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SummaryRecord as context manager

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pull/1069/head
李鸿章 5 years ago
parent 0695bb4c18
commit 32c1d558f4
12 changed files with 444 additions and 857 deletions
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131
mindspore/train/summary/_event_writer.py
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@ -14,91 +14,74 @@
# ============================================================================
"""Writes events to disk in a logdir."""
import os
import time
import stat
from mindspore import log as logger
from collections import deque
from multiprocessing import Pool, Process, Queue, cpu_count
from ..._c_expression import EventWriter_
from ._summary_adapter import package_init_event
from ._summary_adapter import package_summary_event
class _WrapEventWriter(EventWriter_):
"""
Wrap the c++ EventWriter object.
def _pack(result, step):
summary_event = package_summary_event(result, step)
return summary_event.SerializeToString()
Args:
full_file_name (str): Include directory and file name.
"""
def __init__(self, full_file_name):
if full_file_name is not None:
EventWriter_.__init__(self, full_file_name)
class EventRecord:
class EventWriter(Process):
"""
Creates a `EventFileWriter` and write event to file.
Creates a `EventWriter` and write event to file.
Args:
full_file_name (str): Summary event file path and file name.
flush_time (int): The flush seconds to flush the pending events to disk. Default: 120.
filepath (str): Summary event file path and file name.
flush_interval (int): The flush seconds to flush the pending events to disk. Default: 120.
"""
def __init__(self, full_file_name: str, flush_time: int = 120):
self.full_file_name = full_file_name
# The first event will be flushed immediately.
self.flush_time = flush_time
self.next_flush_time = 0
# create event write object
self.event_writer = self._create_event_file()
self._init_event_file()
# count the events
self.event_count = 0
def _create_event_file(self):
"""Create the event write file."""
with open(self.full_file_name, 'w'):
os.chmod(self.full_file_name, stat.S_IWUSR | stat.S_IRUSR)
# create c++ event write object
event_writer = _WrapEventWriter(self.full_file_name)
return event_writer
def _init_event_file(self):
"""Send the init event to file."""
self.event_writer.Write((package_init_event()).SerializeToString())
self.flush()
return True
def write_event_to_file(self, event_str):
"""Write the event to file."""
self.event_writer.Write(event_str)
def get_data_count(self):
"""Return the event count."""
return self.event_count
def flush_cycle(self):
"""Flush file by timer."""
self.event_count = self.event_count + 1
# Flush the event writer every so often.
now = int(time.time())
if now > self.next_flush_time:
self.flush()
# update the flush time
self.next_flush_time = now + self.flush_time
def count_event(self):
"""Count event."""
logger.debug("Write the event count is %r", self.event_count)
self.event_count = self.event_count + 1
return self.event_count
def __init__(self, filepath: str, flush_interval: int) -> None:
super().__init__()
with open(filepath, 'w'):
os.chmod(filepath, stat.S_IWUSR | stat.S_IRUSR)
self._writer = EventWriter_(filepath)
self._queue = Queue(cpu_count() * 2)
self.start()
def run(self):
with Pool() as pool:
deq = deque()
while True:
while deq and deq[0].ready():
self._writer.Write(deq.popleft().get())
if not self._queue.empty():
action, data = self._queue.get()
if action == 'WRITE':
if not isinstance(data, (str, bytes)):
deq.append(pool.apply_async(_pack, data))
else:
self._writer.Write(data)
elif action == 'FLUSH':
self._writer.Flush()
elif action == 'END':
break
for res in deq:
self._writer.Write(res.get())
self._writer.Shut()
def write(self, data) -> None:
"""
Write the event to file.
Args:
data (Optional[str, Tuple[list, int]]): The data to write.
"""
self._queue.put(('WRITE', data))
def flush(self):
"""Flush the event file to disk."""
self.event_writer.Flush()
"""Flush the writer."""
self._queue.put(('FLUSH', None))
def close(self):
"""Flush the event file to disk and close the file."""
self.flush()
self.event_writer.Shut()
def close(self) -> None:
"""Close the writer."""
self._queue.put(('END', None))
self.join()

