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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) |