61 lines
1.3 KiB
Python
61 lines
1.3 KiB
Python
import numpy as np
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from tqdm import tqdm
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from cfg import *
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from mimii import MIMII
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from models.ae import AE
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import torch.nn as nn
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import torch.optim as optim
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import random
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torch.manual_seed(42)
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torch.cuda.manual_seed(42)
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np.random.seed(42)
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random.seed(42)
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dataset_path = ALL_DATASET_PATHS[0]
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print(f'Training on {dataset_path.name}')
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mimii = MIMII(dataset_path=dataset_path, machine_id=0)
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mimii.to(DEVICE)
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#mimii.preprocess(n_fft=1024, hop_length=256, n_mels=80, center=False, power=2.0)
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dl = mimii.train_dataloader(
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segment_len=NUM_SEGMENTS,
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hop_len=NUM_SEGMENT_HOPS,
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batch_size=BATCH_SIZE,
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num_workers=NUM_WORKERS,
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shuffle=True
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)
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model = AE(400).to(DEVICE)
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model.init_weights()
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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beta_1 = 0.00
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beta_2 = 0.0
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for epoch in range(NUM_EPOCHS):
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print(f'EPOCH #{epoch+1}')
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losses = []
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for batch in tqdm(dl):
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data, labels = batch
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data = data.to(DEVICE)
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y_hat, y = model(data)
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loss = criterion(y_hat, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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losses.append(loss.item())
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print(f'Loss: {np.mean(losses)}')
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auc = mimii.evaluate_model(model, NUM_SEGMENTS, NUM_SEGMENTS)
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print(f'AUC: {auc}')
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