added LPCNet torch implementation

Signed-off-by: Jan Buethe <jbuethe@amazon.de>

dnn/torch/lpcnet/engine/lpcnet_engine.py

@@ -0,0 +1,112 @@
+import torch
+from tqdm import tqdm
+import sys
+
+def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10):
+
+    model.to(device)
+    model.train()
+
+    running_loss = 0
+    previous_running_loss = 0
+
+    # gru states
+    gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device)
+    gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device)
+    gru_states = [gru_a_state, gru_b_state]
+
+    with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
+
+        for i, batch in enumerate(tepoch):
+
+            # set gradients to zero
+            optimizer.zero_grad()
+
+            # zero out initial gru states
+            gru_a_state.zero_()
+            gru_b_state.zero_()
+
+            # push batch to device
+            for key in batch:
+                batch[key] = batch[key].to(device)
+
+            target = batch['target']
+
+            # calculate model output
+            output = model(batch['features'], batch['periods'], batch['signals'], gru_states)
+
+            # calculate loss
+            loss = criterion(output.permute(0, 2, 1), target)
+
+            # calculate gradients
+            loss.backward()
+
+            # update weights
+            optimizer.step()
+
+            # update learning rate
+            scheduler.step()
+
+            # call sparsifier
+            model.sparsify()
+
+            # update running loss
+            running_loss += float(loss.cpu())
+
+            # update status bar
+            if i % log_interval == 0:
+                tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}")
+                previous_running_loss = running_loss
+
+
+    running_loss /= len(dataloader)
+
+    return running_loss
+
+def evaluate(model, criterion, dataloader, device, log_interval=10):
+
+    model.to(device)
+    model.eval()
+
+    running_loss = 0
+    previous_running_loss = 0
+
+    # gru states
+    gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device)
+    gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device)
+    gru_states = [gru_a_state, gru_b_state]
+
+    with torch.no_grad():
+        with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
+
+            for i, batch in enumerate(tepoch):
+
+
+                # zero out initial gru states
+                gru_a_state.zero_()
+                gru_b_state.zero_()
+
+                # push batch to device
+                for key in batch:
+                    batch[key] = batch[key].to(device)
+
+                target = batch['target']
+
+                # calculate model output
+                output = model(batch['features'], batch['periods'], batch['signals'], gru_states)
+
+                # calculate loss
+                loss = criterion(output.permute(0, 2, 1), target)
+
+                # update running loss
+                running_loss += float(loss.cpu())
+
+                # update status bar
+                if i % log_interval == 0:
+                    tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}")
+                    previous_running_loss = running_loss
+
+
+        running_loss /= len(dataloader)
+
+        return running_loss