510 lines
20 KiB
Python
510 lines
20 KiB
Python
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import copy
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import torch
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from torch import nn
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import numpy as np
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from PIL import ImageColor, Image, ImageDraw, ImageFont
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import networks
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import tools
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to_np = lambda x: x.detach().cpu().numpy()
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def symlog(x):
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return torch.sign(x) * torch.log(torch.abs(x) + 1.0)
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def symexp(x):
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return torch.sign(x) * (torch.exp(torch.abs(x)) - 1.0)
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class RewardEMA(object):
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"""running mean and std"""
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def __init__(self, device, alpha=1e-2):
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self.device = device
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self.scale = torch.zeros((1,)).to(device)
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self.alpha = alpha
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self.range = torch.tensor([0.05, 0.95]).to(device)
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def __call__(self, x):
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flat_x = torch.flatten(x.detach())
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x_quantile = torch.quantile(input=flat_x, q=self.range)
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scale = x_quantile[1] - x_quantile[0]
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new_scale = self.alpha * scale + (1 - self.alpha) * self.scale
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self.scale = new_scale
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return x / torch.clip(self.scale, min=1.0)
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class WorldModel(nn.Module):
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def __init__(self, step, config):
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super(WorldModel, self).__init__()
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self._step = step
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self._use_amp = True if config.precision == 16 else False
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self._config = config
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self.encoder = networks.ConvEncoder(
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config.grayscale,
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config.cnn_depth,
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config.act,
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config.norm,
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config.encoder_kernels,
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)
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if config.size[0] == 64 and config.size[1] == 64:
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embed_size = (
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(64 // 2 ** (len(config.encoder_kernels))) ** 2
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* config.cnn_depth
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* 2 ** (len(config.encoder_kernels) - 1)
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)
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else:
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raise NotImplemented(f"{config.size} is not applicable now")
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self.dynamics = networks.RSSM(
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config.dyn_stoch,
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config.dyn_deter,
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config.dyn_hidden,
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config.dyn_input_layers,
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config.dyn_output_layers,
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config.dyn_rec_depth,
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config.dyn_shared,
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config.dyn_discrete,
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config.act,
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config.norm,
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config.dyn_mean_act,
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config.dyn_std_act,
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config.dyn_temp_post,
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config.dyn_min_std,
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config.dyn_cell,
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config.unimix_ratio,
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config.num_actions,
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embed_size,
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config.device,
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)
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self.heads = nn.ModuleDict()
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channels = 1 if config.grayscale else 3
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shape = (channels,) + config.size
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if config.dyn_discrete:
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feat_size = config.dyn_stoch * config.dyn_discrete + config.dyn_deter
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else:
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feat_size = config.dyn_stoch + config.dyn_deter
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self.heads["image"] = networks.ConvDecoder(
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feat_size, # pytorch version
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config.cnn_depth,
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config.act,
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config.norm,
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shape,
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config.decoder_kernels,
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)
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if config.reward_head == "twohot":
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self.heads["reward"] = networks.DenseHead(
