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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
#------------
# base class
#------------
class DecisionPredictorBase(nn.Module):
def __init__(self, coord_dim, node_dim, state_dim, emb_dim, num_mlp_layers, num_classes, dropout):
super().__init__()
self.coord_dim = coord_dim
self.node_dim = node_dim
self.emb_dim = emb_dim
self.state_dim = state_dim
self.num_mlp_layers = num_mlp_layers
self.norm_factor = 1 / math.sqrt(emb_dim)
# initial embedding
self.init_linear_node = nn.Linear(node_dim, emb_dim)
self.init_linear_depot = nn.Linear(coord_dim, emb_dim)
if state_dim > 0:
self.init_linear_state = nn.Linear(state_dim, emb_dim)
# An attention layer
self.w_q = nn.Parameter(torch.FloatTensor((2 + int(state_dim > 0)) * emb_dim, emb_dim))
self.w_k = nn.Parameter(torch.FloatTensor(2 * emb_dim, emb_dim))
self.w_v = nn.Parameter(torch.FloatTensor(2 * emb_dim, emb_dim))
# MLP
self.mlp = nn.ModuleList()
for i in range(self.num_mlp_layers):
self.mlp.append(nn.Linear(emb_dim, emb_dim, bias=True))
self.mlp.append(nn.Linear(emb_dim, num_classes, bias=True))
# Dropout
self.dropout = nn.Dropout(dropout)
self.reset_parameters()
def reset_parameters(self):
for param in self.parameters():
stdv = 1. / math.sqrt(param.size(-1))
param.data.uniform_(-stdv, stdv)
def forward(self, inputs):
"""
Paramters
---------
inputs: dict
curr_node_id: torch.LongTensor [batch_size]
next_node_id: torch.LongTensor [batch_size]
node_feat: torch.FloatTensor [batch_size x num_nodes x node_dim]
mask: torch.LongTensor [batch_size x num_nodes]
state: torch.FloatTensor [batch_size x state_dim]
Returns
-------
probs: torch.tensor [batch_size x num_classes]
"""
#----------------
# input features
#----------------
curr_node_id = inputs["curr_node_id"]
next_node_id = inputs["next_node_id"]
node_feat = inputs["node_feats"]
mask = inputs["mask"]
state = inputs["state"]
#---------------------------
# initial linear projection
#---------------------------
node_emb = self.init_linear_node(node_feat[:, 1:, :]) # [batch_size x num_loc x emb_dim]
depot_emb = self.init_linear_depot(node_feat[:, 0:1, :2]) # [batch_size x 1 x emb_dim]
new_node_feat = torch.cat((depot_emb, node_emb), 1) # [batch_size x num_nodes x emb_dim]
new_node_feat = self.dropout(new_node_feat)
#---------------
# preprocessing
#---------------
batch_size = curr_node_id.size(0)
curr_emb = new_node_feat.gather(1, curr_node_id.unsqueeze(-1).expand(batch_size, 1, self.emb_dim))
next_emb = new_node_feat.gather(1, next_node_id.unsqueeze(-1).expand(batch_size, 1, self.emb_dim))
if state is not None and self.state_dim > 0:
state_emb = self.init_linear_state(state) # [batch_size x emb_dim]
input_q = torch.cat((curr_emb, next_emb, state_emb[:, None, :]), -1) # [batch_size x 1 x (3*emb_dim)]
else:
input_q = torch.cat((curr_emb, next_emb), -1) # [batch_size x 1 x (2*emb_dim)]
input_kv = torch.cat((curr_emb.expand_as(new_node_feat), new_node_feat), -1) # [batch_size x num_nodes x (2*emb_dim)]
#--------------------
# An attention layer
#--------------------
q = torch.matmul(input_q, self.w_q) # [batch_size x 1 x emb_dim]
k = torch.matmul(input_kv, self.w_k) # [batch_size x num_nodes x emb_dim]
v = torch.matmul(input_kv, self.w_v) # [batch_size x num_nodes x emb_dim]
compatibility = self.norm_factor * torch.matmul(q, k.transpose(-2, -1)) # [batch_size x 1 x num_nodes]
compatibility[(~mask).unsqueeze(1).expand_as(compatibility)] = -math.inf
