model.py 75.0 KB
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import inspect
import os
import pickle
import numpy as np
import six
import warnings
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import time
import socket
import contextlib
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from collections import Iterable

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import paddle
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from paddle import fluid
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from paddle.fluid import core
from paddle.fluid.framework import in_dygraph_mode, Variable, ParamBase, _current_expected_place
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from paddle.fluid.framework import in_dygraph_mode, Variable, _get_paddle_place
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from paddle.fluid.framework import _current_expected_place as _get_device
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from paddle.fluid.executor import global_scope
from paddle.fluid.io import is_belong_to_optimizer
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph.parallel import ParallelEnv
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from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator, FunctionSpec
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from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
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from paddle.fluid.layers.utils import flatten
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from paddle.fluid.layers import collective
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from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
from paddle.fluid.incubate.fleet.base import role_maker
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from paddle.io import DataLoader, Dataset, DistributedBatchSampler
from paddle.fluid.executor import scope_guard, Executor
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from paddle.fluid.dygraph.layers import Layer
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from paddle.metric import Metric
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from paddle.static import InputSpec as Input
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import paddle.distributed as dist
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from .callbacks import config_callbacks, EarlyStopping
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from .model_summary import summary
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__all__ = ['Model', ]

_parallel_context_initialized = False


def to_list(value):
    if value is None:
        return value
    if isinstance(value, (list, tuple)):
        return list(value)
    return [value]


def to_numpy(var):
    assert isinstance(var, (Variable, fluid.core.VarBase)), "not a variable"
    if isinstance(var, fluid.core.VarBase):
        return var.numpy()
    t = global_scope().find_var(var.name).get_tensor()
    return np.array(t)


def flatten_list(l):
    assert isinstance(l, list), "not a list"
    outl = []
    splits = []
    for sl in l:
        assert isinstance(sl, list), "sub content not a list"
        splits.append(len(sl))
        outl += sl
    return outl, splits


def restore_flatten_list(l, splits):
    outl = []
    for split in splits:
        assert len(l) >= split, "list length invalid"
        sl, l = l[:split], l[split:]
        outl.append(sl)
    return outl


def extract_args(func):
    if hasattr(inspect, 'getfullargspec'):
        return inspect.getfullargspec(func)[0]
    else:
        return inspect.getargspec(func)[0]


def _all_gather(x, nranks, ring_id=0, use_calc_stream=True):
    return collective._c_allgather(
        x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)


def wait_server_ready(endpoints):
    assert not isinstance(endpoints, six.string_types)
    while True:
        all_ok = True
        not_ready_endpoints = []
        for ep in endpoints:
            ip_port = ep.split(":")
            with contextlib.closing(
                    socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
                sock.settimeout(2)
                result = sock.connect_ex((ip_port[0], int(ip_port[1])))
                if result != 0:
                    all_ok = False
                    not_ready_endpoints.append(ep)
        if not all_ok:
            time.sleep(3)
        else:
            break


def init_communicator(program, rank, nranks, wait_port, current_endpoint,
                      endpoints):
    if nranks < 2:
        return
    other_endpoints = endpoints[:]
    other_endpoints.remove(current_endpoint)
    if rank == 0 and wait_port:
        wait_server_ready(other_endpoints)
    block = program.global_block()
    nccl_id_var = block.create_var(
        name=fluid.unique_name.generate('nccl_id'),
        persistable=True,
        type=fluid.core.VarDesc.VarType.RAW)

    block.append_op(
        type='c_gen_nccl_id',
        inputs={},
        outputs={'Out': nccl_id_var},
        attrs={
            'rank': rank,
            'endpoint': current_endpoint,
            'other_endpoints': other_endpoints
        })

    block.append_op(
        type='c_comm_init',
        inputs={'X': nccl_id_var},
        outputs={},
        attrs={
            'nranks': nranks,
            'rank': rank,
            'ring_id': 0,
        })


def prepare_distributed_context(place=None):
    if place is None:
        place = fluid.CUDAPlace(ParallelEnv().dev_id) if ParallelEnv().nranks > 1 \
            else fluid.CUDAPlace(0)

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    place = _get_paddle_place(place)
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    strategy = fluid.dygraph.parallel.ParallelStrategy()
    strategy.nranks = ParallelEnv().nranks
    strategy.local_rank = ParallelEnv().local_rank
    strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
    strategy.current_endpoint = ParallelEnv().current_endpoint

    if strategy.nranks < 2:
        return

    global _parallel_context_initialized

    if not _parallel_context_initialized and isinstance(place, fluid.CUDAPlace):

        def _init_context():
            communicator_prog = fluid.Program()
            init_communicator(communicator_prog, strategy.local_rank,
                              strategy.nranks, True, strategy.current_endpoint,
                              strategy.trainer_endpoints)
            exe = fluid.Executor(place)
            exe.run(communicator_prog)

        if fluid.in_dygraph_mode():
            fluid.disable_dygraph()
            _init_context()
            fluid.enable_dygraph(place)
        else:
            _init_context()

    else:
        assert ("Only support CUDAPlace for now.")

    _parallel_context_initialized = True
    return strategy
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def _update_input_info(inputs):
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    "Get input shape list by given inputs in Model initialization."
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    shapes = None
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    dtypes = None
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    if isinstance(inputs, Input):
        shapes = [list(inputs.shape)]
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        dtypes = [inputs.dtype]
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    elif isinstance(inputs, list):
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        shapes = [list(input.shape) for input in inputs]
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        dtypes = [input.dtype for input in inputs]
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    elif isinstance(inputs, dict):
        shapes = [list(inputs[name].shape) for name in inputs]
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        dtypes = [inputs[name].dtype for name in inputs]
    else:
        return None
    return shapes, dtypes
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class StaticGraphAdapter(object):
    """
    Model traning/inference with a static graph.
    """

    def __init__(self, model):
        super(StaticGraphAdapter, self).__init__()
        self.model = model
        # with `_build_once` gone, parameters are now created in `__init__`
        # so we need to keep track of the parameters already created
        self._startup_prog = fluid.default_startup_program()
        self._orig_prog = fluid.default_main_program()

        self._label_vars = {}  # label variables
        self._input_vars = {}  # label variables
        self._endpoints = {}
        self._loss_endpoint = None
        self._executor = None
        self._progs = {}
        self._compiled_progs = {}

        self._merge_count = {
            'eval_total': 0,
            'test_total': 0,
            'eval_batch': 0,
            'test_batch': 0
        }

        self._nranks = ParallelEnv().nranks
        self._local_rank = ParallelEnv().local_rank

    @property
    def mode(self):
        return self.model.mode

    @mode.setter
    def mode(self, value):
        self.model.mode = value

    def train_batch(self, inputs, labels=None):
        assert self.model._optimizer, \
            "model not ready, please call `model.prepare()` first"
        self.mode = 'train'
        return self._run(inputs, labels)

    def eval_batch(self, inputs, labels=None):
        self.mode = 'eval'
        return self._run(inputs, labels)

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    def predict_batch(self, inputs):
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        self.mode = 'test'
        return self._run(inputs, None)

    def parameters(self, *args, **kwargs):
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        return self.model.network.parameters(*args, **kwargs)
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    def save(self, path):
        def _save(state, path):
            if not state:
                return
            state = {
                k: to_numpy(v) if isinstance(v, Variable) else v
                for k, v in state.items()
            }
            with open(path, 'wb') as f:
                pickle.dump(state, f)

        base = os.path.basename(path)
        assert base != "", "path should be of 'dirname/filename' format"
        dir_name = os.path.dirname(path)
        if dir_name and not os.path.exists(dir_name):
            os.makedirs(dir_name)
        param_path = path + ".pdparams"
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        _save(self.model.network.state_dict(), param_path)
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        prog = self._progs.get('train', None)
        if prog is None or self.model._optimizer is None:
            return
        # XXX `optimizer.state_dict()` only work in dygraph mode
        optim_path = path + ".pdopt"
        optim = {
            p.name: p
            for p in filter(is_belong_to_optimizer, prog.list_vars())
        }
        if not optim:
            return