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feat_size, # pytorch version
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(255,),
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config.reward_layers,
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config.units,
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config.act,
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config.norm,
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dist=config.reward_head,
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)
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else:
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self.heads["reward"] = networks.DenseHead(
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feat_size, # pytorch version
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[],
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config.reward_layers,
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config.units,
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config.act,
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config.norm,
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dist=config.reward_head,
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)
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# added this
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self.heads["reward"].apply(tools.weight_init)
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if config.pred_discount:
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self.heads["discount"] = networks.DenseHead(
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feat_size, # pytorch version
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[],
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config.discount_layers,
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config.units,
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config.act,
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config.norm,
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dist="binary",
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)
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for name in config.grad_heads:
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assert name in self.heads, name
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self._model_opt = tools.Optimizer(
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"model",
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self.parameters(),
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config.model_lr,
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config.opt_eps,
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config.grad_clip,
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config.weight_decay,
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opt=config.opt,
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use_amp=self._use_amp,
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)
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self._scales = dict(reward=config.reward_scale, discount=config.discount_scale)
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def _train(self, data):
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# action (batch_size, batch_length, act_dim)
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# image (batch_size, batch_length, h, w, ch)
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# reward (batch_size, batch_length)
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# discount (batch_size, batch_length)
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data = self.preprocess(data)
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with tools.RequiresGrad(self):
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with torch.cuda.amp.autocast(self._use_amp):
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embed = self.encoder(data)
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post, prior = self.dynamics.observe(embed, data["action"])
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kl_free = tools.schedule(self._config.kl_free, self._step)
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kl_lscale = tools.schedule(self._config.kl_lscale, self._step)
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kl_rscale = tools.schedule(self._config.kl_rscale, self._step)
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kl_loss, kl_value, loss_lhs, loss_rhs = self.dynamics.kl_loss(
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post, prior, self._config.kl_forward, kl_free, kl_lscale, kl_rscale
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)
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losses = {}
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likes = {}
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for name, head in self.heads.items():
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grad_head = name in self._config.grad_heads
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feat = self.dynamics.get_feat(post)
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feat = feat if grad_head else feat.detach()
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pred = head(feat)
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# if name == 'image':
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# losses[name] = torch.nn.functional.mse_loss(pred.mode(), data[name], 'sum')
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like = pred.log_prob(data[name])
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likes[name] = like
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losses[name] = -torch.mean(like) * self._scales.get(name, 1.0)
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model_loss = sum(losses.values()) + kl_loss
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metrics = self._model_opt(model_loss, self.parameters())
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metrics.update({f"{name}_loss": to_np(loss) for name, loss in losses.items()})
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metrics["kl_free"] = kl_free
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metrics["kl_lscale"] = kl_lscale
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metrics["kl_rscale"] = kl_rscale
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metrics["loss_lhs"] = to_np(loss_lhs)
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metrics["loss_rhs"] = to_np(loss_rhs)
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metrics["kl"] = to_np(torch.mean(kl_value))
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with torch.cuda.amp.autocast(self._use_amp):
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metrics["prior_ent"] = to_np(
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torch.mean(self.dynamics.get_dist(prior).entropy())
