def __init__(self, parent, devices):
"""
:type parent: TaskThread
"""
threading.Thread.__init__(self,
name="DeviceThread %s" %
" ".join([dev.name for dev in devices]))
self.alloc_devices = devices
self.parent = parent
self.devices_batches_idx = None
self.run_start_batch_idx = None
self.eval_info = None
" :type: dict[str] | None "
self.allocated = False
self.processing = False
self.finished = True
self.crashed = False
self.num_frames = NumbersDict(0)
self.run_frames = NumbersDict(0)
self.daemon = True
self.active = True
self.result = {
'batchess': [],
'results': [],
'result_format': None,
'num_frames': 0
}
if self.alloc_devices:
self.start()
def analyze_data(config): # pylint: disable=redefined-outer-name
"""
:param Config config:
"""
dss = config.value('analyze_dataset', 'train')
ds = {"train": train_data, "dev": dev_data, "eval": eval_data}[dss]
epoch = config.int('epoch', 1)
print("Analyze dataset", dss, "epoch", epoch, file=log.v1)
ds.init_seq_order(epoch=epoch)
stat_prefix = config.value('statistics_save_prefix', 'statistics')
dtype = config.value('statistics_dtype', 'float64')
target = config.value('target', 'classes')
data_key = config.value('data_key', 'data')
assert ds.is_data_sparse(target), "need for prior calculation"
assert not ds.is_data_sparse(data_key), "needed for mean/var estimation"
from returnn.util.basic import inplace_increment, progress_bar_with_time, NumbersDict
priors = numpy.zeros((ds.get_data_dim(target), ), dtype=dtype)
mean = numpy.zeros((ds.get_data_dim(data_key), ), dtype=dtype)
mean_sq = numpy.zeros((ds.get_data_dim(data_key), ), dtype=dtype)
total_targets_len = 0
total_data_len = 0
# Note: This is not stable! See :class:`Util.Stats` for a better alternative.
seq_idx = 0
while ds.is_less_than_num_seqs(seq_idx):
progress_bar_with_time(ds.get_complete_frac(seq_idx))
ds.load_seqs(seq_idx, seq_idx + 1)
targets = ds.get_data(seq_idx, target)
inplace_increment(priors, targets, 1)
total_targets_len += targets.shape[0]
data = ds.get_data(seq_idx, data_key)
new_total_data_len = total_data_len + data.shape[0]
f = float(total_data_len) / new_total_data_len
mean = mean * f + numpy.sum(data, axis=0) * (1.0 - f)
mean_sq = mean_sq * f + numpy.sum(data * data, axis=0) * (1.0 - f)
total_data_len = new_total_data_len
seq_idx += 1
log_priors = numpy.log(priors)
log_priors -= numpy.log(NumbersDict(ds.get_num_timesteps())[target])
std_dev = numpy.sqrt(mean_sq - mean * mean)
print("Finished. %i total target frames, %i total data frames" %
(total_targets_len, total_data_len),
file=log.v1)
priors_fn = stat_prefix + ".log_priors.txt"
mean_fn = stat_prefix + ".mean.txt"
std_dev_fn = stat_prefix + ".std_dev.txt"
print("Dump priors to", priors_fn, file=log.v1)
numpy.savetxt(priors_fn, log_priors)
print("Dump mean to", mean_fn, file=log.v1)
numpy.savetxt(mean_fn, mean)
print("Dump std dev to", std_dev_fn, file=log.v1)
numpy.savetxt(std_dev_fn, std_dev)
print("Done.", file=log.v1)
def __init__(self,
task,
network,
devices,
data,
batches,
eval_batch_size=0,
start_batch=0,
share_batches=False,
reduction_rate=1.0,
report_prefix=None,
exclude=None,
epoch=None):
"""
:type task: str
:type network: Network.LayerNetwork
:type devices: list[Device.Device]
:type data: Dataset.Dataset
:type batches: EngineBatch.BatchSetGenerator
:type start_batch: int
:param str report_prefix: such as epoch or so. only for reporting
"""
threading.Thread.__init__(self, name="TaskThread %s" % task)
assert len(devices) > 0
if eval_batch_size == 0:
eval_batch_size = sys.maxsize
self.share_batches = share_batches
self.eval_batch_size = eval_batch_size
self.eval_batch_idx = 0
self.start_batch = start_batch
