def __init__(self, env): super().__init__(env) # Assumes raycast observation space of len 20 self.observation_space = gym.spaces.Discrete(5) self.v = VAE.load_from_checkpoint( "/home/denpak/logs/vae_model/last.ckpt") for param in self.v.parameters(): param.requires_grad = False
def _data_loader(self, dataset: Dataset, shuffle: bool = False) -> DataLoader: return DataLoader(dataset, batch_size=self.batch_size, shuffle=shuffle, num_workers=self.num_workers, drop_last=self.drop_last, pin_memory=self.pin_memory) #model = VAE(64,latent_dim=5) #trainer = pl.Trainer(gpus=1) data = FishDataModule() #trainer.fit(model,data) v = VAE.load_from_checkpoint( "/home/denpak/Research/BBEEncoders/vae_model/last.ckpt") d = FishDataset(data_dir="/home/denpak/Research/BBEProject/Recordings", labels_file="labels.csv") x = torch.unsqueeze(d[0][0], 0) x = v.encoder(x) print(v.fc_mu(x)[0]) print(v.fc_var(x)[0]) x = torch.unsqueeze(d[1][0], 0) x = v.encoder(x) print(v.fc_mu(x)[0].detach().numpy()) print(v.fc_var(x)[0])