attn = torch.softmax(compatibility, dim=-1)
h = torch.matmul(attn, v) # [batch_size x 1 x emb_dim]
h = h.squeeze(1) # [batch_size x emb_dim]
#---------------
# MLP (decoder)
#---------------
for i in range(self.num_mlp_layers):
h = self.dropout(h)
h = torch.relu(self.mlp[i](h))
h = self.dropout(h)
logits = self.mlp[-1](h)
probs = F.log_softmax(logits, dim=-1)
return probs
def get_inputs(self, tour, first_explained_step, node_feats):
"""
For TSPTW
TODO: refactoring
Parameters
----------
tour: list [seq_length]
first_explained_step: int
node_feats np.array [num_nodes x node_dim]
Returns
-------
out: dict (key: data type [data_size])
curr_node_id: torch.tensor [num_explained_paths]
next_node_id: torch.tensor [num_explained_paths]
node_feats: torch.tensor [num_explained_paths x num_nodes x node_dim]
mask: torch.tensor [num_explained_paths x num_nodes]
state: torch.tensor [num_explained_paths x state_dim]
"""
node_feats = {
key: torch.from_numpy(node_feat.astype(np.float32).copy()).clone()
if isinstance(node_feat, np.ndarray) else
torch.tensor([node_feat])
for key, node_feat in node_feats.items()
}
if isinstance(tour, np.ndarray):
tour = torch.from_numpy(tour.astype(np.long).copy()).clone()
else:
tour = torch.LongTensor(tour)
out = {"curr_node_id": [], "next_node_id": [], "mask": [], "state": []}
for step in range(first_explained_step, len(tour) - 1):
# node ids
curr_node_id = tour[step]
next_node_id = tour[step + 1]
# mask & state
max_coord = node_feats["grid_size"]
coord = node_feats["coords"] / max_coord # [num_nodes x coord_dim]
time_window = node_feats["time_window"] # [num_nodes x 2(start, end)]
time_window = (time_window - time_window[1:].min()) / time_window[1:].max() # min-max normalization
curr_time = torch.FloatTensor([0.0])
raw_coord = node_feats["coords"]
raw_time_window = node_feats["time_window"]
raw_curr_time = torch.FloatTensor([0.0])
num_nodes = len(node_feats["coords"])
mask = torch.ones(num_nodes, dtype=torch.long) # feasible -> 1, infeasible -> 0
for i in range(step + 1):
curr_id = tour[i]
if i > 0:
prev_id = tour[i - 1]
raw_curr_time += torch.norm(raw_coord[curr_id] - raw_coord[prev_id])
curr_time += torch.norm(coord[curr_id] - coord[prev_id])
# visited?
mask[curr_id] = 0
# curr_time exceeds the time window?
mask[curr_time > time_window[:, 1]] = 0
curr_time = (raw_curr_time - raw_time_window[1:].min()) / raw_time_window[1:].max() # min-max normalization
out["curr_node_id"].append(curr_node_id)
out["next_node_id"].append(next_node_id)
out["mask"].append(mask)
out["state"].append(curr_time)
out = {key: torch.stack(value, 0) for key, value in out.items()}
node_feats = {
key: node_feat.unsqueeze(0).expand(out["mask"].size(0), *node_feat.size())
for key, node_feat in node_feats.items()
}
out.update({"node_feats": node_feats})
return out
#---------------
# general class
#---------------
class DecisionPredictor(DecisionPredictorBase):
def __init__(self, problem, emb_dim, num_mlp_layers, num_classes, drop):
coord_dim = 2
self.problem = problem
if problem == "tsptw":
node_dim = coord_dim + 2 # + time_window(start, end)
state_dim = 1 # current_time
elif problem == "cvrp":
node_dim = coord_dim + 1 # + demand
state_dim = 1 # used_capacity
elif problem == "cvrptw":
node_dim = coord_dim + 1 + 2 # + demand + time_window(start, end)
state_dim = 2 # used_capacity + current_time
else:
assert False, f"problem {problem} is not supported!"
super().__init__(coord_dim, node_dim, state_dim, emb_dim, num_mlp_layers, num_classes, drop) |