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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence
from torch.cuda.amp import autocast
# config
import yaml
config = yaml.safe_load(open("./config.yaml", 'r'))
class ResBlock(nn.Module):
def __init__(self, channel):
super().__init__()
self.block1 = nn.Conv2d(channel, channel, 3, 1, 1)
self.block2 = nn.Conv2d(channel, channel, 3, 1, 1)
def forward(self, x):
identity = x
x = self.block1(x)
x = F.relu(x)
x = self.block2(x)
x += identity
x = F.relu(x)
return x
class MultiHeadAttention(nn.Module):
def __init__(self, input_dim, output_dim, num_heads):
super().__init__()
self.num_heads = num_heads
self.input_dim = input_dim
self.output_dim = output_dim
self.W_Q = nn.Linear(input_dim, output_dim * num_heads)
self.W_K = nn.Linear(input_dim, output_dim * num_heads)
self.W_V = nn.Linear(input_dim, output_dim * num_heads)
self.W_O = nn.Linear(output_dim * num_heads, output_dim, bias=False)
def forward(self, input, attn_mask):
# input: [batch_size x num_agents x input_dim]
batch_size, num_agents, input_dim = input.size()
assert input_dim == self.input_dim
# (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)
q_s = self.W_Q(input).view(batch_size, num_agents, self.num_heads, -1).transpose(1,2) # q_s: [batch_size x num_heads x num_agents x output_dim]
k_s = self.W_K(input).view(batch_size, num_agents, self.num_heads, -1).transpose(1,2) # k_s: [batch_size x num_heads x num_agents x output_dim]
v_s = self.W_V(input).view(batch_size, num_agents, self.num_heads, -1).transpose(1,2) # v_s: [batch_size x num_heads x num_agents x output_dim]
if attn_mask.dim() == 2:
attn_mask = attn_mask.unsqueeze(0)
assert attn_mask.size(0) == batch_size, 'mask dim {} while batch size {}'.format(attn_mask.size(0), batch_size)
attn_mask = attn_mask.unsqueeze(1).repeat_interleave(self.num_heads, 1) # attn_mask : [batch_size x num_heads x num_agents x num_agents]
assert attn_mask.size() == (batch_size, self.num_heads, num_agents, num_agents)
# context: [batch_size x num_heads x num_agents x output_dim]
with autocast(enabled=False):
scores = torch.matmul(q_s.float(), k_s.float().transpose(-1, -2)) / (self.output_dim**0.5) # scores : [batch_size x n_heads x num_agents x num_agents]
scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.
attn = F.softmax(scores, dim=-1)
context = torch.matmul(attn, v_s)
context = context.transpose(1, 2).contiguous().view(batch_size, num_agents, self.num_heads*self.output_dim) # context: [batch_size x len_q x n_heads * d_v]
output = self.W_O(context)
return output # output: [batch_size x num_agents x output_dim]
class CommBlock(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.input_dim = input_dim
self.output_dim = config['comm_output_dim']
self.num_heads = config['num_comm_heads']
self.num_layers = config['num_comm_layers']
self.batch_size = config['batch_size']
self.self_attn = MultiHeadAttention(input_dim, self.output_dim, self.num_heads)
self.update_cell = nn.GRUCell(self.output_dim, input_dim)
def forward(self, latent, comm_mask):
'''
latent shape: batch_size x num_agents x latent_dim
'''
num_agents = latent.size(1)
# agent indices of agent that use communication
update_mask = comm_mask.sum(dim=-1) > 1
comm_idx = update_mask.nonzero(as_tuple=True)
# no agent use communication, return
if len(comm_idx[0]) == 0:
return latent
if len(comm_idx)>1:
update_mask = update_mask.unsqueeze(2)
attn_mask = comm_mask==False
for _ in range(self.num_layers):
info = self.self_attn(latent, attn_mask=attn_mask)
# latent = attn_layer(latent, attn_mask=attn_mask)
if len(comm_idx)==1:
batch_idx = torch.zeros(len(comm_idx[0]), dtype=torch.long)
latent[batch_idx, comm_idx[0]] = self.update_cell(info[batch_idx, comm_idx[0]], latent[batch_idx, comm_idx[0]])