203
mindspore/train/summary/_summary_adapter.py
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308
mindspore/train/summary/_summary_scheduler.py
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198
mindspore/train/summary/summary_record.py
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15
tests/st/summary/test_davinci_summary.py
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@ -53,14 +53,13 @@ def me_train_tensor(net, input_np, label_np, epoch_size=2):
_network = wrap.WithLossCell(net, loss)
_train_net = MsWrapper(wrap.TrainOneStepCell(_network, opt))
_train_net.set_train()
summary_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=_train_net)
for epoch in range(0, epoch_size):
print(f"epoch %d" % (epoch))
output = _train_net(Tensor(input_np), Tensor(label_np))
summary_writer.record(i)
print("********output***********")
print(output.asnumpy())
summary_writer.close()
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=_train_net) as summary_writer:
for epoch in range(0, epoch_size):
print(f"epoch %d" % (epoch))
output = _train_net(Tensor(input_np), Tensor(label_np))
summary_writer.record(i)
print("********output***********")
print(output.asnumpy())
def me_infer_tensor(net, input_np):

11
tests/st/summary/test_gpu_summary.py
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@ -91,15 +91,14 @@ def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
def me_scalar_summary(steps, tag=None, value=None):
test_writer = SummaryRecord(SUMMARY_DIR_ME_TEMP)
with SummaryRecord(SUMMARY_DIR_ME_TEMP) as test_writer:
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
out_me_dict = train_summary_record_scalar_for_1(test_writer, steps, x, y)
out_me_dict = train_summary_record_scalar_for_1(test_writer, steps, x, y)
test_writer.close()
return out_me_dict
return out_me_dict
@pytest.mark.level0

29
tests/ut/python/train/summary/test_graph_summary.py
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@ -106,18 +106,17 @@ def test_graph_summary_sample():
optim = Momentum(net.trainable_params(), 0.1, 0.9)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=model._train_network)
model.train(2, dataset)
# step 2: create the Event
for i in range(1, 5):
test_writer.record(i)
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=model._train_network) as test_writer:
model.train(2, dataset)
# step 2: create the Event
for i in range(1, 5):
test_writer.record(i)
# step 3: send the event to mq
# step 3: send the event to mq
# step 4: accept the event and write the file
test_writer.close()
# step 4: accept the event and write the file
log.debug("finished test_graph_summary_sample")
log.debug("finished test_graph_summary_sample")
def test_graph_summary_callback():
@ -127,9 +126,9 @@ def test_graph_summary_callback():
optim = Momentum(net.trainable_params(), 0.1, 0.9)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=model._train_network)
summary_cb = SummaryStep(test_writer, 1)
model.train(2, dataset, callbacks=summary_cb)
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=model._train_network) as test_writer:
summary_cb = SummaryStep(test_writer, 1)
model.train(2, dataset, callbacks=summary_cb)
def test_graph_summary_callback2():
@ -139,6 +138,6 @@ def test_graph_summary_callback2():
optim = Momentum(net.trainable_params(), 0.1, 0.9)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=net)
summary_cb = SummaryStep(test_writer, 1)
model.train(2, dataset, callbacks=summary_cb)
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=net) as test_writer:
summary_cb = SummaryStep(test_writer, 1)
model.train(2, dataset, callbacks=summary_cb)