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)
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metrics["post_ent"] = to_np(
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torch.mean(self.dynamics.get_dist(post).entropy())
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)
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context = dict(
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embed=embed,
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feat=self.dynamics.get_feat(post),
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kl=kl_value,
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postent=self.dynamics.get_dist(post).entropy(),
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)
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post = {k: v.detach() for k, v in post.items()}
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return post, context, metrics
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def preprocess(self, obs):
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obs = obs.copy()
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if self._config.obs_trans == "normalize":
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obs["image"] = torch.Tensor(obs["image"]) / 255.0 - 0.5
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elif self._config.obs_trans == "identity":
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obs["image"] = torch.Tensor(obs["image"])
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elif self._config.obs_trans == "symlog":
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obs["image"] = symlog(torch.Tensor(obs["image"]))
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else:
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raise NotImplemented(f"{self._config.reward_trans} is not implemented")
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if self._config.reward_trans == "tanh":
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# (batch_size, batch_length) -> (batch_size, batch_length, 1)
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obs["reward"] = torch.tanh(torch.Tensor(obs["reward"])).unsqueeze(-1)
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elif self._config.reward_trans == "identity":
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# (batch_size, batch_length) -> (batch_size, batch_length, 1)
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obs["reward"] = torch.Tensor(obs["reward"]).unsqueeze(-1)
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elif self._config.reward_trans == "symlog":
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obs["reward"] = symlog(torch.Tensor(obs["reward"])).unsqueeze(-1)
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else:
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raise NotImplemented(f"{self._config.reward_trans} is not implemented")
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if "discount" in obs:
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obs["discount"] *= self._config.discount
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# (batch_size, batch_length) -> (batch_size, batch_length, 1)
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obs["discount"] = torch.Tensor(obs["discount"]).unsqueeze(-1)
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obs = {k: torch.Tensor(v).to(self._config.device) for k, v in obs.items()}
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return obs
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def video_pred(self, data):
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data = self.preprocess(data)
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embed = self.encoder(data)
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states, _ = self.dynamics.observe(embed[:6, :5], data["action"][:6, :5])
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recon = self.heads["image"](self.dynamics.get_feat(states)).mode()[:6]
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reward_post = self.heads["reward"](self.dynamics.get_feat(states)).mode()[:6]
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init = {k: v[:, -1] for k, v in states.items()}
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prior = self.dynamics.imagine(data["action"][:6, 5:], init)
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openl = self.heads["image"](self.dynamics.get_feat(prior)).mode()
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reward_prior = self.heads["reward"](self.dynamics.get_feat(prior)).mode()
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# observed image is given until 5 steps
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model = torch.cat([recon[:, :5], openl], 1)
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if self._config.obs_trans == "normalize":
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truth = data["image"][:6] + 0.5
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model += 0.5
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elif self._config.obs_trans == "symlog":
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truth = symexp(data["image"][:6]) / 255.0
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model = symexp(model) / 255.0
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error = (model - truth + 1) / 2
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return torch.cat([truth, model, error], 2)
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class ImagBehavior(nn.Module):
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def __init__(self, config, world_model, stop_grad_actor=True, reward=None):
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super(ImagBehavior, self).__init__()
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self._use_amp = True if config.precision == 16 else False
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self._config = config
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self._world_model = world_model
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self._stop_grad_actor = stop_grad_actor
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self._reward = reward
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if config.dyn_discrete:
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feat_size = config.dyn_stoch * config.dyn_discrete + config.dyn_deter
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else:
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feat_size = config.dyn_stoch + config.dyn_deter
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self.actor = networks.ActionHead(
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feat_size, # pytorch version
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config.num_actions,
