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more wandb logging

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This commit is contained in:
Dominik Moritz Roth 2024-05-26 23:56:12 +02:00
parent 16ba578737
commit 5eab625cae
1 changed files with 11 additions and 2 deletions
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13
main.py
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@ -47,7 +47,7 @@ class SpikeRunner(Slate_Runner):
latent_size = slate.consume(config, 'latent_projector.latent_size') latent_size = slate.consume(config, 'latent_projector.latent_size')
input_size = slate.consume(config, 'latent_projector.input_size') input_size = slate.consume(config, 'latent_projector.input_size')
region_latent_size = slate.consume(config, 'middle_out.region_latent_size') region_latent_size = slate.consume(config, 'middle_out.region_latent_size')
device = slate.consume(training_config, 'device') device = slate.consume(training_config, 'device', 'auto')
if device == 'auto': if device == 'auto':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.device = device self.device = device
@ -110,6 +110,7 @@ class SpikeRunner(Slate_Runner):
total_loss = 0 total_loss = 0
errs = [] errs = []
rels = [] rels = []
derrs = []
for batch_num in range(self.num_batches): for batch_num in range(self.num_batches):
# Create indices for training data and shuffle them # Create indices for training data and shuffle them
@ -119,6 +120,7 @@ class SpikeRunner(Slate_Runner):
stacked_segments = [] stacked_segments = []
peer_metrics = [] peer_metrics = []
targets = [] targets = []
lasts = []
for idx in indices[:self.batch_size]: for idx in indices[:self.batch_size]:
lead_data = self.train_data[idx][:min_length] lead_data = self.train_data[idx][:min_length]
@ -143,6 +145,8 @@ class SpikeRunner(Slate_Runner):
stacked_segments.append(stacked_segment) stacked_segments.append(stacked_segment)
target = lead_data[i + self.input_size + 1] target = lead_data[i + self.input_size + 1]
targets.append(target) targets.append(target)
last = lead_data[i + self.input_size]
lasts.append(last)
# Pass the batch through the projector # Pass the batch through the projector
latents = self.projector(torch.stack(stacked_segments)/self.value_scale) latents = self.projector(torch.stack(stacked_segments)/self.value_scale)
@ -164,10 +168,13 @@ class SpikeRunner(Slate_Runner):
# Calculate loss and backpropagate # Calculate loss and backpropagate
tar = torch.tensor(targets, dtype=torch.float32).unsqueeze(-1).to(device) tar = torch.tensor(targets, dtype=torch.float32).unsqueeze(-1).to(device)
las = torch.tensor(lasts, dtype=torch.float32).unsqueeze(-1).numpy()
loss = self.criterion(prediction, tar) loss = self.criterion(prediction, tar)
err = np.sum(np.abs(prediction.cpu().detach().numpy() - tar.cpu().detach().numpy())) err = np.sum(np.abs(prediction.cpu().detach().numpy() - tar.cpu().detach().numpy()))
derr = np.sum(np.abs(las - tar.cpu().detach().numpy()))
rel = err / np.sum(tar.cpu().detach().numpy()) rel = err / np.sum(tar.cpu().detach().numpy())
total_loss += loss.item() total_loss += loss.item()
derrs.append(derr/np.prod(tar.size()).item())
errs.append(err/np.prod(tar.size()).item()) errs.append(err/np.prod(tar.size()).item())
rels.append(rel.item()) rels.append(rel.item())
self.optimizer.zero_grad() self.optimizer.zero_grad()
@ -175,8 +182,10 @@ class SpikeRunner(Slate_Runner):
self.optimizer.step() self.optimizer.step()
tot_err = sum(errs)/len(errs) tot_err = sum(errs)/len(errs)
tot_derr = sum(derrs)/len(derrs)
adv_delta = tot_derr / tot_err
approx_ratio = 1/(sum(rels)/len(rels)) approx_ratio = 1/(sum(rels)/len(rels))
wandb.log({"epoch": epoch, "loss": total_loss, "err": tot_err, "approx_ratio": approx_ratio}, step=epoch) wandb.log({"epoch": epoch, "loss": total_loss, "err": tot_err, "approx_ratio": approx_ratio, "adv_delta": adv_delta}, step=epoch)
print(f'Epoch {epoch + 1}/{self.epochs}, Loss: {total_loss}') print(f'Epoch {epoch + 1}/{self.epochs}, Loss: {total_loss}')
if self.eval_freq != -1 and (epoch + 1) % self.eval_freq == 0: if self.eval_freq != -1 and (epoch + 1) % self.eval_freq == 0: