本文整理汇总了Python中mxnet.MXNetError方法的典型用法代码示例。如果您正苦于以下问题:Python mxnet.MXNetError方法的具体用法?Python mxnet.MXNetError怎么用?Python mxnet.MXNetError使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mxnet
的用法示例。
在下文中一共展示了mxnet.MXNetError方法的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_invalid_operations
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def test_invalid_operations():
def check_invalid_gluon_trainer_reset():
params = mx.gluon.ParameterDict()
x = params.get('x', shape=(4, 2), lr_mult=1.0, stype='row_sparse')
params.initialize(ctx=mx.cpu(0), init='zeros')
trainer = mx.gluon.Trainer(params, 'sgd', {'learning_rate': 0.1}, kvstore=kv)
params.save('test_gluon_trainer_reset_' + str(my_rank) + '.params')
row_id = mx.nd.arange(0, 4)
w = x.row_sparse_data(row_id)
assert trainer._kv_initialized and trainer._update_on_kvstore
mx.nd.waitall()
# load would fail to reset kvstore since update_on_kvstore is True
assert_exception(params.load, RuntimeError, 'test_gluon_trainer_reset_' + str(my_rank) + '.params')
print('worker ' + str(my_rank) + ' passed check_invalid_gluon_trainer_reset')
def check_invalid_pull():
kv.init(keys_invalid[0], mx.nd.ones((2,2)).tostype('row_sparse'))
out = mx.nd.ones((2,2)).tostype('row_sparse')
assert_exception(kv.pull, mx.MXNetError, 'invalid_key', out=out, ignore_sparse=False)
print('worker ' + str(my_rank) + ' passed check_invalid_pull')
check_invalid_gluon_trainer_reset()
check_invalid_pull()
示例2: run_metric
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def run_metric(name, data_gen_cls, i, n, c, pred_ctx, label_ctx, **kwargs):
""" Helper function for running one metric benchmark """
metric = mx.metric.create(name, **kwargs)
data_gen = data_gen_cls(n, c, pred_ctx, label_ctx)
try:
label, pred = data_gen.data()
mx.nd.waitall()
before = time.time()
metric.update([label] * i, [pred] * i)
mx.nd.waitall()
elapsed = time.time() - before
elapsed_str = "{:<.5}".format(elapsed)
except mx.MXNetError:
elapsed_str = "FAILED"
print("{metric:<15}{pctx:<10}{lctx:<12}{niter:<12}{bs:<15}{out_dim:<15}{elapsed:<}".format(
metric=name, pctx=str(pred_ctx), lctx=str(label_ctx), niter=i * n, bs=data_gen.batch_size,
out_dim=data_gen.output_dim, elapsed=elapsed_str), file=sys.stderr)
示例3: get_gpu_memory_usage
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def get_gpu_memory_usage(ctx: Union[mx.context.Context, List[mx.context.Context]]) -> Dict[int, Tuple[int, int]]:
"""
Returns used and total memory for GPUs identified by the given context list.
:param ctx: List of MXNet context devices.
:return: Dictionary of device id mapping to a tuple of (memory used, memory total).
"""
if isinstance(ctx, mx.context.Context):
ctx = [ctx]
ctx = [c for c in ctx if c.device_type == 'gpu']
if not ctx:
return {}
memory_data = {} # type: Dict[int, Tuple[int, int]]
for c in ctx:
try:
free, total = mx.context.gpu_memory_info(device_id=c.device_id) # in bytes
used = total - free
memory_data[c.device_id] = (used * 1e-06, total * 1e-06)
except mx.MXNetError:
logger.exception("Failed retrieving memory data for gpu%d", c.device_id)
continue
log_gpu_memory_usage(memory_data)
return memory_data
示例4: get_context
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def get_context() -> mx.context:
"""
Returns the a list of all available gpu contexts for a given machine.
If no gpus are available, returns [mx.cpu()].
Use it to automatically return MxNet contexts (uses max number of gpus or cpu)
:return: List of mxnet contexts of a gpu or [mx.cpu()] if gpu not available
"""
context_list = []
for gpu_number in range(16):
try:
_ = mx.nd.array([1, 2, 3], ctx=mx.gpu(gpu_number))
context_list.append(mx.gpu(gpu_number))
except mx.MXNetError:
pass
if len(context_list) == 0:
context_list.append(mx.cpu())
return context_list
示例5: is_cuda_available
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def is_cuda_available():
# TODO: Does MXNet have a convenient function to test GPU availability/compilation?
try:
a = nd.array([1, 2, 3], ctx=mx.gpu())
return True
except mx.MXNetError:
return False
示例6: get_num_gpus
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def get_num_gpus() -> int:
"""
Gets the number of GPUs available on the host.