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config.actor_layers,
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config.units,
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config.act,
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config.norm,
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config.actor_dist,
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config.actor_init_std,
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config.actor_min_std,
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config.actor_dist,
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config.actor_temp,
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config.actor_outscale,
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) # action_dist -> action_disc?
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if config.value_head == "twohot":
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self.value = networks.DenseHead(
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feat_size, # pytorch version
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(255,),
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config.value_layers,
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config.units,
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config.act,
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config.norm,
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config.value_head,
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)
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else:
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self.value = networks.DenseHead(
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feat_size, # pytorch version
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[],
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config.value_layers,
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config.units,
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config.act,
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config.norm,
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config.value_head,
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)
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self.value.apply(tools.weight_init)
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if config.slow_value_target or config.slow_actor_target:
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self._slow_value = copy.deepcopy(self.value)
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self._updates = 0
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kw = dict(wd=config.weight_decay, opt=config.opt, use_amp=self._use_amp)
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self._actor_opt = tools.Optimizer(
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"actor",
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self.actor.parameters(),
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config.actor_lr,
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config.ac_opt_eps,
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config.actor_grad_clip,
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**kw,
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)
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self._value_opt = tools.Optimizer(
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"value",
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self.value.parameters(),
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config.value_lr,
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config.ac_opt_eps,
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config.value_grad_clip,
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**kw,
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)
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if self._config.reward_EMA:
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self.reward_ema = RewardEMA(device=self._config.device)
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def _train(
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self,
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start,
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objective=None,
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action=None,
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reward=None,
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imagine=None,
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tape=None,
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repeats=None,
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):
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objective = objective or self._reward
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self._update_slow_target()
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metrics = {}
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with tools.RequiresGrad(self.actor):
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with torch.cuda.amp.autocast(self._use_amp):
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imag_feat, imag_state, imag_action = self._imagine(
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start, self.actor, self._config.imag_horizon, repeats
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)
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reward = objective(imag_feat, imag_state, imag_action)
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if self._config.reward_trans == "symlog":
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# rescale predicted reward by head['reward']
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reward = symexp(reward)
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actor_ent = self.actor(imag_feat).entropy()
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state_ent = self._world_model.dynamics.get_dist(imag_state).entropy()
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# this target is not scaled
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# slow is flag to indicate whether slow_target is used for lambda-return
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target, weights = self._compute_target(
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imag_feat,
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imag_state,
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imag_action,
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reward,
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actor_ent,
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state_ent,
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self._config.slow_actor_target,
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)
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actor_loss, mets = self._compute_actor_loss(
|
||
|
|
