def train_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
if args.param:
files.append(args.param)
class TrainConfig:
pass
config = TrainConfig()
info = load.load(files)
logger.log(99, 'Train with contexts {}'.format(available_contexts))
config.global_config = info.global_config
config.training_config = info.training_config
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
# Training
max_iter = config.training_config.max_epoch * \
config.training_config.iter_per_epoch
if max_iter > 0:
data_iterators = {'optimizer': {}, 'monitor': {}}
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(
o.optimizer.data_iterator())
for name, m in config.monitors.items():
m.data_iterator = stack.enter_context(
m.monitor.data_iterator())
train(args, config)
else:
# save parameters without training (0 epoch learning)
save_parameters(os.path.join(args.outdir, 'parameters.h5'))
logger.log(99, 'Training Completed.')
progress(None)
def profile_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
class TrainConfig:
pass
config = TrainConfig()
info = load.load(files)
config.global_config = info.global_config
config.training_config = info.training_config
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
ext_module = import_extension_module(
config.global_config.default_context.backend[0].split(':')[0])
def synchronize():
return ext_module.synchronize(
device_id=config.global_config.default_context.device_id)
result_array = [['time in ms']]
# Profile Optimizer
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(o.optimizer.data_iterator())
result_array = profile_optimizer(config, result_array, synchronize)
# Write profiling result
import csv
with open(args.outdir + os.sep + 'profile.csv', 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(result_array)
logger.log(99, 'Profile Completed.')
progress(None)
return True
def train_command(args):
logger.log(99, 'Train with contexts {}'.format(available_contexts))
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
if args.param:
files.append(args.param)
class TrainConfig:
pass
config = TrainConfig()
info = load.load(files)
config.global_config = info.global_config
config.training_config = info.training_config
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
# Training
max_iter = config.training_config.max_epoch * \
config.training_config.iter_per_epoch
if max_iter > 0:
data_iterators = {'optimizer': {}, 'monitor': {}}
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(
o.optimizer.data_iterator())
for name, m in config.monitors.items():
m.data_iterator = stack.enter_context(
m.monitor.data_iterator())
train(args, config)
else:
# save parameters without training (0 epoch learning)
save_parameters(os.path.join(
args.outdir, 'parameters.h5'))
logger.log(99, 'Training Completed.')
progress(None)
def infer_command(args):
files = []
files.append(args.config)
if args.param:
files.append(args.param)
batch_size = args.batch_size
if batch_size < 1:
batch_size = None
class ForwardConfig:
pass
config = ForwardConfig
# To improve load performance
os.environ['NNABLA_CUDNN_ALGORITHM_BY_HEURISTIC'] = '1'
info = load.load(files, prepare_data_iterator=False, batch_size=batch_size)
config.executors = info.executors.values()
config.networks = []
for e in config.executors:
if e.network.name in info.networks.keys():
config.networks.append(info.networks[e.network.name])
else:
logger.critical('Network {} is not found.'.format(
config.executor.network.name))
return False
normalize = True
for d in info.datasets.values():
normalize = d.normalize
input_file_index = 0
inputs = []
for e in config.executors:
for v, d in e.dataset_assign.items():
input_filename = args.inputs[input_file_index]
if "int32" in input_filename:
data = np.fromfile(input_filename, np.int32).reshape(
v.variable_instance.d.shape)
else:
data = np.fromfile(input_filename, np.float32).reshape(
v.variable_instance.d.shape)
inputs.append((d, data))
input_file_index += 1
data = []
variables = []
for v, d in inputs:
variables.append(v)
data.append(d)
result, outputs = _forward(args, 0, config, data, variables, False)
for i, o in enumerate(outputs):
if args.output is not None:
(np.array(o).astype(np.float32)).tofile("{}_{}.bin".format(
args.output, i))
return True
def _get_current_parameter(args):
globname = os.path.join(args.outdir, 'results_current_*.nnp')
exists = glob.glob(globname)
if len(exists) > 0:
ex_list = {}
for ex in exists:
n = int(ex.rsplit('_', 1)[1].rsplit('.', 1)[0])
ex_list[n] = ex
last_epoch = sorted(ex_list.keys())[0]
last_parameter = ex_list[last_epoch]
logger.log(99, "Load parameter from [{}]".format(
os.path.basename(last_parameter)))
load.load([last_parameter], parameter_only=True)
return last_epoch
return 0
def infer_command(args):
files = []
files.append(args.config)
if args.param:
files.append(args.param)
batch_size = args.batch_size
if batch_size < 1:
batch_size = None
class ForwardConfig:
pass
config = ForwardConfig
info = load.load(files, prepare_data_iterator=False, batch_size=batch_size)
config.global_config = info.global_config
config.executors = info.executors.values()
config.networks = []
for e in config.executors:
if e.network.name in info.networks.keys():
config.networks.append(info.networks[e.network.name])
else:
logger.critical('Network {} does not found.'.format(
config.executor.network.name))
return
normalize = True
for d in info.datasets.values():
normalize = d.normalize
input_file_index = 0
inputs = []
for e in config.executors:
for v, d in e.dataset_assign.items():
data = np.fromfile(args.inputs[input_file_index],
np.float32).reshape(v.variable_instance.d.shape)
inputs.append((d, data))
input_file_index += 1
data = []
variables = []
for v, d in inputs:
variables.append(v)
data.append(d)
result, outputs = forward(args, 0, config, data, variables, False)
for i, o in enumerate(outputs):
if args.output is None:
print(o)
else:
print(o)
(np.array(o).astype(np.float32)).tofile("{}_{}.bin".format(
args.output, i))
def test_load_and_infer_improvement(nntxt_idx, parameter_format, dataset_sample_num):
'''This case tests improvement features, comparing legacy implementation,
legacy cannot load or infer successfully, while refactor-ed is OK.