105
tests/ut/python/train/summary/test_histogram_summary.py
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@ -52,12 +52,11 @@ def _wrap_test_data(input_data: Tensor):
def test_histogram_summary():
"""Test histogram summary."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
test_data = _wrap_test_data(Tensor([[1, 2, 3], [4, 5, 6]]))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
test_data = _wrap_test_data(Tensor([[1, 2, 3], [4, 5, 6]]))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -68,20 +67,18 @@ def test_histogram_summary():
def test_histogram_multi_summary():
"""Test histogram multiple step."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
rng = np.random.RandomState(10)
size = 50
num_step = 5
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
for i in range(num_step):
arr = rng.normal(size=size)
rng = np.random.RandomState(10)
size = 50
num_step = 5
test_data = _wrap_test_data(Tensor(arr))
_cache_summary_tensor_data(test_data)
test_writer.record(step=i)
for i in range(num_step):
arr = rng.normal(size=size)
test_writer.close()
test_data = _wrap_test_data(Tensor(arr))
_cache_summary_tensor_data(test_data)
test_writer.record(step=i)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -93,12 +90,11 @@ def test_histogram_multi_summary():
def test_histogram_summary_scalar_tensor():
"""Test histogram summary, input is a scalar tensor."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
test_data = _wrap_test_data(Tensor(1))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
test_data = _wrap_test_data(Tensor(1))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -109,12 +105,11 @@ def test_histogram_summary_scalar_tensor():
def test_histogram_summary_empty_tensor():
"""Test histogram summary, input is an empty tensor."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
test_data = _wrap_test_data(Tensor([]))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
test_data = _wrap_test_data(Tensor([]))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -125,15 +120,14 @@ def test_histogram_summary_empty_tensor():
def test_histogram_summary_same_value():
"""Test histogram summary, input is an ones tensor."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
dim1 = 100
dim2 = 100
dim1 = 100
dim2 = 100
test_data = _wrap_test_data(Tensor(np.ones([dim1, dim2])))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
test_data = _wrap_test_data(Tensor(np.ones([dim1, dim2])))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -146,15 +140,14 @@ def test_histogram_summary_same_value():
def test_histogram_summary_high_dims():
"""Test histogram summary, input is a 4-dimension tensor."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
dim = 10
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
dim = 10
rng = np.random.RandomState(0)
tensor_data = rng.normal(size=[dim, dim, dim, dim])
test_data = _wrap_test_data(Tensor(tensor_data))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
rng = np.random.RandomState(0)
tensor_data = rng.normal(size=[dim, dim, dim, dim])
test_data = _wrap_test_data(Tensor(tensor_data))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -167,20 +160,19 @@ def test_histogram_summary_high_dims():
def test_histogram_summary_nan_inf():
"""Test histogram summary, input tensor has nan."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
dim1 = 100
dim2 = 100
dim1 = 100
dim2 = 100
arr = np.ones([dim1, dim2])
arr[0][0] = np.nan
arr[0][1] = np.inf
arr[0][2] = -np.inf
test_data = _wrap_test_data(Tensor(arr))
arr = np.ones([dim1, dim2])
arr[0][0] = np.nan
arr[0][1] = np.inf
arr[0][2] = -np.inf
test_data = _wrap_test_data(Tensor(arr))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)
@ -193,12 +185,11 @@ def test_histogram_summary_nan_inf():
def test_histogram_summary_all_nan_inf():
"""Test histogram summary, input tensor has no valid number."""
with tempfile.TemporaryDirectory() as tmp_dir:
test_writer = SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM")
with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
test_data = _wrap_test_data(Tensor(np.array([np.nan, np.nan, np.nan, np.inf, -np.inf])))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
test_writer.close()
test_data = _wrap_test_data(Tensor(np.array([np.nan, np.nan, np.nan, np.inf, -np.inf])))
_cache_summary_tensor_data(test_data)
test_writer.record(step=1)
file_name = os.path.join(tmp_dir, test_writer.event_file_name)
reader = SummaryReader(file_name)

