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cl_ewc.py
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"""
Training script of KWS models using EWC as the CL method.
Reference: Overcoming catastrophic forgetting in neural networks
https://arxiv.org/abs/1612.00796
@author huangyz0918
@date 05/09/2021
"""
import time
import neptune
import argparse
import numpy as np
import torch
import torch.nn.functional as F
from model import STFT_TCResnet, MFCC_TCResnet, STFT_MLP, MFCC_RNN, parameter_number
from model import Trainer, Evaluator, get_dataloader_keyword
# for EWC method to calculate the importance of the weight.
fisher_dict = {}
optpar_dict = {}
def on_task_update(task_id, model, optimizer, device, loader_mem):
"""
Update the regularization after each task learning.
"""
model.train()
optimizer.zero_grad()
# accumulating gradients.
for _, (waveform, labels) in enumerate(loader_mem):
waveform, labels = waveform.to(device), labels.to(device)
logits = model(waveform)
loss = F.cross_entropy(logits, labels)
loss.backward()
fisher_dict[task_id] = {}
optpar_dict[task_id] = {}
# gradients accumulated can be used to calculate fisher.
for name, param in model.named_parameters():
optpar_dict[task_id][name] = param.data.clone()
fisher_dict[task_id][name] = param.grad.data.clone().pow(2)
if __name__ == "__main__":
def options(config):
parser = argparse.ArgumentParser(description="Input optional guidance for training")
parser.add_argument("--epoch", default=10, type=int, help="The number of training epoch")
parser.add_argument("--lr", default=0.01, type=float, help="Learning rate")
parser.add_argument("--elambda", default=6, type=float, help="EWC Lambda, the regularization strength")
parser.add_argument("--batch", default=128, type=int, help="Training batch size")
parser.add_argument("--log", default=False, action='store_true',
help="record the experiment into web neptune.ai")
parser.add_argument("--ek", default=False, action='store_true', help="evaluate the CL by keywords")
parser.add_argument("--step", default=30, type=int, help="Training step size")
parser.add_argument("--gpu", default=4, type=int, help="Number of GPU device")
parser.add_argument("--dpath", default="./dataset", type=str, help="The path of dataset")
parser.add_argument("--tqdm", default=False, action='store_true', help="enable terminal tqdm output.")
parser.add_argument("--model", default="stft", type=str, help="[stft, mfcc]")
parser.add_argument("--cha", default=config["tc-resnet8"], type=list,
help="The channel of model layers (in list)")
parser.add_argument("--scale", default=1, type=float, help="The scale of model channel")
parser.add_argument("--freq", default=30, type=int, help="Model saving frequency (in step)")
parser.add_argument("--save", default="stft", type=str, help="The save name")
args = parser.parse_args()
return args
class_list_0 = ["yes", "no", "nine",
"three", "bed", "up",
"down", "wow", "happy",
"four", "stop", "go",
"dog", "cat", "five"]
class_list_1 = ["tree", "one", "eight"]
class_list_2 = ["left", "right", "bird"]
class_list_3 = ["seven", "six", "two"]
class_list_4 = ["marvin", "on", "sheila"]
class_list_5 = ["off", "house", "zero"]
config = {
"tc-resnet8": [16, 24, 32, 48],
"tc-resnet14": [16, 24, 24, 32, 32, 48, 48]}
parameters = options(config)
# initialize and setup Neptune
if parameters.log:
neptune.init('huangyz0918/kws')
neptune.create_experiment(name='kws_model', tags=['pytorch', 'KWS', 'GSC', 'TC-ResNet', 'EWC'],
params=vars(parameters))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# build a multi-head setting for learning process.
total_class_list = []
learning_tasks = [class_list_0, class_list_1, class_list_2, class_list_3, class_list_4, class_list_5]
for x in learning_tasks:
total_class_list += x
total_class_num = len([i for j, i in enumerate(total_class_list) if i not in total_class_list[:j]])
class_list = []
for task in learning_tasks:
class_list += task
class_encoding = {category: index for index, category in enumerate(class_list)}
# load the model.
if parameters.model == "stft":
model = STFT_TCResnet(
filter_length=256, hop_length=129, bins=129,
channels=parameters.cha, channel_scale=parameters.scale, num_classes=total_class_num)
elif parameters.model == "mfcc":
model = MFCC_TCResnet(bins=40, channels=parameters.cha, channel_scale=parameters.scale,
num_classes=total_class_num)
elif parameters.model == "stft-mlp":
model = STFT_MLP(filter_length=256, hop_length=129, bins=129, num_classes=total_class_num)
elif parameters.model == "rnn":
model = MFCC_RNN(n_mfcc=12, sampling_rate=16000,
num_classes=total_class_num) # sample length for the dataset is 16000.
else:
model = None
# continuous learning by EWC.
la_list = []
acc_list = []
bwt_list = []
learned_class_list = []
trainer = Trainer(parameters, model)
optimizer = torch.optim.SGD(model.parameters(), lr=parameters.lr, momentum=0.9)
start_time = time.time()
for task_id, task_class in enumerate(learning_tasks):
print(">>> Learned Class: ", learned_class_list, " To Learn: ", task_class)
learned_class_list += task_class
train_loader, test_loader = get_dataloader_keyword(parameters.dpath, task_class, class_encoding,
parameters.batch)
# starting training.
if parameters.log:
trainer.ewc_train(task_id, optimizer, train_loader, test_loader,
fisher_dict, optpar_dict, parameters.elambda, tag=task_id)
else:
trainer.ewc_train(task_id, optimizer, train_loader, test_loader,
fisher_dict, optpar_dict, parameters.elambda)
# update the EWC parameters.
on_task_update(task_id, trainer.model, optimizer, device, train_loader)
# start evaluating the CL on previous tasks.
total_learned_acc = 0
if parameters.ek:
evaluate_list = class_list
else:
evaluate_list = learning_tasks
for val_id, task in enumerate(evaluate_list):
print(f">>> Testing on task {val_id}, Keywords: {task}")
_, val_loader = get_dataloader_keyword(parameters.dpath, task, class_encoding, parameters.batch)
if parameters.log:
log_data = Evaluator(trainer.model, tag=f't{task_id}v{val_id}').evaluate(val_loader)
else:
log_data = Evaluator(trainer.model).evaluate(val_loader)
if parameters.log:
neptune.log_metric(f'TASK-{task_id}-acc', log_data["test_accuracy"])
if val_id <= task_id:
total_learned_acc += log_data["test_accuracy"]
if val_id == task_id:
la_list.append(log_data["test_accuracy"])
acc_list.append(total_learned_acc / (task_id + 1))
print(f'ACC on TASK {task_id}: {total_learned_acc / (task_id + 1)}')
if task_id > 0:
bwt_list.append(np.mean([acc_list[i + 1] - acc_list[i] for i in range(len(acc_list) - 1)]))
duration = time.time() - start_time
print(f'Total time {duration}, Avg: {duration / len(learning_tasks)}s')
print(f'ACC: {np.mean(acc_list)}, std: {np.std(acc_list)}')
print(f'LA: {np.mean(la_list)}, std: {np.std(la_list)}')
print(f'BWT: {np.mean(bwt_list)}, std: {np.std(bwt_list)}')
print(f'Parameter: {parameter_number(trainer.model) / 1024} K')