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- import math
- import torch
- import os
- import argparse
- import numpy as np
- import itertools
- from tqdm import tqdm
- from utils import load_model, move_to
- from utils.data_utils import save_dataset
- from torch.utils.data import DataLoader
- import time
- from datetime import timedelta
- from utils.functions import parse_softmax_temperature
- mp = torch.multiprocessing.get_context('spawn')
- import greedy_solver
- def get_best(sequences, cost, ids=None, batch_size=None):
- """
- Ids contains [0, 0, 0, 1, 1, 2, ..., n, n, n] if 3 solutions found for 0th instance, 2 for 1st, etc
- :param sequences:
- :param lengths:
- :param ids:
- :return: list with n sequences and list with n lengths of solutions
- """
- if ids is None:
- idx = cost.argmin()
- return sequences[idx:idx+1, ...], cost[idx:idx+1, ...]
- splits = np.hstack([0, np.where(ids[:-1] != ids[1:])[0] + 1])
- mincosts = np.minimum.reduceat(cost, splits)
- group_lengths = np.diff(np.hstack([splits, len(ids)]))
- all_argmin = np.flatnonzero(np.repeat(mincosts, group_lengths) == cost)
- result = np.full(len(group_lengths) if batch_size is None else batch_size, -1, dtype=int)
- result[ids[all_argmin[::-1]]] = all_argmin[::-1]
- return [sequences[i] if i >= 0 else None for i in result], [cost[i] if i >= 0 else math.inf for i in result]
- def eval_dataset_mp(args):
- (dataset_path, width, softmax_temp, opts, i, num_processes) = args
- model, _ = load_model(opts.model)
- val_size = opts.val_size // num_processes
- dataset = model.problem.make_dataset(filename=dataset_path, num_samples=val_size, offset=opts.offset + val_size * i)
- device = torch.device("cuda:{}".format(i))
- return _eval_dataset(model, dataset, width, softmax_temp, opts, device)
- def eval_dataset(dataset_path, width, softmax_temp, opts):
- # Even with multiprocessing, we load the model here since it contains the name where to write results
- model, _ = load_model(opts.model)
- use_cuda = torch.cuda.is_available() and not opts.no_cuda
- if opts.multiprocessing:
- assert use_cuda, "Can only do multiprocessing with cuda"
- num_processes = torch.cuda.device_count()
- assert opts.val_size % num_processes == 0
- with mp.Pool(num_processes) as pool:
- results = list(itertools.chain.from_iterable(pool.map(
- eval_dataset_mp,
- [(dataset_path, width, softmax_temp, opts, i, num_processes) for i in range(num_processes)]
- )))
- else:
- device = torch.device("cuda:0" if use_cuda else "cpu")
- dataset = model.problem.make_dataset(filename=dataset_path, num_samples=opts.val_size, offset=opts.offset)
- results = _eval_dataset(model, dataset, width, softmax_temp, opts, device)
- # This is parallelism, even if we use multiprocessing (we report as if we did not use multiprocessing, e.g. 1 GPU)
- parallelism = opts.eval_batch_size
- costs, tours, durations, greedy_costs = zip(*results) # Not really costs since they should be negative
- print("Average cost : {} +- {}".format(np.mean(costs), 2 * np.std(costs) / np.sqrt(len(costs))))
- print("Average greedy cost: {} +- {}".format(np.mean(greedy_costs), 2 * np.std(greedy_costs) / np.sqrt(len(greedy_costs))))
- print("Average serial duration: {} +- {}".format(
- np.mean(durations), 2 * np.std(durations) / np.sqrt(len(durations))))
- print("Average parallel duration: {}".format(np.mean(durations) / parallelism))
- print("Calculated total duration: {}".format(timedelta(seconds=int(np.sum(durations) / parallelism))))
- dataset_basename, ext = os.path.splitext(os.path.split(dataset_path)[-1])
- model_name = "_".join(os.path.normpath(os.path.splitext(opts.model)[0]).split(os.sep)[-2:])
- if opts.o is None:
- results_dir = os.path.join(opts.results_dir, model.problem.NAME, dataset_basename)
- os.makedirs(results_dir, exist_ok=True)
- out_file = os.path.join(results_dir, "{}-{}-{}{}-t{}-{}-{}{}".format(
- dataset_basename, model_name,
- opts.decode_strategy,
- width if opts.decode_strategy != 'greedy' else '',
- softmax_temp, opts.offset, opts.offset + len(costs), ext
- ))
- else:
- out_file = opts.o
- assert opts.f or not os.path.isfile(
- out_file), "File already exists! Try running with -f option to overwrite."
