# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Model."""
from collections.abc import Iterable
import os
import math
import numpy as np
from mindspore import log as logger
from ..common.tensor import Tensor
from ..nn.metrics import get_metrics
from .._checkparam import check_input_data, check_output_data, Validator
from .callback import _InternalCallbackParam, RunContext, _CallbackManager, Callback
from .. import context
from ..parallel._utils import _get_parallel_mode, _get_device_num, _get_global_rank, \
_get_parameter_broadcast, _device_number_check, _parameter_broadcast_check, _parallel_predict_check, \
_check_task_sink_envs
from ..parallel._ps_context import _is_role_pserver, _is_role_sched
from ..nn.metrics import Loss
from .. import nn
from ..nn.wrap.cell_wrapper import _VirtualDatasetCell
from ..boost import AutoBoost
from ..context import ParallelMode
from ..parallel._cost_model_context import _set_multi_subgraphs
from .dataset_helper import DatasetHelper, connect_network_with_dataset
from . import amp
from ..common.api import _pynative_executor
def _transfer_tensor_to_tuple(inputs):
"""
If the input is a tensor, convert it to a tuple. If not, the output is unchanged.
"""
if isinstance(inputs, Tensor):
return (inputs,)
return inputs
class _StepSync(Callback):
@staticmethod
def step_end(run_context):
_pynative_executor.sync()
[docs]class Model:
"""
High-Level API for training or inference.
`Model` groups layers into an object with training and inference features.
Args:
network (Cell): A training or testing network.
loss_fn (Cell): Objective function, if loss_fn is None, the
network should contain the logic of loss and grads calculation,
and parallel if needed. Default: None.
optimizer (Cell): Optimizer for updating the weights. Default: None.
metrics (Union[dict, set]): A Dictionary or a set of metrics to be evaluated by the model during
training and inference. eg: {'accuracy', 'recall'}. Default: None.
eval_network (Cell): Network for evaluation. If not defined, `network` and `loss_fn` would be wrapped as
`eval_network` . Default: None.
eval_indexes (list): When defining the `eval_network`, if `eval_indexes` is None, all outputs of the
`eval_network` would be passed to metrics, otherwise `eval_indexes` must contain three
elements, including the positions of loss value, predicted value and label. The loss
value would be passed to the `Loss` metric, the predicted value and label would be passed
to other metric. Default: None.
amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network` , level for mixed
precision training. Supports ["O0", "O2", "O3", "auto"]. Default: "O0".
- O0: Do not change.
- O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale.
- O3: Cast network to float16, with additional property `keep_batchnorm_fp32=False` .
- auto: Set to level to recommended level in different devices. Set level to O2 on GPU, Set
level to O3 Ascend. The recommended level is choose by the export experience, cannot
always general. User should specify the level for special network.
O2 is recommended on GPU, O3 is recommended on Ascend.The more detailed explanation of `amp_level` setting
can be found at `mindspore.amp.build_train_network` .
boost_level (str): Option for argument `level` in `mindspore.boost` , level for boost mode
training. Supports ["O0", "O1", "O2"]. Default: "O0".
- O0: Do not change.
- O1: Enable the boost mode, the performance is improved by about 20%, and
the accuracy is the same as the original accuracy.
- O2: Enable the boost mode, the performance is improved by about 30%, and
the accuracy is reduced by less than 3%.
Examples:
>>> from mindspore import Model, nn
>>>
>>> class Net(nn.Cell):
... def __init__(self, num_class=10, num_channel=1):
... super(Net, self).__init__()
... self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
... self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
... self.fc1 = nn.Dense(16*5*5, 120, weight_init='ones')
... self.fc2 = nn.Dense(120, 84, weight_init='ones')
... self.fc3 = nn.Dense(84, num_class, weight_init='ones')
... self.relu = nn.ReLU()
... self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
... self.flatten = nn.Flatten()
...
... def construct(self, x):
... x = self.max_pool2d(self.relu(self.conv1(x)))
... x = self.max_pool2d(self.relu(self.conv2(x)))
... x = self.flatten(x)
... x = self.relu(self.fc1(x))
... x = self.relu(self.fc2(x))
... x = self.fc3(x)
... return x
>>>
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
>>> # For details about how to build the dataset, please refer to the tutorial
>>> # document on the official website.
