File size: 6,706 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 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from ding.utils import MODEL_REGISTRY, deep_merge_dicts
from ding.config import read_config
from dizoo.gfootball.model.conv1d.conv1d_default_config import conv1d_default_config
@MODEL_REGISTRY.register('conv1d')
class GfootballConv1DModel(nn.Module):
def __init__(
self,
cfg: dict = {},
) -> None:
super(GfootballConv1DModel, self).__init__()
self.cfg = deep_merge_dicts(conv1d_default_config, cfg)
self.fc_player = nn.Linear(
self.cfg.feature_embedding.player.input_dim, self.cfg.feature_embedding.player.output_dim
)
self.fc_ball = nn.Linear(self.cfg.feature_embedding.ball.input_dim, self.cfg.feature_embedding.ball.output_dim)
self.fc_left = nn.Linear(
self.cfg.feature_embedding.left_team.input_dim, self.cfg.feature_embedding.left_team.output_dim
)
self.fc_right = nn.Linear(
self.cfg.feature_embedding.right_team.input_dim, self.cfg.feature_embedding.right_team.output_dim
)
self.fc_left_closest = nn.Linear(
self.cfg.feature_embedding.left_closest.input_dim, self.cfg.feature_embedding.left_closest.output_dim
)
self.fc_right_closest = nn.Linear(
self.cfg.feature_embedding.right_closest.input_dim, self.cfg.feature_embedding.right_closest.output_dim
)
self.conv1d_left = nn.Conv1d(
self.cfg.feature_embedding.left_team.output_dim,
self.cfg.feature_embedding.left_team.conv1d_output_channel,
1,
stride=1
)
self.conv1d_right = nn.Conv1d(
self.cfg.feature_embedding.right_team.output_dim,
self.cfg.feature_embedding.right_team.conv1d_output_channel,
1,
stride=1
)
self.fc_left2 = nn.Linear(
self.cfg.feature_embedding.left_team.conv1d_output_channel * 10,
self.cfg.feature_embedding.left_team.fc_output_dim
)
self.fc_right2 = nn.Linear(
self.cfg.feature_embedding.right_team.conv1d_output_channel * 11,
self.cfg.feature_embedding.right_team.fc_output_dim
)
self.fc_cat = nn.Linear(self.cfg.fc_cat.input_dim, self.cfg.lstm_size)
self.norm_player = nn.LayerNorm(64)
self.norm_ball = nn.LayerNorm(64)
self.norm_left = nn.LayerNorm(48)
self.norm_left2 = nn.LayerNorm(96)
self.norm_left_closest = nn.LayerNorm(48)
self.norm_right = nn.LayerNorm(48)
self.norm_right2 = nn.LayerNorm(96)
self.norm_right_closest = nn.LayerNorm(48)
self.norm_cat = nn.LayerNorm(self.cfg.lstm_size)
self.lstm = nn.LSTM(self.cfg.lstm_size, self.cfg.lstm_size)
self.fc_pi_a1 = nn.Linear(self.cfg.lstm_size, self.cfg.policy_head.hidden_dim)
self.fc_pi_a2 = nn.Linear(self.cfg.policy_head.hidden_dim, self.cfg.policy_head.act_shape)
self.norm_pi_a1 = nn.LayerNorm(164)
self.fc_pi_m1 = nn.Linear(self.cfg.lstm_size, 164)
self.fc_pi_m2 = nn.Linear(164, 8)
self.norm_pi_m1 = nn.LayerNorm(164)
self.fc_v1 = nn.Linear(self.cfg.lstm_size, self.cfg.value_head.hidden_dim)
self.norm_v1 = nn.LayerNorm(164)
self.fc_v2 = nn.Linear(self.cfg.value_head.hidden_dim, self.cfg.value_head.output_dim, bias=False)
def forward(self, state_dict):
player_state = state_dict["player"].unsqueeze(0)
ball_state = state_dict["ball"].unsqueeze(0)
left_team_state = state_dict["left_team"].unsqueeze(0)
left_closest_state = state_dict["left_closest"].unsqueeze(0)
right_team_state = state_dict["right_team"].unsqueeze(0)
right_closest_state = state_dict["right_closest"].unsqueeze(0)
avail = state_dict["avail"].unsqueeze(0)
player_embed = self.norm_player(self.fc_player(player_state))
ball_embed = self.norm_ball(self.fc_ball(ball_state))
left_team_embed = self.norm_left(self.fc_left(left_team_state)) # horizon, batch, n, dim
left_closest_embed = self.norm_left_closest(self.fc_left_closest(left_closest_state))
right_team_embed = self.norm_right(self.fc_right(right_team_state))
right_closest_embed = self.norm_right_closest(self.fc_right_closest(right_closest_state))
[horizon, batch_size, n_player, dim] = left_team_embed.size()
left_team_embed = left_team_embed.view(horizon * batch_size, n_player,
dim).permute(0, 2, 1) # horizon * batch, dim1, n
left_team_embed = F.relu(self.conv1d_left(left_team_embed)).permute(0, 2, 1) # horizon * batch, n, dim2
left_team_embed = left_team_embed.reshape(horizon * batch_size,
-1).view(horizon, batch_size, -1) # horizon, batch, n * dim2
left_team_embed = F.relu(self.norm_left2(self.fc_left2(left_team_embed)))
right_team_embed = right_team_embed.view(horizon * batch_size, n_player + 1,
dim).permute(0, 2, 1) # horizon * batch, dim1, n
right_team_embed = F.relu(self.conv1d_right(right_team_embed)).permute(0, 2, 1) # horizon * batch, n * dim2
## Usually we need to call reshape() or contiguous() after permute, transpose, etc to make sure
# tensor on memory is contiguous
right_team_embed = right_team_embed.reshape(horizon * batch_size, -1).view(horizon, batch_size, -1)
## view() can only be used on contiguous tensor, reshape() don't have this limit.
right_team_embed = F.relu(self.norm_right2(self.fc_right2(right_team_embed)))
cat = torch.cat(
[player_embed, ball_embed, left_team_embed, right_team_embed, left_closest_embed, right_closest_embed], 2
)
cat = F.relu(self.norm_cat(self.fc_cat(cat)))
hidden = state_dict.pop('prev_state', None)
if hidden is None:
h_in = (
torch.zeros([1, batch_size, self.cfg.lstm_size],
dtype=torch.float), torch.zeros([1, batch_size, self.cfg.lstm_size], dtype=torch.float)
)
else:
h_in = hidden
out, h_out = self.lstm(cat, h_in)
a_out = F.relu(self.norm_pi_a1(self.fc_pi_a1(out)))
a_out = self.fc_pi_a2(a_out)
logit = a_out + (avail - 1) * 1e7
prob = F.softmax(logit, dim=2)
v = F.relu(self.norm_v1(self.fc_v1(out)))
v = self.fc_v2(v)
return {'logit': prob.squeeze(0), 'value': v.squeeze(0), 'next_state': h_out}
|