self.reduction_rate = reduction_rate
self.devices = devices
self.network = network
self.batches = batches
self.exclude = exclude
self.task = task
self.data = data
self.daemon = True
self.elapsed = 0
self.finalized = False
self.score = {}
self.error = {}
self.results = {}
self.num_frames = NumbersDict(0)
self.batch_idx = None
" :type: int | None "
self.device_crash_batch = None
" :type: int | None "
self.report_prefix = report_prefix or self.task
self.epoch = epoch
self.lock = threading.Lock()
self.start()
def allocate(self):
self.devices_batches_idx = self.parent.batches.get_current_batch_idx(
)
self.allocated_devices_batches = self.parent.allocate_devices(
self.alloc_devices)
self.run_frames = NumbersDict(0)
for batches, device in zip(self.allocated_devices_batches,
self.alloc_devices):
assert batches
assert batches[0].seqs
#assert batches[0].seqs[0].frame_length[1] > 0
device.num_updates += 1 if not device.update_specs[
'block_size'] else int(
ceil(
sum([len(batch.seqs) for batch in batches]) /
float(device.update_specs['block_size'])))
self.run_frames += sum(
[batch.get_total_num_frames() for batch in batches])
if self.parent.share_batches:
self.run_frames /= len(self.alloc_devices)
assert self.run_frames.max_value() > 0
self.allocated = True
def allocate(self):
self.devices_batches_idx = self.parent.batches.get_current_batch_idx()
assert len(self.alloc_devices) == 1
self.devices_batches = [None] * len(self.alloc_devices)
self.num_frames = NumbersDict(13)
batch_dim = 1
self.alloc_devices[0].alloc_data(
shapes={
"data": (self.num_frames["data"], batch_dim,
config.typed_value("num_inputs")),
"classes": (self.num_frames["classes"], batch_dim)
})
self.parent.num_frames += self.num_frames
self.allocated = True
def main(argv):
"""
Main entry.
"""
argparser = argparse.ArgumentParser(description=__doc__)
argparser.add_argument("config_file",
type=str,
help="RETURNN config, or model-dir")
argparser.add_argument("--epoch", type=int)
argparser.add_argument(
'--data',
default="train",
help=
"e.g. 'train', 'config:train', or sth like 'config:get_dataset('dev')'"
)
argparser.add_argument('--do_search', default=False, action='store_true')
argparser.add_argument('--beam_size', default=12, type=int)
argparser.add_argument('--dump_dir', help="for npy or png")
argparser.add_argument("--output_file", help="hdf")
argparser.add_argument("--device", help="gpu or cpu (default: automatic)")
argparser.add_argument("--layers",
default=["att_weights"],
action="append",
help="Layer of subnet to grab")
argparser.add_argument("--rec_layer",
default="output",
help="Subnet layer to grab from; decoder")
argparser.add_argument("--enc_layer", default="encoder")
argparser.add_argument("--batch_size", type=int, default=5000)
argparser.add_argument("--seq_list",
default=[],
action="append",
help="predefined list of seqs")
argparser.add_argument("--min_seq_len",
default="0",
help="can also be dict")
argparser.add_argument("--num_seqs",
default=-1,
type=int,
help="stop after this many seqs")
argparser.add_argument("--output_format",
default="npy",
help="npy, png or hdf")
argparser.add_argument("--dropout",
default=None,
type=float,
help="if set, overwrites all dropout values")
argparser.add_argument("--train_flag", action="store_true")
argparser.add_argument("--reset_partition_epoch", type=int, default=1)
argparser.add_argument("--reset_seq_ordering", default="sorted_reverse")
argparser.add_argument("--reset_epoch_wise_filter", default=None)
args = argparser.parse_args(argv[1:])
layers = args.layers
assert isinstance(layers, list)
config_fn = args.config_file
explicit_model_dir = None
if os.path.isdir(config_fn):
