def semseg_train(network, exp_dir, exp_prefix, args):
if args.cpu:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(0)
if exp_prefix:
snapshot_prefix = os.path.join(exp_dir, exp_prefix)
exp_prefix = exp_prefix + '_'
else:
snapshot_prefix = os.path.join(exp_dir, 'snapshot')
exp_prefix = ''
if network == 'seq':
batch_norm = True
conv_weight_filler = 'xavier'
network = models.semseg_seq(arch_str=args.arch,
skip_str=args.skips,
dataset=args.dataset,
dataset_params=args.dataset_params,
feat_dims_str=args.feat,
lattice_dims_str=args.lattice,
sample_size=args.sample_size,
batch_size=args.batch_size,
batchnorm=batch_norm,
conv_weight_filler=conv_weight_filler,
save_path=os.path.join(
exp_dir, exp_prefix + 'net.prototxt'))
models.semseg_seq(deploy=True,
arch_str=args.arch,
skip_str=args.skips,
dataset=args.dataset,
dataset_params=args.dataset_params,
feat_dims_str=args.feat,
lattice_dims_str=args.lattice,
sample_size=args.sample_size,
batchnorm=batch_norm,
save_path=os.path.join(
exp_dir, exp_prefix + 'net_deploy.prototxt'))
else:
assert network.endswith(
'.prototxt'), 'Please provide a valid prototxt file'
print('Using network defined at {}'.format(network))
random_seed = 0
debug_info = False
solver = create_solver.standard_solver(network,
network,
snapshot_prefix,
base_lr=args.base_lr,
gamma=args.lr_decay,
stepsize=args.stepsize,
test_iter=args.test_iter,
test_interval=args.test_interval,
max_iter=args.num_iter,
snapshot=args.snapshot_interval,
solver_type=args.solver_type,
weight_decay=args.weight_decay,
iter_size=args.iter_size,
debug_info=debug_info,
random_seed=random_seed,
save_path=os.path.join(
exp_dir,
exp_prefix + 'solver.prototxt'))
solver = caffe.get_solver(solver)
if args.init_model:
if args.init_model.endswith('.solverstate'):
solver.restore(args.init_model)
elif args.init_model.endswith('.caffemodel'):
solver.net.copy_from(args.init_model)
else:
raise ValueError('Invalid file: {}'.format(args.init_model))
import pdb
pdb.set_trace()
solver.solve()
def partseg_train_single_model(network, exp_dir, args):
if args.cpu:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(0)
if network == 'seq':
batch_norm = True
conv_weight_filler = 'xavier'
network = models.partseg_seq_combined_categories(arch_str=args.arch,
skip_str=args.skips,
renorm_class=args.renorm_class,
dataset=args.dataset,
dataset_params=args.dataset_params,
feat_dims_str=args.feat,
lattice_dims_str=args.lattice,
sample_size=args.sample_size,
batch_size=args.batch_size,
batchnorm=batch_norm,
conv_weight_filler=conv_weight_filler,
save_path=os.path.join(exp_dir, 'net.prototxt'))
models.partseg_seq_combined_categories(deploy=True,
arch_str=args.arch,
skip_str=args.skips,
renorm_class=args.renorm_class,
dataset=args.dataset,
dataset_params=args.dataset_params,
feat_dims_str=args.feat,
lattice_dims_str=args.lattice,
sample_size=args.sample_size,
batchnorm=batch_norm,
save_path=os.path.join(exp_dir, 'net_deploy.prototxt'))
else:
assert network.endswith('.prototxt'), 'Please provide a valid prototxt file'
print('Using network defined at {}'.format(network))
random_seed = 0
debug_info = False
solver = create_solver.standard_solver(network,
network,
os.path.join(exp_dir, 'snapshot'),
base_lr=args.base_lr,
gamma=args.lr_decay,
stepsize=args.stepsize,
test_iter=args.test_iter,
test_interval=args.test_interval,
