +import mxnet as mx +from mxnet import gluon, nd, autograd +from datasets import AudioFolderDataset +from transforms import MFCC +import model + +def evaluate_accuracy(data_iterator, net): + """Function to evaluate accuracy of any data iterator passed to it as an argument""" + acc = mx.metric.Accuracy() + for data, label in data_iterator: + output = net(data) + predictions = nd.argmax(output, axis=1) + predictions = predictions.reshape((-1, 1)) + acc.update(preds=predictions, labels=label) + return acc.get()[1] + + +def train(train_dir=None, train_csv=None, epochs=30, batch_size=32): + """The function responsible for running the training the model.""" + + if not train_dir or not os.path.exists(train_dir) or not train_csv: + warnings.warn("No train directory could be found ") + return + # Make a dataset from the local folder containing Audio data + print("\nMaking an Audio Dataset...\n") + tick = time.time() + aud_dataset = AudioFolderDataset(train_dir, train_csv=train_csv, file_format='.wav', skip_header=True) + tock = time.time() + + print("Loading the dataset took ", (tock-tick), " seconds.") + print("\n=======================================\n") + print("Number of output classes = ", len(aud_dataset.synsets)) + print("\nThe labels are : \n") + print(aud_dataset.synsets) + # Get the model to train + net = model.get_net(len(aud_dataset.synsets)) + print("\nNeural Network = \n") + print(net) + print("\nModel - Neural Network Generated!\n") + print("=======================================\n") + + #Define the loss - Softmax CE Loss + softmax_loss = gluon.loss.SoftmaxCELoss(from_logits=False, sparse_label=True) + print("Loss function initialized!\n") + print("=======================================\n") + + #Define the trainer with the optimizer + trainer = gluon.Trainer(net.collect_params(), 'adadelta') + print("Optimizer - Trainer function initialized!\n") + print("=======================================\n") + print("Loading the dataset to the Gluon's OOTB Dataloader...") + + #Getting the data loader out of the AudioDataset and passing the transform + aud_transform = MFCC() + tick = time.time() + + audio_train_loader = gluon.data.DataLoader(aud_dataset.transform_first(aud_transform), batch_size=32, shuffle=True) + tock = time.time() + print("Time taken to load data and apply transform here is ", (tock-tick), " seconds.") + print("=======================================\n") + + + print("Starting the training....\n") + # Training loop + tick = time.time() + batch_size = batch_size + num_examples = len(aud_dataset) + + for e in range(epochs): + cumulative_loss = 0 + for data, label in audio_train_loader: + with autograd.record(): + output = net(data) + loss = softmax_loss(output, label) + loss.backward() + + trainer.step(batch_size) + cumulative_loss += mx.nd.sum(loss).asscalar() + + if e%5 == 0: + train_accuracy = evaluate_accuracy(audio_train_loader, net) + print("Epoch %s. Loss: %s Train accuracy : %s " % (e, cumulative_loss/num_examples, train_accuracy)) Review comment: nit: unify the print style ```suggestion print("Epoch {}. Loss: {} Train accuracy : {} ".format(e, cumulative_loss/ num_examples, train_accuracy)) ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
