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pointer_net.py
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pointer_net.py
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import torch
import torch.nn as nn
import torch.autograd as autograd
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from torch.distributions import Normal
import math
import numpy as np
import pyscipopt as scip
from pyscipopt import SCIP_RESULT
# from beam_search import Beam
from utils import cut_feature_generator
from logger import logger
LOG_STD_MAX = 2
LOG_STD_MIN = -20
class Encoder(nn.Module):
"""Maps a graph represented as an input sequence
to a hidden vector"""
def __init__(self, input_dim, hidden_dim, use_cuda):
super(Encoder, self).__init__()
self.hidden_dim = hidden_dim
self.lstm = nn.LSTM(input_dim, hidden_dim) # default layers=1
self.use_cuda = use_cuda
self.enc_init_hx = nn.Parameter(torch.zeros(hidden_dim),requires_grad=False)
self.enc_init_cx = nn.Parameter(torch.zeros(hidden_dim),requires_grad=False)
self.enc_init_state = (self.enc_init_hx, self.enc_init_cx)
def forward(self, x, hidden):
output, hidden = self.lstm(x, hidden)
return output, hidden
def init_hidden(self, hidden_dim):
"""Trainable initial hidden state"""
enc_init_hx = nn.Parameter(torch.zeros(hidden_dim),requires_grad=False)
# enc_init_hx = Variable(torch.zeros(hidden_dim), requires_grad=False)
# if self.use_cuda:
# enc_init_hx = enc_init_hx.cuda()
# enc_init_hx.data.uniform_(-(1. / math.sqrt(hidden_dim)),
# 1. / math.sqrt(hidden_dim))
enc_init_cx = nn.Parameter(torch.zeros(hidden_dim),requires_grad=False)
# enc_init_cx = Variable(torch.zeros(hidden_dim), requires_grad=False)
# if self.use_cuda:
# enc_init_cx = enc_init_cx.cuda()
#enc_init_cx = nn.Parameter(enc_init_cx)
# enc_init_cx.data.uniform_(-(1. / math.sqrt(hidden_dim)),
# 1. / math.sqrt(hidden_dim))
return (enc_init_hx, enc_init_cx)
class Attention(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10, use_cuda=True):
super(Attention, self).__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, dim)
self.project_ref = nn.Conv1d(dim, dim, 1, 1) # TODO: check 为何会有卷积
self.C = C # tanh exploration
self.tanh = nn.Tanh()
# v = torch.FloatTensor(dim)
# if use_cuda:
# v = v.cuda()
self.v = nn.Parameter(torch.FloatTensor(dim))
self.v.data.uniform_(-(1. / math.sqrt(dim)) , 1. / math.sqrt(dim))
def forward(self, query, ref):
"""
Args:
query: is the hidden state of the decoder at the current
time step. batch x dim
ref: the set of hidden states from the encoder.
sourceL x batch x hidden_dim
"""
# ref is now [batch_size x hidden_dim x sourceL]
ref = ref.permute(1, 2, 0)
q = self.project_query(query).unsqueeze(2) # batch x dim x 1
e = self.project_ref(ref) # batch_size x hidden_dim x sourceL
# expand the query by sourceL
# batch x dim x sourceL
expanded_q = q.repeat(1, 1, e.size(2))
# batch x 1 x hidden_dim
v_view = self.v.unsqueeze(0).expand(
expanded_q.size(0), len(self.v)).unsqueeze(1)
# [batch_size x 1 x hidden_dim] * [batch_size x hidden_dim x sourceL]
u = torch.bmm(v_view, self.tanh(expanded_q + e)).squeeze(1)
if self.use_tanh:
logits = self.C * self.tanh(u)
else:
logits = u
return e, logits
# TODO:保留beam search,我们的setting 用不了beam search,还得增加一个模式是sample 的模式
class Decoder(nn.Module):
def __init__(self,
embedding_dim,
hidden_dim,
tanh_exploration,
use_tanh,
n_glimpses=1,
beam_size=0,
use_cuda=True):
super(Decoder, self).__init__()
self.embedding_dim = embedding_dim
self.hidden_dim = hidden_dim
self.n_glimpses = n_glimpses
self.beam_size = beam_size
self.use_cuda = use_cuda
self.input_weights = nn.Linear(embedding_dim, 4 * hidden_dim)
self.hidden_weights = nn.Linear(hidden_dim, 4 * hidden_dim)
self.pointer = Attention(hidden_dim, use_tanh=use_tanh, C=tanh_exploration, use_cuda=self.use_cuda)
self.glimpse = Attention(hidden_dim, use_tanh=False, use_cuda=self.use_cuda)
self.sm = nn.Softmax()
def apply_mask_to_logits(self, step, logits, mask, prev_idxs):
if mask is None:
mask = torch.zeros(logits.size()).byte().to(self.pointer.v.device)
# if self.use_cuda:
# mask = mask.cuda()
maskk = mask.clone()