# Assume we gave a model dir.
explicit_model_dir = config_fn
train_log_dir_config_pattern = "%s/train-*/*.config" % config_fn
train_log_dir_configs = sorted(glob(train_log_dir_config_pattern))
assert train_log_dir_configs
config_fn = train_log_dir_configs[-1]
print("Using this config via model dir:", config_fn)
else:
assert os.path.isfile(config_fn)
model_name = ".".join(config_fn.split("/")[-1].split(".")[:-1])
init_returnn(config_fn=config_fn, args=args)
if explicit_model_dir:
config.set(
"model", "%s/%s" %
(explicit_model_dir, os.path.basename(config.value('model', ''))))
print("Model file prefix:", config.value('model', ''))
if args.do_search:
raise NotImplementedError
min_seq_length = NumbersDict(eval(args.min_seq_len))
assert args.output_format in ["npy", "png", "hdf"]
if args.output_format in ["npy", "png"]:
assert args.dump_dir
if not os.path.exists(args.dump_dir):
os.makedirs(args.dump_dir)
plt = ticker = None
if args.output_format == "png":
import matplotlib.pyplot as plt # need to import early? https://stackoverflow.com/a/45582103/133374
import matplotlib.ticker as ticker
dataset_str = args.data
if dataset_str in ["train", "dev", "eval"]:
dataset_str = "config:%s" % dataset_str
extra_dataset_kwargs = {}
if args.reset_partition_epoch:
print("NOTE: We are resetting partition epoch to %i." %
(args.reset_partition_epoch, ))
extra_dataset_kwargs["partition_epoch"] = args.reset_partition_epoch
if args.reset_seq_ordering:
print("NOTE: We will use %r seq ordering." %
(args.reset_seq_ordering, ))
extra_dataset_kwargs["seq_ordering"] = args.reset_seq_ordering
if args.reset_epoch_wise_filter:
extra_dataset_kwargs["epoch_wise_filter"] = eval(
args.reset_epoch_wise_filter)
dataset = init_dataset(dataset_str, extra_kwargs=extra_dataset_kwargs)
if hasattr(dataset,
"epoch_wise_filter") and args.reset_epoch_wise_filter is None:
if dataset.epoch_wise_filter:
print("NOTE: Resetting epoch_wise_filter to None.")
dataset.epoch_wise_filter = None
if args.reset_partition_epoch:
assert dataset.partition_epoch == args.reset_partition_epoch
if args.reset_seq_ordering:
assert dataset.seq_ordering == args.reset_seq_ordering
init_net(args, layers)
network = rnn.engine.network
hdf_writer = None
if args.output_format == "hdf":
assert args.output_file
assert len(layers) == 1
sub_layer = network.get_layer("%s/%s" % (args.rec_layer, layers[0]))
from returnn.datasets.hdf import SimpleHDFWriter
hdf_writer = SimpleHDFWriter(filename=args.output_file,
dim=sub_layer.output.dim,
ndim=sub_layer.output.ndim)
extra_fetches = {
"output":
network.layers[args.rec_layer].output.get_placeholder_as_batch_major(),
"output_len":
network.layers[
args.rec_layer].output.get_sequence_lengths(), # decoder length
"encoder_len":
network.layers[
args.enc_layer].output.get_sequence_lengths(), # encoder length
"seq_idx":
network.get_extern_data("seq_idx"),
"seq_tag":
network.get_extern_data("seq_tag"),
"target_data":
network.get_extern_data(network.extern_data.default_input),
"target_classes":
network.get_extern_data(network.extern_data.default_target),
}
for layer in layers:
sub_layer = rnn.engine.network.get_layer("%s/%s" %
(args.rec_layer, layer))
extra_fetches[
"rec_%s" %
layer] = sub_layer.output.get_placeholder_as_batch_major()
dataset.init_seq_order(
epoch=1, seq_list=args.seq_list
or None) # use always epoch 1, such that we have same seqs
dataset_batch = dataset.generate_batches(
recurrent_net=network.recurrent,
batch_size=args.batch_size,
max_seqs=rnn.engine.max_seqs,
max_seq_length=sys.maxsize,
min_seq_length=min_seq_length,
max_total_num_seqs=args.num_seqs,
used_data_keys=network.used_data_keys)
stats = {layer: Stats() for layer in layers}
# (**dict[str,numpy.ndarray|str|list[numpy.ndarray|str])->None
def fetch_callback(seq_idx, seq_tag, target_data, target_classes, output,
output_len, encoder_len, **kwargs):
"""
:param list[int] seq_idx: len is n_batch
:param list[str] seq_tag: len is n_batch
:param numpy.ndarray target_data: extern data default input (e.g. "data"), shape e.g. (B,enc-T,...)
