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import math
from typing import List, Dict, Any, Tuple
from collections import namedtuple
import torch
import torch.nn as nn
from torch.optim import Adam, SGD, AdamW
from torch.optim.lr_scheduler import LambdaLR
from ding.policy import Policy
from ding.model import model_wrap
from ding.torch_utils import to_device
from ding.utils import EasyTimer
from ding.utils import POLICY_REGISTRY
@POLICY_REGISTRY.register('pc_bfs')
class ProcedureCloningBFSPolicy(Policy):
def default_model(self) -> Tuple[str, List[str]]:
return 'pc_bfs', ['ding.model.template.procedure_cloning']
config = dict(
type='pc',
cuda=False,
on_policy=False,
continuous=False,
max_bfs_steps=100,
learn=dict(
update_per_collect=1,
batch_size=32,
learning_rate=1e-5,
lr_decay=False,
decay_epoch=30,
decay_rate=0.1,
warmup_lr=1e-4,
warmup_epoch=3,
optimizer='SGD',
momentum=0.9,
weight_decay=1e-4,
),
collect=dict(
unroll_len=1,
noise=False,
noise_sigma=0.2,
noise_range=dict(
min=-0.5,
max=0.5,
),
),
eval=dict(),
other=dict(replay_buffer=dict(replay_buffer_size=10000)),
)
def _init_learn(self):
assert self._cfg.learn.optimizer in ['SGD', 'Adam']
if self._cfg.learn.optimizer == 'SGD':
self._optimizer = SGD(
self._model.parameters(),
lr=self._cfg.learn.learning_rate,
weight_decay=self._cfg.learn.weight_decay,
momentum=self._cfg.learn.momentum
)
elif self._cfg.learn.optimizer == 'Adam':
if self._cfg.learn.weight_decay is None:
self._optimizer = Adam(
self._model.parameters(),
lr=self._cfg.learn.learning_rate,
)
else:
self._optimizer = AdamW(
self._model.parameters(),
lr=self._cfg.learn.learning_rate,
weight_decay=self._cfg.learn.weight_decay
)
if self._cfg.learn.lr_decay:
def lr_scheduler_fn(epoch):
if epoch <= self._cfg.learn.warmup_epoch:
return self._cfg.learn.warmup_lr / self._cfg.learn.learning_rate
else:
ratio = (epoch - self._cfg.learn.warmup_epoch) // self._cfg.learn.decay_epoch
return math.pow(self._cfg.learn.decay_rate, ratio)
self._lr_scheduler = LambdaLR(self._optimizer, lr_scheduler_fn)
self._timer = EasyTimer(cuda=True)
self._learn_model = model_wrap(self._model, 'base')
self._learn_model.reset()
self._max_bfs_steps = self._cfg.max_bfs_steps
self._maze_size = self._cfg.maze_size
self._num_actions = self._cfg.num_actions
self._loss = nn.CrossEntropyLoss()
def process_states(self, observations, maze_maps):
"""Returns [B, W, W, 3] binary values. Channels are (wall; goal; obs)"""
loc = torch.nn.functional.one_hot(
(observations[:, 0] * self._maze_size + observations[:, 1]).long(),
self._maze_size * self._maze_size,
).long()
loc = torch.reshape(loc, [observations.shape[0], self._maze_size, self._maze_size])
states = torch.cat([maze_maps, loc], dim=-1).long()
return states
def _forward_learn(self, data):
if self._cuda:
collated_data = to_device(data, self._device)
else:
collated_data = data
observations = collated_data['obs'],
bfs_input_maps, bfs_output_maps = collated_data['bfs_in'].long(), collated_data['bfs_out'].long()
states = observations
bfs_input_onehot = torch.nn.functional.one_hot(bfs_input_maps, self._num_actions + 1).float()
bfs_states = torch.cat([
states,
bfs_input_onehot,
], dim=-1)
logits = self._model(bfs_states)['logit']
logits = logits.flatten(0, -2)
labels = bfs_output_maps.flatten(0, -1)
loss = self._loss(logits, labels)
preds = torch.argmax(logits, dim=-1)
acc = torch.sum((preds == labels)) / preds.shape[0]
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
pred_loss = loss.item()
cur_lr = [param_group['lr'] for param_group in self._optimizer.param_groups]
cur_lr = sum(cur_lr) / len(cur_lr)
return {'cur_lr': cur_lr, 'total_loss': pred_loss, 'acc': acc}
def _monitor_vars_learn(self):
return ['cur_lr', 'total_loss', 'acc']
def _init_eval(self):
self._eval_model = model_wrap(self._model, wrapper_name='base')
self._eval_model.reset()
def _forward_eval(self, data):
if self._cuda:
data = to_device(data, self._device)
max_len = self._max_bfs_steps
data_id = list(data.keys())
output = {}
for ii in data_id:
states = data[ii].unsqueeze(0)
bfs_input_maps = self._num_actions * torch.ones([1, self._maze_size, self._maze_size]).long()
if self._cuda:
bfs_input_maps = to_device(bfs_input_maps, self._device)
xy = torch.where(states[:, :, :, -1] == 1)
observation = (xy[1][0].item(), xy[2][0].item())
i = 0
while bfs_input_maps[0, observation[0], observation[1]].item() == self._num_actions and i < max_len:
bfs_input_onehot = torch.nn.functional.one_hot(bfs_input_maps, self._num_actions + 1).long()
bfs_states = torch.cat([
states,
bfs_input_onehot,
], dim=-1)
logits = self._model(bfs_states)['logit']
bfs_input_maps = torch.argmax(logits, dim=-1)
i += 1
output[ii] = bfs_input_maps[0, observation[0], observation[1]]
if self._cuda:
output[ii] = {'action': to_device(output[ii], 'cpu'), 'info': {}}
if output[ii]['action'].item() == self._num_actions:
output[ii]['action'] = torch.randint(low=0, high=self._num_actions, size=[1])[0]
return output
def _init_collect(self) -> None:
raise NotImplementedError
def _forward_collect(self, data: Dict[int, Any], **kwargs) -> Dict[int, Any]:
raise NotImplementedError
def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> dict:
raise NotImplementedError
def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
raise NotImplementedError
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