# print(f"debug log maskk device: {maskk.device}")
# to prevent them from being reselected.
# Or, allow re-selection and penalize in the objective function
if prev_idxs is not None:
# set most recently selected idx values to 1
maskk[[x for x in range(logits.size(0))],
prev_idxs.data] = 1
logits[maskk] = -np.inf
return logits, maskk
def logprobs(self, decoder_input, embedded_inputs, hidden, context, max_length, seled_idxes):
def recurrence(x, hidden, logit_mask, prev_idxs, step):
hx, cx = hidden # batch_size x hidden_dim
gates = self.input_weights(x) + self.hidden_weights(hx)
ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
ingate = F.sigmoid(ingate)
forgetgate = F.sigmoid(forgetgate)
cellgate = F.tanh(cellgate)
outgate = F.sigmoid(outgate)
cy = (forgetgate * cx) + (ingate * cellgate)
hy = outgate * F.tanh(cy) # batch_size x hidden_dim
g_l = hy
for i in range(self.n_glimpses):
ref, logits = self.glimpse(g_l, context)
logits, logit_mask = self.apply_mask_to_logits(step, logits, logit_mask, prev_idxs)
# [batch_size x h_dim x sourceL] * [batch_size x sourceL x 1] =
# [batch_size x h_dim x 1]
g_l = torch.bmm(ref, self.sm(logits).unsqueeze(2)).squeeze(2)
_, logits = self.pointer(g_l, context) # logits 代表基于context vector 的概率分布
logits, logit_mask = self.apply_mask_to_logits(step, logits, logit_mask, prev_idxs)
probs = self.sm(logits)
return hy, cy, probs, logit_mask
batch_size = context.size(1)
outputs = []
single_probs = []
steps = range(max_length) # or until terminating symbol ?
inps = []
idxs = None
mask = None
for i in steps:
hx, cx, probs, mask = recurrence(decoder_input, hidden, mask, idxs, i)
hidden = (hx, cx)
# select the next inputs for the decoder [batch_size x hidden_dim]
decoder_input, prob = self.decode_logp(
probs,
embedded_inputs,
seled_idxes[i]) # 每一次decode 都是随机sample 一个输出
inps.append(decoder_input)
idxs = torch.tensor([seled_idxes[i]], dtype=torch.int64).to(self.pointer.v.device)
# if self.use_cuda:
# idxs = idxs.cuda()
# use outs to point to next object
outputs.append(probs)
single_probs.append(prob)
return (outputs, single_probs), hidden
def decode_logp(self, probs, embedded_inputs, idxs):
batch_size = probs.size(0)
# due to race conditions, might need to resample here
sels = embedded_inputs[idxs, [i for i in range(batch_size)], :]
return sels, probs[:,idxs]
def forward(self, decoder_input, embedded_inputs, hidden, context, max_length, decode_type): # TODO: max decode length 以参数传入forward 函数
"""
Args:
decoder_input: The initial input to the decoder
size is [batch_size x embedding_dim]. Trainable parameter.
embedded_inputs: [sourceL x batch_size x embedding_dim]
hidden: the prev hidden state, size is [batch_size x hidden_dim].