:param numpy.ndarray target_classes: extern data default target (e.g. "classes"), shape e.g. (B,dec-T,...)
:param numpy.ndarray output: rec layer output, shape e.g. (B,dec-T,...)
:param numpy.ndarray output_len: rec layer seq len, i.e. decoder length, shape (B,)
:param numpy.ndarray encoder_len: encoder seq len, shape (B,)
:param kwargs: contains "rec_%s" % l for l in layers, the sub layers (e.g att weights) we are interested in
"""
n_batch = len(seq_idx)
for i in range(n_batch):
# noinspection PyShadowingNames
for layer in layers:
att_weights = kwargs["rec_%s" % layer][i]
stats[layer].collect(att_weights.flatten())
if args.output_format == "npy":
data = {}
for i in range(n_batch):
data[i] = {
'tag': seq_tag[i],
'data': target_data[i],
'classes': target_classes[i],
'output': output[i],
'output_len': output_len[i],
'encoder_len': encoder_len[i],
}
# noinspection PyShadowingNames
for layer in [("rec_%s" % layer) for layer in layers]:
assert layer in kwargs
out = kwargs[layer][i]
assert out.ndim >= 2
assert out.shape[0] >= output_len[i] and out.shape[
1] >= encoder_len[i]
data[i][layer] = out[:output_len[i], :encoder_len[i]]
fname = args.dump_dir + '/%s_ep%03d_data_%i_%i.npy' % (
model_name, rnn.engine.epoch, seq_idx[0], seq_idx[-1])
np.save(fname, data)
elif args.output_format == "png":
for i in range(n_batch):
# noinspection PyShadowingNames
for layer in layers:
extra_postfix = ""
if args.dropout is not None:
extra_postfix += "_dropout%.2f" % args.dropout
elif args.train_flag:
extra_postfix += "_train"
fname = args.dump_dir + '/%s_ep%03d_plt_%05i_%s%s.png' % (
model_name, rnn.engine.epoch, seq_idx[i], layer,
extra_postfix)
att_weights = kwargs["rec_%s" % layer][i]
att_weights = att_weights.squeeze(axis=2) # (out,enc)
assert att_weights.shape[0] >= output_len[
i] and att_weights.shape[1] >= encoder_len[i]
att_weights = att_weights[:output_len[i], :encoder_len[i]]
print("Seq %i, %s: Dump att weights with shape %r to: %s" %
(seq_idx[i], seq_tag[i], att_weights.shape, fname))
plt.matshow(att_weights)
title = seq_tag[i]
if dataset.can_serialize_data(
network.extern_data.default_target):
title += "\n" + dataset.serialize_data(
network.extern_data.default_target,
target_classes[i][:output_len[i]])
ax = plt.gca()
tick_labels = [
dataset.serialize_data(
network.extern_data.default_target,
np.array([x], dtype=target_classes[i].dtype))
for x in target_classes[i][:output_len[i]]
]
ax.set_yticklabels([''] + tick_labels, fontsize=8)
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.title(title)
plt.savefig(fname)
plt.close()
elif args.output_format == "hdf":
assert len(layers) == 1
att_weights = kwargs["rec_%s" % layers[0]]
hdf_writer.insert_batch(inputs=att_weights,
seq_len={
0: output_len,
1: encoder_len
},
seq_tag=seq_tag)
else:
raise Exception("output format %r" % args.output_format)
runner = Runner(engine=rnn.engine,
dataset=dataset,
batches=dataset_batch,
train=False,
train_flag=bool(args.dropout) or args.train_flag,
extra_fetches=extra_fetches,
extra_fetches_callback=fetch_callback)
runner.run(report_prefix="att-weights epoch %i" % rnn.engine.epoch)
for layer in layers:
stats[layer].dump(stream_prefix="Layer %r " % layer)
if not runner.finalized:
print("Some error occured, not finalized.")
