forked from semtim/RB_ZTF
/
train_vae.py
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/
train_vae.py
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import torch
import numpy as np
from itertools import chain
from torch.utils.data import DataLoader, random_split
from tqdm import tqdm
from datasets import *
from vae import *
from losses import *
set_random_seed(7)
######################################
oids, targets = get_only_r_oids('akb.ztf.snad.space.json')
frames_dataset = AllFramesDataset(oids)
train_loader = DataLoader(frames_dataset, batch_size=256, shuffle=True, num_workers=32)
latent_dim = 78
learning_rate = 5e-5
encoder = VAEEncoder(latent_dim=latent_dim * 2)
decoder = Decoder(latent_dim=latent_dim)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = encoder.to(device)
decoder = decoder.to(device)
optimizer = torch.optim.Adam(
chain(encoder.parameters(), decoder.parameters()), lr=learning_rate
)
losses = []
for i in tqdm(range(1, 101)):
losses.append(
train_vae(
enc=encoder,
dec=decoder,
optimizer=optimizer,
loader=train_loader,
epoch=i,
single_pass_handler=vae_pass_handler,
loss_handler=vae_loss_handler,
device=device
)
)
torch.save(encoder.state_dict(), 'trained_models/vae/encoder_ld78.zip')
torch.save(decoder.state_dict(), 'trained_models/vae/decoder_ld78.zip')
np.save('trained_models/vae/loss_ld78.npy', losses)