nodes/city96-SD-Latent-Interposer
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Log eval loss

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City
2023-08-04 04:11:15 +02:00
parent 8d619afdf8
commit 7e071f34f9
1 changed files with 13 additions and 6 deletions
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train.py
+13 -6
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@@ -77,10 +77,13 @@ if __name__ == "__main__":
if not os.path.isdir("models"): os.mkdir("models")
log = open(f"models/{latent_src}-to-{latent_dst}_interposer.csv", "w")
if os.path.isfile(f"test_{latent_src}.npy"):
sample_latent = torch.from_numpy(np.load(f"test_{latent_src}.npy")).to(target_dev)
if os.path.isfile(f"test_{latent_src}.npy") and os.path.isfile(f"test_{latent_dst}.npy"):
ss_latent = torch.from_numpy(np.load(f"test_{latent_src}.npy")).to(target_dev)
st_latent = torch.from_numpy(np.load(f"test_{latent_dst}.npy")).to(target_dev)
else:
sample_latent = random.choice(latents).src
sample_latent = random.choice(latents)
ss_latent = sample_latent.src.to(target_dev)
st_latent = sample_latent.dst.to(target_dev)
model = Interposer()
if args.resume:
@@ -107,13 +110,17 @@ if __name__ == "__main__":
# print loss
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")
# test loss
with torch.no_grad():
t_pred = model(ss_latent)
t_loss = criterion(t_pred, st_latent)
tqdm.write(f"{step} - {loss.data.item()/args.bs:.2f}|{t_loss.data.item()/args.bs:.2f}")
log.write(f"{step},{loss.data.item()/args.bs:.2f},{t_loss.data.item()/args.bs:.2f}\n")
log.flush()
# sample/save
if step%args.save == 0:
out = model(sample_latent)
out = model(ss_latent)
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")