:return: The number of GPUs on the system.
"""
try:
return mx.context.num_gpus()
except mx.MXNetError:
# Some builds of MXNet will raise a CUDA error when CUDA is not
# installed on the host. In this case, zero GPUs are available.
return 0
示例7: get_module
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def get_module(self, iterator, fixed_layer_parameters=None, random_layer_parameters=None):
"""
Return MXNet Module using the model symbol and parameters.
:param iterator: MXNet iterator to be used with model.
:type iterator: :class:`mxnet.io.DataIter`
:param list(str) fixed_layer_parameters: List of layer parameters to keep fixed.
:param list(str) random_layer_parameters: List of layer parameters to randomise.
:return: MXNet module
:rtype: :class:`mx.module.Module`
"""
if fixed_layer_parameters is not None:
fixed_layer_parameters = self._prune_parameters(fixed_layer_parameters)
if random_layer_parameters is None:
arg_params, aux_params = self.arg_params.copy(), self.aux_params.copy()
else:
arg_params, aux_params = self._remove_random_parameters(random_layer_parameters)
mod = mx.mod.Module(symbol=self.symbol, context=self.devices, fixed_param_names=fixed_layer_parameters,
label_names=(self.layer_names[-1] + "_label",), data_names=(self.data_name,))
mod.bind(data_shapes=iterator.provide_data, label_shapes=iterator.provide_label)
mod.init_params(mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2))
try:
mod.set_params(arg_params, aux_params, allow_missing=True, force_init=True)
except mx.MXNetError as e:
exceptions._handle_mxnet_error(e)
return mod
示例8: test_sample_multinomial
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def test_sample_multinomial():
for dtype in ['uint8', 'int32', 'float16', 'float32', 'float64']: # output array types
for x in [mx.nd.array([[0,1,2,3,4],[4,3,2,1,0]])/10.0, mx.nd.array([0,1,2,3,4])/10.0]:
dx = mx.nd.ones_like(x)
mx.contrib.autograd.mark_variables([x], [dx])
# Adding rtol and increasing samples needed to pass with seed 2951820647
samples = 10000
with mx.autograd.record():
y, prob = mx.nd.random.multinomial(x, shape=samples, get_prob=True, dtype=dtype)
r = prob * 5
r.backward()
assert(np.dtype(dtype) == y.dtype)
y = y.asnumpy()
x = x.asnumpy()
dx = dx.asnumpy()
if len(x.shape) is 1:
x = x.reshape((1, x.shape[0]))
dx = dx.reshape(1, dx.shape[0])
y = y.reshape((1, y.shape[0]))
prob = prob.reshape((1, prob.shape[0]))
for i in range(x.shape[0]):
freq = np.bincount(y[i,:].astype('int32'), minlength=5)/np.float32(samples)*x[i,:].sum()
mx.test_utils.assert_almost_equal(freq, x[i], rtol=0.20, atol=1e-1)
rprob = x[i][y[i].astype('int32')]/x[i].sum()
mx.test_utils.assert_almost_equal(np.log(rprob), prob.asnumpy()[i], atol=1e-5)
real_dx = np.zeros((5,))
for j in range(samples):
real_dx[int(y[i][j])] += 5.0 / rprob[j]
mx.test_utils.assert_almost_equal(real_dx, dx[i, :], rtol=1e-4, atol=1e-5)
for dtype in ['uint8', 'float16', 'float32']:
# Bound check for the output data types. 'int32' and 'float64' require large memory so are skipped.
x = mx.nd.zeros(2 ** 25) # Larger than the max integer in float32 without precision loss.