>>> dataset = create_custom_dataset()
>>> model.train(2, dataset)
"""
def __init__(self, network, loss_fn=None, optimizer=None, metrics=None, eval_network=None,
eval_indexes=None, amp_level="O0", boost_level="O0", **kwargs):
self._network = network
self._loss_fn = loss_fn
self._optimizer = optimizer
self._loss_scale_manager = None
self._loss_scale_manager_set = False
self._keep_bn_fp32 = True
self._check_kwargs(kwargs)
self._amp_level = amp_level
self._boost_level = boost_level
self._eval_network = eval_network
self._process_amp_args(kwargs)
self._parallel_mode = _get_parallel_mode()
self._device_number = _get_device_num()
self._global_rank = _get_global_rank()
self._parameter_broadcast = _get_parameter_broadcast()
self._metrics = metrics
self._check_amp_level_arg(optimizer, amp_level)
self._check_for_graph_cell(kwargs)
self._build_boost_network(kwargs)
self._train_network = self._build_train_network()
self._build_eval_network(metrics, self._eval_network, eval_indexes)
self._build_predict_network()
def _check_for_graph_cell(self, kwargs):
"""Check for graph cell"""
if not isinstance(self._network, nn.GraphCell):
return
if self._amp_level != "O0":
logger.warning("amp_level will not work when network is a GraphCell.")
if self._loss_fn is not None or self._optimizer is not None:
raise ValueError("For 'Model', 'loss_fn' and 'optimizer' should be None when network is a GraphCell, "
"but got 'loss_fn': {}, 'optimizer': {}.".format(self._loss_fn, self._optimizer))
if kwargs:
raise ValueError("For 'Model', the '**kwargs' argument should be empty when network is a GraphCell.")
def _process_amp_args(self, kwargs):
if self._amp_level in ["O0", "O3"]:
self._keep_bn_fp32 = False
if 'keep_batchnorm_fp32' in kwargs:
self._keep_bn_fp32 = kwargs['keep_batchnorm_fp32']
if 'loss_scale_manager' in kwargs:
self._loss_scale_manager = kwargs['loss_scale_manager']
self._loss_scale_manager_set = True
def _check_amp_level_arg(self, optimizer, amp_level):
if optimizer is None and amp_level != "O0":
raise ValueError(
"Auto mixed precision will not work because 'optimizer' is None.Please set amp_level='O0' "
"to disable auto mixed precision or set 'optimizer' not be None to use auto mixed precision.")
def _check_kwargs(self, kwargs):
for arg in kwargs:
if arg not in ['loss_scale_manager', 'keep_batchnorm_fp32']:
raise ValueError(f"The argument in 'kwargs' should be 'loss_scale_manager' or "
f"'keep_batchnorm_fp32', but got '{arg}'.")
def _check_reuse_dataset(self, dataset):
if not hasattr(dataset, '__model_hash__'):
dataset.__model_hash__ = hash(self)
if hasattr(dataset, '__model_hash__') and dataset.__model_hash__ != hash(self):
raise RuntimeError('The Dataset cannot be bound to different models, please create a new dataset.')
def _build_boost_network(self, kwargs):
"""Build the boost network."""
processor = AutoBoost(self._boost_level, kwargs)
if processor.level not in ["O1", "O2"]:
return
if self._optimizer is None:
logger.warning("In boost mode, the optimizer must be defined.")
return
if self._eval_network is None and self._metrics is None:
logger.warning("In boost mode, the eval_network and metrics cannot be undefined at the same time.")
return
self._network, self._optimizer = processor.network_auto_process_train(self._network, self._optimizer)
if self._eval_network is not None:
self._eval_network = processor.network_auto_process_eval(self._eval_network)
def _build_train_network(self):
"""Build train network"""
network = self._network
if self._loss_scale_manager is not None and self._optimizer is None:
raise ValueError("The argument 'optimizer' can not be None when set 'loss_scale_manager'.")
if self._optimizer:
if self._loss_scale_manager_set:
network = amp.build_train_network(network,
self._optimizer,
self._loss_fn,
level=self._amp_level,
boost_level=self._boost_level,
loss_scale_manager=self._loss_scale_manager,
keep_batchnorm_fp32=self._keep_bn_fp32)
else:
network = amp.build_train_network(network,
self._optimizer,
self._loss_fn,
level=self._amp_level,
boost_level=self._boost_level,
keep_batchnorm_fp32=self._keep_bn_fp32)
elif self._loss_fn:
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
network = _VirtualDatasetCell(network)
network = nn.WithLossCell(network, self._loss_fn)
# If need to check if loss_fn is not None, but optimizer is None
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
network.set_auto_parallel()
if self._optimizer is None:
# In this case, multiple optimizer(s) is supposed to be included in 'self._network'
_set_multi_subgraphs()
return network
def _build_eval_network(self, metrics, eval_network, eval_indexes):
"""Build the network for evaluation."""