        _save(optim, optim_path)

    def load(self, param_state_pairs, optim_state):
        if self._executor is None:
            executor = fluid.Executor(fluid.CPUPlace())._default_executor
        else:
            executor = self._executor._default_executor

        # restore parameter states
        fluid.core._create_loaded_parameter(
            [param for param, state in param_state_pairs],
            global_scope(), executor)
        for param, state in param_state_pairs:
            self._set_var(param, state)

        # restore optimizer states
        # FIXME what if a different optimizer is used?
        if not self.model._optimizer or not optim_state:
            return
        self._load_optimizer(optim_state, executor)

    def _load_optimizer(self, state, executor):
        prog = self._progs.get('train', None)
        optim = list(filter(is_belong_to_optimizer, prog.list_vars()))
        if not optim:
            return

        fluid.core._create_loaded_parameter(optim, global_scope(), executor)

        converted_state = dict(state)
        for var in optim:
            if var.name in ["@LR_DECAY_COUNTER@", "global_step"]:
                # When using learning rate scheduler, dygraph would name the
                # global step var as "global_step" to save, while static-graph
                # would has a state var named as "@LR_DECAY_COUNTER@".
                # NOTE: dygraph saved global_step is 1 larger than that in
                # static-graph, since the time of global_step to increase is
                # different.
                state_val = (
                    np.array(converted_state.pop("global_step")) - 1
                ) if "global_step" in converted_state else converted_state.pop(
                    "@LR_DECAY_COUNTER@", None)
                if state_val is not None:
                    converted_state[var.name] = state_val
            elif var.name.startswith("learning_rate_"):
                # When using static learning rate, static-graph would make it
                # a persistable var named 'unique_name.generate("learning_rate")',
                # However, dygraph wouldn't save it.
                if var.name not in state:
                    continue
            else:
                # moment and other accumulators
                if var.name not in converted_state:
                    # try to convert from dygraph name
                    opt_name = self.model._optimizer._name
                    opt_cls_name = self.model._optimizer.__class__.__name__
                    opt_unq_name = None
                    for name in self.model._optimizer._accumulators.keys():
                        accum_name = name if opt_name is None else name[len(
                            opt_name) + 1:]
                        for param_name, state_var in self.model._optimizer._accumulators[
                                name].items():
                            if opt_unq_name is None:
                                # can not infer out the exact unique(opt_name),
                                # thus try to extract rather than generate
                                for state_key in sorted(
                                        state.keys(),
                                        key=lambda x: len(x),
                                        reverse=True):
                                    prefix = param_name + "_" + (
                                        opt_cls_name
                                        if opt_name is None else opt_name) + "_"
                                    if state_key.startswith(prefix):
                                        prefix_offset = state_key[len(
                                            prefix):].find("_") + len(prefix)
                                        opt_unq_name = state_key[len(
                                            param_name + "_"):prefix_offset]
                                        # TODO: assert
                                        # assert opt_unq_name is None
                                    # gen(param.name + "_" + gen(opt_name) + "_" + accum_name)
                                    # always end with "_0" since the unique optimizer._name
                            dy_state_name = (param_name + "_" + opt_unq_name +
                                             "_" + accum_name + "_0")
                            converted_state[
                                state_var.name] = converted_state.pop(
                                    dy_state_name)

            assert var.name in converted_state, \
                "variable [{}] is not in optimizer state file".format(var.name)
            self._set_var(var, converted_state[var.name])

    def _set_var(self, var, ndarray):
        t = global_scope().find_var(var.name).get_tensor()
        p = t._place()
        if p.is_cpu_place():
            place = fluid.CPUPlace()
        elif p.is_cuda_pinned_place():
            place = fluid.CUDAPinnedPlace()
        else:
            p = fluid.core.Place()
            p.set_place(t._place())
            place = fluid.CUDAPlace(p.gpu_device_id())

        t.set(ndarray, place)

    def _run(self, inputs, labels=None):
        compiled_prog = self._compiled_progs.get(self.mode, None)
        assert compiled_prog, \
            "Model is not ready, please call `model.prepare()` first"

        inputs = to_list(inputs)
        if labels is not None:
            labels = to_list(labels)
        assert len(inputs) == len(self._input_vars[self.mode]), \
            "number of inputs" \
            + " does not match number of arguments of `forward` method"

        feed = {}
        input_names = [v.name for v in self._input_vars[self.mode]]
        for idx, n in enumerate(input_names):
            # train and test may take different arguments
            if inputs[idx] is not None:
                feed[n] = inputs[idx]
        if labels is not None:
            for idx, v in enumerate(self._label_vars[self.mode]):
                feed[v.name] = labels[idx]

        endpoints = self._endpoints[self.mode]
        if self.mode == 'test':
            fetch_list = endpoints['output']
        else:
            metric_list, metric_splits = flatten_list(endpoints['metric'])
            fetch_list = endpoints['loss'] + metric_list
            num_loss = len(endpoints['loss'])

        # if fetch Variable is same as input Variable, do not fetch
        # from program, get it from input directly
        pruned_fetch_list = []
        pruned_fetch_idx_name_map = [""] * len(fetch_list)
        for i, fetch_var in enumerate(fetch_list):
            if fetch_var.name in feed.keys():
                pruned_fetch_idx_name_map[i] = fetch_var.name
            else:
                pruned_fetch_list.append(fetch_var)

        rets = self._executor.run(compiled_prog,
                                  feed=feed,
                                  fetch_list=pruned_fetch_list,
                                  return_numpy=False)

        # restore pruned fetch_list Variable from feeds
        for i, name in enumerate(pruned_fetch_idx_name_map):
            if len(name) > 0:
                rets.insert(i, feed[name])

        # LoDTensor cannot be fetch as numpy directly
        rets = [np.array(v) for v in rets]
        if self.mode == 'test':
            return rets[:]
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        metric_states = restore_flatten_list(rets[num_loss:], metric_splits)
        metrics = []
        for metric, state in zip(self.model._metrics, metric_states):
            # cut off padding size
            if self.mode != 'train' and self.model._test_dataloader is not None \
                    and isinstance(self.model._test_dataloader, DataLoader) \
                    and self._nranks > 1:
                total_size = len(self.model._test_dataloader.dataset)
                # TODO: fixme if have better way to get batch size
                samples = state[0].shape[0]
                current_count = self._merge_count.get(self.mode + '_total', 0)
                if current_count + samples >= total_size:
                    state = [
                        s[:int(total_size - current_count), ...] for s in state
                    ]
                    self._merge_count[self.mode + '_total'] = 0
                    self._merge_count[self.mode + '_batch'] = int(total_size -
                                                                  current_count)
                else:
                    self._merge_count[self.mode + '_total'] += samples
                    self._merge_count[self.mode + '_batch'] = samples

            metrics.append(metric.update(*state))
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        if num_loss and len(metrics):
            return rets[:num_loss], metrics
        else:
            return rets[:num_loss] if num_loss else metrics
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    def prepare(self):
        modes = ['train', 'eval', 'test']
        for mode in modes:
            self._make_program(mode)
            self._compile_and_initialize(self._progs[mode], mode)

    def _make_program(self, mode):
        prog = self._progs.get(mode, None)
        if prog is not None:
            return

        prog = self._orig_prog.clone()
        # NOTE: When defining learning rate scheduling in static-graph, ops to
        # increase the global step var and calculate learning rate would be
        # prepended into _orig_prog. test program maked by `_orig_prog.clone`
        # also would include these ops. Thus must prune these ops in test
        # program, otherwise the global step would be changed in test.
        if mode != 'train':
            for op in list(prog.global_block().ops):
                prog.global_block()._remove_op(0)
        if mode == 'train' and self.model._optimizer \
                and self.model._optimizer._learning_rate_map:
            # HACK workaround learning rate map issue
            lr_var = self.model._optimizer._learning_rate_map[self._orig_prog]
            new_lr_var = prog.global_block().vars[lr_var.name]
            self.model._optimizer._learning_rate_map[prog] = new_lr_var

        losses = []
        metrics = []
        with fluid.program_guard(prog, self._startup_prog):
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            inputs = self.model._inputs
            labels = self.model._labels if self.model._labels else []
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            inputs = [k._create_feed_layer() for k in to_list(inputs)]
            labels = [k._create_feed_layer() for k in to_list(labels)]
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            self._label_vars[mode] = labels
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            outputs = to_list(self.model.network.forward(*inputs))
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            if mode != 'test' and self.model._loss:
                losses = self.model._loss(*(outputs + labels))
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            if self._nranks > 1 and mode != 'train':
                outputs = [_all_gather(o, self._nranks) for o in outputs]
                if mode != 'test':
                    labels = [_all_gather(l, self._nranks) for l in labels]

            if mode != 'test':
                for metric in self.model._metrics:
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                    metrics.append(to_list(metric.compute(*(outputs + labels))))
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            if mode == 'train' and self.model._optimizer:
                self._loss_endpoint = fluid.layers.sum(losses)
                if self._nranks > 1:
                    role = role_maker.PaddleCloudRoleMaker(is_collective=True)
                    fleet.init(role)
                    dist_strategy = DistributedStrategy()
                    dist_strategy.mode = "collective"
                    dist_strategy.collective_mode = "grad_allreduce"
                    self.model._optimizer = fleet.distributed_optimizer(
                        self.model._optimizer, strategy=dist_strategy)

                self.model._optimizer.minimize(self._loss_endpoint)

        if mode != 'train':  # clone again to put it in test mode
            prog = prog.clone(for_test=True)

        self._input_vars[mode] = inputs

        self._progs[mode] = prog
        self._endpoints[mode] = {
            "output": outputs,
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            "loss": to_list(losses),
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            "metric": metrics
        }

    def _compile_and_initialize(self, prog, mode):
        compiled_prog = self._compiled_progs.get(mode, None)
        if compiled_prog is not None:
            return compiled_prog

        assert self.model._place is not None, \
            "device is not set, please call `model.prepare()` first"

        place = self.model._place

        # XXX *ALL WEIGHTS* should be initialized upon model construction
        # even if `forward()` may run different code path for different mode
        # therefore startup program only needs to run once
        if self._executor is None:
            self._executor = fluid.Executor(place)
            # XXX incremental initialization
            uninitialized = []
            for var_py in self._startup_prog.list_vars():
                var = fluid.global_scope().find_var(var_py.name)
                if not var_py.name.startswith('nccl_id') and var and \
                        var.get_tensor()._is_initialized():
                    continue

                uninitialized.append(var_py)
            if uninitialized:
                startup_prog = self._startup_prog._prune(uninitialized)
                self._executor.run(startup_prog)

        if self._nranks < 2:
            compiled_prog = fluid.CompiledProgram(prog)
        else:
            compiled_prog = prog

        self._compiled_progs[mode] = compiled_prog


class DynamicGraphAdapter(object):
    def __init__(self, model):
        super(DynamicGraphAdapter, self).__init__()
        self.model = model
        self._nranks = ParallelEnv().nranks
        self._local_rank = ParallelEnv().local_rank
        self._merge_count = {
            'eval_total': 0,
            'test_total': 0,
            'eval_batch': 0,
            'test_batch': 0
        }

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        self._input_info = None
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        if self._nranks > 1:
            stradegy = fluid.dygraph.parallel.ParallelStrategy()
            stradegy.nranks = ParallelEnv().nranks
            stradegy.local_rank = ParallelEnv().local_rank
            stradegy.trainer_endpoints = ParallelEnv().trainer_endpoints
            stradegy.current_endpoint = ParallelEnv().current_endpoint
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            self.ddp_model = fluid.dygraph.parallel.DataParallel(
                self.model.network, stradegy)
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    @property
    def mode(self):
        return self.model.mode

    @mode.setter
    def mode(self, value):
        self.model.mode = value

    # TODO multi device in dygraph mode not implemented at present time
    def train_batch(self, inputs, labels=None):
        assert self.model._optimizer, \
            "model not ready, please call `model.prepare()` first"
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        self.model.network.train()
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        self.mode = 'train'
        inputs = to_list(inputs)
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        self._input_info = _update_input_info(inputs)
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        labels = labels or []
        labels = [to_variable(l) for l in to_list(labels)]

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        if self._nranks > 1:
            outputs = self.ddp_model.forward(* [to_variable(x) for x in inputs])
        else:
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            outputs = self.model.network.forward(
                * [to_variable(x) for x in inputs])
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        losses = self.model._loss(*(to_list(outputs) + labels))
        losses = to_list(losses)
        final_loss = fluid.layers.sum(losses)
        final_loss.backward()
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        self.model._optimizer.minimize(final_loss)
663
        self.model.network.clear_gradients()
664

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        metrics = []
        for metric in self.model._metrics:
667
            metric_outs = metric.compute(*(to_list(outputs) + labels))
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            m = metric.update(* [to_numpy(m) for m in to_list(metric_outs)])
            metrics.append(m)

        return ([to_numpy(l) for l in losses], metrics) \
            if len(metrics) > 0 else [to_numpy(l) for l in losses]

    def eval_batch(self, inputs, labels=None):
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        self.model.network.eval()
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        self.mode = 'eval'
        inputs = to_list(inputs)
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        self._input_info = _update_input_info(inputs)
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        labels = labels or []
        labels = [to_variable(l) for l in to_list(labels)]

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        outputs = self.model.network.forward(* [to_variable(x) for x in inputs])
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        if self.model._loss:
            losses = self.model._loss(*(to_list(outputs) + labels))
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            losses = to_list(losses)

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        if self._nranks > 1:
            outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
            labels = [_all_gather(l, self._nranks) for l in labels]
        metrics = []
        for metric in self.model._metrics:
            # cut off padding value.
            if self.model._test_dataloader is not None and self._nranks > 1 \
                    and isinstance(self.model._test_dataloader, DataLoader):
                total_size = len(self.model._test_dataloader.dataset)
                samples = outputs[0].shape[0]
                current_count = self._merge_count.get(self.mode + '_total', 0)
                if current_count + samples >= total_size:
                    outputs = [
                        o[:int(total_size - current_count)] for o in outputs
                    ]
                    labels = [
                        l[:int(total_size - current_count)] for l in labels
                    ]
                    self._merge_count[self.mode + '_total'] = 0
                    self._merge_count[self.mode + '_batch'] = int(total_size -
                                                                  current_count)
                else:
                    self._merge_count[self.mode + '_total'] += samples
                    self._merge_count[self.mode + '_batch'] = samples

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            metric_outs = metric.compute(*(to_list(outputs) + labels))
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            m = metric.update(* [to_numpy(m) for m in to_list(metric_outs)])
            metrics.append(m)

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        if self.model._loss and len(metrics):
717
            return [to_numpy(l) for l in losses], metrics
718
        elif self.model._loss:
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            return [to_numpy(l) for l in losses]
        else:
            return metrics
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    def predict_batch(self, inputs):
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        self.model.network.eval()
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        self.mode = 'test'
        inputs = [to_variable(x) for x in to_list(inputs)]
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        self._input_info = _update_input_info(inputs)
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        outputs = self.model.network.forward(*inputs)
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        if self._nranks > 1 and isinstance(self.model._place, fluid.CUDAPlace):
            outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]

        return [to_numpy(o) for o in to_list(outputs)]

    def parameters(self, *args, **kwargs):
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        return self.model.network.parameters(*args, **kwargs)
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    def save(self, path):
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        params = self.model.network.state_dict()
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        fluid.save_dygraph(params, path)
        if self.model._optimizer is None:
            return
        if self.model._optimizer.state_dict():
            optim = self.model._optimizer.state_dict()
            fluid.save_dygraph(optim, path)

    def load(self, param_state_pairs, optim_state):
        # restore parameter states
        for param, state in param_state_pairs:
            param.set_value(state)