bound_check = False
try:
y = mx.nd.random.multinomial(x, dtype=dtype)
except mx.MXNetError as e:
bound_check = True
assert bound_check
# Test the generators with the chi-square testing
示例9: test_random_seed_setting_for_context
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def test_random_seed_setting_for_context():
seed_to_test = 1234
num_temp_seeds = 25
probs = [0.125, 0.25, 0.25, 0.0625, 0.125, 0.1875]
num_samples = 100000
dev_type = mx.context.current_context().device_type
for dtype in ['float16', 'float32', 'float64']:
samples_imp = []
samples_sym = []
# Collect random number samples from the generators of all devices, each seeded with the same number.
for dev_id in range(0, 16 if dev_type == 'gpu' else 1):
# Currently python API does not provide a method to get the number of gpu devices.
# Waiting for PR #10354, which provides the method, to be merged.
# As a temporal workaround, try first and catch the exception caused by the absence of the device with `dev_id`.
try:
with mx.Context(dev_type, dev_id):
ctx = mx.context.current_context()
seed = set_seed_variously_for_context(ctx, 1, num_temp_seeds, seed_to_test)
# Check imperative. `multinomial` uses non-parallel rng.
rnds = mx.nd.random.multinomial(data=mx.nd.array(probs, dtype=dtype), shape=num_samples)
samples_imp.append(rnds.asnumpy())
# Check symbolic. `multinomial` uses non-parallel rng.
P = mx.sym.Variable("P")
X = mx.sym.random.multinomial(data=P, shape=num_samples, get_prob=False)
exe = X.bind(ctx, {"P": mx.nd.array(probs, dtype=dtype)})
set_seed_variously_for_context(ctx, seed, num_temp_seeds, seed_to_test)
exe.forward()
samples_sym.append(exe.outputs[0].asnumpy())
except mx.MXNetError as e:
if str(e).find("invalid device ordinal") != -1:
break
else:
raise e
# The samples should be identical across different gpu devices.
for i in range(1, len(samples_imp)):
assert same(samples_imp[i - 1], samples_imp[i])
for i in range(1, len(samples_sym)):
assert same(samples_sym[i - 1], samples_sym[i])
# Tests that seed setting of parallel rng for specific context is synchronous w.r.t. rng use before and after.
示例10: test_parallel_random_seed_setting_for_context
# 需要导入模块: import mxnet [as 别名]
# 或者: from mxnet import MXNetError [as 别名]
def test_parallel_random_seed_setting_for_context():
seed_to_test = 1234
dev_type = mx.context.current_context().device_type
for dtype in ['float16', 'float32', 'float64']:
samples_imp = []
samples_sym = []
# Collect random number samples from the generators of all devices, each seeded with the same number.
for dev_id in range(0, 16 if dev_type == 'gpu' else 1):
# Currently python API does not provide a method to get the number of gpu devices.
# Waiting for PR #10354, which provides the method, to be merged.
# As a temporal workaround, try first and catch the exception caused by the absence of the device with `dev_id`.
try:
with mx.Context(dev_type, dev_id):
ctx = mx.context.current_context()
# Avoid excessive test cpu runtimes.
num_temp_seeds = 25 if dev_type == 'gpu' else 1
# To flush out a possible race condition, run multiple times.
for _ in range(20):
# Create enough samples such that we get a meaningful distribution.
shape = (200, 200)
params = { 'low': -1.5, 'high': 3.0 }
params.update(shape=shape, dtype=dtype)
# Check imperative. `uniform` uses parallel rng.
seed = set_seed_variously_for_context(ctx, 1, num_temp_seeds, seed_to_test)
rnds = mx.nd.random.uniform(**params)
samples_imp.append(rnds.asnumpy())
# Check symbolic. `uniform` uses parallel rng.
X = mx.sym.Variable("X")
Y = mx.sym.random.uniform(**params) + X
x = mx.nd.zeros(shape, dtype=dtype)
xgrad = mx.nd.zeros(shape, dtype=dtype)
yexec = Y.bind(ctx, {'X' : x}, {'X': xgrad})
set_seed_variously_for_context(ctx, seed, num_temp_seeds, seed_to_test)
yexec.forward(is_train=True)
yexec.backward(yexec.outputs[0])
samples_sym.append(yexec.outputs[0].asnumpy())
except mx.MXNetError as e:
if str(e).find("invalid device ordinal") != -1:
break
else:
raise e
# The samples should be identical across different gpu devices.
for i in range(1, len(samples_imp)):
assert same(samples_imp[i - 1], samples_imp[i])
for i in range(1, len(samples_sym)):
assert same(samples_sym[i - 1], samples_sym[i])