sys.exit(1)
if hdf_writer:
hdf_writer.close()
rnn.finalize()
def __init__(self):
self.num_frames = NumbersDict(0)
self.y = {}
self.j = {}
def run_inner(self):
self.start_time = time.time()
for device in self.devices:
device.prepare(epoch=self.epoch, **self.get_device_prepare_args())
self.initialize()
terminal_width, _ = terminal_size()
self.interactive = (log.v[3] and terminal_width >= 0)
print("starting task", self.task, file=log.v5)
for device in self.devices:
device.eval_batch_idx = -1
device.start_epoch_stats()
device.num_frames = 0
device.num_updates = 0
device.tot = 0
num_device_runs = 1 if self.share_batches else len(self.devices)
deviceRuns = [
self.DeviceBatchRun(
self,
[self.devices[i]] if not self.share_batches else self.devices)
for i in range(num_device_runs)
]
results = {'batchess': [], 'results': [], 'num_frames': NumbersDict(0)}
run_frames = NumbersDict(0)
cost_result_format = -1
crashed = False
assert num_device_runs > 0
while True:
if getattr(sys, "exited", False):
# This happens when we exit Python.
# Without this check, this thread would keep running until all exit handlers of Python are done.
print("%s stopped" % self, file=log.v5)
crashed = True
break
for i in range(num_device_runs):
if deviceRuns[i].crashed or not deviceRuns[i].is_alive():
crashed = True
break
if deviceRuns[i].finished:
results['batchess'] += deviceRuns[i].result['batchess'][:]
results['results'] += deviceRuns[i].result['results'][:]
results['result_format'] = deviceRuns[i].result[
'result_format']
deviceRuns[i].finished = False
if crashed:
break
if cost_result_format < 0 and deviceRuns[i].result['result_format']:
for idx, fmt in enumerate(
deviceRuns[i].result['result_format']):
if fmt and fmt.startswith('cost:'):
cost_result_format = idx
total_cost = 0
if results['results'] and cost_result_format >= 0:
total_cost = numpy.asarray(
results['results'])[:, cost_result_format].sum()
if total_cost >= self.eval_batch_size or not self.batches.has_more(
):
if all(not (dev.finished or dev.allocated or dev.processing)
for dev in deviceRuns):
results['num_frames'] = run_frames
self.num_frames += run_frames
if self.share_batches: run_frames *= len(self.devices)
self.reduce(run_frames)
self.eval_batch_idx += 1
run_frames = NumbersDict(0)
results['batchess'] = []
results['results'] = []
for device in self.devices:
device.num_frames = 0
device.num_updates = 0
if not self.batches.has_more():
break
else:
time.sleep(0.01)
match = True
while self.batches.has_more(
) and total_cost < self.eval_batch_size and match:
self.batch_idx = self.batches.get_current_batch_idx()
if self.batch_idx < self.start_batch:
self.batches.advance(1)
break
match = False
for i in range(num_device_runs):
if not deviceRuns[i].allocated:
deviceRuns[i].allocate()
run_frames += deviceRuns[i].run_frames
match = True
break
if not match:
time.sleep(0.01)
for run in deviceRuns:
run.stop()
if crashed: return
for device in self.devices:
device.finish_epoch_stats()
self.finalize()
if self.interactive: progress_bar()
self.elapsed = (time.time() - self.start_time)
class DeviceBatchRun(threading.Thread):
def __init__(self, parent, devices):
"""
:type parent: TaskThread
"""
threading.Thread.__init__(self,
name="DeviceThread %s" %
" ".join([dev.name for dev in devices]))
self.alloc_devices = devices
self.parent = parent
self.devices_batches_idx = None
self.run_start_batch_idx = None
self.eval_info = None
" :type: dict[str] | None "
self.allocated = False
self.processing = False
self.finished = True
self.crashed = False
self.num_frames = NumbersDict(0)
self.run_frames = NumbersDict(0)
self.daemon = True
self.active = True
self.result = {
'batchess': [],
'results': [],
'result_format': None,
'num_frames': 0
}
if self.alloc_devices:
self.start()
def allocate(self):
self.devices_batches_idx = self.parent.batches.get_current_batch_idx(
)
self.allocated_devices_batches = self.parent.allocate_devices(
self.alloc_devices)
self.run_frames = NumbersDict(0)
for batches, device in zip(self.allocated_devices_batches,
