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run.py
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run.py
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#!/usr/bin/env python
import os
import json
import pprint as pp
import torch
import torch.optim as optim
from tensorboard_logger import Logger as TbLogger
from nets.critic_network import CriticNetwork
from options import get_options
from train_multiple_resolution import train_epoch, validate, get_inner_model
# from train_multiple_resolution import train_epoch, validate, get_inner_model
from reinforce_baselines import NoBaseline, ExponentialBaseline, CriticBaseline, RolloutBaseline, WarmupBaseline
from nets.attention_model import AttentionModel
from nets.pointer_network import PointerNetwork, CriticNetworkLSTM
from utils import torch_load_cpu, load_problem
def run(opts):
# Pretty print the run args
pp.pprint(vars(opts))
# Set the random seed
torch.manual_seed(opts.seed)
# Optionally configure tensorboard
tb_logger = None
if not opts.no_tensorboard:
tb_logger = TbLogger(os.path.join(opts.log_dir, "{}_{}".format(opts.problem, opts.graph_size), opts.run_name))
os.makedirs(opts.save_dir)
# Save arguments so exact configuration can always be found
with open(os.path.join(opts.save_dir, "args.json"), 'w') as f:
json.dump(vars(opts), f, indent=True)
# Set the device
opts.device = torch.device("cuda:0" if opts.use_cuda else "cpu")
# Figure out what's the problem
problem = load_problem(opts.problem)
# Load data from load_path
load_data = {}
assert opts.load_path is None or opts.resume is None, "Only one of load path and resume can be given"
load_path = opts.load_path if opts.load_path is not None else opts.resume
if load_path is not None:
print(' [*] Loading data from {}'.format(load_path))
load_data = torch_load_cpu(load_path)
# Initialize model
model_list=[]
for _ in range(2):
model_class = {
'attention': AttentionModel,
'pointer': PointerNetwork
}.get(opts.model, None)
assert model_class is not None, "Unknown model: {}".format(model_class)
model = model_class(
opts.embedding_dim,
opts.hidden_dim,
problem,
n_encode_layers=opts.n_encode_layers,
mask_inner=True,
mask_logits=True,
normalization=opts.normalization,
tanh_clipping=opts.tanh_clipping,
checkpoint_encoder=opts.checkpoint_encoder,
shrink_size=opts.shrink_size
).to(opts.device)
if opts.use_cuda and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model_list.append(model)
# Overwrite model parameters by parameters to load
model_ = get_inner_model(model_list[0])
model_.load_state_dict({**model_.state_dict(), **load_data.get('model', {})})
model_1 = get_inner_model(model_list[1])
model_1.load_state_dict({**model_1.state_dict(), **load_data.get('model', {})})
print(model_list[0])
print(model_list[1])
# Initialize baseline
if opts.baseline == 'exponential':
baseline = ExponentialBaseline(opts.exp_beta)
elif opts.baseline == 'critic' or opts.baseline == 'critic_lstm':
assert problem.NAME == 'tsp', "Critic only supported for TSP"
baseline = CriticBaseline(
(
CriticNetworkLSTM(
2,
opts.embedding_dim,
opts.hidden_dim,
opts.n_encode_layers,
opts.tanh_clipping
)
if opts.baseline == 'critic_lstm'
else
CriticNetwork(
2,
opts.embedding_dim,
opts.hidden_dim,
opts.n_encode_layers,
opts.normalization
)
).to(opts.device)
)
elif opts.baseline == 'rollout':
baseline = RolloutBaseline(model_list[0], problem, opts)
baseline_1 = RolloutBaseline(model_list[1], problem, opts)
else:
assert opts.baseline is None, "Unknown baseline: {}".format(opts.baseline)
baseline = NoBaseline()
baseline_1 = NoBaseline()
if opts.bl_warmup_epochs > 0:
baseline = WarmupBaseline(baseline, opts.bl_warmup_epochs, warmup_exp_beta=opts.exp_beta)
baseline_1 = WarmupBaseline(baseline_1, opts.bl_warmup_epochs, warmup_exp_beta=opts.exp_beta)
# Load baseline from data, make sure script is called with same type of baseline
if 'baseline' in load_data:
baseline.load_state_dict(load_data['baseline'])
baseline_1.load_state_dict(load_data['baseline'])
# Initialize optimizer
optimizer = optim.Adam(
[{'params': model_list[0].parameters(), 'lr': opts.lr_model}]
+[{'params': model_list[1].parameters(), 'lr': opts.lr_model}]
+ (
[{'params': baseline.get_learnable_parameters(), 'lr': opts.lr_critic}]
if len(baseline.get_learnable_parameters()) > 0
else []
)
+
(
[{'params': baseline_1.get_learnable_parameters(), 'lr': opts.lr_critic}]
if len(baseline_1.get_learnable_parameters()) > 0
else []
)
)
# Load optimizer state
if 'optimizer' in load_data:
optimizer.load_state_dict(load_data['optimizer'])
for state in optimizer.state.values():
for k, v in state.items():
# if isinstance(v, torch.Tensor):
if torch.is_tensor(v):
state[k] = v.to(opts.device)
# Initialize learning rate scheduler, decay by lr_decay once per epoch!
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: opts.lr_decay ** epoch)
# Start the actual training loop
val_dataset = problem.make_dataset(
size=opts.graph_size, num_samples=opts.val_size, filename=opts.val_dataset, distribution=opts.data_distribution)
if opts.resume:
epoch_resume = int(os.path.splitext(os.path.split(opts.resume)[-1])[0].split("-")[1])
torch.set_rng_state(load_data['rng_state'])
if opts.use_cuda:
torch.cuda.set_rng_state_all(load_data['cuda_rng_state'])
# Set the random states
# Dumping of state was done before epoch callback, so do that now (model is loaded)
baseline.epoch_callback(model_list[0], epoch_resume)
baseline_1.epoch_callback(model_list[1], epoch_resume)
print("Resuming after {}".format(epoch_resume))
opts.epoch_start = epoch_resume + 1
if opts.eval_only:
validate(model_list[0], val_dataset, opts)
else:
reward=0
for epoch in range(opts.epoch_start, opts.epoch_start + opts.n_epochs):
reward=train_epoch(
model_list,
optimizer,
baseline,
baseline_1,
lr_scheduler,
epoch,
val_dataset,
problem,
tb_logger,
opts,
reward
)
if __name__ == "__main__":
run(get_options())