From 3d91f2f024207c8bfc0dae62cdaead227f4558c7 Mon Sep 17 00:00:00 2001 From: yh Date: Sat, 1 Dec 2018 15:00:06 +0800 Subject: [PATCH] =?UTF-8?q?trainer=E8=BF=AD=E4=BB=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- fastNLP/core/tester.py | 18 ++++--- fastNLP/core/trainer.py | 117 +++++++++++++++++++++++++++------------- fastNLP/core/utils.py | 63 ++++++++++++++++++++-- 3 files changed, 148 insertions(+), 50 deletions(-) diff --git a/fastNLP/core/tester.py b/fastNLP/core/tester.py index ee1354fe..5d264b80 100644 --- a/fastNLP/core/tester.py +++ b/fastNLP/core/tester.py @@ -6,33 +6,34 @@ import torch from fastNLP.core.batch import Batch from fastNLP.core.sampler import RandomSampler from fastNLP.core.utils import _build_args +from fastNLP.core.utils import get_func_signature class Tester(object): """An collection of model inference and evaluation of performance, used over validation/dev set and test set. """ - def __init__(self, data, model, batch_size=16, use_cuda=False): + def __init__(self, data, model, metrics, batch_size=16, use_cuda=False, verbose=0): super(Tester, self).__init__() self.use_cuda = use_cuda self.data = data self.batch_size = batch_size + self.verbose = verbose if torch.cuda.is_available() and self.use_cuda: self._model = model.cuda() else: self._model = model if hasattr(self._model, 'predict'): - assert callable(self._model.predict) + if not callable(self._model.predict): + raise TypeError(f"{get_func_signature(model.predict)} must be callable to be used " + f"for evaluation.") self._predict_func = self._model.predict else: self._predict_func = self._model - assert hasattr(model, 'evaluate') - self._evaluator = model.evaluate - self.eval_history = [] # evaluation results of all batches + def test(self): # turn on the testing mode; clean up the history network = self._model self.mode(network, is_test=True) - self.eval_history.clear() output, truths = defaultdict(list), defaultdict(list) data_iterator = Batch(self.data, self.batch_size, sampler=RandomSampler(), as_numpy=False) @@ -48,9 +49,10 @@ class Tester(object): output[k] = itertools.chain(*v) for k, v in truths.items(): truths[k] = itertools.chain(*v) - args = _build_args(self._evaluator, **output, **truths) + # args = _build_args(self._evaluator, **output, **truths) eval_results = self._evaluator(**args) - print("[tester] {}".format(self.print_eval_results(eval_results))) + if self.verbose >= 0: + print("[tester] {}".format(self.print_eval_results(eval_results))) self.mode(network, is_test=False) return eval_results diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py index 6b0398b5..63eb963e 100644 --- a/fastNLP/core/trainer.py +++ b/fastNLP/core/trainer.py @@ -9,6 +9,7 @@ import shutil from tensorboardX import SummaryWriter import torch +from torch import nn from fastNLP.core.batch import Batch from fastNLP.core.loss import Loss @@ -21,12 +22,13 @@ from fastNLP.core.utils import _check_arg_dict_list from fastNLP.core.utils import _build_args from fastNLP.core.utils import _syn_model_data from fastNLP.core.utils import get_func_signature +from fastNLP.core.dataset import DataSet class Trainer(object): """Main Training Loop """ - def __init__(self, train_data, model, n_epochs=3, batch_size=32, print_every=-1, validate_every=-1, + def __init__(self, train_data, model, losser=None, metrics=None, n_epochs=3, batch_size=32, print_every=-1, validate_every=-1, dev_data=None, use_cuda=False, save_path="./save", optimizer=Optimizer("Adam", lr=0.01, weight_decay=0), need_check_code=True, **kwargs): @@ -35,6 +37,8 @@ class Trainer(object): self.train_data = train_data self.dev_data = dev_data # If None, No validation. self.model = model + self.losser = losser + self.metrics = metrics self.n_epochs = int(n_epochs) self.batch_size = int(batch_size) self.use_cuda = bool(use_cuda) @@ -43,23 +47,22 @@ class Trainer(object): self.validate_every = int(validate_every) self._best_accuracy = 0 - if need_check_code: - _check_code(dataset=train_data, model=model, dev_data=dev_data) - model_name = model.__class__.