'''
with generate_case_from_nntxt_str(NNTXT_IMPROVEMENT_CASES[nntxt_idx], parameter_format, dataset_sample_num) as nnp_file:
with pytest.raises(ValueError) as excinfo:
ref_info = ref_load(nnp_file)
ref_result = partial(
common_forward, forward_func=_ref_forward)(ref_info)
print(excinfo)
info = load.load(nnp_file)
result = partial(common_forward, forward_func=_forward)(info)
def get_context(device_id):
# for cli app use
try:
context = 'cudnn'
ctx = get_extension_context(context, device_id=device_id)
except ModuleNotFoundError:
context = 'cpu'
ctx = get_extension_context(context, device_id=device_id)
# for nnc use
config_filename = 'net.nntxt'
if os.path.isfile(config_filename):
config_info = load([config_filename])
ctx = config_info.global_config.default_context
return ctx
def profile_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
class TrainConfig:
pass
config = TrainConfig()
info = load.load(files)
config.global_config = info.global_config
config.training_config = info.training_config
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
result_array = [['time in ms']]
# Profile Optimizer
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(
o.optimizer.data_iterator())
result_array = profile_optimizer(config, result_array)
# Write profiling result
import csv
with open(args.outdir + os.sep + 'profile.csv', 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(result_array)
logger.log(99, 'Profile Completed.')
progress(None)
def test_load_and_infer_equivalence(nntxt_idx, parameter_format, dataset_sample_num):
'''These cases tends to test equivalence before and after
refactoring NNP load functions. The scope of refactor includes network part and load function.
This test firstly generated .nnp from nntxt_str, according to specified parameter_format
and replace dataset's uri with a temporarily generated random dataset, then performs inferring
operation similar to what is done in cli/forward.py.
'''
with generate_case_from_nntxt_str(NNTXT_EQUIVALENCE_CASES[nntxt_idx], parameter_format, dataset_sample_num) as nnp_file:
ref_info = ref_load(nnp_file)
ref_result = partial(
common_forward, forward_func=_ref_forward)(ref_info)
info = load.load(nnp_file)
result = partial(common_forward, forward_func=_forward)(info)
assert_tensor_equal(result, ref_result)
def load_model_from_utils_load_and_forward(nnp_file, batch_size):
g = load.load(nnp_file, batch_size=batch_size).proto_graph
inputs = [
g.default_graph().variables[i].variable_instance
for i in g.default_graph().inputs
]
for i in inputs:
i.d = np.random.random(i.d.shape)
outputs = [
g.default_graph().variables[i].variable_instance
for i in g.default_graph().outputs
]
y = F.sink(*outputs)
y.forward()
out = outputs[0].d
return out
def infer_command(args):
files = []
files.append(args.config)
if args.param:
files.append(args.param)
batch_size = args.batch_size
if batch_size < 1:
batch_size = None
class ForwardConfig:
pass
config = ForwardConfig
# To improve load performance
os.environ['NNABLA_CUDNN_ALGORITHM_BY_HEURISTIC'] = '1'
info = load.load(files, prepare_data_iterator=False, batch_size=batch_size)
inputs = []
for input_filename in args.inputs:
if args.data_type == 'uint8':
inputs.append(np.fromfile(input_filename, np.uint8))
elif args.data_type == 'int32':
inputs.append(np.fromfile(input_filename, np.int32))
elif args.data_type == 'float32':
if 'int32' in input_filename:
inputs.append(np.fromfile(input_filename, np.int32))
elif 'uint8' in input_filename:
inputs.append(np.fromfile(input_filename, np.uint8))
else:
inputs.append(np.fromfile(input_filename, np.float32))
else:
logger.critical('Type is one of ("uint8", "int32" or "float32").')
return False
result, outputs = infer(info, inputs)
for i, o in enumerate(outputs):
if args.output is not None:
(np.array(o).astype(np.float32)).tofile("{}_{}.bin".format(
args.output, i))
return True
def test_resume_suspend_equivalence(nntxt_idx, parameter_format,
dataset_sample_num, batch_size):
'''These cases tends to test equivalence before and after refactoring.
