83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
if __name__ == '__main__':
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import numpy as np
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import random
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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, SubSpecCAE
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import pickle
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import torch.optim as optim
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from models.layers import Subspectrogram
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def train(dataset_path, machine_id, band, norm='batch', loss_fn='mse', seed=42):
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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np.random.seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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random.seed(seed)
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print(f'Training on {dataset_path.name}')
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mimii = MIMII(dataset_path=dataset_path, machine_id=machine_id)
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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) # 80 x 80
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tfms = Subspectrogram(SUB_SPEC_HEIGT, SUB_SPEC_HOP)
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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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transform=tfms
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)
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model = SubSpecCAE(norm=norm, loss_fn=loss_fn, band=band).to(DEVICE)
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model.init_weights()
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# print(model(torch.randn(128, 1, 20, 80).to(DEVICE)).shape)
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optimizer = optim.Adam(model.parameters(), lr=0.0005)
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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) # torch.Size([128, 4, 20, 80]) batch x subs_specs x height x width
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loss = model.train_loss(data)
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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, transform=tfms)
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print(f'AUC: {auc}, Machine: {machine_id}, Band: {band}, Norm: {norm}, Seed: {seed}')
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return auc
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loss_fn = 'mse'
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results = []
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for norm in ['batch']:
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for seed in SEEDS:
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for dataset_path in ALL_DATASET_PATHS:
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if '-6_dB' in dataset_path.name:
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for machine_id in [4]:
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for band in range(7):
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auc = train(dataset_path, machine_id, band, norm, loss_fn, seed)
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results.append([dataset_path.name, machine_id, seed, band, norm, auc])
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with open(f'results2_hard_{norm}_{loss_fn}.pkl', 'wb') as f:
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pickle.dump(results, f)
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