else:
update_info = self.update_cell(info.view(-1, self.output_dim), latent.view(-1, self.input_dim)).view(self.batch_size, num_agents, self.input_dim)
# update_mask = update_mask.unsqueeze(2)
latent = torch.where(update_mask, update_info, latent)
# latent[comm_idx] = self.update_cell(info[comm_idx], latent[comm_idx])
# latent = self.update_cell(info, latent)
return latent
class PolicyNet(nn.Module):
def __init__(self):
super().__init__()
self.obs_radius = config['obs_radius']
self.input_shape = (6, 2*self.obs_radius+1, 2*self.obs_radius+1)
self.latent_dim = 16*7*7
self.hidden_dim = config['hidden_dim']
self.max_num_agents = config['max_num_agents']
self.max_comm_agents = config['max_comm_agents']
self.num_channels = config['num_channels']
self.batch_size = config['batch_size']
self.obs_encoder = nn.Sequential(
nn.Conv2d(self.input_shape[0], self.num_channels, 3, 1),
nn.ReLU(True),
ResBlock(self.num_channels),
ResBlock(self.num_channels),
ResBlock(self.num_channels),
nn.Conv2d(self.num_channels, 16, 1, 1),
nn.ReLU(True),
nn.Flatten(),
)
self.recurrent = nn.GRUCell(self.latent_dim, self.hidden_dim)
self.comm = CommBlock(self.hidden_dim)
# dueling q structure
self.adv = nn.Linear(self.hidden_dim, 5)
self.state = nn.Linear(self.hidden_dim, 1)
self.hidden = None
for _, m in self.named_modules():
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.no_grad()
def step(self, obs, pos):
num_agents = obs.size(0)
latent = self.obs_encoder(obs)
if self.hidden is None:
self.hidden = self.recurrent(latent)
else:
self.hidden = self.recurrent(latent, self.hidden)
# from num_agents x hidden_dim to 1 x num_agents x hidden_dim
self.hidden = self.hidden.unsqueeze(0)
# masks for communication block
agents_pos = pos
pos_mat = (agents_pos.unsqueeze(1)-agents_pos.unsqueeze(0)).abs()
dist_mat = (pos_mat[:,:,0]**2+pos_mat[:,:,1]**2).sqrt()
# mask out agents that out of range of FOV
in_obs_mask = (pos_mat<=self.obs_radius).all(2)
# mask out agents that are far away
_, ranking = dist_mat.topk(min(self.max_comm_agents, num_agents), dim=1, largest=False)
dist_mask = torch.zeros((num_agents, num_agents), dtype=torch.bool)
dist_mask.scatter_(1, ranking, True)
comm_mask = torch.bitwise_and(in_obs_mask, dist_mask)
self.hidden = self.comm(self.hidden, comm_mask)
self.hidden = self.hidden.squeeze(0)
adv_val = self.adv(self.hidden)
state_val = self.state(self.hidden)
q_val = state_val + adv_val - adv_val.mean(1, keepdim=True)
actions = torch.argmax(q_val, 1).tolist()
return actions, q_val.numpy(), self.hidden.numpy(), comm_mask.numpy()
def reset(self):
self.hidden = None
@autocast()
def forward(self, obs, steps, hidden, comm_mask):
# comm_mask shape: batch_size x seq_len x max_num_agents x max_num_agents
max_steps = obs.size(1)
num_agents = comm_mask.size(2)
assert comm_mask.size(2) == self.max_num_agents
obs = obs.transpose(1, 2)
obs = obs.contiguous().view(-1, *self.input_shape)
latent = self.obs_encoder(obs)
latent = latent.view(self.batch_size*num_agents, max_steps, self.latent_dim).transpose(0, 1)
hidden_buffer = []
for i in range(max_steps):
# hidden size: batch_size*num_agents x self.hidden_dim
hidden = self.recurrent(latent[i], hidden)
hidden = hidden.view(self.batch_size, num_agents, self.hidden_dim)
hidden = self.comm(hidden, comm_mask[:, i])
# only hidden from agent 0
hidden_buffer.append(hidden[:, 0])
hidden = hidden.view(self.batch_size*num_agents, self.hidden_dim)
# hidden buffer size: batch_size x seq_len x self.hidden_dim
hidden_buffer = torch.stack(hidden_buffer).transpose(0, 1)
# hidden size: batch_size x self.hidden_dim
hidden = hidden_buffer[torch.arange(self.batch_size), steps-1]
adv_val = self.adv(hidden)
state_val = self.state(hidden)
q_val = state_val + adv_val - adv_val.mean(1, keepdim=True)
return q_val