imag_feat,
|
||
|
|
imag_state,
|
||
|
|
imag_action,
|
||
|
|
target,
|
||
|
|
actor_ent,
|
||
|
|
state_ent,
|
||
|
|
weights,
|
||
|
|
)
|
||
|
|
metrics.update(mets)
|
||
|
|
if self._config.slow_value_target != self._config.slow_actor_target:
|
||
|
|
target, weights = self._compute_target(
|
||
|
|
imag_feat,
|
||
|
|
imag_state,
|
||
|
|
imag_action,
|
||
|
|
reward,
|
||
|
|
actor_ent,
|
||
|
|
state_ent,
|
||
|
|
self._config.slow_value_target,
|
||
|
|
)
|
||
|
|
value_input = imag_feat
|
||
|
|
|
||
|
|
with tools.RequiresGrad(self.value):
|
||
|
|
with torch.cuda.amp.autocast(self._use_amp):
|
||
|
|
value = self.value(value_input[:-1].detach())
|
||
|
|
target = torch.stack(target, dim=1)
|
||
|
|
# only critic target is processed using symlog(not actor)
|
||
|
|
if self._config.critic_trans == "symlog":
|
||
|
|
metrics["unscaled_target_mean"] = to_np(torch.mean(target))
|
||
|
|
target = symlog(target)
|
||
|
|
# (time, batch, 1), (time, batch, 1) -> (time, batch)
|
||
|
|
value_loss = -value.log_prob(target.detach())
|
||
|
|
if self._config.value_decay:
|
||
|
|
value_loss += self._config.value_decay * value.mode()
|
||
|
|
# (time, batch, 1), (time, batch, 1) -> (1,)
|
||
|
|
value_loss = torch.mean(weights[:-1] * value_loss[:, :, None])
|
||
|
|
|
||
|
|
metrics["value_mean"] = to_np(torch.mean(value.mode()))
|
||
|
|
metrics["value_max"] = to_np(torch.max(value.mode()))
|
||
|
|
metrics["value_min"] = to_np(torch.min(value.mode()))
|
||
|
|
metrics["value_std"] = to_np(torch.std(value.mode()))
|
||
|
|
metrics["target_mean"] = to_np(torch.mean(target))
|
||
|
|
metrics["reward_mean"] = to_np(torch.mean(reward))
|
||
|
|
metrics["reward_std"] = to_np(torch.std(reward))
|
||
|
|
metrics["actor_ent"] = to_np(torch.mean(actor_ent))
|
||
|
|
with tools.RequiresGrad(self):
|
||
|
|
metrics.update(self._actor_opt(actor_loss, self.actor.parameters()))
|
||
|
|
metrics.update(self._value_opt(value_loss, self.value.parameters()))
|
||
|
|
return imag_feat, imag_state, imag_action, weights, metrics
|
||
|
|
|
||
|
|
def _imagine(self, start, policy, horizon, repeats=None):
|
||
|
|
dynamics = self._world_model.dynamics
|
||
|
|
if repeats:
|
||
|
|
raise NotImplemented("repeats is not implemented in this version")
|
||
|
|
flatten = lambda x: x.reshape([-1] + list(x.shape[2:]))
|
||
|
|
start = {k: flatten(v) for k, v in start.items()}
|
||
|
|
|
||
|
|
def step(prev, _):
|
||
|
|
state, _, _ = prev
|
||
|
|
feat = dynamics.get_feat(state)
|
||
|
|
inp = feat.detach() if self._stop_grad_actor else feat
|
||
|
|
action = policy(inp).sample()
|
||
|
|
succ = dynamics.img_step(state, action, sample=self._config.imag_sample)
|
||
|
|
return succ, feat, action
|
||
|
|
|
||
|
|
feat = 0 * dynamics.get_feat(start)
|
||
|
|
action = policy(feat).mode()
|
||
|
|
succ, feats, actions = tools.static_scan(
|
||
|
|
step, [torch.arange(horizon)], (start, feat, action)
|
||
|
|
)
|
||
|
|
states = {k: torch.cat([start[k][None], v[:-1]], 0) for k, v in succ.items()}
|
||
|
|
if repeats:
|
||
|
|
raise NotImplemented("repeats is not implemented in this version")
|
||
|
|
|
||
|
|
return feats, states, actions
|
||
|
|
|
||
|
|
def _compute_target(
|
||
|
|
self, imag_feat, imag_state, imag_action, reward, actor_ent, state_ent, slow
|
||
|
|
):
|
||
|
|
if "discount" in self._world_model.heads:
|
||
|
|
inp = self._world_model.dynamics.get_feat(imag_state)
|
||
|
|
discount = self._world_model.heads["discount"](inp).mean
|
||
|
|
else:
|
||
|
|
discount = self._config.discount * torch.ones_like(reward)
|
||
|
|
if self._config.future_entropy and self._config.actor_entropy() > 0:
|
||
|
|
reward += self._config.actor_entropy() * actor_ent
|
||
|
|
if self._config.future_entropy and self._config.actor_state_entropy() > 0:
|
||
|
|
reward += self._config.actor_state_entropy() * state_ent
|
||
|
|
if slow:
|
||
|
|
value = self._slow_value(imag_feat).mode()
|
||
|
|
else:
|
||
|
|
value = self.value(imag_feat).mode()
|
||
|
|
if self._config.critic_trans == "symlog":
|
||
|
|
# After adding this line there is issue
|
||
|
|
value = symexp(value)
|
||
|
|
target = tools.lambda_return(
|
||
|
|
reward[:-1],
|
||
|
|
value[:-1],
|
||
|
|
discount[:-1],
|
||
|
|
bootstrap=value[-1],
|
||
|
|
lambda_=self._config.discount_lambda,
|
||
|
|
axis=0,
|
||
|
|
)
|
||
|
|
weights = torch.cumprod(
|
||
|
|
torch.cat([torch.ones_like(discount[:1]), discount[:-1]], 0), 0
|
||
|
|
).detach()
|
||
|
|
return target, weights
|
||
|
|
|
||
|
|
def _compute_actor_loss(
|
||
|
|
self, imag_feat, imag_state, imag_action, target, actor_ent, state_ent, weights
|
||
|
|
):
|
||
|
|
metrics = {}
|
||
|
|
inp = imag_feat.detach() if self._stop_grad_actor else imag_feat
|
||
|
|
policy = self.actor(inp)
|
||
|
|
actor_ent = policy.entropy()
|
||
|
|
# Q-val for actor is not transformed using symlog
|
||
|
|
target = torch.stack(target, dim=1)
|
||
|
|
if self._config.reward_EMA:
|
||
|
|
target = self.reward_ema(target)
|
||
|
|
metrics["EMA_scale"] = to_np(self.reward_ema.scale)
|
||
|
|
|
||
|
|
if self._config.imag_gradient == "dynamics":
|
||
|
|
actor_target = target
|
||
|
|
elif self._config.imag_gradient == "reinforce":
|
||
|
|
actor_target = (
|
||
|
|
policy.log_prob(imag_action)[:-1][:, :, None]
|
||
|
|
* (target - self.value(imag_feat[:-1]).mode()).detach()
|
||
|
|
)
|
||
|
|
elif self._config.imag_gradient == "both":
|
||
|
|
actor_target = (
|
||
|
|
policy.log_prob(imag_action)[:-1][:, :, None]
|
||
|
|
* (target - self.value(imag_feat[:-1]).mode()).detach()
|
||
|
|
)
|
||
|
|
mix = self._config.imag_gradient_mix()
|
||
|
|
actor_target = mix * target + (1 - mix) * actor_target
|
||
|
|
metrics["imag_gradient_mix"] = mix
|
||
|
|
else:
|
||
|
|
raise NotImplementedError(self._config.imag_gradient)
|
||
|
|
if not self._config.future_entropy and (self._config.actor_entropy() > 0):
|
||
|
|
actor_entropy = self._config.actor_entropy() * actor_ent[:-1][:, :, None]
|
||
|
|
actor_target += actor_entropy
|
||
|
|
metrics["actor_entropy"] = to_np(torch.mean(actor_entropy))
|
||
|
|
if not self._config.future_entropy and (self._config.actor_state_entropy() > 0):
|
||
|
|
state_entropy = self._config.actor_state_entropy() * state_ent[:-1]
|
||
|
|
actor_target += state_entropy
|
||
|
|
metrics["actor_state_entropy"] = to_np(torch.mean(state_entropy))
|
||
|
|
actor_loss = -torch.mean(weights[:-1] * actor_target)
|
||
|
|
return actor_loss, metrics
|
||
|
|
|
||
|
|
def _update_slow_target(self):
|
||
|
|
if self._config.slow_value_target or self._config.slow_actor_target:
|
||
|
|
if self._updates % self._config.slow_target_update == 0:
|
||
|
|
mix = self._config.slow_target_fraction
|
||
|
|
for s, d in zip(self.value.parameters(), self._slow_value.parameters()):
|
||
|
|
d.data = mix * s.data + (1 - mix) * d.data
|
||
|
|
self._updates += 1
|