File size: 6,878 Bytes
079c32c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
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