self.alloc_devices):
assert batches
assert batches[0].seqs
#assert batches[0].seqs[0].frame_length[1] > 0
device.num_updates += 1 if not device.update_specs[
'block_size'] else int(
ceil(
sum([len(batch.seqs) for batch in batches]) /
float(device.update_specs['block_size'])))
self.run_frames += sum(
[batch.get_total_num_frames() for batch in batches])
if self.parent.share_batches:
self.run_frames /= len(self.alloc_devices)
assert self.run_frames.max_value() > 0
self.allocated = True
def finish(self):
"""
:returns whether everything is fine.
"""
device_results, outputs_format = self.device_collect_results()
if device_results is None:
if not getattr(sys, "exited", False):
print("device crashed on batch",
self.run_start_batch_idx,
file=log.v3)
self.parent.device_crash_batch = self.run_start_batch_idx
self.crashed = True
return False
assert len(device_results) == len(self.alloc_devices) == len(
self.running_devices_batches)
if outputs_format and any(
[k.startswith("gparam:") for k in outputs_format]):
# WARNING: this code is untested and likely broken!
for i in range(len(self.alloc_devices)):
res = Device.make_result_dict(device_results[i],
outputs_format)
self.alloc_devices[i].sync_net_train_params()
devnet = self.alloc_devices[i].get_net_train_params(
self.parent.network)
vars = self.parent.network.get_all_params_vars()
for p, q in zip(vars, devnet):
p.set_value(q)
gparams = {}
for p in vars:
gparams[p] = numpy.zeros(p.get_value(
borrow=True, return_internal_type=True).shape,
dtype=theano.config.floatX)
for p in vars:
q = res["gparam:%s" % p.name]
if q.shape == p.get_value().shape:
gparams[p] = q
elif q.shape:
print(
"warning: shape for gradient does not match:",
p.get_value().shape,
q.shape,
file=log.v2)
self.parent.updater.setNetParamDeltas(gparams)
self.parent.updater.update()
self.alloc_devices[i].set_net_params(self.parent.network)
self.result = {
'batchess': self.running_devices_batches,
'results': device_results,
'result_format': outputs_format,
'num_frames': self.num_frames
}
self.eval_info = self.parent.evaluate(**self.result)
self.parent.lock.acquire()
self.print_process()
self.parent.lock.release()
return True
def run(self):
try:
while self.active and not getattr(sys, "exited", False):
if self.allocated and not self.finished:
self.device_run()
self.num_frames = self.run_frames
self.processing = True
self.allocated = False
self.finish()
self.finished = True
self.processing = False
else:
time.sleep(0.01)
except BaseException:
self.crashed = True
sys.excepthook(*sys.exc_info())
finally:
self.finished = True
def stop(self):
self.active = False
def device_run(self):
batch_idx = self.run_start_batch_idx = self.devices_batches_idx
assert len(self.alloc_devices) == len(
self.allocated_devices_batches)
self.running_devices_batches = self.allocated_devices_batches
for device, batches in zip(self.alloc_devices,
self.running_devices_batches):
if self.parent.network.recurrent:
print("running", device.targets["data"].shape[1], \
"sequence slices (%i nts)" % (device.targets["data"].shape[0] * device.targets["data"].shape[1]), end=' ', file=log.v5)
else:
print("running",
device.targets["data"].shape[0] *
device.targets["data"].shape[1],
"frames",
end=' ',
file=log.v5)
if device.num_batches == 1:
print("of batch %i" % batch_idx, end=' ', file=log.v5)
else:
print("of batches %i-%i" %
(batch_idx, batch_idx + device.num_batches - 1),
end=' ',
file=log.v5)
print("on device", device.name, file=log.v5)
device.run(self.parent.task)
#if not self.share batch_idx += device.num_batches
def device_collect_results(self):
device_results = []
outputs_format = None
for i, device in enumerate(self.alloc_devices):
try:
result, outputs_format_new = device.result()
except RuntimeError:
return None, None
if result is None:
return None, None
assert isinstance(result, list)
assert len(result) > 0 # we always expect to get some result
if i >= 1:
assert outputs_format == outputs_format_new, "We expect to always get the same output format."
