Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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outputlist=profile.get_outputlist(),
losslist=lossweights.keys(),
**cudadata,
**(profile.get_ae_args() if hasattr(profile, "get_ae_args") else {}))
# compute final loss
loss = sum([
lossweights[k] * (torch.sum(v[0]) / torch.sum(v[1]) if isinstance(v, tuple) else torch.mean(v))
for k, v in losses.items()])
# print current information
print("Iteration {}: loss = {:.5f}, ".format(iternum, float(loss.item())) +
", ".join(["{} = {:.5f}".format(k,
float(torch.sum(v[0]) / torch.sum(v[1]) if isinstance(v, tuple) else torch.mean(v)))
for k, v in losses.items()]), end="")
if iternum % 10 == 0:
endtime = time.time()
ips = 10. / (endtime - starttime)
print(", iter/sec = {:.2f}".format(ips))
starttime = time.time()
else:
print()
# update parameters
optim.zero_grad()
loss.backward()
optim.step()
# compute evaluation output
if not args.noprogress and iternum in evalpoints:
with torch.no_grad():
testoutput, _ = ae(
trainiter=iternum,
outputlist=progressprof.get_outputlist() + ["rmtime"],
losslist=[],
**utils.tocuda(testbatch),
**progressprof.get_ae_args())
print("Iteration {}: rmtime = {:.5f}".format(iternum, testoutput["rmtime"] * 1000.))
writer.batch(iternum, iternum * profile.batchsize + torch.arange(0), **testbatch, **testoutput)
if not args.nostab and (loss.item() > 400 * prevloss or not np.isfinite(loss.item())):
print("unstable loss function; resetting")
ae.load_state_dict(torch.load("{}/aeparams.pt".format(outpath)), strict=False)
optim = profile.get_optimizer(ae)
prevloss = loss.item()
# save intermediate results
if iternum % 1000 == 0:
torch.save(ae.state_dict(), "{}/aeparams.pt".format(outpath))
torch.save(optim.state_dict(), "{}/optimparams.pt".format(outpath))
if iternum >= profile.maxiter:
break
iternum += 1
if iternum >= profile.maxiter:
break
# cleanup
writer.finalize()
# <FILESEP>
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This script is a simplified version of the training script in detectron2/tools.
"""
from functools import partial
import copy
import itertools
import logging
import os
from collections import OrderedDict
from typing import Any, Dict, List, Set
import detectron2.utils.comm as comm
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.evaluation import (
CityscapesSemSegEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
verify_results,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from detectron2.utils.events import CommonMetricPrinter, JSONWriter