        # resotre optimizer states
        if not self.model._optimizer or not optim_state:
            return

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        # If optimizer performs set_state_dict when state vars haven't been created,
        # which would happen when set_state_dict before minimize, the state would be
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        # stored in optimizer._accumulators_holder and loaded lazily.
        # To contrive this when loading from static-graph saved states, extend
        # state dict to include keys named accoring to dygraph naming rules.
        # TODO: if len(self.model._optimizer._accumulators) > 0
        converted_state = dict(optim_state)
        opt_unq_name = self.model._optimizer._name
        if opt_unq_name is None:
            opt_unq_name = ''

        opt_cls_name = self.model._optimizer.__class__.__name__
        opt_name = opt_unq_name[:opt_unq_name.rfind("_")]  # remove suffix idx
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        param_names = [param.name for param in self.model.network.parameters()]
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        for var_name, state_var in sorted(
                optim_state.items(), key=lambda x: len(x[0]), reverse=True):
            if var_name in ["@LR_DECAY_COUNTER@", "global_step"]:
                # NOTE: dygraph saved global_step is 1 larger than that in
                # static-graph, since the time of global_step to increase is
                # different.
                if var_name == "@LR_DECAY_COUNTER@":
                    converted_state["global_step"] = np.array(
                        converted_state.pop("@LR_DECAY_COUNTER@")) + 1
            else:
                # moment and other accumulators
                # extend state dict to include promising dygraph names
                for param_name in param_names:
                    if var_name.startswith(param_name + "_" + opt_name):
                        # when init optimizer with name
                        accum_name = var_name[len(param_name + "_" + opt_name +
                                                  "_"):]
                    elif var_name.startswith(param_name +
                                             "_") and opt_name == opt_cls_name:
                        # when init optimizer without name
                        accum_name = var_name[len(param_name + "_"):]
                    else:
                        continue
                    # remove suffix idx
                    accum_name = accum_name[:accum_name.rfind("_")]
                    # state names always end with "_0" in dygraph because of the
                    # unique optimizer._name
                    dy_state_name = (param_name + "_" + opt_unq_name + "_" +
                                     accum_name + "_0")
                    converted_state[dy_state_name] = state_var

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        if not hasattr(self.model._optimizer, 'set_state_dict'):
            warnings.warn(
802
                "paddle.fluid.optimizer is deprecated in API 2.0, please use paddle.optimizer instead."
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            )
            self.model._optimizer.set_dict(converted_state)
        else:
            self.model._optimizer.set_state_dict(converted_state)
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809
class Model(object):
810 811 812
    """
    An Model object is network with training and inference features.
    Dynamic graph and static graph are supported at the same time,
813
    switched by `paddle.enable_static()`. The usage is as follows.
814
    But note, the switching between dynamic and static should be before
815
    instantiating a Model. The input description, i.e, paddle.static.InputSpec,
816
    must be required for static graph.
817

818
    Args:
819 820
        network (paddle.nn.Layer): The network is an instance of
            paddle.nn.Layer.
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        inputs (InputSpec|list|dict|None): `inputs`, entry points of network,
            could be a InputSpec instance, or lits of InputSpec instances,
823 824
            or dict ({name: InputSpec}), and it couldn't be None in static
            graph.
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        labels (InputSpec|list|None): `labels`, entry points of network,
            could be a InputSpec instnace or lits of InputSpec instances,
            or None. For static graph, if labels is required in loss,
828 829 830
            labels must be set. Otherwise, it could be None.


831
    Examples:
832 833
        .. code-block:: python

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          import paddle
          import paddle.nn as nn
          import paddle.vision.transforms as T
          from paddle.static import InputSpec
  
          device = paddle.set_device('cpu') # or 'gpu'
  
          net = nn.Sequential(
              nn.Flatten(1),
              nn.Linear(784, 200),
              nn.Tanh(),
              nn.Linear(200, 10))
  
          # inputs and labels are not required for dynamic graph.
          input = InputSpec([None, 784], 'float32', 'x')
          label = InputSpec([None, 1], 'int64', 'label')
          
          model = paddle.Model(net, input, label)
          optim = paddle.optimizer.SGD(learning_rate=1e-3,
              parameters=model.parameters())
          model.prepare(optim,
                        paddle.nn.CrossEntropyLoss(),
                        paddle.metric.Accuracy())
          
          transform = T.Compose([
              T.Transpose(),
              T.Normalize([127.5], [127.5])
          ])
          data = paddle.vision.datasets.MNIST(mode='train', transform=transform)
          model.fit(data, epochs=2, batch_size=32, verbose=1)
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    """

866
    def __init__(self, network, inputs=None, labels=None):
867
        self.mode = 'train'
868
        self.network = network
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        self._inputs = None
        self._labels = None
871
        self._loss = None
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        self._loss_weights = None
        self._optimizer = None
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        self._input_info = None
875
        self._is_shape_inferred = False
876
        self._test_dataloader = None
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        self.stop_training = False
878

879 880 881 882 883
        if not in_dygraph_mode():
            if not isinstance(inputs, (list, dict, Input)):
                raise TypeError(
                    "'inputs' must be list or dict, and couldn't be None.")
        elif inputs:
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            self._input_info = _update_input_info(inputs)
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886
        self._inputs = self._verify_spec(inputs, is_input=True)
887
        self._labels = self._verify_spec(labels)
888

889 890
        # init backend
        if fluid.in_dygraph_mode():
891
            dist.init_parallel_env()
892 893 894 895 896 897 898 899 900
            self._adapter = DynamicGraphAdapter(self)
        else:
            self._adapter = StaticGraphAdapter(self)

    def train_batch(self, inputs, labels=None):
        """
        Run one training step on a batch of data.

        Args:
901 902 903 904 905 906 907
            inputs (numpy.ndarray|Tensor|list): Batch of input data. It could 
                be a numpy array or paddle.Tensor, or a list of arrays or 
                tensors (in case the model has multiple inputs).
            labels (numpy.ndarray|Tensor|list): Batch of labels. It could be 
                a numpy array or paddle.Tensor, or a list of arrays or tensors 
                (in case the model has multiple labels). If has no labels, 
                set None. Default is None.
908 909 910 911 912 913 914 915 916 917 918

        Returns:
            A list of scalar training loss if the model has no metrics,
            or a tuple (list of scalar loss, list of metrics) if the model
            set metrics.

        Examples:

            .. code-block:: python
            
              import numpy as np
919
              import paddle
920 921
              import paddle.nn as nn
              from paddle.static import InputSpec
922

923
              device = paddle.set_device('cpu') # or 'gpu'
924

925 926 927 928 929 930 931 932
              net = nn.Sequential(
                  nn.Linear(784, 200),
                  nn.Tanh(),
                  nn.Linear(200, 10))

              input = InputSpec([None, 784], 'float32', 'x')
              label = InputSpec([None, 1], 'int64', 'label')
              model = paddle.Model(net, input, label)
933
              optim = paddle.optimizer.SGD(learning_rate=1e-3,
934
                  parameters=model.parameters())
935
              model.prepare(optim, paddle.nn.CrossEntropyLoss())
936 937 938 939 940
              data = np.random.random(size=(4,784)).astype(np.float32)
              label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
              loss = model.train_batch([data], [label])
              print(loss)
        """
941
        loss = self._adapter.train_batch(inputs, labels)
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        if fluid.in_dygraph_mode() and self._input_info is None:
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            self._update_inputs()
944
        return loss
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    def eval_batch(self, inputs, labels=None):
        """
        Run one evaluating step on a batch of data.

        Args:
951 952 953 954 955 956 957
            inputs (numpy.ndarray|Tensor|list): Batch of input data. It could 
                be a numpy array or paddle.Tensor, or a list of arrays or 
                tensors (in case the model has multiple inputs).
            labels (numpy.ndarray|Tensor|list): Batch of labels. It could be 
                a numpy array or paddle.Tensor, or a list of arrays or tensors 
                (in case the model has multiple labels). If has no labels, 
                set None. Default is None.
958 959 960 961 962 963 964 965 966 967 968

        Returns:
            A list of scalar testing loss if the model has no metrics,
            or a tuple (list of scalar loss, list of metrics) if the model
            set metrics.