Initially this is set to (enc_h[-1], enc_c[-1])
context: encoder outputs, [sourceL x batch_size x hidden_dim]
"""
def recurrence(x, hidden, logit_mask, prev_idxs, step):
hx, cx = hidden # batch_size x hidden_dim
gates = self.input_weights(x) + self.hidden_weights(hx)
ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
ingate = F.sigmoid(ingate)
forgetgate = F.sigmoid(forgetgate)
cellgate = F.tanh(cellgate)
outgate = F.sigmoid(outgate)
cy = (forgetgate * cx) + (ingate * cellgate)
hy = outgate * F.tanh(cy) # batch_size x hidden_dim
g_l = hy
for i in range(self.n_glimpses):
ref, logits = self.glimpse(g_l, context)
logits, logit_mask = self.apply_mask_to_logits(step, logits, logit_mask, prev_idxs)
# [batch_size x h_dim x sourceL] * [batch_size x sourceL x 1] =
# [batch_size x h_dim x 1]
g_l = torch.bmm(ref, self.sm(logits).unsqueeze(2)).squeeze(2)
_, logits = self.pointer(g_l, context) # logits 代表基于context vector 的概率分布
logits, logit_mask = self.apply_mask_to_logits(step, logits, logit_mask, prev_idxs)
probs = self.sm(logits)
return hy, cy, probs, logit_mask
batch_size = context.size(1)
outputs = []
selections = []
steps = range(max_length) # or until terminating symbol ?
inps = []
idxs = None
mask = None
if decode_type in ["stochastic", "greedy"]:
for i in steps:
hx, cx, probs, mask = recurrence(decoder_input, hidden, mask, idxs, i)
hidden = (hx, cx)
# select the next inputs for the decoder [batch_size x hidden_dim]
decoder_input, idxs = self.decode(
probs,
embedded_inputs,
selections,
decode_type) # 每一次decode 都是随机sample 一个输出
inps.append(decoder_input)
# use outs to point to next object
outputs.append(probs)
selections.append(idxs)
return (outputs, selections), hidden
elif decode_type == "beam_search":
raise NotImplementedError
# Expand input tensors for beam search
# decoder_input = Variable(decoder_input.data.repeat(self.beam_size, 1))
# context = Variable(context.data.repeat(1, self.beam_size, 1))
# hidden = (Variable(hidden[0].data.repeat(self.beam_size, 1)),
# Variable(hidden[1].data.repeat(self.beam_size, 1)))
# beam = [
# Beam(self.beam_size, max_length, cuda=self.use_cuda)
# for k in range(batch_size)
# ]
# for i in steps:
# hx, cx, probs, mask = recurrence(decoder_input, hidden, mask, idxs, i)
# hidden = (hx, cx)
# probs = probs.view(self.beam_size, batch_size, -1
# ).transpose(0, 1).contiguous()
# n_best = 1
# # select the next inputs for the decoder [batch_size x hidden_dim]
# decoder_input, idxs, active = self.decode_beam(probs,
# embedded_inputs, beam, batch_size, n_best, i)
# inps.append(decoder_input)
# # use probs to point to next object
# if self.beam_size > 1:
# outputs.append(probs[:, 0,:])
# else:
# outputs.append(probs.squeeze(0))
# # Check for indexing
# selections.append(idxs)
# # Should be done decoding
# if len(active) == 0:
# break
# decoder_input = Variable(decoder_input.data.repeat(self.beam_size, 1))
# return (outputs, selections), hidden
else:
# TODO: 实现每轮输出最大概率对应的index
raise NotImplementedError
def decode(self, probs, embedded_inputs, selections, decode_type):
"""
Return the next input for the decoder by selecting the
input with sampling
Args:
probs: [batch_size x sourceL]
embedded_inputs: [sourceL x batch_size x embedding_dim]
selections: list of all of the previously selected indices during decoding
Returns:
Tensor of size [batch_size x sourceL] containing the embeddings
from the inputs corresponding to the [batch_size] indices
selected for this iteration of the decoding, as well as the
corresponding indicies
"""
batch_size = probs.size(0)
# idxs is [batch_size]
if decode_type == "stochastic":
idxs = probs.multinomial(num_samples=1).squeeze(1) # TODO: multinomial() 函数添加参数num_samples,应该也是torch 版本的问题
elif decode_type == "greedy":
max_probs, idxs = probs.max(1)
# due to race conditions, might need to resample here
# TODO: check,这里的mask 操作是O(n) 感觉不够快
# for old_idxs in selections:
# # compare new idxs
# # elementwise with the previous idxs. If any matches,
# # then need to resample
# if old_idxs.eq(idxs).data.any():
# print(' [!] resampling due to race condition')
# if decode_type == "stochastic":
# idxs = probs.multinomial(num_samples=1).squeeze(1) # TODO: multinomial() 函数添加参数num_samples,应该也是torch 版本的问题
# elif decode_type == "greedy":
# max_probs, idxs = probs.max(1)
# break
assert idxs not in set(selections)
sels = embedded_inputs[idxs.data, [i for i in range(batch_size)], :]
return sels, idxs
# def decode_beam(self, probs, embedded_inputs, beam, batch_size, n_best, step):
# active = []
# for b in range(batch_size):
# if beam[b].done:
# continue
# if not beam[b].advance(probs.data[b]):
# active += [b]
# all_hyp, all_scores = [], []
# for b in range(batch_size):
# scores, ks = beam[b].sort_best()
# all_scores += [scores[:n_best]]
# hyps = zip(*[beam[b].get_hyp(k) for k in ks[:n_best]])
# all_hyp += [hyps]
# all_idxs = Variable(torch.LongTensor([[x for x in hyp] for hyp in all_hyp]).squeeze())
# if all_idxs.dim() == 2:
# if all_idxs.size(1) > n_best:
# idxs = all_idxs[:,-1]
# else:
# idxs = all_idxs
# elif all_idxs.dim() == 3:
# idxs = all_idxs[:, -1, :]
# else:
# if all_idxs.size(0) > 1:
# idxs = all_idxs[-1]
# else:
# idxs = all_idxs
# # if self.use_cuda:
# # idxs = idxs.cuda()
# idxs = idxs.to(self.pointer.v.device)
# if idxs.dim() > 1:
# x = embedded_inputs[idxs.transpose(0,1).contiguous().data,
# [x for x in range(batch_size)], :]
# else:
# x = embedded_inputs[idxs.data, [x for x in range(batch_size)], :]
# return x.view(idxs.size(0) * n_best, embedded_inputs.size(2)), idxs, active
class PointerNetwork(nn.Module):
"""The pointer network, which is the core seq2seq
model"""
def __init__(self,
embedding_dim,
hidden_dim,
n_glimpses, # TODO:这个参数代表什么意思?