'''
verbose = True
a_few_iter = 10
half_iter = 5
output_network_topology = False
with generate_case_from_nntxt_str(NNTXT_EQUIVALENCE_CASES[nntxt_idx],
parameter_format, dataset_sample_num,
batch_size) as nnp_file:
with create_temp_with_dir(
"saved_parameter.nnp") as saved_parameter_nnp:
class Callback:
pass
class ModelSaver:
def __init__(self, info):
self.info = info
def __call__(self, config):
if config.iter != half_iter:
return
_save_parameters(saved_parameter_nnp, config,
NNTXT_EQUIVALENCE_CASES[nntxt_idx])
new_config = TrainConfig()
new_config.start_iteration = 0
new_config.end_iteration = a_few_iter
new_config.save_optimizer_variable = False
new_config.save_evaluation_variable = False
new_cb = Callback()
new_cb.forward = lambda x: x.target.forward(clear_no_need_grad=True
)
new_cb.backward = lambda x, b: x.target.backward()
new_config.cb = new_cb
new_config.impl = "ref"
ref_result = []
ref_info = load.load(nnp_file, batch_size=batch_size)
print("load.load")
if output_network_topology:
for n, opt in ref_info.optimizers.items():
print(n)
opt.network.execute_on_proto(Verifier())
new_config.on_iter = ModelSaver(ref_info)
for cost, error in partial(train, config=new_config)(ref_info):
ref_result.append((cost, error))
new_config.on_iter = None
new_config.start_iteration = half_iter
new_config.end_iteration = a_few_iter
new_config.impl = "new"
result = []
nn.clear_parameters()
info = load.load(nnp_file,
batch_size=batch_size,
exclude_parameter=True)
print("load.load")
# Here, `info` is different `config`, but optimizer is same.
load_train_state(saved_parameter_nnp, info)
for cost, error in partial(train, config=new_config)(info):
result.append((cost, error))
compare_info(ref_info, info)
for i, ((cost_ref, error_ref),
(cost, error)) in enumerate(zip(ref_result, result)):
if verbose:
print("{}: cost: {} <--> {}".format(i, cost_ref, cost))
print("{}: error: {} <--> {}".format(i, error_ref, error))
if i > new_config.start_iteration:
assert_allclose(np.array([cost_ref, error_ref]),
np.array([cost, error]),
rtol=1e-2,
atol=1e-5,
err_msg="Error: {}".format(nntxt_idx))
def test_workingmemory_layer():
from nnabla.utils.load import load
with create_temp_with_dir("network.nntxt") as fn:
with open(fn, "w") as f:
f.write(nntxt_mixup)
load(fn)
def forward_command(args):
callback.update_status(args)
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
if args.param:
files.append(args.param)
batch_size = args.batch_size
if batch_size < 1:
batch_size = None
class ForwardConfig:
pass
config = ForwardConfig
info = load.load(files, prepare_data_iterator=False, batch_size=batch_size)
config.global_config = info.global_config
config.executors = info.executors.values()
config.networks = []
for e in config.executors:
if e.network.name in info.networks.keys():
config.networks.append(info.networks[e.network.name])
else:
logger.critical('Network {} is not found.'.format(
config.executor.network.name))
return False
normalize = True
for d in info.datasets.values():
if d.uri == args.dataset or d.cache_dir == args.dataset:
normalize = d.normalize
for e in config.executors:
normalize = normalize and not e.no_image_normalization
orders = {}
# With CSV
if os.path.splitext(args.dataset)[1] == '.csv':
data_iterator = (lambda: data_iterator_csv_dataset(
uri=args.dataset,
batch_size=config.networks[0].batch_size,
shuffle=False,
normalize=normalize,
with_memory_cache=False,
with_file_cache=False))
# load dataset as csv
filereader = FileReader(args.dataset)
with filereader.open(textmode=True, encoding='utf-8-sig') as f:
rows = [row for row in csv.reader(f)]
row0 = rows.pop(0)
if args.replace_path:
root_path = os.path.dirname(args.dataset)
root_path = os.path.abspath(root_path.replace('/|\\', os.path.sep))
else:
root_path = '.'
rows = [row for row in rows if len(row)]
rows = list(
map(
lambda row: list(
map(
lambda i, x: x if row0[i][0] == '#' or is_float(
x) else compute_full_path(root_path, x),
range(len(row)), row)), rows))
for i in range(len(rows)):
orders[i] = i
# With Cache
elif os.path.splitext(args.dataset)[1] == '.cache':
data_iterator = (lambda: data_iterator_cache(uri=args.dataset,
batch_size=config.
networks[0].batch_size,
shuffle=False,
normalize=normalize))
# Get original CSV
original_csv = os.path.join(args.dataset, 'original.csv')
try:
# load dataset as csv
filereader = FileReader(original_csv)
with filereader.open(textmode=True, encoding='utf-8-sig') as f:
rows = [row for row in csv.reader(f)]
row0 = rows.pop(0)
root_path = '.'