        Examples:

            .. code-block:: python
            
              import numpy as np
969
              import paddle
970 971
              import paddle.nn as nn
              from paddle.static import InputSpec
972

973
              device = paddle.set_device('cpu') # or 'gpu'
974

975 976 977 978 979 980 981 982
              net = nn.Sequential(
                  nn.Linear(784, 200),
                  nn.Tanh(),
                  nn.Linear(200, 10))

              input = InputSpec([None, 784], 'float32', 'x')
              label = InputSpec([None, 1], 'int64', 'label')
              model = paddle.Model(net, input, label)
983
              optim = paddle.optimizer.SGD(learning_rate=1e-3,
984
                  parameters=model.parameters())
985
              model.prepare(optim,
986
                            paddle.nn.CrossEntropyLoss())
987 988 989 990 991
              data = np.random.random(size=(4,784)).astype(np.float32)
              label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
              loss = model.eval_batch([data], [label])
              print(loss)
        """
992
        loss = self._adapter.eval_batch(inputs, labels)
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        if fluid.in_dygraph_mode() and self._input_info is None:
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            self._update_inputs()
995
        return loss
996

997
    def predict_batch(self, inputs):
998
        """
999
        Run one predicting step on a batch of data.
1000 1001

        Args:
1002 1003 1004
            inputs (numpy.ndarray|Tensor|list): Batch of input data. It could 
                be a numpy array or paddle.Tensor, or a list of arrays or 
                tensors (in case the model has multiple inputs).
1005 1006 1007 1008 1009 1010 1011 1012 1013 1014

        Returns:
            A list of numpy.ndarray of predictions, that is the outputs
            of Model forward.

        Examples:

            .. code-block:: python
            
              import numpy as np
1015
              import paddle
1016
              import paddle.nn as nn
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              from paddle.static import InputSpec
1018

1019
              device = paddle.set_device('cpu') # or 'gpu'
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              input = InputSpec([None, 784], 'float32', 'x')
              label = InputSpec([None, 1], 'int64', 'label')
1023

1024 1025 1026 1027 1028 1029
              net = nn.Sequential(
                  nn.Linear(784, 200),
                  nn.Tanh(),
                  nn.Linear(200, 10),
                  nn.Softmax())

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              model = paddle.Model(net, input, label)
1031
              model.prepare()
1032
              data = np.random.random(size=(4,784)).astype(np.float32)
1033
              out = model.predict_batch([data])
1034 1035
              print(out)
        """
1036
        loss = self._adapter.predict_batch(inputs)
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        if fluid.in_dygraph_mode() and self._input_info is None:
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            self._update_inputs()
1039
        return loss
1040

1041 1042 1043 1044 1045
    def save(self, path, training=True):
        """  
        This function saves parameters, optimizer information or model and 
        paramters only for inference to path. It depends on the parameter
        `training`.
1046

1047 1048
        If `training` is set to True, the parameters saved contain all 
        the trainable Variable, will save to a file with suffix ".pdparams".
1049 1050 1051 1052
        The optimizer information contains all the variable used by optimizer.
        For Adam optimizer, contains beta1, beta2, momentum etc. All the
        information will save to a file with suffix ".pdopt". (If the optimizer
        have no variable need to save (like SGD), the fill will not generated).
1053
        This function will silently overwrite existing file at the target location.
1054

1055
        If `training` is set to False, only inference model will be saved.
1056 1057

        Args:
1058 1059 1060
            path (str): The file prefix to save model. The format
                is 'dirname/file_prefix' or 'file_prefix'. if empty str.
                A exception will be raised.
1061 1062
            training (bool, optional): Whether to save for training. If not, save
                for inference only. Default: True.
1063 1064 1065 1066 1067 1068 1069

        Returns:
            None

        Examples:

            .. code-block:: python
1070

1071
                import paddle
1072
                import paddle.nn as nn
1073
                import paddle.vision.transforms as T
1074
                from paddle.static import InputSpec
1075

1076
                class Mnist(nn.Layer):
1077
                    def __init__(self):
1078
                        super(Mnist, self).__init__()
1079
                        self.net = nn.Sequential(
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                            nn.Flatten(1),
1081 1082 1083 1084
                            nn.Linear(784, 200),
                            nn.Tanh(),
                            nn.Linear(200, 10),
                            nn.Softmax())
1085

1086
                    def forward(self, x):
1087
                        return self.net(x)
1088

1089
                dynamic = True  # False
1090
                # if use static graph, do not set
1091 1092
                if not dynamic:
                    paddle.enable_static()
1093

1094 1095 1096
                input = InputSpec([None, 784], 'float32', 'x')
                label = InputSpec([None, 1], 'int64', 'label')
                model = paddle.Model(Mnist(), input, label)
1097
                optim = paddle.optimizer.SGD(learning_rate=1e-3,
1098
                    parameters=model.parameters())
1099
                model.prepare(optim, paddle.nn.CrossEntropyLoss())
1100 1101 1102 1103 1104 1105 1106
                
                transform = T.Compose([
                    T.Transpose(),
                    T.Normalize([127.5], [127.5])
                ])
                data = paddle.vision.datasets.MNIST(mode='train', transform=transform)
                
1107
                model.fit(data, epochs=1, batch_size=32, verbose=0)
1108 1109
                model.save('checkpoint/test')  # save for training
                model.save('inference_model', False)  # save for inference
1110
        """
1111

1112
        if ParallelEnv().local_rank == 0:
1113 1114 1115 1116
            if not training:
                self._save_inference_model(path)
            else:
                self._adapter.save(path)
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    def load(self, path, skip_mismatch=False, reset_optimizer=False):
        """
        Load from files storing the model states and optimizer states. The file
        for optimizer states is not necessary if no need to restore the optimizer.

        NOTE: parameters are retrieved out from the file storing model states
        accoring to their structured names.

        For fine-tuning or transfer-learning models where some of the layers have
        changed, keep parameters needed to restore have same structured names in
        the pre-trained model and fine-tuning model.

        Args:
            path (str): The prefix of files storing the model states and
                optimizer states. The files would be `path.pdparams` and
                `path.pdopt` separately, and the latter is not necessary
                when no need to restore.
            skip_mismatch (bool): Whether to skip the loading of mismatch
                parameter or raise an error when mismatch happens (not found
                the parameter in file storing model states of or receives a
                mismatch shape).
            reset_optimizer (bool): If True, ignore the providing file storing
                optimizer states and initialize optimizer states from scratch.
                Otherwise, restore optimizer states from `path.pdopt` if
                a optimizer has been set to the model. Default False.

        Returns:
            None

        Examples:

            .. code-block:: python
            
1151
              import paddle
1152
              import paddle.nn as nn
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              from paddle.static import InputSpec

1155
              device = paddle.set_device('cpu')
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              input = InputSpec([None, 784], 'float32', 'x')
1158 1159 1160 1161 1162

              model = paddle.Model(nn.Sequential(
                  nn.Linear(784, 200),
                  nn.Tanh(),
                  nn.Linear(200, 10),
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                  nn.Softmax()), input)

1165
              model.save('checkpoint/test')
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              model.load('checkpoint/test')
        """

        def _load_state_from_path(path):
            if not os.path.exists(path):
                return
            with open(path, 'rb') as f:
                return pickle.load(f) if six.PY2 else pickle.load(
                    f, encoding='latin1')

        def _check_match(key, param):
            state = param_state.get(key, None)
            if state is None:
                raise ValueError(
                    "{} is not found in the providing file.".format(key))
            if list(state.shape) != list(param.shape):
                raise ValueError(
                    "{} receives a shape {}, but the expected shape is {}.".
                    format(key, list(state.shape), list(param.shape)))
            return param, state

        def _strip_postfix(path):
            path, ext = os.path.splitext(path)
            assert ext in ['', '.pdparams', '.pdopt', '.pdmodel'], \
                    "Unknown postfix {} from weights".format(ext)
            return path

        path = _strip_postfix(path)
        param_state = _load_state_from_path(path + ".pdparams")
        assert param_state, "Failed to load parameters, please check path."

        matched_param_state = []
1198
        for key, param in self.network.state_dict().items():
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            try:
                match_res = _check_match(key, param)
            except ValueError as err:
                if skip_mismatch:
                    warnings.warn(
                        ("Skip loading for {}. ".format(key) + str(err)))
                    # reset optimizer when mismatch happens
                    reset_optimizer = True
                else:
                    raise err
            matched_param_state.append(match_res)

        optim_state = None if reset_optimizer else _load_state_from_path(
            path + ".pdopt")
        return self._adapter.load(matched_param_state, optim_state)

    def parameters(self, *args, **kwargs):
        """
        Returns a list of parameters of the model.