max_iter=args.num_iter,
snapshot=args.snapshot_interval,
solver_type=args.solver_type,
weight_decay=args.weight_decay,
iter_size=args.iter_size,
debug_info=debug_info,
random_seed=random_seed,
save_path=os.path.join(exp_dir, 'solver.prototxt'))
solver = caffe.get_solver(solver)
if args.init_model:
if args.init_model.endswith('.caffemodel'):
solver.net.copy_from(args.init_model)
else:
solver.net.copy_from(os.path.join(exp_dir, 'snapshot_iter_{}.caffemodel'.format(args.init_model)))
if args.init_state:
if args.init_state.endswith('.solverstate'):
solver.restore(args.init_state)
else:
solver.restore(os.path.join(exp_dir, 'snapshot_iter_{}.solverstate'.format(args.init_state)))
solver.solve()
def partseg_train(network, exp_dir, category, args):
def solve2(solver, args, uid, rank):
if args.cpu:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpus[rank])
caffe.set_solver_count(len(args.gpus))
caffe.set_solver_rank(rank)
caffe.set_multiprocess(True)
solver = caffe.get_solver(solver)
if args.init_model:
if args.init_model.endswith('.caffemodel'):
solver.net.copy_from(args.init_model)
else:
solver.net.copy_from(os.path.join(exp_dir, '{}_iter_{}.caffemodel'.format(category, args.init_model)))
if args.init_state:
if args.init_state.endswith('.solverstate'):
solver.restore(args.init_state)
else:
solver.restore(os.path.join(exp_dir, '{}_iter_{}.solverstate'.format(category, args.init_state)))
nccl = caffe.NCCL(solver, uid)
nccl.bcast()
if solver.param.layer_wise_reduce:
solver.net.after_backward(nccl)
print(rank)
#pdb.set_trace()
solver.step(solver.param.max_iter)
#solver.solve()
#caffe.set_device(0)
if network == 'seq':
batch_norm = True
conv_weight_filler = 'xavier'
network = models.partseg_seq(arch_str=args.arch,
skip_str=args.skips,
dataset=args.dataset,
dataset_params=args.dataset_params,
category=category,
feat_dims_str=args.feat,
lattice_dims_str=args.lattice,
sample_size=args.sample_size,
batch_size=args.batch_size,
batchnorm=batch_norm,
conv_weight_filler=conv_weight_filler,
save_path=os.path.join(exp_dir, category + '_net.prototxt'))
models.partseg_seq(deploy=True,
arch_str=args.arch,
skip_str=args.skips,
dataset=args.dataset,
dataset_params=args.dataset_params,
category=category,
feat_dims_str=args.feat,
lattice_dims_str=args.lattice,
sample_size=args.sample_size,
batchnorm=batch_norm,
save_path=os.path.join(exp_dir, category + '_net_deploy.prototxt'))
else:
assert network.endswith('.prototxt'), 'Please provide a valid prototxt file'
print('Using network defined at {}'.format(network))
random_seed = 0
debug_info = False
solver = create_solver.standard_solver(network,
network,
os.path.join(exp_dir, category)+'_' +args.prefix,
base_lr=args.base_lr,
gamma=args.lr_decay,
stepsize=args.stepsize,
test_iter=args.test_iter,
test_interval=args.test_interval,
max_iter=args.num_iter,
snapshot=args.snapshot_interval,
solver_type=args.solver_type,
weight_decay=args.weight_decay,
iter_size=args.iter_size,
debug_info=debug_info,
random_seed=random_seed,
save_path=os.path.join(exp_dir, category+'_solver.prototxt'))
## Multiple GPUs
uid = caffe.NCCL.new_uid()
caffe.init_log(0, True)
caffe.log('Using devices %s' % str(args.gpus))
procs = []
for rank in range(len(args.gpus)):
p = Process(target=solve2,
args=(solver, args, uid, rank))
p.daemon = True
p.start()
procs.append(p)
for p in procs:
p.join()