tanh_exploration, # tanh exploration coefficient
use_tanh,
beam_size,
use_cuda):
super(PointerNetwork, self).__init__()
self.embedding_dim = embedding_dim
self.encoder = Encoder(
embedding_dim,
hidden_dim,
use_cuda)
self.decoder = Decoder(
embedding_dim,
hidden_dim,
tanh_exploration=tanh_exploration,
use_tanh=use_tanh,
n_glimpses=n_glimpses,
beam_size=beam_size,
use_cuda=use_cuda)
# Trainable initial hidden states
# dec_in_0 = torch.FloatTensor(embedding_dim)
# if use_cuda:
# dec_in_0 = dec_in_0.cuda()
self.decoder_in_0 = nn.Parameter(torch.FloatTensor(embedding_dim))
self.decoder_in_0.data.uniform_(-(1. / math.sqrt(embedding_dim)),
1. / math.sqrt(embedding_dim))
def forward(self, inputs, max_decode_len, decode_type):
""" Propagate inputs through the network
Args:
inputs: [sourceL x batch_size x embedding_dim]
"""
(encoder_hx, encoder_cx) = self.encoder.enc_init_state
encoder_hx = encoder_hx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
encoder_cx = encoder_cx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
# encoder forward pass
enc_h, (enc_h_t, enc_c_t) = self.encoder(inputs, (encoder_hx, encoder_cx))
dec_init_state = (enc_h_t[-1], enc_c_t[-1])
# repeat decoder_in_0 across batch
decoder_input = self.decoder_in_0.unsqueeze(0).repeat(inputs.size(1), 1)
(pointer_probs, input_idxs), dec_hidden_t = self.decoder(decoder_input,
inputs,
dec_init_state,
enc_h,
max_decode_len,
decode_type)
return pointer_probs, input_idxs
def _prob_to_logp(self, prob):
logprob = 0
for p in prob:
logp = torch.log(p)
logprob += logp
# TODO: 添加截断过小logprob 的trick
# logprob[(logprob < -10000).detach()] = 0.
return logprob
def logprobs(self, inputs, max_decode_len, seled_idxes):
""" Propagate inputs through the network
Args:
inputs: [sourceL x batch_size x embedding_dim]
"""
(encoder_hx, encoder_cx) = self.encoder.enc_init_state
encoder_hx = encoder_hx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
encoder_cx = encoder_cx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
# encoder forward pass
enc_h, (enc_h_t, enc_c_t) = self.encoder(inputs, (encoder_hx, encoder_cx))
dec_init_state = (enc_h_t[-1], enc_c_t[-1])
# repeat decoder_in_0 across batch
decoder_input = self.decoder_in_0.unsqueeze(0).repeat(inputs.size(1), 1)
(pointer_probs, probs), dec_hidden_t = self.decoder.logprobs(decoder_input,
inputs,
dec_init_state,
enc_h,
max_decode_len,
seled_idxes)
logprob = self._prob_to_logp(probs)
return [pointer_prob.cpu().detach() for pointer_prob in pointer_probs], logprob
class CriticNetwork(nn.Module):
"""Useful as a baseline in REINFORCE updates"""
def __init__(self,
embedding_dim,
hidden_dim,
n_process_block_iters,
tanh_exploration,
use_tanh,
use_cuda):
super(CriticNetwork, self).__init__()
# TODO: check embedding_dim 是否还有必要呢?
self.hidden_dim = hidden_dim
self.n_process_block_iters = n_process_block_iters
self.encoder = Encoder(
embedding_dim,
hidden_dim,
use_cuda)
self.process_block = Attention(hidden_dim,
use_tanh=use_tanh, C=tanh_exploration, use_cuda=use_cuda)
self.sm = nn.Softmax()
self.decoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1)
)
def forward(self, inputs):
"""
Args:
inputs: [embedding_dim x batch_size x sourceL] of embedded inputs
"""
(encoder_hx, encoder_cx) = self.encoder.enc_init_state
encoder_hx = encoder_hx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
encoder_cx = encoder_cx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
# encoder forward pass
enc_outputs, (enc_h_t, enc_c_t) = self.encoder(inputs, (encoder_hx, encoder_cx))
# grab the hidden state and process it via the process block
process_block_state = enc_h_t[-1]
for i in range(self.n_process_block_iters):
ref, logits = self.process_block(process_block_state, enc_outputs)
process_block_state = torch.bmm(ref, self.sm(logits).unsqueeze(2)).squeeze(2)
# produce the final scalar output
out = self.decoder(process_block_state)
return out
class CutsPercentPolicy(nn.Module):
def __init__(self,
embedding_dim,
hidden_dim,
