Add batch size

This is probably not how it's supposed to be done but it stops the horrible coil whine at least.

train.py

@@ -14,6 +14,8 @@ from vae import get_vae
 def parse_args():
 	parser = argparse.ArgumentParser(description="Train latent interposer model")
 	parser.add_argument("--steps", type=int, default=500000, help="No. of training steps")
+	parser.add_argument('--bs', type=int, default=1, help="Batch size")
+	parser.add_argument('--lr', default="1e-8", help="Learning rate")
 	parser.add_argument("-n", "--save_every_n", type=int, dest="save", default=50000, help="Save model/sample periodically")
 	parser.add_argument('--src', choices=["v1","xl"], required=True, help="Source latent format")
 	parser.add_argument('--dst', choices=["v1","xl"], required=True, help="Destination latent format")
@@ -21,6 +23,10 @@ def parse_args():
 	args = parser.parse_args()
 	if args.src == args.dst:
 		parser.error("--src and --dst can't be the same")
+	try:
+		float(args.lr)
+	except:
+		parser.error("--lr must be a valid float eg. 0.001 or 1e-3")
 	return args
 
 class Latent:
@@ -63,8 +69,6 @@ def sample_decode(latent, filename, version):
 if __name__ == "__main__":
 	args = parse_args()
 	target_dev = "cuda"
-	target_steps = args.steps
-	save_every_n = args.save
 	latent_src = args.src
 	latent_dst = args.dst
 
@@ -84,14 +88,17 @@ if __name__ == "__main__":
 	model.to(target_dev)
 
 	criterion = torch.nn.MSELoss(size_average=False)
-	optimizer = torch.optim.SGD(model.parameters(), lr=1e-8)
+	optimizer = torch.optim.SGD(model.parameters(), lr=float(args.lr)/args.bs)
 
-	for t in tqdm(range(target_steps)):
-		# io = latents[t%len(latents)]
-		io = random.choice(latents)
+	for t in tqdm(range(int(args.steps/args.bs)), unit_scale=args.bs):
+		step = t*args.bs
+		# input batch
+		lts = [random.choice(latents) for _ in range(args.bs)]
+		src = torch.cat([x.src for x in lts],0)
+		dst = torch.cat([x.dst for x in lts],0)
 
-		y_pred = model(io.src) # forward
-		loss = criterion(y_pred, io.dst) # loss
+		y_pred = model(src) # forward
+		loss = criterion(y_pred, dst) # loss
 
 		# backward
 		optimizer.zero_grad()
@@ -99,18 +106,19 @@ if __name__ == "__main__":
 		optimizer.step()
 
 		# print loss
-		if t%1000 == 0:
-			tqdm.write(f"{t} - {loss.data.item():.2f}")
-			log.write(f"{t},{loss.data.item():.2f}\n")
+		if step%1000 == 0:
+			tqdm.write(f"{step} - {loss.data.item()/args.bs:.2f}")
+			log.write(f"{step},{loss.data.item()/args.bs:.2f}\n")
+			log.flush()
 
 		# sample/save
-		if t%save_every_n == 0:
+		if step%args.save == 0:
 			out = model(sample_latent)
-			output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{t/1000}k"
+			output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{step/1000}k"
 			sample_decode(out, f"{output_name}.png", latent_dst)
 			save_file(model.state_dict(), f"{output_name}.safetensors")
 	# save final output
-	output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{target_steps/1000}k"
+	output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{args.steps/1000}k"
 	sample_decode(out, f"{output_name}.png", "v1")
 	save_file(model.state_dict(), f"{output_name}.safetensors")
 	log.close()