self._metric_fns = get_metrics(metrics)
if not self._metric_fns:
return
if eval_network is not None:
if eval_indexes is not None and not (isinstance(eval_indexes, list) and len(eval_indexes) == 3):
raise ValueError("The argument 'eval_indexes' must be a list or None. If 'eval_indexes' is a list, "
"length of it must be three. But got 'eval_indexes' {}".format(eval_indexes))
self._eval_network = eval_network
self._eval_indexes = eval_indexes
else:
if self._loss_fn is None:
raise ValueError(f"If `metrics` is set, `eval_network` must not be None. Do not set `metrics` if you"
f" don't want an evaluation.\n"
f"If evaluation is required, you need to specify `eval_network`, which will be used in"
f" the framework to evaluate the model.\n"
f"For the simple scenarios with one data, one label and one logits, `eval_network` is"
f" optional, and then you can set `eval_network` or `loss_fn`. For the latter case,"
f" framework will automatically build an evaluation network with `network` and"
f" `loss_fn`.")
self._eval_network = nn.WithEvalCell(self._network, self._loss_fn, self._amp_level in ["O2", "O3", "auto"])
self._eval_indexes = [0, 1, 2]
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
if self._optimizer:
self._eval_network = _VirtualDatasetCell(self._eval_network)
if self._optimizer is None:
# In this case, multiple optimizer(s) is supposed to be included in 'self._network'
_set_multi_subgraphs()
self._eval_network.set_auto_parallel()
def _build_predict_network(self):
"""Build the network for prediction."""
self._predict_network = self._network
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
self._predict_network = _VirtualDatasetCell(self._network)
# Unlike the cases in build_train_network() and build_eval_network(), 'multi_subgraphs' is not set
self._predict_network.set_auto_parallel()
def _clear_metrics(self):
"""Clear metrics local values."""
for metric in self._metric_fns.values():
metric.clear()
def _update_metrics(self, outputs):
"""Update metrics local values."""
if isinstance(outputs, Tensor):
outputs = (outputs,)
if not isinstance(outputs, tuple):
raise ValueError(f"The argument 'outputs' should be tuple, but got {type(outputs)}.")
if self._eval_indexes is not None and len(outputs) < 3:
raise ValueError("The length of 'outputs' must be >= 3, but got {}".format(len(outputs)))
for metric in self._metric_fns.values():
if self._eval_indexes is None:
metric.update(*outputs)
else:
if isinstance(metric, Loss):
metric.update(outputs[self._eval_indexes[0]])
else:
metric.update(outputs[self._eval_indexes[1]], outputs[self._eval_indexes[2]])
def _get_metrics(self):
"""Get metrics local values."""
metrics = dict()
for key, value in self._metric_fns.items():
metrics[key] = value.eval()
return metrics
def _get_scaling_sens(self):
"""get the scaling sens"""
scaling_sens = 1
if self._loss_scale_manager is not None:
scaling_sens = self._loss_scale_manager.get_loss_scale()
if self._parallel_mode == ParallelMode.DATA_PARALLEL:
scaling_sens /= self._device_number
return scaling_sens
def _exec_preprocess(self, is_train, dataset, dataset_sink_mode, sink_size=-1, epoch_num=1, dataset_helper=None):
"""Initializes dataset."""
if is_train:
network = self._train_network
phase = 'train'
else:
network = self._eval_network
phase = 'eval'
if dataset_sink_mode and not is_train:
dataset.__loop_size__ = 1
if dataset_helper is None:
dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)
if dataset_sink_mode:
network = connect_network_with_dataset(network, dataset_helper)
network.set_train(is_train)
network.phase = phase
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
network.set_auto_parallel()
return dataset_helper, network
def _warmup_dataset(self, epoch, train_dataset, sink_size=-1):
"""
Trigger dataset pipeline running before graph compiling.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If `train_dataset` is defined, training graphs will be
initialized. Default: None.
sink_size (int): Control the amount of data in each sink. Default: -1.
"""
if sink_size == -1:
epoch_num = epoch
else:
epoch_num = math.ceil(epoch * sink_size / train_dataset.get_dataset_size())
train_dataset.__total_batch__ = epoch * sink_size
dataset_helper = None
dataset_helper, _ = self._exec_preprocess(is_train=True,
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size,
epoch_num=epoch_num,
dataset_helper=dataset_helper)
train_dataset._dataset_helper = dataset_helper
train_dataset._warmup_epoch = epoch
def _init(self, train_dataset=None, valid_dataset=None, sink_size=-1, epoch=1):
"""
Initialize compute graphs and data graphs with the sink mode.
Note:
Pre-init process only supports `GRAPH_MODE` and `Ascend` target currently.
Args:
train_dataset (Dataset): A training dataset iterator. If `train_dataset` is defined, training graphs will be
initialized. Default: None.
valid_dataset (Dataset): A evaluating dataset iterator. If `valid_dataset` is defined, evaluation graphs
will be initialized, and `metrics` in `Model` can not be None. Default: None.
sink_size (int): Control the amount of data in each sink. Default: -1.
epoch (int): Total number of iterations on the data. Default: 1.
"""
if context.get_context("mode") != context.GRAPH_MODE or context.get_context("device_target") != "Ascend":
raise RuntimeError('Pre-init process only supports GRAPH MODE and Ascend target currently.')
if not train_dataset and not valid_dataset:
raise ValueError("The argument 'train_dataset' and 'valid_dataset' can not both be None or empty.")
_device_number_check(self._parallel_mode, self._device_number)
if train_dataset:
_parameter_broadcast_check(self._parallel_mode, self._parameter_broadcast)
if self._parameter_broadcast:
self._train_network.set_broadcast_flag()
train_dataset.__no_send__ = True
train_dataset_helper, train_network = self._exec_preprocess(is_train=True,
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size)
self._warmup_dataset(epoch, train_dataset, sink_size)
self._train_network = train_network
if context.get_auto_parallel_context("pipeline_stages") > 1 and valid_dataset:
self._train_network.add_flags_recursive(is_first_iteration=True)
for inputs in train_dataset_helper:
self._train_network.compile(*inputs)
break
if valid_dataset:
if not self._metric_fns:
raise RuntimeError("If define `valid_dataset`, metric fn can not be None or empty, "
"you should set the argument 'metrics' for model.")
valid_dataset.__no_send__ = True
valid_dataset_helper, eval_network = self._exec_preprocess(is_train=False,
dataset=valid_dataset,
dataset_sink_mode=True)
self._eval_network = eval_network
if context.get_auto_parallel_context("pipeline_stages") > 1:
self._eval_network.add_flags_recursive(is_first_iteration=False)
for inputs in valid_dataset_helper:
self._eval_network.compile(*inputs)
break
def _train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1):
"""
Training.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiple data (data1, data2, data3, ...) will be
returned and passed to the network. Otherwise, a tuple (data, label) will
be returned. The data and label would be passed to the network and loss
function respectively.
callbacks (list): List of callback objects which should be executed while training. Default: None.
dataset_sink_mode (bool): Determine whether the data should be passed through the dataset channel.
Default: True.
Configure pynative mode or CPU, the training process will be performed with
dataset not sink.
sink_size (int): Control the amount of data in each sink. Default: -1.
"""
epoch = Validator.check_positive_int(epoch)
if context.get_context("device_target") == "Ascend" and \
context.get_context("mode") == context.GRAPH_MODE and not \
_check_task_sink_envs() and \
dataset_sink_mode:
dataset_sink_mode = False
logger.warning("The Ascend cannot support dataset sink when performed with nontask sink mode."
"So the training process will be performed with dataset not sink.")
if self._parameter_broadcast:
self._train_network.set_broadcast_flag()
cb_params = _InternalCallbackParam()
cb_params.train_network = self._train_network
cb_params.epoch_num = epoch
if dataset_sink_mode and sink_size > 0:
cb_params.batch_num = sink_size
else:
cb_params.batch_num = train_dataset.get_dataset_size()
cb_params.mode = "train"
cb_params.loss_fn = self._loss_fn
cb_params.optimizer = self._optimizer
cb_params.parallel_mode = self._parallel_mode
cb_params.device_number = self._device_number
cb_params.train_dataset = train_dataset
cb_params.list_callback = self._transform_callbacks(callbacks)
if context.get_context("mode") == context.PYNATIVE_MODE:
cb_params.list_callback.insert(0, _StepSync())
callbacks = cb_params.list_callback
cb_params.train_dataset_element = None
cb_params.network = self._network
if _is_role_pserver() or _is_role_sched():
epoch = 1
# build callback list
with _CallbackManager(callbacks) as list_callback:
self._check_reuse_dataset(train_dataset)
if not dataset_sink_mode:
self._train_process(epoch, train_dataset, list_callback, cb_params)
elif context.get_context("device_target") == "CPU":
logger.warning("The CPU cannot support dataset sink mode currently."
"So the training process will be performed with dataset not sink.")
self._train_process(epoch, train_dataset, list_callback, cb_params)
else:
self._train_dataset_sink_process(epoch, train_dataset, list_callback, cb_params, sink_size)
@staticmethod
def _transform_callbacks(callbacks):
"""Transform callback to a list."""
if callbacks is None:
return []
if isinstance(callbacks, Iterable):
return list(callbacks)
return [callbacks]
def _train_dataset_sink_process(self, epoch, train_dataset, list_callback=None, cb_params=None, sink_size=-1):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiple data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned. The data and label would be passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
sink_size (int): Control the amount of data in each sink. Default: -1.
"""
is_graph = (context.get_context("mode") == context.GRAPH_MODE)
if sink_size == -1:
epoch_num = epoch
else:
epoch_num = math.ceil(epoch * sink_size / train_dataset.get_dataset_size())
train_dataset.__total_batch__ = epoch * sink_size
cb_params.cur_step_num = 0
cb_params.dataset_sink_mode = True
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
dataset_helper = None
if hasattr(train_dataset, '_dataset_helper'):
dataset_helper = train_dataset._dataset_helper
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
dataset_helper, train_network = self._exec_preprocess(is_train=True,
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size,
epoch_num=epoch_num,
dataset_helper=dataset_helper)
self._train_network = train_network
cb_params.train_network = self._train_network
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:
cb_params.train_dataset_element = inputs
list_callback.step_begin(run_context)
outputs = self._train_network(*inputs)
if is_graph:
cb_params.cur_step_num += dataset_helper.sink_size()
else:
cb_params.cur_step_num += 1
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
if _is_role_pserver():
os._exit(0)
dataset_helper.continue_send()
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
dataset_helper.stop_send()
dataset_helper.release()
list_callback.end(run_context)
def _train_process(self, epoch, train_dataset, list_callback=None, cb_params=None):
"""
Training process. The data would be passed to network directly.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiple data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned. The data and label would be passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
dataset_helper, _ = self._exec_preprocess(is_train=True,
dataset=train_dataset,
dataset_sink_mode=False,
epoch_num=epoch)
cb_params.cur_step_num = 0
cb_params.dataset_sink_mode = False
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
for next_element in dataset_helper:
len_element = len(next_element)
next_element = _transfer_tensor_to_tuple(next_element)
if self._loss_fn and len_element != 2:
raise ValueError("When 'loss_fn' is not None, 'train_dataset' should return "
"two elements, but got {}, please check the number of elements "
"returned by 'train_dataset'".format(len_element))
cb_params.cur_step_num += 1
cb_params.train_dataset_element = next_element
list_callback.step_begin(run_context)
outputs = self._train_network(*next_element)
cb_params.net_outputs = outputs
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update():
_, overflow, _ = outputs
overflow = np.all(overflow.asnumpy())
self._loss_scale_manager.update_loss_scale(overflow)
list_callback.step_end(run_context)
if _is_role_pserver():
os._exit(0)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
train_dataset.reset()
# if param is cache enable, flush data from cache to host before epoch end
self._flush_from_cache(cb_params)
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)
[docs] def train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1):
"""
Training API where the iteration is controlled by python front-end.
When setting pynative mode or CPU, the training process will be performed with dataset not sink.
Note:
If dataset_sink_mode is True, data will be sent to device. If the device is Ascend, features
of data will be transferred one by one. The limitation of data transmission per time is 256M.
When dataset_sink_mode is True, the step_end method of the Callback class will be executed when
the epoch_end method is called.
If sink_size > 0, each epoch of the dataset can be traversed unlimited times until you get sink_size
elements of the dataset. The next epoch continues to traverse from the end position of the previous
traversal. The interface builds the computational graphs and then executes the computational graphs.
However, when the 'model.build' is executed first, it only performs the graphs execution.
Args:
epoch (int): Generally, total number of iterations on the data per epoch.
When dataset_sink_mode is set to true and sink_size>0, each epoch sink sink_size
steps on the data instead of total number of iterations.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiple data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned. The data and label would be passed to the network and loss
function respectively.
callbacks (Optional[list[Callback], Callback]): List of callback objects or callback object,
which should be executed while training.
Default: None.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel.
Configure pynative mode or CPU, the training process will be performed with
dataset not sink. Default: True.
sink_size (int): Control the amount of data in each sink.
If sink_size = -1, sink the complete dataset for each epoch.
If sink_size > 0, sink sink_size data for each epoch.
If dataset_sink_mode is False, set sink_size as invalid.
Default: -1.
Examples:
>>> from mindspore import Model, nn, FixedLossScaleManager
>>>
>>> # For details about how to build the dataset, please refer to the tutorial
>>> # document on the official website.
>>> dataset = create_custom_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> loss_scale_manager = FixedLossScaleManager()
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager)
>>> model.train(2, dataset)
"""
dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
if isinstance(self._train_network, nn.GraphCell) and dataset_sink_mode is True:
raise ValueError("Dataset sink mode is currently not supported when training with a GraphCell.")
if hasattr(train_dataset, '_warmup_epoch') and train_dataset._warmup_epoch != epoch:
raise ValueError("Use Model.build to initialize model, but the value of parameter `epoch` in Model.build "
"is not equal to value in Model.train, got {} and {} separately."
.format(train_dataset._warmup_epoch, epoch))
Validator.check_is_int(sink_size)
dataset_size = train_dataset.get_dataset_size()
if dataset_size == 0:
raise ValueError("There is no valid data in dataset, please check dataset file firstly.")
if sink_size == -1:
sink_size = dataset_size
if sink_size < -1 or sink_size == 0:
raise ValueError("The argument 'sink_size' must be -1 or positive, but got {}.".format(sink_size))
_device_number_check(self._parallel_mode, self._device_number)
self._train(epoch,
train_dataset,
callbacks=callbacks,
dataset_sink_mode=dataset_sink_mode,
sink_size=sink_size)
[docs] def build(self, train_dataset=None, valid_dataset=None, sink_size=-1, epoch=1):
"""
Build computational graphs and data graphs with the sink mode.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
Note:
Pre-build process only supports `GRAPH_MODE` and `Ascend` target currently.
The interface builds the computational graphs, when the interface is executed first,
'model.train' only performs the graphs execution.
It only support dataset sink mode.
Args:
train_dataset (Dataset): A training dataset iterator. If `train_dataset` is defined, training graphs will be
initialized. Default: None.
valid_dataset (Dataset): An evaluating dataset iterator. If `valid_dataset` is defined, evaluation graphs
will be initialized, and `metrics` in `Model` can not be None. Default: None.
sink_size (int): Control the amount of data in each sink. Default: -1.
epoch (int): Control the training epochs. Default: 1.
Examples:
>>> from mindspore import Model, nn, FixedLossScaleManager
>>>
>>> # For details about how to build the dataset, please refer to the tutorial
>>> # document on the official website.
>>> dataset = create_custom_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> loss_scale_manager = FixedLossScaleManager()
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager)
>>> model.build(dataset, epoch=2)
>>> model.train(2, dataset)
"""
self._init(train_dataset, valid_dataset, sink_size, epoch)
def _eval_dataset_sink_process(self, valid_dataset, list_callback=None, cb_params=None):
"""
Evaluation. The data would be passed to network through dataset channel.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
Returns:
Dict, which returns the loss value and metrics values for the model in the test mode.
"""
run_context = RunContext(cb_params)
dataset_helper, eval_network = self._exec_preprocess(is_train=False,
dataset=valid_dataset,
dataset_sink_mode=True)
self._eval_network = eval_network
cb_params.eval_network = self._eval_network
cb_params.dataset_sink_mode = True
list_callback.begin(run_context)
list_callback.epoch_begin(run_context)
for inputs in dataset_helper:
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
outputs = self._eval_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
self._update_metrics(outputs)
list_callback.epoch_end(run_context)
metrics = self._get_metrics()
cb_params.metrics = metrics
list_callback.end(run_context)
return metrics
def _eval_process(self, valid_dataset, list_callback=None, cb_params=None):
"""
Evaluation. The data would be passed to network directly.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
Returns:
Dict, which returns the loss value and metrics values for the model in the test mode.
"""
run_context = RunContext(cb_params)
cb_params.dataset_sink_mode = False
list_callback.begin(run_context)
dataset_helper, _ = self._exec_preprocess(is_train=False,
dataset=valid_dataset,
dataset_sink_mode=False)
list_callback.epoch_begin(run_context)
for next_element in dataset_helper:
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
next_element = _transfer_tensor_to_tuple(next_element)
outputs = self._eval_network(*next_element)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
self._update_metrics(outputs)
list_callback.epoch_end(run_context)
valid_dataset.reset()
metrics = self._get_metrics()
cb_params.metrics = metrics
list_callback.end(run_context)
return metrics
[docs] def eval(self, valid_dataset, callbacks=None, dataset_sink_mode=True):
"""
Evaluation API where the iteration is controlled by python front-end.
Configure to pynative mode or CPU, the evaluating process will be performed with dataset non-sink mode.
Note:
If dataset_sink_mode is True, data will be sent to device. If the device is Ascend, features
of data will be transferred one by one. The limitation of data transmission per time is 256M.
When dataset_sink_mode is True, the step_end method of the Callback class will be executed when
the epoch_end method is called.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
callbacks (Optional[list(Callback)]): List of callback objects which should be executed
while training. Default: None.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel.
Default: True.
Returns:
Dict, the key is the metric name defined by users and the value is the metrics value for
the model in the test mode.
Examples:
>>> from mindspore import Model, nn
>>>
>>> # For details about how to build the dataset, please refer to the tutorial
>>> # document on the official website.
>>> dataset = create_custom_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'})
>>> acc = model.eval(dataset, dataset_sink_mode=False)
"""
dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
_device_number_check(self._parallel_mode, self._device_number)
if not self._metric_fns:
raise ValueError("The model argument 'metrics' can not be None or empty, "
"you should set the argument 'metrics' for model.")
if isinstance(self._eval_network, nn.GraphCell) and dataset_sink_mode is True:
raise ValueError("Sink mode is currently not supported when evaluating with a GraphCell.")
cb_params = _InternalCallbackParam()
cb_params.eval_network = self._eval_network
cb_params.valid_dataset = valid_dataset
cb_params.batch_num = valid_dataset.get_dataset_size()
cb_params.mode = "eval"
cb_params.cur_step_num = 0
cb_params.list_callback = self._transform_callbacks(callbacks)
cb_params.network = self._network
self._clear_metrics()
if context.get_context("device_target") == "CPU" and dataset_sink_mode:
dataset_sink_mode = False
logger.warning("CPU cannot support dataset sink mode currently."
"So the evaluating process will be performed with dataset non-sink mode.")
if context.get_context("device_target") == "Ascend" and \
context.get_context("mode") == context.GRAPH_MODE and not \
_check_task_sink_envs() and \
dataset_sink_mode:
dataset_sink_mode = False
logger.warning("The Ascend cannot support dataset sink when performed with nontask sink mode."
"So the training process will be performed with dataset not sink.")
with _CallbackManager(callbacks) as list_callback:
if dataset_sink_mode:
return self._eval_dataset_sink_process(valid_dataset, list_callback, cb_params)
return self._eval_process(valid_dataset, list_callback, cb_params)
[docs] def predict(self, *predict_data):
"""
Generate output predictions for the input samples.
Data could be a single tensor, a list of tensor, or a tuple of tensor.
Note:
This is a pre-compile function. The arguments should be the same with model.predict() function.
Args:
predict_data (Optional[Tensor, list[Tensor], tuple[Tensor]]): The predict data, can be a single tensor,
a list of tensor, or a tuple of tensor.
Returns:
Tensor, array(s) of predictions.
Examples:
>>> import mindspore as ms
>>> from mindspore import Model, Tensor
>>>
>>> input_data = Tensor(np.random.randint(0, 255, [1, 1, 32, 32]), ms.float32)
>>> model = Model(Net())
>>> result = model.predict(input_data)
"""
self._predict_network.set_train(False)
check_input_data(*predict_data, data_class=(int, float, str, None, Tensor))
_parallel_predict_check()
result = self._predict_network(*predict_data)
check_output_data(result)
return result
def _infer_train_check(self, train_dataset, dataset_sink_mode, sink_size):
"""
Check arguments of training.
Args:
train_dataset (Dataset): A training dataset iterator.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel.
sink_size (int): Control the amount of data in each sink.
"""
if context.get_context("mode") != context.GRAPH_MODE:
raise RuntimeError("Pre-compile process that generate parameter layout for the train network "
"only supports GRAPH MODE and Ascend target currently.")
if _get_parallel_mode() not in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
raise RuntimeError("'infer_train_layout' only supports 'semi_auto_parallel' and 'auto_parallel' "
"mode, but got {}.".format(_get_parallel_mode()))
dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
if not dataset_sink_mode:
raise ValueError("Only dataset sink mode is supported for now.")
if isinstance(self._train_network, nn.GraphCell) and dataset_sink_mode is True:
raise ValueError("Dataset sink mode is currently not supported when training with a GraphCell.")