n_process_block_iters,
tanh_exploration,
use_tanh,
use_cuda):
super(CutsPercentPolicy, self).__init__()
self.hidden_dim = hidden_dim
self.n_process_block_iters = n_process_block_iters
self.encoder = Encoder(
embedding_dim,
hidden_dim,
use_cuda)
self.process_block = Attention(hidden_dim,
use_tanh=use_tanh, C=tanh_exploration, use_cuda=use_cuda)
self.sm = nn.Softmax()
self.decoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 2)
)
self.use_cuda = use_cuda
def forward(self, inputs):
"""
Args:
inputs: [embedding_dim x batch_size x sourceL] of embedded inputs
"""
(encoder_hx, encoder_cx) = self.encoder.enc_init_state
encoder_hx = encoder_hx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
encoder_cx = encoder_cx.unsqueeze(0).repeat(inputs.size(1), 1).unsqueeze(0)
# encoder forward pass
enc_outputs, (enc_h_t, enc_c_t) = self.encoder(inputs, (encoder_hx, encoder_cx))
# grab the hidden state and process it via the process block
process_block_state = enc_h_t[-1]
for i in range(self.n_process_block_iters):
ref, logits = self.process_block(process_block_state, enc_outputs)
process_block_state = torch.bmm(ref, self.sm(logits).unsqueeze(2)).squeeze(2)
# produce the final scalar output
out = self.decoder(process_block_state)
return out
def action(self, states, deterministic=False):
mean, log_std = self.get_mean_std(states)
std = torch.exp(log_std)
# normal = Normal(mean, std)
if deterministic:
action = mean
else:
if self.use_cuda:
sample = Normal(torch.zeros_like(mean).to(states.device), torch.ones_like(mean).to(states.device)).sample()
else:
sample = Normal(torch.zeros_like(mean), torch.ones_like(mean)).sample()
action = mean + std * sample
tanh_action = torch.tanh(action)
return tanh_action
def get_mean_std(self, states):
out = self.forward(states)
mean, log_std = torch.chunk(out,2,-1)
log_std = torch.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
log_std = log_std.expand(mean.shape)
return mean, log_std
def log_prob(self, states, action=None, pretanh_action=None):
if pretanh_action is None:
assert action is not None
pretanh_action = torch.log((1+action)/(1-action) +1e-6) / 2
else:
assert pretanh_action is not None
action = torch.tanh(pretanh_action)
mean, log_std = self.get_mean_std(states)
std = torch.exp(log_std)
normal = Normal(mean, std)
pre_log_prob = normal.log_prob(pretanh_action)
log_prob = pre_log_prob.sum(-1, keepdim=True) - torch.log(1 - action * action + 1e-6).sum(-1, keepdim=True)
info = {}
info['pre_log_prob'] = pre_log_prob
info['mean'] = mean
info['std'] = std
info['entropy'] = normal.entropy()
return log_prob, info
class CutSelectAgent(scip.Cutsel):
def __init__(
self,
scip_model,
pointer_net,
value_net,
sel_cuts_percent,
device,
decode_type,
baseline_type
):
super().__init__()
self.scip_model = scip_model
self.policy = pointer_net
self.value = value_net
self.sel_cuts_percent = sel_cuts_percent
self.device = device
self.decode_type = decode_type
self.baseline_type = baseline_type
self.data = {}
def cutselselect(self, cuts, forcedcuts, root, maxnselectedcuts):
'''first method called in each iteration in the main solving loop. '''
# this method needs to be implemented by the user
logger.log(f"forcedcuts length: {len(forcedcuts)}")
logger.log(f"len cuts: {len(cuts)}")
num_cuts = len(cuts)
if num_cuts <= 1:
return {
'cuts': cuts, # selected sorted cuts
'nselectedcuts': 1, # num of selected cuts
'result': SCIP_RESULT.SUCCESS
}
sel_cuts_num = min(int(num_cuts * self.sel_cuts_percent), int(maxnselectedcuts))
sel_cuts_num = max(sel_cuts_num, 2)
cuts_features = cut_feature_generator(self.scip_model, cuts)
input_cuts = torch.from_numpy(cuts_features).to(self.device)
input_cuts = input_cuts.reshape(input_cuts.shape[0], 1, input_cuts.shape[1])
if self.decode_type == 'greedy': # evaluate disable gradient calculation
with torch.no_grad():
pointer_probs, input_idxs = self.policy(input_cuts.float(), sel_cuts_num, self.decode_type) # (list of tensor, list of tensor)
baseline_value = 0.