rows = list(
map(
lambda row: list(
map(
lambda x: x if is_float(x) else compute_full_path(
root_path, x), row)), rows))
except:
print('Cannot open', original_csv)
pass
# Get original Data order.
order_csv = os.path.join(args.dataset, 'order.csv')
try:
filereader = FileReader(order_csv)
with filereader.open(textmode=True) as f:
for original, shuffled in [[int(x) for x in row]
for row in csv.reader(f)]:
orders[original] = shuffled
except:
print('Cannot open', order_csv)
for i in range(len(rows)):
orders[i] = i
else:
print('Unsupported extension "{}" in "{}".'.format(
os.path.splitext(args.dataset)[1], args.dataset))
callback.update_status(('data.max', len(rows)))
callback.update_status(('data.current', 0))
callback.update_status('processing', True)
result_csv_filename = os.path.join(args.outdir, args.outfile)
with open(result_csv_filename, 'w', encoding='utf-8') as f:
writer = csv.writer(f, lineterminator='\n')
with data_iterator() as di:
index = 0
while index < di.size:
data = di.next()
result, outputs = _forward(args, index, config, data,
di.variables)
if index == 0:
for name, dim in zip(result.names, result.dims):
if dim == 1:
if e.repeat_evaluation_type == "std":
name = "Uncertainty(Std)"
row0.append(name)
else:
for d in range(dim):
row0.append(name + '__' + str(d))
writer.writerow(row0)
for i, output in enumerate(outputs):
if index + i < len(rows):
import copy
row = copy.deepcopy(rows[orders[index + i]])
row.extend(output)
writer.writerow(row)
index += len(outputs)
callback.update_status(('data.current', min([index,
len(rows)])))
callback.update_forward_time()
callback.update_status()
logger.log(
99, 'data {} / {}'.format(min([index, len(rows)]),
len(rows)))
callback.process_evaluation_result(args.outdir, result_csv_filename)
logger.log(99, 'Forward Completed.')
progress(None)
callback.update_status(('output_result.csv_header', ','.join(row0)))
callback.update_status(('output_result.column_num', len(row0)))
callback.update_status(('output_result.data_num', len(rows)))
callback.update_status('finished')
return True
def train_command(args):
if single_or_rankzero():
configure_progress(os.path.join(args.outdir, 'progress.txt'))
info = load.load([args.config], exclude_parameter=True)
# Check dataset uri is empty.
dataset_error = False
for dataset in info.datasets.values():
if dataset.uri.strip() == '':
dataset_error = True
if dataset_error:
logger.log(99, 'Fatal error. Dataset URI is empty.')
return False
class TrainConfig:
pass
config = TrainConfig()
config.timelimit = -1
if args.param:
load.load([args.param], parameter_only=True)
config.global_config = info.global_config
config.training_config = info.training_config
if single_or_rankzero():
logger.log(99, 'Train with contexts {}'.format(available_contexts))
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
# Training
comm = current_communicator()
config.training_config.iter_per_epoch //= comm.size if comm else 1
max_iteration = config.training_config.max_epoch * \
config.training_config.iter_per_epoch
global _save_parameter_info
_save_parameter_info = {}
_, config_ext = os.path.splitext(args.config)
if config_ext == '.prototxt' or config_ext == '.nntxt':
_save_parameter_info['config'] = args.config
elif config_ext == '.nnp':
with zipfile.ZipFile(args.config, 'r') as nnp:
for name in nnp.namelist():
_, ext = os.path.splitext(name)
if ext == '.nntxt' or ext == '.prototxt':
nnp.extract(name, args.outdir)
_save_parameter_info['config'] = os.path.join(
args.outdir, name)
result = False
if max_iteration > 0:
data_iterators = {'optimizer': {}, 'monitor': {}}
rng = np.random.RandomState(comm.rank if comm else 0)
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(
o.optimizer.data_iterator())
if comm and comm.size > 1:
o.data_iterator = o.data_iterator.slice(
rng, comm.size, comm.rank)
for name, m in config.monitors.items():
m.data_iterator = stack.enter_context(
m.monitor.data_iterator())
if comm and comm.size > 1:
m.data_iterator = m.data_iterator.slice(
rng, comm.size, comm.rank)
result = _train(args, config)
else:
# save parameters without training (0 epoch learning)
logger.log(99, '0 epoch learning. (Just save parameter.)')
if single_or_rankzero():
_save_parameters(args, 'current', 0, True)
result = True
if single_or_rankzero():
if result:
logger.log(99, 'Training Completed.')
else:
logger.log(99, 'Training Incompleted.')
if single_or_rankzero():
progress(None)
return True
def main():
print(sys.argv)
print(load([sys.argv[1]]))
def compare_with_cpu_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
class TrainConfig:
pass
class OptConfig:
pass
class MonConfig:
pass
# Load config with current context
files = []
files.append(args.config)
with nn.parameter_scope('current'):
info = load.load(files)
parameters = get_parameters(grad_only=False)
config = TrainConfig()
config.global_config = info.global_config
config.training_config = info.training_config
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
# Load config with cpu context
files = []
files.append(args.config2)
with nn.parameter_scope('cpu'):
info_cpu = load.load(files)
cpu_parameters = get_parameters(grad_only=False)
config_cpu = TrainConfig()
config_cpu.global_config = info_cpu.global_config
config_cpu.training_config = info_cpu.training_config
config_cpu.optimizers = OrderedDict()
for name, opt in info_cpu.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config_cpu.optimizers[name] = o
config_cpu.monitors = OrderedDict()
for name, mon in info_cpu.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config_cpu.monitors[name] = m
result_array = [['1-Correl']]
# Profile Optimizer
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(
o.optimizer.data_iterator())
for name, o in config_cpu.optimizers.items():
o.data_iterator = stack.enter_context(
o.optimizer.data_iterator())
result_array = compare_optimizer(
config, parameters, config_cpu, cpu_parameters, result_array)
# Write profiling result
import csv
with open(args.outdir + os.sep + 'compare_with_cpu.csv', 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(result_array)
logger.log(99, 'Compare with CPU Completed.')
