def process_args(args):
""" Process the options got from get_args()
"""
if not common_train_lib.validate_chunk_width(args.chunk_width):
raise Exception("--egs.chunk-width has an invalid value")
if not common_train_lib.validate_minibatch_size_str(args.num_chunk_per_minibatch):
raise Exception("--trainer.num-chunk-per-minibatch has an invalid value")
if args.chunk_left_context < 0:
raise Exception("--egs.chunk-left-context should be non-negative")
if args.chunk_right_context < 0:
raise Exception("--egs.chunk-right-context should be non-negative")
if args.left_deriv_truncate is not None:
args.deriv_truncate_margin = -args.left_deriv_truncate
logger.warning(
"--chain.left-deriv-truncate (deprecated) is set by user, and "
"--trainer.deriv-truncate-margin is set to negative of that "
"value={0}. We recommend using the option "
"--trainer.deriv-truncate-margin.".format(args.deriv_truncate_margin)
)
if not os.path.exists(args.dir) or not os.path.exists(args.dir + "/configs"):
raise Exception(
"This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script"
)
if args.transform_dir is None:
args.transform_dir = args.lat_dir
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make"""
)
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = ""
run_opts.combine_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. " "nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = args.egs_command if args.egs_command is not None else args.command
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if not common_train_lib.validate_chunk_width(args.chunk_width):
raise Exception("--egs.chunk-width has an invalid value")
if not common_train_lib.validate_minibatch_size_str(
args.num_chunk_per_minibatch):
raise Exception(
"--trainer.rnn.num-chunk-per-minibatch has an invalid value")
if args.chunk_left_context < 0:
raise Exception("--egs.chunk-left-context should be non-negative")
if args.chunk_right_context < 0:
raise Exception("--egs.chunk-right-context should be non-negative")
if (not os.path.exists(args.dir)
or not os.path.exists(args.dir + "/configs")):
raise Exception("This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script")
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu in ["true", "false"]:
args.use_gpu = ("yes" if args.use_gpu == "true" else "no")
if args.use_gpu in ["yes", "wait"]:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if not common_train_lib.validate_chunk_width(args.chunk_width):
raise Exception("--egs.chunk-width has an invalid value")
if not common_train_lib.validate_minibatch_size_str(args.num_chunk_per_minibatch):
raise Exception("--trainer.rnn.num-chunk-per-minibatch has an invalid value")
if args.chunk_left_context < 0:
raise Exception("--egs.chunk-left-context should be non-negative")
if args.chunk_right_context < 0:
raise Exception("--egs.chunk-right-context should be non-negative")
if (not os.path.exists(args.dir)
or not os.path.exists(args.dir+"/configs")):
raise Exception("This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script")
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu in ["true", "false"]:
args.use_gpu = ("yes" if args.use_gpu == "true" else "no")
if args.use_gpu in ["yes", "wait"]:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else
args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.frames_per_eg < 1:
raise Exception("--egs.frames-per-eg should have a minimum value of 1")
if not common_train_lib.validate_minibatch_size_str(args.minibatch_size):
raise Exception(
"--trainer.optimization.minibatch-size has an invalid value")
if (not os.path.exists(args.dir)):
raise Exception("Directory specified with --dir={0} "
"does not exist.".format(args.dir))
if (not os.path.exists(args.dir + "/configs") and
(args.input_model is None or not os.path.exists(args.input_model))):
raise Exception(
"Either --trainer.input-model option should be supplied, "
"and exist; or the {0}/configs directory should exist."
"{0}/configs is the output of make_configs.py"
"".format(args.dir))
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu in ["true", "false"]:
args.use_gpu = ("yes" if args.use_gpu == "true" else "no")
if args.use_gpu in ["yes", "wait"]:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.chunk_width < 1:
raise Exception("--egs.chunk-width should have a minimum value of 1")
if args.chunk_left_context < 0:
raise Exception("--egs.chunk-left-context should be non-negative")
if args.chunk_right_context < 0:
raise Exception("--egs.chunk-right-context should be non-negative")
if args.left_deriv_truncate is not None:
args.deriv_truncate_margin = -args.left_deriv_truncate
logger.warning(
"--chain.left-deriv-truncate (deprecated) is set by user, and "
"--trainer.deriv-truncate-margin is set to negative of that "
"value={0}. We recommend using the option "
"--trainer.deriv-truncate-margin.".format(
args.deriv_truncate_margin))
if (not os.path.exists(args.dir)
or not os.path.exists(args.dir+"/configs")):
raise Exception("This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script")
if args.transform_dir is None:
args.transform_dir = args.lat_dir
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = ""
run_opts.combine_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else
args.command)
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.frames_per_eg < 1:
raise Exception("--egs.frames-per-eg should have a minimum value of 1")
if not common_train_lib.validate_minibatch_size_str(args.minibatch_size):
raise Exception("--trainer.optimization.minibatch-size has an invalid value")
if (not os.path.exists(args.dir)):
raise Exception("This script expects --dir={0} to exist.")
if (not os.path.exists(args.dir+"/configs") and
(args.input_model is None or not os.path.exists(args.input_model))):
raise Exception("Either --trainer.input-model option should be supplied, "
"and exist; or the {0}/configs directory should exist."
"{0}/configs is the output of make_configs.py"
"".format(args.dir))
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu in ["true", "false"]:
args.use_gpu = ("yes" if args.use_gpu == "true" else "no")
if args.use_gpu in ["yes", "wait"]:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu={}".format(args.use_gpu)
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else
args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.frames_per_eg < 1:
raise Exception("--egs.frames-per-eg should have a minimum value of 1")
if not common_train_lib.validate_minibatch_size_str(args.minibatch_size):
raise Exception("--trainer.rnn.num-chunk-per-minibatch has an invalid value")
if (not os.path.exists(args.dir)
or (not os.path.exists(args.dir+"/configs") and
not os.path.exists(args.input_model))):
raise Exception("This script expects {0} to exist. Also either "
"--trainer.input-model option as initial 'raw' model "
"(used as 0.raw in the script) should be supplied or "
"{0}/configs directory which is the output of "
"make_configs.py script should be provided."