else:
pointer_probs, input_idxs = self.policy(input_cuts.float(), sel_cuts_num, self.decode_type) # (list of tensor, list of tensor)
if self.baseline_type == 'net':
baseline_value = self.value(input_cuts.float())
else:
baseline_value = 0.
idxes = [input.cpu().detach().item() for input in input_idxs]
assert len(set(idxes))==len(idxes) # 保证选择的idxes 没有重复的!
all_idxes = list(range(num_cuts))
not_sel_idxes = list(set(all_idxes).difference(idxes))
sorted_cuts = [cuts[idx] for idx in idxes]
not_sel_cuts = [cuts[n_idx] for n_idx in not_sel_idxes]
sorted_cuts.extend(not_sel_cuts)
# debug
# sorted_cuts = cuts
self.data = {
"raw_cuts": cuts_features,
"len_raw_cuts": num_cuts,
"selected_idx": idxes,
"pointer_probs": [prob[:,idx] for prob, idx in zip(pointer_probs, idxes)],
"raw_seq_pointer_probs": [pointer_prob.cpu().detach() for pointer_prob in pointer_probs],
"baseline_value": baseline_value
}
return {
'cuts': sorted_cuts, # selected sorted cuts
'nselectedcuts': sel_cuts_num, # num of selected cuts
'result': SCIP_RESULT.SUCCESS
}
def _get_lp_info(self):
lp_info = {}
lp_info['lp_solution_value'] = self.scip_model.getLPObjVal()
cols = self.scip_model.getLPColsData()
lp_info['lp_solution_integer_var_value'] = [col.getPrimsol() for col in cols if col.isIntegral()]
return lp_info
def get_data(self):
return self.data
def free_problem(self):
self.scip_model.freeProb()
# test
if __name__ == "__main__":
import time
# from ipdb import set_trace
# embedding_dim = 13
# hidden_dim = 128
# n_process_block_iters = 3
# tanh_exploration = 10
# use_tanh = True
# use_cuda = True
# cutsel_policy = CutsPercentPolicy(
# embedding_dim=embedding_dim,
# hidden_dim=hidden_dim,
# n_process_block_iters=n_process_block_iters,
# tanh_exploration=tanh_exploration,
# use_tanh=use_tanh,
# use_cuda=use_cuda
# ).to('cuda:5')
# input_x = torch.randn((1000, 1, 13)).to('cuda:5')
# for _ in range(20):
# st = time.time()
# y = cutsel_policy.action(input_x)
# logp, info = cutsel_policy.log_prob(input_x, action=y.detach())
# et = time.time() - st
# print(f"pred: {y}")
# print(f"lop: {logp}")
# for k in info.keys():
# print(f"{k}: {info[k]}")
# print(f"time: {et}")
embedding_dim = 13
hidden_dim = 128
max_decoding_len = 40
decode_type = 'greedy'
n_glimpses = 2
tanh_exploration = 10
use_tanh = True
use_cuda = True
beam_size = 1
ptr_net = PointerNetwork(
embedding_dim=embedding_dim,
hidden_dim=hidden_dim,
n_glimpses=n_glimpses,
tanh_exploration=tanh_exploration,
use_tanh=use_tanh,
beam_size=beam_size,
use_cuda=use_cuda
).to('cuda:4')
# (seq_len, batch_size, feature_dim)
input_x = torch.randn((40, 1, 13)).to('cuda:4')
for _ in range(10):
st = time.time()
with torch.no_grad():
decode_y = ptr_net(input_x, max_decoding_len,'stochastic')
et = time.time() - st
print(f"time: {et}")