        Returns:
            A list of Parameter in static graph.
            A list of ParamBase in dynamic graph.

        Examples:

            .. code-block:: python

1227
              import paddle
1228
              import paddle.nn as nn
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              from paddle.static import InputSpec
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              input = InputSpec([None, 784], 'float32', 'x')
              
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              model = paddle.Model(nn.Sequential(
                  nn.Linear(784, 200),
                  nn.Tanh(),
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                  nn.Linear(200, 10)), input)

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              params = model.parameters()
        """
        return self._adapter.parameters()

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    def prepare(self, optimizer=None, loss=None, metrics=None):
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        """
        Configures the model before runing.

        Args:
            optimizer (Optimizer|None): Optimizer must be set in training
                and should be a Optimizer instance. It can be None in eval
                and test mode.
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            loss (Loss|callable function|None): Loss function can
                be a `paddle.nn.Layer` instance or any callable function
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                taken the predicted values and ground truth values as input.
                It can be None when there is no loss.
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            metrics (Metric|list of Metric|None): If metrics is set, all
                metrics will be calculated and output in train/eval mode.

        Returns:
            None
        """

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        self._place = _get_device()
        if isinstance(self._place, fluid.CUDAPlace):
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            global _parallel_context_initialized
            if ParallelEnv().nranks > 1 and not _parallel_context_initialized:
                if fluid.in_dygraph_mode():
                    main_prog_seed = fluid.default_main_program().random_seed
                    startup_prog_seed = fluid.default_startup_program(
                    ).random_seed
                    fluid.disable_dygraph()
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                    paddle.disable_static(self._place)
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                    # enable_dygraph would create and switch to a new program,
                    # thus also copy seed to the new program
                    fluid.default_main_program().random_seed = main_prog_seed
                    fluid.default_startup_program(
                    ).random_seed = startup_prog_seed
                else:
                    prepare_distributed_context(self._place)
                _parallel_context_initialized = True

        self._optimizer = optimizer
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        if loss is not None:
            if not isinstance(loss, paddle.nn.Layer) and not callable(loss):
                raise TypeError("'loss' must be sub classes of " \
                    "`paddle.nn.Layer` or any callable function.")
        self._loss = loss
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        metrics = metrics or []
        for metric in to_list(metrics):
            assert isinstance(metric, Metric), \
                "{} is not sub class of Metric".format(
                    metric.__class__.__name__)
        self._metrics = to_list(metrics)

        if not in_dygraph_mode():
            self._adapter.prepare()

    def fit(
            self,
            train_data=None,
            eval_data=None,
            batch_size=1,
            epochs=1,
            eval_freq=1,
            log_freq=10,
            save_dir=None,
            save_freq=1,
            verbose=2,
            drop_last=False,
            shuffle=True,
            num_workers=0,
            callbacks=None, ):
        """
        Trains the model for a fixed number of epochs. If `eval_data` is set,
        evaluation will be done at the end of each epoch.

        Args:
            train_data (Dataset|DataLoader): An iterable data loader is used for 
                train. An instance of paddle paddle.io.Dataset or 
                paddle.io.Dataloader is recomended. Default: None.
            eval_data (Dataset|DataLoader): An iterable data loader is used for
                evaluation at the end of epoch. If None, will not do evaluation. 
                An instance of paddle.io.Dataset or paddle.io.Dataloader 
                is recomended. Default: None.
            batch_size (int): Integer number. The batch size of train_data
                and eval_data. When train_data and eval_data are both the
                instance of Dataloader, this parameter will be ignored.
                Default: 1.
            epochs (int): Integer number. The number of epochs to train
                the model. Default: 1.
            eval_freq (int): The frequency, in number of epochs, an evalutation
                is performed. Default: 1.
            log_freq (int): The frequency, in number of steps, the training logs
                are printed. Default: 10.
            save_dir(str|None): The directory to save checkpoint during training.
                If None, will not save checkpoint. Default: None.
            save_freq (int): The frequency, in number of epochs, to save
                checkpoint. Default: 1.
            verbose (int): The verbosity mode, should be 0, 1, or 2. 0 = silent,
                1 = progress bar, 2 = one line per epoch. Default: 2.
            drop_last (bool): Whether drop the last incomplete batch of
                train_data when dataset size is not divisible by the batch size.
                When train_data is an instance of Dataloader, this parameter
                will be ignored. Default: False.
            shuffle (bool): Whther to shuffle train_data. When train_data is
                an instance of Dataloader, this parameter will be ignored.
                Default: True.
            num_workers (int): The number of subprocess to load data, 0 for no
                subprocess used and loading data in main process.
                When train_data and eval_data are both the instance of
                Dataloader, this parameter will be ignored. Default: 0.
            callbacks (Callback|None): A list of `Callback` instances to apply
                during training. If None, `ProgBarLogger` and `ModelCheckpoint`
                are automatically inserted. Default: None.

        Returns:
            None

        Examples:
            1. An example use Dataset and set btch size, shuffle in fit.
               How to make a batch is done internally.

            .. code-block:: python

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              import paddle
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              import paddle.vision.transforms as T
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              from paddle.vision.datasets import MNIST
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              from paddle.static import InputSpec
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              dynamic = True
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              if not dynamic:
                  paddle.enable_static()

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              transform = T.Compose([
                  T.Transpose(),
                  T.Normalize([127.5], [127.5])
              ])
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              train_dataset = MNIST(mode='train', transform=transform)
              val_dataset = MNIST(mode='test', transform=transform)
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              input = InputSpec([None, 1, 28, 28], 'float32', 'image')
              label = InputSpec([None, 1], 'int64', 'label')
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              model = paddle.Model(
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                  paddle.vision.models.LeNet(),
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                  input, label)
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              optim = paddle.optimizer.Adam(
                  learning_rate=0.001, parameters=model.parameters())
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              model.prepare(
                  optim,
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                  paddle.nn.CrossEntropyLoss(),
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                  paddle.metric.Accuracy(topk=(1, 2)))
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              model.fit(train_dataset,
                        val_dataset,
                        epochs=2,
                        batch_size=64,
                        save_dir='mnist_checkpoint')

            2. An example use DataLoader, batch size and shuffle is set in
               DataLoader.

            .. code-block:: python

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              import paddle
1404
              import paddle.vision.transforms as T
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              from paddle.vision.datasets import MNIST
1406
              from paddle.static import InputSpec
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              dynamic = True
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              if not dynamic:
                  paddle.enable_static()
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              transform = T.Compose([
                    T.Transpose(),
                    T.Normalize([127.5], [127.5])
                ])
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              train_dataset = MNIST(mode='train', transform=transform)
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              train_loader = paddle.io.DataLoader(train_dataset,
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                  batch_size=64)
              val_dataset = MNIST(mode='test', transform=transform)
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              val_loader = paddle.io.DataLoader(val_dataset,
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                  batch_size=64)
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              input = InputSpec([None, 1, 28, 28], 'float32', 'image')
              label = InputSpec([None, 1], 'int64', 'label')
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              model = paddle.Model(
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                  paddle.vision.models.LeNet(), input, label)
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              optim = paddle.optimizer.Adam(
                  learning_rate=0.001, parameters=model.parameters())
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              model.prepare(
                  optim,
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                  paddle.nn.CrossEntropyLoss(),
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                  paddle.metric.Accuracy(topk=(1, 2)))
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              model.fit(train_loader,
                        val_loader,
                        epochs=2,
                        save_dir='mnist_checkpoint')
        """

        assert train_data is not None, \
                "train_data must be given!"

        if isinstance(train_data, Dataset):
            train_sampler = DistributedBatchSampler(
                train_data,
                batch_size=batch_size,
                shuffle=shuffle,
                drop_last=drop_last)
            train_loader = DataLoader(
                train_data,
                batch_sampler=train_sampler,
                places=self._place,
                num_workers=num_workers,
                return_list=True)
        else:
            train_loader = train_data

        if eval_data is not None and isinstance(eval_data, Dataset):
            eval_sampler = DistributedBatchSampler(
                eval_data, batch_size=batch_size)
            eval_loader = DataLoader(
                eval_data,
                batch_sampler=eval_sampler,
                places=self._place,
                num_workers=num_workers,
                return_list=True)
        elif eval_data is not None:
            eval_loader = eval_data
        else:
            eval_loader = None

        do_eval = eval_loader is not None
        self._test_dataloader = eval_loader

        steps = self._len_data_loader(train_loader)
        cbks = config_callbacks(
            callbacks,
            model=self,
            epochs=epochs,
            steps=steps,
            log_freq=log_freq,
            save_freq=save_freq,
            save_dir=save_dir,
            verbose=verbose,
            metrics=self._metrics_name(), )

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        if any(isinstance(k, EarlyStopping) for k in cbks) and not do_eval:
            warnings.warn("EarlyStopping needs validation data.")