64
tests/ut/python/train/summary/test_image_summary.py
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@ -74,23 +74,21 @@ def test_image_summary_sample():
""" test_image_summary_sample """
log.debug("begin test_image_summary_sample")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE")
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
# step 1: create the test data for summary
# step 1: create the test data for summary
# step 2: create the Event
for i in range(1, 5):
test_data = get_test_data(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
test_writer.flush()
# step 2: create the Event
for i in range(1, 5):
test_data = get_test_data(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
test_writer.flush()
# step 3: send the event to mq
# step 3: send the event to mq
# step 4: accept the event and write the file
test_writer.close()
log.debug("finished test_image_summary_sample")
# step 4: accept the event and write the file
log.debug("finished test_image_summary_sample")
class Net(nn.Cell):
@ -174,23 +172,21 @@ def test_image_summary_train():
log.debug("begin test_image_summary_sample")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE")
# step 1: create the test data for summary
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
# step 2: create the Event
# step 1: create the test data for summary
model = get_model()
fn = ImageSummaryCallback(test_writer)
summary_recode = SummaryStep(fn, 1)
model.train(2, dataset, callbacks=summary_recode)
# step 2: create the Event
# step 3: send the event to mq
model = get_model()
fn = ImageSummaryCallback(test_writer)
summary_recode = SummaryStep(fn, 1)
model.train(2, dataset, callbacks=summary_recode)
# step 4: accept the event and write the file
test_writer.close()
# step 3: send the event to mq
log.debug("finished test_image_summary_sample")
# step 4: accept the event and write the file
log.debug("finished test_image_summary_sample")
def test_image_summary_data():
@ -209,18 +205,12 @@ def test_image_summary_data():
log.debug("begin test_image_summary_sample")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE")
# step 1: create the test data for summary
# step 2: create the Event
_cache_summary_tensor_data(test_data_list)
test_writer.record(1)
test_writer.flush()
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
# step 3: send the event to mq
# step 1: create the test data for summary
# step 4: accept the event and write the file
test_writer.close()
# step 2: create the Event
_cache_summary_tensor_data(test_data_list)
test_writer.record(1)
log.debug("finished test_image_summary_sample")
log.debug("finished test_image_summary_sample")

151
tests/ut/python/train/summary/test_summary.py
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@ -65,22 +65,21 @@ def test_scalar_summary_sample():
""" test_scalar_summary_sample """
log.debug("begin test_scalar_summary_sample")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR")
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
# step 1: create the test data for summary
# step 1: create the test data for summary
# step 2: create the Event
for i in range(1, 500):
test_data = get_test_data(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
# step 2: create the Event
for i in range(1, 500):
test_data = get_test_data(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
# step 3: send the event to mq
# step 3: send the event to mq
# step 4: accept the event and write the file
test_writer.close()
# step 4: accept the event and write the file
log.debug("finished test_scalar_summary_sample")
log.debug("finished test_scalar_summary_sample")
def get_test_data_shape_1(step):
@ -110,22 +109,21 @@ def test_scalar_summary_sample_with_shape_1():
""" test_scalar_summary_sample_with_shape_1 """
log.debug("begin test_scalar_summary_sample_with_shape_1")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR")
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
# step 1: create the test data for summary
# step 1: create the test data for summary
# step 2: create the Event
for i in range(1, 100):
test_data = get_test_data_shape_1(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
# step 2: create the Event
for i in range(1, 100):
test_data = get_test_data_shape_1(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
# step 3: send the event to mq
# step 3: send the event to mq
# step 4: accept the event and write the file
test_writer.close()
# step 4: accept the event and write the file
log.debug("finished test_scalar_summary_sample")
log.debug("finished test_scalar_summary_sample")
# Test: test with ge
@ -152,26 +150,24 @@ def test_scalar_summary_with_ge():
log.debug("begin test_scalar_summary_with_ge")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR")
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net = SummaryDemo()
net.set_train()
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net = SummaryDemo()
net.set_train()
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
net(x, y)
test_writer.record(i)
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
net(x, y)
test_writer.record(i)
# step 3: close the writer
test_writer.close()
log.debug("finished test_scalar_summary_with_ge")
log.debug("finished test_scalar_summary_with_ge")
# test the problem of two consecutive use cases going wrong
@ -180,55 +176,52 @@ def test_scalar_summary_with_ge_2():
log.debug("begin test_scalar_summary_with_ge_2")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR")
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net = SummaryDemo()
net.set_train()
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
# step 2: create the Event
steps = 100
for i in range(1, steps):
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net(x, y)
test_writer.record(i)
net = SummaryDemo()
net.set_train()
# step 3: close the writer
test_writer.close()
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net(x, y)
test_writer.record(i)
log.debug("finished test_scalar_summary_with_ge_2")
log.debug("finished test_scalar_summary_with_ge_2")
def test_validate():
sr = SummaryRecord(SUMMARY_DIR)
with pytest.raises(ValueError):
SummaryStep(sr, 0)
with pytest.raises(ValueError):
SummaryStep(sr, -1)
with pytest.raises(ValueError):
SummaryStep(sr, 1.2)
with pytest.raises(ValueError):
SummaryStep(sr, True)
with pytest.raises(ValueError):
SummaryStep(sr, "str")
sr.record(1)
with pytest.raises(ValueError):
sr.record(False)
with pytest.raises(ValueError):
sr.record(2.0)
with pytest.raises(ValueError):
sr.record((1, 3))
with pytest.raises(ValueError):
sr.record([2, 3])
with pytest.raises(ValueError):
sr.record("str")
with pytest.raises(ValueError):
sr.record(sr)
sr.close()
def test_validate():
with SummaryRecord(SUMMARY_DIR) as sr:
with pytest.raises(ValueError):
SummaryStep(sr, 0)
with pytest.raises(ValueError):
SummaryStep(sr, -1)
with pytest.raises(ValueError):
SummaryStep(sr, 1.2)
with pytest.raises(ValueError):
SummaryStep(sr, True)
with pytest.raises(ValueError):
SummaryStep(sr, "str")
sr.record(1)
with pytest.raises(ValueError):
sr.record(False)
with pytest.raises(ValueError):
sr.record(2.0)
with pytest.raises(ValueError):
sr.record((1, 3))
with pytest.raises(ValueError):
sr.record([2, 3])
with pytest.raises(ValueError):
sr.record("str")
with pytest.raises(ValueError):
sr.record(sr)
SummaryStep(sr, 1)
with pytest.raises(ValueError):