progress(None)
def forward_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
if args.param:
files.append(args.param)
batch_size = args.batch_size
if batch_size < 1:
batch_size = None
class ForwardConfig:
pass
config = ForwardConfig
info = load.load(files, prepare_data_iterator=False, batch_size=batch_size)
config.global_config = info.global_config
config.executors = info.executors.values()
config.networks = []
for e in config.executors:
if e.network.name in info.networks.keys():
config.networks.append(info.networks[e.network.name])
else:
logger.critical('Network {} is not found.'.format(
config.executor.network.name))
return False
normalize = True
for d in info.datasets.values():
if d.uri == args.dataset:
normalize = d.normalize
for e in config.executors:
normalize = normalize and not e.no_image_normalization
data_iterator = (lambda: data_iterator_csv_dataset(uri=args.dataset,
batch_size=config.
networks[0].batch_size,
shuffle=False,
normalize=normalize,
with_memory_cache=False,
with_file_cache=False))
# load dataset as csv
filereader = FileReader(args.dataset)
with filereader.open(textmode=True) as f:
rows = [row for row in csv.reader(f)]
row0 = rows.pop(0)
root_path = os.path.dirname(args.dataset)
root_path = os.path.abspath(root_path.replace('/|\\', os.path.sep))
rows = list(
map(
lambda row: list(
map(
lambda x: x
if is_float(x) else compute_full_path(root_path, x), row)),
rows))
with open(os.path.join(args.outdir, 'output_result.csv'), 'w') as f:
writer = csv.writer(f, lineterminator='\n')
with data_iterator() as di:
index = 0
while index < di.size:
data = di.next()
result, outputs = _forward(args, index, config, data,
di.variables)
if index == 0:
for name, dim in zip(result.names, result.dims):
if dim == 1:
row0.append(name)
else:
for d in range(dim):
row0.append(name + '__' + str(d))
writer.writerow(row0)
for i, output in enumerate(outputs):
if index + i < len(rows):
import copy
row = copy.deepcopy(rows[index + i])
row.extend(output)
writer.writerow(row)
index += len(outputs)
logger.log(
99, 'data {} / {}'.format(min([index, len(rows)]),
len(rows)))
logger.log(99, 'Forward Completed.')
progress(None)
return True
def profile_command(args):
callback.update_status(args)
configure_progress(os.path.join(args.outdir, 'progress.txt'))
class TrainConfig:
pass
config = TrainConfig()
info = load.load(args.config)
config.global_config = info.global_config
config.training_config = info.training_config
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterators = []
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterators = []
config.monitors[name] = m
ext_module = import_extension_module(
config.global_config.default_context.backend[0].split(':')[0])
def synchronize():
return ext_module.synchronize(
device_id=config.global_config.default_context.device_id)
result_array = [['time in ms']]
callback.update_status('processing', True)
# Profile Optimizer
with ExitStack() as stack:
# Create data_iterator instance only once for each dataset in optimizers
optimizer_data_iterators = {}
for name, o in config.optimizers.items():
for di in o.optimizer.data_iterators.values():
if di not in optimizer_data_iterators:
di_instance = stack.enter_context(di())
optimizer_data_iterators[di] = di_instance
else:
di_instance = optimizer_data_iterators[di]
o.data_iterators.append(di_instance)
result_array = profile_optimizer(config, result_array, synchronize)
# Write profiling result
import csv
with open(args.outdir + os.sep + 'profile.csv', 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(result_array)
logger.log(99, 'Profile Completed.')
progress(None)
callback.update_status('finished')
return True
def compare_with_cpu_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
class TrainConfig:
pass
class OptConfig:
pass
class MonConfig:
pass
# Load config with current context
files = []
files.append(args.config)
with nn.parameter_scope('current'):
info = load.load(files)
parameters = get_parameters(grad_only=False)
config = TrainConfig()
config.global_config = info.global_config
config.training_config = info.training_config
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config.optimizers[name] = o
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config.monitors[name] = m
# Load config with cpu context
files = []
files.append(args.config2)
with nn.parameter_scope('cpu'):
info_cpu = load.load(files)
cpu_parameters = get_parameters(grad_only=False)
config_cpu = TrainConfig()
config_cpu.global_config = info_cpu.global_config
config_cpu.training_config = info_cpu.training_config
config_cpu.optimizers = OrderedDict()
for name, opt in info_cpu.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterator = None
config_cpu.optimizers[name] = o
config_cpu.monitors = OrderedDict()
for name, mon in info_cpu.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterator = None
config_cpu.monitors[name] = m
result_array = [['1-Correl']]
# Profile Optimizer
with ExitStack() as stack:
for name, o in config.optimizers.items():
o.data_iterator = stack.enter_context(o.optimizer.data_iterator())
for name, o in config_cpu.optimizers.items():
o.data_iterator = stack.enter_context(o.optimizer.data_iterator())
result_array = compare_optimizer(config, parameters, config_cpu,
cpu_parameters, result_array)
# Write profiling result
import csv
with open(args.outdir + os.sep + 'compare_with_cpu.csv', 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(result_array)
logger.log(99, 'Compare with CPU Completed.')