"".format(args.dir))
if args.transform_dir is None:
args.transform_dir = args.ali_dir
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = ""
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu=yes"
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else
args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.frames_per_eg < 1:
raise Exception("--egs.frames-per-eg should have a minimum value of 1")
if not common_train_lib.validate_minibatch_size_str(args.minibatch_size):
raise Exception("--trainer.rnn.num-chunk-per-minibatch has an invalid value")
if (not os.path.exists(args.dir)
or not os.path.exists(args.dir+"/configs")):
raise Exception("This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script")
if args.transform_dir is None:
args.transform_dir = args.ali_dir
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = ""
run_opts.combine_gpu_opt = ""
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu=yes"
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else
args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.frames_per_eg < 1:
raise Exception("--egs.frames-per-eg should have a minimum value of 1")
if not common_train_lib.validate_minibatch_size_str(args.minibatch_size):
raise Exception(
"--trainer.optimization.minibatch-size has an invalid value")
if (not os.path.exists(args.dir)
or not os.path.exists(args.dir + "/configs")):
raise Exception("This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script")
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = ""
run_opts.combine_gpu_opt = ""
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu=yes"
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if not common_train_lib.validate_chunk_width(args.chunk_width):
raise Exception("--egs.chunk-width has an invalid value")
if not common_train_lib.validate_minibatch_size_str(
args.num_chunk_per_minibatch):
raise Exception(
"--trainer.num-chunk-per-minibatch has an invalid value")
if args.chunk_left_context < 0:
raise Exception("--egs.chunk-left-context should be non-negative")
if args.chunk_right_context < 0:
raise Exception("--egs.chunk-right-context should be non-negative")
if args.left_deriv_truncate is not None:
args.deriv_truncate_margin = -args.left_deriv_truncate
logger.warning(
"--chain.left-deriv-truncate (deprecated) is set by user, and "
"--trainer.deriv-truncate-margin is set to negative of that "
"value={0}. We recommend using the option "
"--trainer.deriv-truncate-margin.".format(
args.deriv_truncate_margin))
if (not os.path.exists(args.dir)):
raise Exception("This script expects --dir={0} to exist.")
if (not os.path.exists(args.dir + "/configs") and
(args.input_model is None or not os.path.exists(args.input_model))):
raise Exception(
"Either --trainer.input-model option should be supplied, "
"and exist; or the {0}/configs directory should exist."
"".format(args.dir))
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu in ["true", "false"]:
args.use_gpu = ("yes" if args.use_gpu == "true" else "no")
if args.use_gpu in ["yes", "wait"]:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = "--use-gpu={}".format(args.use_gpu)
run_opts.combine_queue_opt = "--gpu 1"
run_opts.combine_gpu_opt = "--use-gpu={}".format(args.use_gpu)
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.combine_gpu_opt = "--use-gpu=no"
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else args.command)
return [args, run_opts]
def process_args(args):
""" Process the options got from get_args()
"""
if args.chunk_width < 1:
raise Exception("--egs.chunk-width should have a minimum value of 1")
if args.chunk_left_context < 0:
raise Exception("--egs.chunk-left-context should be non-negative")
if args.chunk_right_context < 0:
raise Exception("--egs.chunk-right-context should be non-negative")
if args.num_bptt_steps is not None:
# -2 is used to compensate for the splicing of the input frame,
# assuming that splicing spans from -2 to 2
args.deriv_truncate_margin = args.num_bptt_steps - args.chunk_width - 2
logger.warning(
"--trainer.rnn.num-bptt-steps (deprecated) is set by user, and "
"--trainer.deriv-truncate-margin is set to (num-bptt-steps - "
"chunk-width - 2) = {0}. We recommend using the option "
"--trainer.deriv-truncate-margin.".format(
args.deriv_truncate_margin))
if (not os.path.exists(args.dir)
or not os.path.exists(args.dir+"/configs")):
raise Exception("This scripts expects {0} to exist and have a configs "
"directory which is the output of "
"make_configs.py script")
if args.transform_dir is None:
args.transform_dir = args.ali_dir
# set the options corresponding to args.use_gpu
run_opts = common_train_lib.RunOpts()
if args.use_gpu:
if not common_lib.check_if_cuda_compiled():
logger.warning(
"""You are running with one thread but you have not compiled
for CUDA. You may be running a setup optimized for GPUs.
If you have GPUs and have nvcc installed, go to src/ and do
./configure; make""")
run_opts.train_queue_opt = "--gpu 1"
run_opts.parallel_train_opts = ""
run_opts.combine_queue_opt = "--gpu 1"
run_opts.prior_gpu_opt = "--use-gpu=yes"
run_opts.prior_queue_opt = "--gpu 1"
else:
logger.warning("Without using a GPU this will be very slow. "
"nnet3 does not yet support multiple threads.")
run_opts.train_queue_opt = ""
run_opts.parallel_train_opts = "--use-gpu=no"
run_opts.combine_queue_opt = ""
run_opts.prior_gpu_opt = "--use-gpu=no"
run_opts.prior_queue_opt = ""
run_opts.command = args.command
run_opts.egs_command = (args.egs_command
if args.egs_command is not None else
args.command)
run_opts.num_jobs_compute_prior = args.num_jobs_compute_prior
return [args, run_opts]