32
tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
Unescape View File

@ -126,23 +126,21 @@ class HistogramSummaryNet(nn.Cell):
def run_case(net):
""" run_case """
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR)
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net.set_train()
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
net(x, y)
test_writer.record(i)
# step 3: close the writer
test_writer.close()
with SummaryRecord(SUMMARY_DIR) as test_writer:
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net.set_train()
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
net(x, y)
test_writer.record(i)
# Test 1: use the repeat tag

54
tests/ut/python/train/summary/test_tensor_summary.py
Unescape View File

@ -80,19 +80,18 @@ def test_tensor_summary_sample():
""" test_tensor_summary_sample """
log.debug("begin test_tensor_summary_sample")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR, file_suffix="_MS_TENSOR")
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_TENSOR") as test_writer:
# step 1: create the Event
for i in range(1, 100):
test_data = get_test_data(i)
# step 1: create the Event
for i in range(1, 100):
test_data = get_test_data(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
_cache_summary_tensor_data(test_data)
test_writer.record(i)
# step 2: accept the event and write the file
test_writer.close()
# step 2: accept the event and write the file
log.debug("finished test_tensor_summary_sample")
log.debug("finished test_tensor_summary_sample")
def get_test_data_check(step):
@ -131,23 +130,20 @@ def test_tensor_summary_with_ge():
log.debug("begin test_tensor_summary_with_ge")
# step 0: create the thread
test_writer = SummaryRecord(SUMMARY_DIR)
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net = SummaryDemo()
net.set_train()
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([[i], [i]]).astype(np.float32))
y = Tensor(np.array([[i + 1], [i + 1]]).astype(np.float32))
net(x, y)
test_writer.record(i)
# step 3: close the writer
test_writer.close()
log.debug("finished test_tensor_summary_with_ge")
with SummaryRecord(SUMMARY_DIR) as test_writer:
# step 1: create the network for summary
x = Tensor(np.array([1.1]).astype(np.float32))
y = Tensor(np.array([1.2]).astype(np.float32))
net = SummaryDemo()
net.set_train()
# step 2: create the Event
steps = 100
for i in range(1, steps):
x = Tensor(np.array([[i], [i]]).astype(np.float32))
y = Tensor(np.array([[i + 1], [i + 1]]).astype(np.float32))
net(x, y)
test_writer.record(i)
log.debug("finished test_tensor_summary_with_ge")

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