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        cbks.on_begin('train')
        for epoch in range(epochs):
            cbks.on_epoch_begin(epoch)
            logs = self._run_one_epoch(train_loader, cbks, 'train')
            cbks.on_epoch_end(epoch, logs)

            if do_eval and epoch % eval_freq == 0:

                eval_steps = self._len_data_loader(eval_loader)
                cbks.on_begin('eval', {
                    'steps': eval_steps,
                    'metrics': self._metrics_name()
                })

                eval_logs = self._run_one_epoch(eval_loader, cbks, 'eval')

                cbks.on_end('eval', eval_logs)
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                if self.stop_training:
                    break
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        cbks.on_end('train', logs)
        self._test_dataloader = None

    def evaluate(
            self,
            eval_data,
            batch_size=1,
            log_freq=10,
            verbose=2,
            num_workers=0,
            callbacks=None, ):
        """
        Evaluate the loss and metrics of the model on input dataset.

        Args:
            eval_data (Dataset|DataLoader): An iterable data loader is used for
                evaluation. An instance of paddle.io.Dataset or 
                paddle.io.Dataloader is recomended.
            batch_size (int): Integer number. The batch size of train_data
                and eval_data.  When eval_data is the instance of Dataloader,
                this argument will be ignored. Default: 1.
            log_freq (int): The frequency, in number of steps, the eval logs
                are printed. Default: 10.
            verbose (int): The verbosity mode, should be 0, 1, or 2. 0 = silent,
                1 = progress bar, 2 = one line per epoch. Default: 2.
            num_workers (int): The number of subprocess to load data,
                0 for no subprocess used and loading data in main process. When
                train_data and eval_data are both the instance of Dataloader,
                this parameter will be ignored. Default: 0.
            callbacks (Callback|None): A list of `Callback` instances to apply
                during training. If None, `ProgBarLogger` and `ModelCheckpoint`
                are automatically inserted. Default: None.
        Returns:
            dict: Result of metric. The key is the names of Metric,
                value is a scalar or numpy.array.

        Examples:
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          .. code-block:: python
1549

1550
            import paddle
1551
            import paddle.vision.transforms as T
1552
            from paddle.static import InputSpec
1553

1554
            # declarative mode
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            transform = T.Compose([
                    T.Transpose(),
                    T.Normalize([127.5], [127.5])
                ])
            val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
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            input = InputSpec([-1, 1, 28, 28], 'float32', 'image')
            label = InputSpec([None, 1], 'int64', 'label')
            model = paddle.Model(paddle.vision.models.LeNet(), input, label)
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            model.prepare(metrics=paddle.metric.Accuracy())
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            result = model.evaluate(val_dataset, batch_size=64)
            print(result)
        """

        if eval_data is not None and isinstance(eval_data, Dataset):
            eval_sampler = DistributedBatchSampler(
                eval_data, batch_size=batch_size)
            eval_loader = DataLoader(
                eval_data,
                batch_sampler=eval_sampler,
                places=self._place,
                num_workers=num_workers,
                return_list=True)
        else:
            eval_loader = eval_data

        self._test_dataloader = eval_loader

        cbks = config_callbacks(
            callbacks,
            model=self,
            log_freq=log_freq,
            verbose=verbose,
            metrics=self._metrics_name(), )

        eval_steps = self._len_data_loader(eval_loader)
        cbks.on_begin('eval',
                      {'steps': eval_steps,
                       'metrics': self._metrics_name()})

        logs = self._run_one_epoch(eval_loader, cbks, 'eval')

        cbks.on_end('eval', logs)

        self._test_dataloader = None

        eval_result = {}
        for k in self._metrics_name():
            eval_result[k] = logs[k]

        return eval_result

    def predict(self,
                test_data,
                batch_size=1,
                num_workers=0,
                stack_outputs=False,
                callbacks=None):
        """
        Compute the output predictions on testing data.

        Args:
            test_data (Dataset|DataLoader): An iterable data loader is used for
                predict. An instance of paddle.io.Dataset or paddle.io.Dataloader
                is recomended.
            batch_size (int): Integer number. The batch size of train_data and eval_data.
                When train_data and eval_data are both the instance of Dataloader, this
                argument will be ignored. Default: 1.
            num_workers (int): The number of subprocess to load data, 0 for no subprocess 
                used and loading data in main process. When train_data and eval_data are
                both the instance of Dataloader, this argument will be ignored. Default: 0.
1626
            stack_outputs (bool): Whether stack output field like a batch, as for an output
1627 1628 1629 1630 1631
                filed of a sample is in shape [X, Y], test_data contains N samples, predict
                output field will be in shape [N, X, Y] if stack_output is True, and will
                be a length N list in shape [[X, Y], [X, Y], ....[X, Y]] if stack_outputs
                is False. stack_outputs as False is used for LoDTensor output situation,
                it is recommended set as True if outputs contains no LoDTensor. Default: False.
1632
            callbacks(Callback): A Callback instance, default None.
1633 1634 1635 1636
        Returns:
            list: output of models.

        Examples:
1637 1638

          .. code-block:: python
1639 1640

            import numpy as np
1641
            import paddle
1642
            from paddle.static import InputSpec
1643

1644
            class MnistDataset(paddle.vision.datasets.MNIST):
1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659
                def __init__(self, mode, return_label=True):
                    super(MnistDataset, self).__init__(mode=mode)
                    self.return_label = return_label

                def __getitem__(self, idx):
                    img = np.reshape(self.images[idx], [1, 28, 28])
                    if self.return_label:
                        return img, np.array(self.labels[idx]).astype('int64')
                    return img,

                def __len__(self):
                    return len(self.images)

            test_dataset = MnistDataset(mode='test', return_label=False)

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            # imperative mode
1661 1662
            input = InputSpec([-1, 1, 28, 28], 'float32', 'image')
            model = paddle.Model(paddle.vision.models.LeNet(), input)
1663
            model.prepare()
1664
            result = model.predict(test_dataset, batch_size=64)
1665
            print(len(result[0]), result[0][0].shape)
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            # declarative mode
1668
            device = paddle.set_device('cpu')
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            paddle.enable_static()
            input = InputSpec([-1, 1, 28, 28], 'float32', 'image')
            model = paddle.Model(paddle.vision.models.LeNet(), input)
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            model.prepare()
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            result = model.predict(test_dataset, batch_size=64)
            print(len(result[0]), result[0][0].shape)
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        """

        if test_data is not None and isinstance(test_data, Dataset):
            test_sampler = DistributedBatchSampler(
                test_data, batch_size=batch_size)
            test_loader = DataLoader(
                test_data,
                batch_sampler=test_sampler,
                places=self._place,
                num_workers=num_workers,
                return_list=True)
        else:
            test_loader = test_data

        self._test_dataloader = test_loader

        cbks = config_callbacks(callbacks, model=self, verbose=1)

        test_steps = self._len_data_loader(test_loader)
        logs = {'steps': test_steps}

1697
        cbks.on_begin('predict', logs)
1698 1699 1700

        outputs = []

1701
        logs, outputs = self._run_one_epoch(test_loader, cbks, 'predict')
1702 1703 1704 1705 1706 1707 1708 1709 1710 1711

        outputs = list(zip(*outputs))