progress(None)
return True
def forward_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
if args.param:
files.append(args.param)
class ForwardConfig:
pass
config = ForwardConfig
info = load.load(files, prepare_data_iterator=False)
config.global_config = info.global_config
config.executors = info.executors.values()
config.networks = []
for e in config.executors:
if e.network.name in info.networks.keys():
config.networks.append(info.networks[e.network.name])
else:
logger.critical('Network {} does not found.'.format(
config.executor.network.name))
return
normalize = True
for d in info.datasets.values():
if d.uri == args.dataset:
normalize = d.normalize
data_iterator = (lambda: data_iterator_csv_dataset(
args.dataset, config.networks[0].batch_size, False, padding=True, normalize=normalize))
# load dataset as csv
with open(args.dataset, 'rt') as f:
rows = [row for row in csv.reader(f)]
row0 = rows.pop(0)
root_path = os.path.dirname(args.dataset)
root_path = os.path.abspath(root_path.replace('/|\\', os.path.sep))
rows = map(lambda row: map(lambda x: x if is_float(
x) else compute_full_path(root_path, x), row), rows)
with data_iterator() as di:
index = 0
while index < di.size:
data = di.next()
result, outputs = forward(args, index, config, data, di.variables)
if index == 0:
for name, dim in zip(result.names, result.dims):
if dim == 1:
row0.append(name)
else:
for d in range(dim):
row0.append(name + '__' + str(d))
for i, output in enumerate(outputs):
if index + i < len(rows):
rows[index + i].extend(output)
index += len(outputs)
logger.log(
99, 'data {} / {}'.format(min([index, len(rows)]), len(rows)))
with open(os.path.join(args.outdir, 'output_result.csv'), 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerow(row0)
writer.writerows(rows)
logger.log(99, 'Forward Completed.')
progress(None)
def test_load_and_train_equivalence(nntxt_idx, parameter_format,
dataset_sample_num, batch_size):
'''These cases tends to test equivalence before and after refactoring.
The operation is similar to what is done in cli/train.py.
'''
# for debugging
save_v = False
output_network_topology = False
verbose = False
m_iter = 10
class Callback:
pass
legacy_config = TrainConfig()
legacy_config.on_iter = None
legacy_config.save_optimizer_variable = False
legacy_config.save_evaluation_variable = False
legacy_config.start_iteration = 0
legacy_config.end_iteration = 10
legacy_config.enable_save_variable = save_v
legacy_cb = Callback()
legacy_cb.forward = lambda o: o.network.forward(o.forward_sequence)
legacy_cb.backward = lambda o, b: o.network.backward(
o.backward_sequence, b)
legacy_config.cb = legacy_cb
legacy_config.impl = "legacy"
new_config = TrainConfig()
new_config.on_iter = None
new_config.save_optimizer_variable = False
new_config.save_evaluation_variable = False
new_config.start_iteration = 0
new_config.end_iteration = 10
new_config.enable_save_variable = save_v
new_cb = Callback()
new_cb.forward = lambda x: x.target.forward(clear_no_need_grad=True)
new_cb.backward = lambda x, b: x.target.backward(clear_buffer=True)
new_config.cb = new_cb
new_config.impl = "new"
with generate_case_from_nntxt_str(NNTXT_EQUIVALENCE_CASES[nntxt_idx],
parameter_format, dataset_sample_num,
batch_size) as nnp_file:
ref_result = []
result = []
nn.clear_parameters()
info = ref_load(nnp_file, batch_size=batch_size)
for cost, error in partial(train, config=legacy_config)(info):
ref_result.append((cost, error))
nn.clear_parameters()
info = load.load(nnp_file, batch_size=batch_size)
if output_network_topology:
for n, opt in info.optimizers.items():
print(n)
opt.network.execute_on_proto(Verifier())
for cost, error in partial(train, config=new_config)(info):
result.append((cost, error))
for i, ((cost_ref, error_ref),
(cost, error)) in enumerate(zip(ref_result, result)):
if verbose:
print("{}: cost: {} <--> {}".format(i, cost_ref, cost))
print("{}: error: {} <--> {}".format(i, error_ref, error))
assert_allclose(np.array([cost_ref, error_ref]),
np.array([cost, error]),
rtol=1e-2,
atol=1e-3,
err_msg="Error: {}".format(nntxt_idx))
def test_load_and_save_equivalence(nntxt_idx, parameter_format,
dataset_sample_num, batch_size,
include_params, variable_batch_size):
'''These cases tends to test equivalence before and after refactoring.