Validator.check_is_int(sink_size)
dataset_size = train_dataset.get_dataset_size()
if dataset_size == 0:
raise ValueError("There is no valid data in dataset, please check dataset file firstly.")
if sink_size == -1:
sink_size = dataset_size
if sink_size < -1 or sink_size == 0:
raise ValueError("The 'sink_size' must be -1 or positive, but got sink_size {}.".format(sink_size))
[docs] def infer_train_layout(self, train_dataset, dataset_sink_mode=True, sink_size=-1):
"""
Generate parameter layout for the train network in auto or semi auto parallel mode.
Only dataset sink mode is supported for now.
.. warning::
This is an experimental prototype that is subject to change and/or deletion.
Note:
This is a pre-compile function. The arguments should be the same with model.train() function.
Args:
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiple data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned. The data and label would be passed to the network and loss
function respectively.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel.
Configure pynative mode or CPU, the training process will be performed with
dataset not sink. Default: True.
sink_size (int): Control the amount of data in each sink.
If sink_size = -1, sink the complete dataset for each epoch.
If sink_size > 0, sink sink_size data for each epoch.
If dataset_sink_mode is False, set sink_size as invalid.
Default: -1.
Returns:
Dict, Parameter layout dictionary used for load distributed checkpoint
Examples:
>>> # This example should be run with multiple devices. Refer to the tutorial > Distributed Training on
>>> # mindspore.cn.
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import Model, context, Tensor, nn, FixedLossScaleManager
>>> from mindspore.context import ParallelMode
>>> from mindspore.communication import init
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
>>>
>>> # For details about how to build the dataset, please refer to the tutorial
>>> # document on the official website.
>>> dataset = create_custom_dataset()
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> loss_scale_manager = FixedLossScaleManager()
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager)
>>> layout_dict = model.infer_train_layout(dataset)
"""
self._infer_train_check(train_dataset, dataset_sink_mode, sink_size)
train_dataset.__no_send__ = True
train_dataset_helper, train_network = self._exec_preprocess(is_train=True,
dataset=train_dataset,
dataset_sink_mode=dataset_sink_mode,
sink_size=sink_size)
self._train_network = train_network
for inputs in train_dataset_helper:
self._train_network.compile(*inputs)
break
train_dataset.__model_hash__ = hash(self)
return self._train_network.parameter_layout_dict
[docs] def infer_predict_layout(self, *predict_data):
"""
Generate parameter layout for the predict network in auto or semi auto parallel mode.
Data could be a single tensor or multiple tensors.
Note:
Batch data should be put together in one tensor.
Args:
predict_data (Tensor): One tensor or multiple tensors of predict data.
Returns:
Dict, Parameter layout dictionary used for load distributed checkpoint.
Raises:
RuntimeError: If get_context is not GRAPH_MODE.
Examples:
>>> # This example should be run with multiple devices. Refer to the tutorial > Distributed Training on
>>> # mindspore.cn.
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import Model, context, Tensor
>>> from mindspore.context import ParallelMode
>>> from mindspore.communication import init
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
>>> context.set_auto_parallel_context(full_batch=True, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
>>> input_data = Tensor(np.random.randint(0, 255, [1, 1, 32, 32]), ms.float32)
>>> model = Model(Net())
>>> predict_map = model.infer_predict_layout(input_data)
"""
if context.get_context("mode") != context.GRAPH_MODE:
raise RuntimeError("Pre-compile process that generate parameter layout for the predict network "
"only supports GRAPH MODE and Ascend target currently.")
if _get_parallel_mode() not in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
raise RuntimeError('Infer predict layout only supports semi auto parallel and auto parallel mode.')
_parallel_predict_check()
check_input_data(*predict_data, data_class=Tensor)
predict_net = self._predict_network
# Unlike the cases in build_train_network() and build_eval_network(), 'multi_subgraphs' is not set
predict_net.set_auto_parallel()
predict_net.set_train(False)
predict_net.compile(*predict_data)
return predict_net.parameter_layout_dict
def _flush_from_cache(self, cb_params):
"""Flush cache data to host if tensor is cache enable."""
params = cb_params.train_network.get_parameters()
for param in params:
if param.cache_enable:
Tensor(param).flush_from_cache()
@property
def train_network(self):
"""Get the model's train_network."""
return self._train_network
@property
def predict_network(self):
"""Get the model's predict_network."""
return self._predict_network
@property
def eval_network(self):
"""Get the model's eval_network."""
return self._eval_network
__all__ = ["Model"]