outputs_format = outputs_format_new
device_results.append(result)
return device_results, outputs_format
def device_mem_usage_str(self, devices):
"""
:type devices: list[Device.Device]
:rtype: str | None
"""
if not devices:
return None
mem_info = [device.get_memory_info() for device in devices]
if len(mem_info) == 1 and mem_info[0] is None:
return None
mem_usage = [info.used if info else None for info in mem_info]
s = [
"%s MB" % (mem /
(1024 * 1024)) if mem is not None else "unknown"
for mem in mem_usage
]
return "/".join(s)
def print_process(self):
if not self.parent.interactive and not log.v[5]:
return
start_elapsed = time.time() - self.parent.start_time
complete = self.parent.batches.completed_frac()
assert complete > 0
total_time_estimated = start_elapsed / complete
remaining_estimated = total_time_estimated - start_elapsed
if log.verbose[5]:
mem_usage = self.device_mem_usage_str(self.alloc_devices)
info = [
self.parent.report_prefix,
"batch %i" % self.run_start_batch_idx
]
if self.eval_info: # Such as score.
info += [
"%s %s" % item
for item in sorted(self.eval_info.items())
]
info += [
"elapsed %s" % hms(start_elapsed),
"exp. remaining %s" % hms(remaining_estimated),
"complete %.02f%%" % (complete * 100)
]
if mem_usage:
info += ["memory %s" % mem_usage]
print(", ".join(filter(None, info)), file=log.v5)
if self.parent.interactive:
progress_bar(complete, hms(remaining_estimated))
def test_iterate_seqs_chunking_varying_sequence_length():
dataset = DummyDatasetMultipleSequenceLength(input_dim=2,
output_dim=3,
num_seqs=2,
seq_len={
'data': 24,
'classes': 12
})
dataset.init_seq_order(1)
seqs = list(
dataset.iterate_seqs(chunk_size={
'data': 12,
'classes': 6
},
chunk_step={
'data': 6,
'classes': 3
},
used_data_keys=None))
for s in seqs:
print(s)
assert_equal(len(seqs), 8)
assert_equal(seqs[0], (0, NumbersDict({
'data': 0,
'classes': 0
}), NumbersDict({
'data': 12,
'classes': 6
})))
assert_equal(seqs[1], (0, NumbersDict({
'data': 6,
'classes': 3
}), NumbersDict({
'data': 18,
'classes': 9
})))
assert_equal(seqs[2], (0, NumbersDict({
'data': 12,
'classes': 6
}), NumbersDict({
'data': 24,
'classes': 12
})))
assert_equal(seqs[3], (0, NumbersDict({
'data': 18,
'classes': 9
}), NumbersDict({
'data': 24,
'classes': 12
})))
assert_equal(seqs[4], (1, NumbersDict({
'data': 0,
'classes': 0
}), NumbersDict({
'data': 12,
'classes': 6
})))
assert_equal(seqs[5], (1, NumbersDict({
'data': 6,
'classes': 3
}), NumbersDict({
'data': 18,
'classes': 9
})))
assert_equal(seqs[6], (1, NumbersDict({
'data': 12,
'classes': 6
}), NumbersDict({
'data': 24,
'classes': 12
})))
assert_equal(seqs[7], (1, NumbersDict({
'data': 18,
'classes': 9
}), NumbersDict({
'data': 24,
'classes': 12
})))
def analyze_dataset(options):
"""
:param options: argparse.Namespace
"""
print("Epoch: %i" % options.epoch, file=log.v3)
print("Dataset keys:", dataset.get_data_keys(), file=log.v3)
print("Dataset target keys:", dataset.get_target_list(), file=log.v3)
assert options.key in dataset.get_data_keys()
terminal_width, _ = util.terminal_size()
