model.py

import torch
from torch import nn
from torch.nn import functional as F
import torchaudio
import pytorch_lightning as pl
from torchmetrics import Accuracy, F1, Precision, Recall

import torch.nn as nn
import torch.nn.functional as F

class M11(pl.LightningModule):
    def __init__(self, hidden_units_1, hidden_units_2, dropout_1, dropout_2, n_input=1, n_output=3, stride=4, n_channel=64, lr=1e-3, l2=1e-5):
        super().__init__()
        self.save_hyperparameters()

        
        self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride)
        self.bn1 = nn.BatchNorm1d(n_channel)
        self.pool1 = nn.MaxPool1d(4)

        self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3,padding=1)
        self.bn2 = nn.BatchNorm1d(n_channel)
        self.conv3 = nn.Conv1d(n_channel, n_channel, kernel_size=3,padding=1)
        self.bn3 = nn.BatchNorm1d(n_channel)
        self.pool2 = nn.MaxPool1d(4)

        self.conv4 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3,padding=1)
        self.bn4 = nn.BatchNorm1d(2 * n_channel)
        self.conv5 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3,padding=1)
        self.bn5 = nn.BatchNorm1d(2 * n_channel)
        self.pool3 = nn.MaxPool1d(4)
 
        self.conv6 = nn.Conv1d(2 * n_channel, 4 * n_channel, kernel_size=3,padding=1)
        self.bn6 = nn.BatchNorm1d(4 * n_channel)
        self.conv7 = nn.Conv1d(4 * n_channel, 4 * n_channel, kernel_size=3,padding=1)
        self.bn7 = nn.BatchNorm1d(4 * n_channel)
        self.conv8 = nn.Conv1d(4 * n_channel, 4 * n_channel, kernel_size=3,padding=1)
        self.bn8 = nn.BatchNorm1d(4 * n_channel)
        self.pool4 = nn.MaxPool1d(4)

        self.conv9 = nn.Conv1d(4 * n_channel, 8 * n_channel, kernel_size=3,padding=1)
        self.bn9 = nn.BatchNorm1d(8 * n_channel)
        self.conv10 = nn.Conv1d(8 * n_channel, 8 * n_channel, kernel_size=3,padding=1)
        self.bn10 = nn.BatchNorm1d(8 * n_channel)

#         self.fc1 = nn.Linear(8 * n_channel, n_output)
        self.mlp = nn.Sequential(
            nn.Linear(8 * n_channel, hidden_units_1),
            nn.ReLU(),
            nn.Dropout(dropout_1),
            nn.Linear(hidden_units_1, hidden_units_2),
            nn.ReLU(),
            nn.Dropout(dropout_2),
            nn.Linear(hidden_units_2, n_output)
        )

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.pool1(x)

        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        x = self.conv3(x)
        x = F.relu(self.bn3(x))
        x = self.pool2(x)

        x = self.conv4(x)
        x = F.relu(self.bn4(x))
        x = self.conv5(x)
        x = F.relu(self.bn5(x))
        x = self.pool3(x)

        x = self.conv6(x)
        x = F.relu(self.bn6(x))
        x = self.conv7(x)
        x = F.relu(self.bn7(x))
        x = self.conv8(x)
        x = F.relu(self.bn8(x))
        x = self.pool4(x)

        x = self.conv9(x)
        x = F.relu(self.bn9(x))
        x = self.conv10(x)
        x = F.relu(self.bn10(x))

        x = F.avg_pool1d(x, x.shape[-1])
        x = x.permute(0, 2, 1)
#         x = self.fc1(x)
        x = self.mlp(x)
        return F.log_softmax(x, dim=2)
    
    def training_step(self, batch, batch_idx):
        # Very simple training loop
        data, target = batch
        logits = self(data)  # this calls self.forward
        preds = torch.argmax(logits, dim=-1).squeeze()
#         loss = cost(logits.squeeze(), target)
        loss = unweighted_cost(logits.squeeze(), target)
        
        f1 = f1_metric(preds, target)
        
        self.log('train_loss', loss, on_epoch=True, prog_bar=True)
        self.log('train_f1', f1, on_epoch=True, prog_bar=True)
        return loss
    
    def validation_step(self, batch, batch_idx):
        data, target = batch
        logits = self(data)
        preds = torch.argmax(logits, dim=-1).squeeze()
#         loss = val_cost(logits.squeeze(), target)
        loss = unweighted_cost(logits.squeeze(), target)
        
        acc = accuracy(preds, target)
        f1 = f1_metric(preds, target)
        prec = precision(preds, target)
        rec = recall(preds, target)
        
        self.log('val_loss', loss, on_epoch=True, prog_bar=True)
        self.log('val_acc', acc, on_epoch=True, prog_bar=True)
        self.log('val_f1', f1, on_epoch=True, prog_bar=True)
        self.log('val_precision', prec, on_epoch=True, prog_bar=True)
        self.log('val_recall', rec, on_epoch=True, prog_bar=True)
        return loss, acc, f1, prec, rec
        
    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr, weight_decay=self.hparams.l2)
        return optimizer

# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# model_PATH = "./model.ckpt"
# audio_PATH = "./sample_audio.wav"


# def _resample_if_necessary(signal, sr, device):
#     if sr != 8_000:
#         resampler = torchaudio.transforms.Resample(sr, 8_000).to(device)
#         signal = resampler(signal)

#     return signal

# def _mix_down_if_necessary(signal):
#     if signal.shape[0] > 1:
#         signal = torch.mean(signal, dim=0, keepdim=True)
    
#     return signal

# def get_likely_index(tensor):
#     # find most likely label index for each element in the batch
#     return tensor.argmax(dim=-1)

# model = M11.load_from_checkpoint(model_PATH).to(DEVICE)
# model.eval()

# audio, sr = torchaudio.load(audio_PATH)
# # resampler = torchaudio.transforms.Resample(sr, 8_000).to(DEVICE)
# processed_audio = _mix_down_if_necessary(_resample_if_necessary(audio, sr, DEVICE))

# print(processed_audio.shape)
# with torch.no_grad():
#     pred = get_likely_index(model(processed_audio.unsqueeze(0).to(DEVICE))).view(-1)
    
# # y_true = target.tolist()
# # y_pred = pred.tolist()
# # target_names = eval_dataset.label_list
# # print(classification_report(y_true, y_pred, target_names=target_names))
# print(pred)