'''
verbose = True
a_few_iter = 10
half_iter = 5
output_network_topology = True
with generate_case_from_nntxt_str(NNTXT_EQUIVALENCE_CASES[nntxt_idx],
parameter_format, dataset_sample_num,
batch_size) as nnp_file:
with create_temp_with_dir("saved.nnp") as saved_nnp_file:
class Callback:
pass
class ModelSaver:
def __init__(self, info):
self.info = info
def __call__(self, config):
if config.iter != half_iter:
return
info = self.info
datasets = []
with ExitStack() as stack:
for d_name, d in info.datasets.items():
ds = {}
ds['name'] = d_name
ds['uri'] = d.uri
ds['cache_dir'] = d.cache_dir
di_instance = stack.enter_context(
d.data_iterator())
ds['variables'] = [
var_name for var_name in di_instance.variables
]
ds['batch_size'] = di_instance.batch_size
ds['no_image_normalization'] = not d.normalize
ds['shuffle'] = di_instance._shuffle
datasets.append(ds)
dataset_assign = set()
for obj in itertools.chain(info.monitors.values(),
info.executors.values(),
info.optimizers.values()):
for pv in obj.dataset_assign.keys():
dataset_assign.add(pv.name)
contents = {
'global_config': {
'default_context':
info.global_config.default_context
},
'training_config': {
'max_epoch': info.training_config.max_epoch,
'iter_per_epoch':
info.training_config.iter_per_epoch,
'save_best': info.training_config.save_best
},
'networks': [{
'name': n_name,
'batch_size': n.batch_size,
'outputs': {
out: n.variables[out].variable_instance
for out in n.outputs
},
'names': {
inp: n.variables[inp].variable_instance
for inp in itertools.chain(
n.inputs, n.outputs)
}
} for n_name, n in info.networks.items()],
'executors': [{
'name':
e_name,
'network':
e.network.name,
'data':
[pv.name for pv in e.dataset_assign.keys()],
'generator_variables':
[pv.name for pv in e.generator_assign.keys()],
'output':
[pv.name for pv in e.output_assign.keys()]
} for e_name, e in info.executors.items()],
'optimizers': [{
'name':
o_name,
'solver':
o.solver,
'network':
o.network.name,
'data_variables':
{pv.name: d
for pv, d in o.dataset_assign.items()},
'generator_variables':
[pv.name for pv in o.generator_assign.keys()],
'loss_variables':
[pv.name for pv in o.loss_variables],
'dataset':
[ds_name for ds_name in o.data_iterators.keys()],
'weight_decay':
o.weight_decay,
'lr_decay':
o.lr_decay,
'lr_decay_interval':
o.lr_decay_interval,
'update_interval':
o.update_interval
} for o_name, o in info.optimizers.items()],
'datasets':
datasets,
'monitors': [{
'name':
m_name,
'network':
m.network.name,
'data_variables':
{pv.name: d
for pv, d in m.dataset_assign.items()},
'generator_variables':
[pv.name for pv in m.generator_assign.keys()],
'monitor_variables':
[pv.name for pv in m.monitor_variables],
'dataset':
[ds_name for ds_name in m.data_iterators.keys()]
} for m_name, m in info.monitors.items()],
}
save.save(saved_nnp_file, contents, include_params,
variable_batch_size)
new_config = TrainConfig()
new_config.start_iteration = 0
new_config.end_iteration = a_few_iter
new_config.save_optimizer_variable = False
new_config.save_evaluation_variable = False
new_cb = Callback()
new_cb.forward = lambda x: x.target.forward(clear_no_need_grad=True
)
new_cb.backward = lambda x, b: x.target.backward(clear_buffer=True)
new_config.cb = new_cb
new_config.impl = "ref"
ref_result = []
ref_info = load.load(nnp_file, batch_size=batch_size)
if output_network_topology:
for n, opt in ref_info.optimizers.items():
print(n)
opt.network.execute_on_proto(Verifier())
new_config.on_iter = ModelSaver(ref_info)
for cost, error in partial(train, config=new_config)(ref_info):
ref_result.append((cost, error))
new_config.on_iter = None
new_config.start_iteration = half_iter
new_config.end_iteration = a_few_iter
new_config.impl = "new"
result = []
nn.clear_parameters()
info = load.load(saved_nnp_file, batch_size=batch_size)
if output_network_topology:
for n, opt in info.optimizers.items():
print(n)
opt.network.execute_on_proto(Verifier())
for cost, error in partial(train, config=new_config)(info):
result.append((cost, error))
compare_info(ref_info, info)
for i, ((cost_ref, error_ref),
(cost, error)) in enumerate(zip(ref_result, result)):
if verbose:
print("{}: cost: {} <--> {}".format(i, cost_ref, cost))
print("{}: error: {} <--> {}".format(i, error_ref, error))
if i > new_config.start_iteration:
assert_allclose(np.array([cost_ref, error_ref]),
np.array([cost, error]),
rtol=1e-2,
atol=1e-5,
err_msg="Error: {}".format(nntxt_idx))
def train_command(args):
callback.update_status(args)
if single_or_rankzero():
configure_progress(os.path.join(args.outdir, 'progress.txt'))
info = load.load([args.config],
prepare_data_iterator=None,
exclude_parameter=True)
# Check dataset uri is empty.
dataset_error = False
for dataset in info.datasets.values():
if dataset.uri.strip() == '':
dataset_error = True
if dataset_error:
logger.log(99, 'Fatal error. Dataset URI is empty.')