        # NOTE: for lod tensor output, we should not stack outputs
        # for stacking may lose its detail info
        if stack_outputs:
            outputs = [np.vstack(outs) for outs in outputs]

        self._test_dataloader = None

1712
        cbks.on_end('predict', logs)
1713 1714
        return outputs

1715
    def _save_inference_model(self, path):
1716
        """
1717
        Save inference model can be used in static or dynamic mode.
1718 1719

        Args:
1720 1721
            path (str): The path prefix to save model. The format is
                ``dirname/file_prefix`` or ``file_prefix``.
1722
        Returns:
1723
            None
1724 1725
        """

1726
        if fluid.in_dygraph_mode():
1727 1728
            with fluid.framework._dygraph_guard(None):
                layer = self.network
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                if self._input_info is None:  # No provided or inferred
1730
                    raise RuntimeError(
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                        "Saving inference model needs 'inputs' or running before saving. Please specify 'inputs' in Model initialization or input training data and perform a training for shape derivation."
1732 1733 1734 1735
                    )
                if self._is_shape_inferred:
                    warnings.warn(
                        "'inputs' was not specified when Model initialization, so the input shape to be saved will be the shape derived from the user's actual inputs. The input shape to be saved is %s. For saving correct input shapes, please provide 'inputs' for Model initialization."
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                        % self._input_info[0])

1738
                paddle.jit.save(layer, path, input_spec=self._inputs)
1739

1740
        else:
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            # path check
            file_prefix = os.path.basename(path)
            if file_prefix == "":
                raise ValueError(
                    "The input path MUST be format of dirname/file_prefix "
                    "[dirname\\file_prefix in Windows system], but received "
                    "file_prefix is empty string.")

            dirname = os.path.dirname(path)
            if dirname and not os.path.exists(dirname):
                os.makedirs(dirname)

            model_path = dirname
            model_filename = file_prefix + INFER_MODEL_SUFFIX
            params_filename = file_prefix + INFER_PARAMS_SUFFIX

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            prog = self._adapter._progs.get('test', None)
            assert prog, \
                "Model is not ready, please call `model.prepare()` first"

            infer_prog = prog.clone(for_test=True)

            input_names = [v.name for v in self._adapter._input_vars['test']]
            endpoints = self._adapter._endpoints['test']['output']

1766 1767
            fluid.io.save_inference_model(
                model_path,
1768 1769 1770 1771 1772
                input_names,
                endpoints,
                self._adapter._executor,
                main_program=infer_prog,
                model_filename=model_filename,
1773
                params_filename=params_filename)
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    def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
        outputs = []
        for step, data in enumerate(data_loader):
            # data might come from different types of data_loader and have
            # different format, as following:
            # 1. DataLoader in static graph:
            #    [[input1, input2, ..., label1, lable2, ...]]
            # 2. DataLoader in dygraph
            #    [input1, input2, ..., label1, lable2, ...]
            # 3. custumed iterator yield concated inputs and labels:
            #   [input1, input2, ..., label1, lable2, ...]
            # 4. custumed iterator yield seperated inputs and labels:
            #   ([input1, input2, ...], [label1, lable2, ...])
            # To handle all of these, flatten (nested) list to list.
            data = flatten(data)
            # LoDTensor.shape is callable, where LoDTensor comes from
            # DataLoader in static graph
1792

1793 1794 1795 1796 1797
            batch_size = data[0].shape()[0] if callable(data[
                0].shape) else data[0].shape[0]

            callbacks.on_batch_begin(mode, step, logs)

1798
            if mode != 'predict':
1799 1800
                outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
                                                      data[len(self._inputs):])
1801
                if self._metrics and self._loss:
1802
                    metrics = [[l[0] for l in outs[0]]]
1803
                elif self._loss:
1804 1805 1806
                    metrics = [[l[0] for l in outs]]
                else:
                    metrics = []
1807 1808 1809 1810 1811 1812 1813 1814 1815 1816

                # metrics
                for metric in self._metrics:
                    res = metric.accumulate()
                    metrics.extend(to_list(res))

                assert len(self._metrics_name()) == len(metrics)
                for k, v in zip(self._metrics_name(), metrics):
                    logs[k] = v
            else:
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                if self._inputs is not None:
1818
                    outs = self.predict_batch(data[:len(self._inputs)])
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                else:
1820
                    outs = self.predict_batch(data)
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                outputs.append(outs)

            logs['step'] = step
            if mode == 'train' or self._adapter._merge_count.get(
                    mode + '_batch', 0) <= 0:
                logs['batch_size'] = batch_size * ParallelEnv().nranks
            else:
                logs['batch_size'] = self._adapter._merge_count[mode + '_batch']

            callbacks.on_batch_end(mode, step, logs)
        self._reset_metrics()

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        if mode == 'predict':
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            return logs, outputs
        return logs

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    def summary(self, input_size=None, dtype=None):
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        """Prints a string summary of the network.

        Args:
            input_size (tuple|InputSpec|list[tuple|InputSpec], optional): size of input tensor. 
                    if not set, input_size will get from ``self._inputs`` if network only have 
                    one input, input_size can be tuple or InputSpec. if model have multiple 
                    input, input_size must be a list which contain every input's shape. 
                    Default: None.
            dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.

        Returns:
            Dict: a summary of the network including total params and total trainable params.

        Examples:
            .. code-block:: python

              import paddle
              from paddle.static import InputSpec
           
              input = InputSpec([None, 1, 28, 28], 'float32', 'image')
              label = InputSpec([None, 1], 'int64', 'label')
           
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              model = paddle.Model(paddle.vision.LeNet(),
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                  input, label)
              optim = paddle.optimizer.Adam(
                  learning_rate=0.001, parameters=model.parameters())
              model.prepare(
                  optim,
                  paddle.nn.CrossEntropyLoss())

              params_info = model.summary()
              print(params_info)

        """
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        assert (input_size is not None or self._inputs is not None
                ), "'input_size' or 'self._input' must be set"
        if input_size is not None:
            _input_size = input_size
        else:
            _input_size = self._inputs
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        return summary(self.network, _input_size, dtype)
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    def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):
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        out_specs = []

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        if specs is None:
            # Note(Aurelius84): If not specific specs of `Input`, using argument names of `forward` function
            # to generate `Input`. But how can we know the actual shape of each input tensor?

            if is_input:
                arg_names = extract_args(self.network.forward)[1:]
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                # While Saving inference model in dygraph, and providing inputs only in running.
                if shapes is not None and dtypes is not None and fluid.in_dygraph_mode(
                ):
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                    out_specs = [
                        Input(
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                            name=n, dtype=dtypes[i], shape=shapes[i])
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                        for i, n in enumerate(arg_names)
                    ]
                else:
                    out_specs = [Input(name=n, shape=[None]) for n in arg_names]
            else:
                out_specs = to_list(specs)
        elif isinstance(specs, dict):
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            assert is_input == False
            out_specs = [specs[n] \
                for n in extract_args(self.network.forward) if n != 'self']
        else:
            out_specs = to_list(specs)
        # Note: checks each element has specificed `name`.
        if out_specs is not None:
            for i, spec in enumerate(out_specs):
                assert isinstance(spec, Input)
                if spec.name is None:
                    raise ValueError(
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                        "Requires Input[{}].name != None, but receive `None` with {}."
                        .format(i, spec))
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        return out_specs

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    def _reset_metrics(self):
        for metric in self._metrics:
            metric.reset()

    def _metrics_name(self):
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        metrics_name = ['loss'] if self._loss else []
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        for m in self._metrics:
            metrics_name.extend(to_list(m.name()))
        return metrics_name

    def _len_data_loader(self, data_loader):
        try:
            steps = len(data_loader)
        except Exception:
            steps = None
        return steps
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    def _update_inputs(self):
        "Update self._inputs according to given inputs."
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        self._input_info = self._adapter._input_info
        if self._input_info is not None and len(self._input_info) == 2:
            self._inputs = self._verify_spec(None, self._input_info[0],
                                             self._input_info[1], True)
            self._is_shape_inferred = True