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import torch
import einops
import torch.nn as nn
import torch.nn.functional as F
from .vector_quantize_pytorch import ResidualVQ
def weights_init_encoder(m):
if isinstance(m, nn.Linear):
nn.init.orthogonal_(m.weight.data)
m.bias.data.fill_(0.0)
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
assert m.weight.size(2) == m.weight.size(3)
m.weight.data.fill_(0.0)
m.bias.data.fill_(0.0)
mid = m.weight.size(2) // 2
gain = nn.init.calculate_gain("relu")
nn.init.orthogonal_(m.weight.data[:, :, mid, mid], gain)
class EncoderMLP(nn.Module):
def __init__(
self,
input_dim,
output_dim=16,
hidden_dim=128,
layer_num=1,
last_activation=None,
):
super(EncoderMLP, self).__init__()
layers = []
layers.append(nn.Linear(input_dim, hidden_dim))
layers.append(nn.ReLU())
for _ in range(layer_num):
layers.append(nn.Linear(hidden_dim, hidden_dim))
layers.append(nn.ReLU())
self.encoder = nn.Sequential(*layers)
self.fc = nn.Linear(hidden_dim, output_dim)
if last_activation is not None:
self.last_layer = last_activation
else:
self.last_layer = None
self.apply(weights_init_encoder)
def forward(self, x):
h = self.encoder(x)
state = self.fc(h)
if self.last_layer:
state = self.last_layer(state)
return state
class VqVae(nn.Module):
def __init__(
self,
input_dim_h=10, # length of action chunk
input_dim_w=9, # action dim
n_latent_dims=512,
vqvae_n_embed=32,
vqvae_groups=4,
encoder_loss_multiplier=1.0,
act_scale=1.0,
):
super(VqVae, self).__init__()
self.n_latent_dims = n_latent_dims
self.input_dim_h = input_dim_h
self.input_dim_w = input_dim_w
self.rep_dim = self.n_latent_dims
self.vqvae_n_embed = vqvae_n_embed
self.vqvae_groups = vqvae_groups
self.encoder_loss_multiplier = encoder_loss_multiplier
self.act_scale = act_scale
discrete_cfg = {"groups": self.vqvae_groups, "n_embed": self.vqvae_n_embed}
self.vq_layer = ResidualVQ(
dim=self.n_latent_dims,
num_quantizers=discrete_cfg["groups"],
codebook_size=self.vqvae_n_embed,
)
self.embedding_dim = self.n_latent_dims
self.encoder = EncoderMLP(
input_dim=input_dim_w * self.input_dim_h, output_dim=n_latent_dims
)
self.decoder = EncoderMLP(
input_dim=n_latent_dims, output_dim=input_dim_w * self.input_dim_h
)
def draw_logits_forward(self, encoding_logits):
z_embed = self.vq_layer.draw_logits_forward(encoding_logits)
return z_embed
def draw_code_forward(self, encoding_indices):
with torch.no_grad():
z_embed = self.vq_layer.get_codes_from_indices(encoding_indices)
z_embed = z_embed.sum(dim=0)
return z_embed
def get_action_from_latent(self, latent) -> torch.Tensor:
output = self.decoder(latent) * self.act_scale
return einops.rearrange(output, "... (T A) -> ... T A", A=self.input_dim_w)
def preprocess(self, state):
state = torch.Tensor(state)
state = state / self.act_scale
if self.input_dim_h == 1:
state = state.squeeze(-2) # state.squeeze(-1)
else:
state = einops.rearrange(state, "... T A -> ... (T A)")
return state
def get_code(self, state):
state = self.preprocess(state)
with torch.no_grad():
state_rep = self.encoder(state)
state_vq, vq_code, vq_loss_state = self.vq_layer(state_rep)
vq_loss_state = torch.sum(vq_loss_state)
return state_vq, vq_code
def forward(self, state):
state = self.preprocess(state)
state_rep = self.encoder(state)
state_rep_shape = state_rep.shape[:-1]
state_rep_flat = state_rep.view(state_rep.size(0), -1, state_rep.size(1))
state_rep_flat, vq_code, vq_loss_state = self.vq_layer(state_rep_flat)
state_vq = state_rep_flat.view(*state_rep_shape, -1)
vq_code = vq_code.view(*state_rep_shape, -1)
dec_out = self.decoder(state_vq)
vq_loss_state = vq_loss_state.sum()
encoder_loss = (state - dec_out).abs().mean()
vqvae_recon_loss = F.mse_loss(state, dec_out)
loss = encoder_loss * self.encoder_loss_multiplier + (vq_loss_state * 5)
loss_dict = {
"loss": loss.detach().clone(),
"vq_loss_state": vq_loss_state.detach().clone(),
"vqvae_recon_loss": vqvae_recon_loss.detach().clone(),
"encoder_loss": encoder_loss.detach().clone(),
}
return loss, vq_code, loss_dict
def state_dict(
self,
destination: str = None,
prefix: str = "",
keep_vars: bool = False,
):
if destination is None:
destination = {}
self.encoder.state_dict(destination, prefix + "encoder", keep_vars)
self.decoder.state_dict(destination, prefix + "decoder", keep_vars)
self.vq_layer.state_dict(destination, prefix + "vq_embedding", keep_vars)
return destination
def load_state_dict(self, state_dict):
self.encoder.load_state_dict(state_dict["encoder"])
self.decoder.load_state_dict(state_dict["decoder"])
self.vq_layer.load_state_dict(state_dict["vq_embedding"])
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