return False
class TrainConfig:
pass
config = TrainConfig()
config.timelimit = -1
if args.param:
load.load([args.param], parameter_only=True)
config.timelimit = callback.get_timelimit(args)
config.global_config = info.global_config
config.training_config = info.training_config
if single_or_rankzero():
logger.log(99, 'Train with contexts {}'.format(available_contexts))
class OptConfig:
pass
config.optimizers = OrderedDict()
for name, opt in info.optimizers.items():
o = OptConfig()
o.optimizer = opt
o.data_iterators = []
config.optimizers[name] = o
class MonConfig:
pass
config.monitors = OrderedDict()
for name, mon in info.monitors.items():
m = MonConfig()
m.monitor = mon
m.data_iterators = []
config.monitors[name] = m
# Training
comm = current_communicator()
config.training_config.iter_per_epoch //= comm.size if comm else 1
max_iteration = config.training_config.max_epoch * \
config.training_config.iter_per_epoch
global _save_parameter_info
_save_parameter_info = {}
_, config_ext = os.path.splitext(args.config)
if config_ext == '.prototxt' or config_ext == '.nntxt':
_save_parameter_info['config'] = args.config
elif config_ext == '.nnp':
with zipfile.ZipFile(args.config, 'r') as nnp:
for name in nnp.namelist():
_, ext = os.path.splitext(name)
if ext == '.nntxt' or ext == '.prototxt':
nnp.extract(name, args.outdir)
_save_parameter_info['config'] = os.path.join(
args.outdir, name)
result = False
restart = False
if max_iteration > 0:
rng = np.random.RandomState(comm.rank if comm else 0)
with ExitStack() as stack:
# Create data_iterator instance only once for each dataset in optimizers
optimizer_data_iterators = {}
for name, o in config.optimizers.items():
for di in o.optimizer.data_iterators.values():
if di not in optimizer_data_iterators:
di_instance = stack.enter_context(di())
if comm and comm.size > 1:
di_instance = di_instance.slice(
rng, comm.size, comm.rank)
optimizer_data_iterators[di] = di_instance
else:
di_instance = optimizer_data_iterators[di]
o.data_iterators.append(di_instance)
# Create data_iterator instance only once for each dataset in monitors
monitor_data_iterators = {}
for name, m in config.monitors.items():
for di in m.monitor.data_iterators.values():
if di not in monitor_data_iterators:
di_instance = stack.enter_context(di())
if comm and comm.size > 1:
di_instance = di_instance.slice(
rng, comm.size, comm.rank)
monitor_data_iterators[di] = di_instance
else:
di_instance = monitor_data_iterators[di]
m.data_iterators.append(di_instance)
monitor_data_iterators.update(optimizer_data_iterators)
result, restart = _train(args, config)
else:
# save parameters without training (0 epoch learning)
logger.log(99, '0 epoch learning. (Just save parameter.)')
if single_or_rankzero():
_save_parameters(args, None, 0, config, True)
result = True
if single_or_rankzero() and not restart:
if result:
logger.log(99, 'Training Completed.')
callback.update_status('finished')
else:
logger.log(99, 'Training Incompleted.')
callback.update_status('failed')
if single_or_rankzero():
progress(None)
return True
def forward_command(args):
configure_progress(os.path.join(args.outdir, 'progress.txt'))
files = []
files.append(args.config)
if args.param:
files.append(args.param)
class ForwardConfig:
pass
config = ForwardConfig
info = load.load(files, prepare_data_iterator=False)
config.global_config = info.global_config
config.executors = info.executors.values()
config.networks = []
for e in config.executors:
if e.network.name in info.networks.keys():
config.networks.append(info.networks[e.network.name])
else:
logger.critical('Network {} does not found.'.format(
config.executor.network.name))
return
normalize = True
for d in info.datasets.values():
if d.uri == args.dataset:
normalize = d.normalize
data_iterator = (lambda: data_iterator_csv_dataset(
args.dataset, config.networks[0].batch_size, False, normalize=normalize))
# load dataset as csv
with open(args.dataset, 'rt') as f:
rows = [row for row in csv.reader(f)]
row0 = rows.pop(0)
root_path = os.path.dirname(args.dataset)
root_path = os.path.abspath(root_path.replace('/|\\', os.path.sep))
rows = list(map(lambda row: list(map(lambda x: x if is_float(
x) else compute_full_path(root_path, x), row)), rows))
with data_iterator() as di:
index = 0
while index < di.size:
data = di.next()
result, outputs = forward(args, index, config, data, di.variables)
if index == 0:
for name, dim in zip(result.names, result.dims):
if dim == 1:
row0.append(name)
else:
for d in range(dim):
row0.append(name + '__' + str(d))
for i, output in enumerate(outputs):
if index + i < len(rows):
rows[index + i].extend(output)
index += len(outputs)
logger.log(
99, 'data {} / {}'.format(min([index, len(rows)]), len(rows)))
with open(os.path.join(args.outdir, 'output_result.csv'), 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerow(row0)
writer.writerows(rows)
logger.log(99, 'Forward Completed.')
progress(None)