show_interactive_process_bar = (log.verbose[3] and (not log.verbose[5])
and terminal_width >= 0)
start_time = time.time()
num_seqs_stats = Stats()
if options.endseq < 0:
options.endseq = float("inf")
recurrent = True
used_data_keys = dataset.get_data_keys()
batch_size = config.typed_value('batch_size', 1)
max_seqs = config.int('max_seqs', -1)
seq_drop = config.float('seq_drop', 0.0)
max_seq_length = config.typed_value(
'max_seq_length', None) or config.float('max_seq_length', 0)
max_pad_size = config.typed_value("max_pad_size", None)
batches = dataset.generate_batches(recurrent_net=recurrent,
batch_size=batch_size,
max_seqs=max_seqs,
max_seq_length=max_seq_length,
max_pad_size=max_pad_size,
seq_drop=seq_drop,
used_data_keys=used_data_keys)
step = 0
total_num_seqs = 0
total_num_frames = NumbersDict()
total_num_used_frames = NumbersDict()
try:
while batches.has_more():
# See FeedDictDataProvider.
batch, = batches.peek_next_n(1)
assert isinstance(batch, Batch)
if batch.start_seq > options.endseq:
break
dataset.load_seqs(batch.start_seq, batch.end_seq)
complete_frac = batches.completed_frac()
start_elapsed = time.time() - start_time
try:
num_seqs_s = str(dataset.num_seqs)
except NotImplementedError:
try:
num_seqs_s = "~%i" % dataset.estimated_num_seqs
except TypeError: # a number is required, not NoneType
num_seqs_s = "?"
progress_prefix = "%i/%s" % (batch.start_seq, num_seqs_s)
progress = "%s (%.02f%%)" % (progress_prefix, complete_frac * 100)
if complete_frac > 0:
total_time_estimated = start_elapsed / complete_frac
remaining_estimated = total_time_estimated - start_elapsed
progress += " (%s)" % hms(remaining_estimated)
batch_max_time = NumbersDict.max(
[seq.frame_length for seq in batch.seqs]) * len(batch.seqs)
batch_num_used_frames = sum(
[seq.frame_length for seq in batch.seqs], NumbersDict())
total_num_seqs += len(batch.seqs)
num_seqs_stats.collect(numpy.array([len(batch.seqs)]))
total_num_frames += batch_max_time
total_num_used_frames += batch_num_used_frames
print("%s, batch %i, num seqs %i, frames %s, used %s (%s)" %
(progress, step, len(
batch.seqs), batch_max_time, batch_num_used_frames,
batch_num_used_frames / batch_max_time),
file=log.v5)
if show_interactive_process_bar:
util.progress_bar_with_time(complete_frac,
prefix=progress_prefix)
step += 1
batches.advance(1)
finally:
print("Done. Total time %s. More seqs which we did not dumped: %s" %
(hms(time.time() - start_time), batches.has_more()),
file=log.v2)
print("Dataset epoch %i, order %r." %
(dataset.epoch, dataset.seq_ordering))
print("Num batches (steps): %i" % step, file=log.v1)
print("Num seqs: %i" % total_num_seqs, file=log.v1)
num_seqs_stats.dump(stream=log.v1, stream_prefix="Batch num seqs ")
for key in used_data_keys:
print("Data key %r:" % key, file=log.v1)
print(" Num frames: %s" % total_num_frames[key], file=log.v1)
print(" Num used frames: %s" % total_num_used_frames[key],
file=log.v1)
print(" Fraction used frames: %s" %
(total_num_used_frames / total_num_frames)[key],
file=log.v1)
dataset.finish_epoch()