- save_dataset((results, parallelism), out_file)
- return costs, tours, durations
- def _eval_dataset(model, dataset, width, softmax_temp, opts, device):
- model.to(device)
- model.eval()
- model.set_decode_type(
- "greedy" if opts.decode_strategy in ('bs', 'greedy') else "sampling",
- temp=softmax_temp)
- dataloader = DataLoader(dataset, batch_size=opts.eval_batch_size)
- results = []
- for batch in tqdm(dataloader, disable=opts.no_progress_bar):
- batch = move_to(batch, device)
- start = time.time()
- with torch.no_grad():
- if opts.decode_strategy in ('sample', 'greedy'):
- if opts.decode_strategy == 'greedy':
- assert width == 0, "Do not set width when using greedy"
- assert opts.eval_batch_size <= opts.max_calc_batch_size, \
- "eval_batch_size should be smaller than calc batch size"
- batch_rep = 1
- iter_rep = 1
- elif width * opts.eval_batch_size > opts.max_calc_batch_size:
- assert opts.eval_batch_size == 1
- assert width % opts.max_calc_batch_size == 0
- batch_rep = opts.max_calc_batch_size
- iter_rep = width // opts.max_calc_batch_size
- else:
- batch_rep = width
- iter_rep = 1
- assert batch_rep > 0
- results_greedy, greedy_costs = greedy_solver.get_route(batch)
- # This returns (batch_size, iter_rep shape)
- sequences, costs = model.sample_many(batch, batch_rep=batch_rep, iter_rep=iter_rep)
- batch_size = len(costs)
- ids = torch.arange(batch_size, dtype=torch.int64, device=costs.device)
- else:
- assert opts.decode_strategy == 'bs'
- cum_log_p, sequences, costs, ids, batch_size = model.beam_search(
- batch, beam_size=width,
- compress_mask=opts.compress_mask,
- max_calc_batch_size=opts.max_calc_batch_size
- )
- if sequences is None:
- sequences = [None] * batch_size
- costs = [math.inf] * batch_size
- else:
- sequences, costs = get_best(
- sequences.cpu().numpy(), costs.cpu().numpy(),
- ids.cpu().numpy() if ids is not None else None,
- batch_size
- )
- duration = time.time() - start
- for seq, cost, greedy_cost in zip(sequences, costs, greedy_costs):
- if model.problem.NAME == "tsp":
- seq = seq.tolist() # No need to trim as all are same length
- elif model.problem.NAME in ("cvrp", "sdvrp"):
- seq = np.trim_zeros(seq).tolist() + [0] # Add depot
- elif model.problem.NAME in ("op", "pctsp"):
- seq = np.trim_zeros(seq) # We have the convention to exclude the depot
- else:
- assert False, "Unkown problem: {}".format(model.problem.NAME)
- # Note VRP only
- results.append((cost, seq, duration, greedy_cost))
- return results
- if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("datasets", nargs='+', help="Filename of the dataset(s) to evaluate")
- parser.add_argument("-f", action='store_true', help="Set true to overwrite")
- parser.add_argument("-o", default=None, help="Name of the results file to write")
- parser.add_argument('--val_size', type=int, default=10000,
- help='Number of instances used for reporting validation performance')
- parser.add_argument('--offset', type=int, default=0,
- help='Offset where to start in dataset (default 0)')
- parser.add_argument('--eval_batch_size', type=int, default=1024,
- help="Batch size to use during (baseline) evaluation")
- # parser.add_argument('--decode_type', type=str, default='greedy',
- # help='Decode type, greedy or sampling')
- parser.add_argument('--width', type=int, nargs='+',
- help='Sizes of beam to use for beam search (or number of samples for sampling), '
- '0 to disable (default), -1 for infinite')
- parser.add_argument('--decode_strategy', type=str,
- help='Beam search (bs), Sampling (sample) or Greedy (greedy)')
- parser.add_argument('--softmax_temperature', type=parse_softmax_temperature, default=1,
- help="Softmax temperature (sampling or bs)")
- parser.add_argument('--model', type=str)
- parser.add_argument('--no_cuda', action='store_true', help='Disable CUDA')
- parser.add_argument('--no_progress_bar', action='store_true', help='Disable progress bar')
- parser.add_argument('--compress_mask', action='store_true', help='Compress mask into long')
- parser.add_argument('--max_calc_batch_size', type=int, default=10000, help='Size for subbatches')
- parser.add_argument('--results_dir', default='results', help="Name of results directory")
- parser.add_argument('--multiprocessing', action='store_true',
- help='Use multiprocessing to parallelize over multiple GPUs')
- opts = parser.parse_args()
- assert opts.o is None or (len(opts.datasets) == 1 and len(opts.width) <= 1), \
- "Cannot specify result filename with more than one dataset or more than one width"
- widths = opts.width if opts.width is not None else [0]
- for width in widths:
- for dataset_path in opts.datasets:
- eval_dataset(dataset_path, width, opts.softmax_temperature, opts)
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