__name__ - assert hasattr(self.model, 'get_loss'), "model {} has to have a 'get_loss' function.".format(model_name) - self.loss_func = self.model.get_loss + # TODO check loss与metrics的类型 + + + + # TODO self._best_accuracy不能表现出当前的metric多种的情况 + if isinstance(optimizer, torch.optim.Optimizer): self.optimizer = optimizer else: self.optimizer = optimizer.construct_from_pytorch(self.model.parameters()) - assert hasattr(self.model, 'evaluate'), "model {} has to have a 'evaluate' function.".format(model_name) - self.evaluator = self.model.evaluate - if self.dev_data is not None: self.tester = Tester(model=self.model, data=self.dev_data, + metrics=self.metrics, batch_size=self.batch_size, use_cuda=self.use_cuda) @@ -71,6 +74,38 @@ class Trainer(object): # print(self.__dict__) + def _check_params(self, train_data, model, losser, metrics=[], n_epochs=3, batch_size=32, print_every=-1, + validate_every=-1, dev_data=None, use_cuda=False, save_path="./save", + optimizer=Optimizer("Adam", lr=0.01, weight_decay=0), need_check_code=True, + **kwargs): + if not isinstance(train_data, DataSet): + raise TypeError("The type of train_data must be fastNLP.DataSet, got {}.".\ + format(type(train_data))) + if not isinstance(model, nn.Module): + raise TypeError("The type of model must be torch.nn.Module, got {}.".\ + format(type(model))) + if losser is not None: + # TODO change + if not isinstance(losser, None): + raise TypeError("The type of losser must be xxx, got {}.".\ + format(type(losser))) + + # check metrics and dev_data + if (not metrics) and dev_data is not None: + raise ValueError("No metric for dev_data evaluation.") + if metrics and (dev_data is None): + raise ValueError("No dev_data for evaluations, pass dev_data or set metrics to None. ") + + # check loss + if isinstance(losser, type): + self.losser = losser() + if not isinstance(self.losser, None): + raise TypeError(f'The type of losser must be `{}`, got {type(self.losser)}.') + + if need_check_code: + _check_code(dataset=train_data, model=model, losser=losser, metrics=metrics, dev_data=dev_data) + + def train(self): """Start Training. @@ -171,6 +206,9 @@ class Trainer(object): def data_forward(self, network, x): x = _build_args(network.forward, **x) y = network(**x) + if not isinstance(y, dict): + + raise TypeError(f"The return value of {get_func_signature(network.forward)} should be dict, got {type(y)}.") return y def grad_backward(self, loss): @@ -231,11 +269,11 @@ IGNORE_CHECK_LEVEL = 0 WARNING_CHECK_LEVEL = 1 STRICT_CHECK_LEVEL = 2 -def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=None, check_level=WARNING_CHECK_LEVEL): +def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_SIZE, + dev_data=None, + check_level=WARNING_CHECK_LEVEL): # check get_loss 方法 model_name = model.__class__.__name__ - if not hasattr(model, 'get_loss'): - raise AttributeError("{} has to have a 'get_loss' function.".format(model_name)) batch = Batch(dataset=dataset, batch_size=batch_size, sampler=SequentialSampler()) for batch_count, (batch_x, batch_y) in enumerate(batch): @@ -248,23 +286,26 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No refined_batch_x = _build_args(model.forward, **batch_x) output = model(**refined_batch_x) func_signature = get_func_signature(model.forward) - assert isinstance(output, dict), "The return value of {} should be dict.".format(func_signature) + if not isinstance(output, dict): + raise TypeError(f"The return value of {func_signature} should be `dict`, not `{type(output)}`.") # loss check - if batch_count == 0: - _check_loss_evaluate(prev_func=model.forward, func=model.get_loss, check_level=check_level, - output=output, batch_y=batch_y) - loss_input = _build_args(model.get_loss, **output, **batch_y) - loss = model.get_loss(**loss_input) + if isinstance(losser, type): # 这种情况，用户传的是losser.CE这种未初始化的loss + # 需要保证output与batch_y是无歧义的？ + # (1) output和batch_y长度为1 + # (2) output和batch_y的key是和losser接受的完全一致 + pass + + loss = losser(output, batch_y) # check loss output if batch_count == 0: if not isinstance(loss, torch.Tensor): - raise ValueError("The return value of {}.get_loss() should be torch.Tensor, but {} got.". - format(model_name, type(loss))) + raise ValueError("The return value of {} should be torch.Tensor, but got {}.". + format(type(losser), type(loss))) if len(loss.size())!=0: - raise ValueError("The size of return value of {}.get_loss() is {}, should be torch.size([])".format( - model_name, loss.size() + raise ValueError("The size of return value of {} is {}, should be torch.size([])".format( + type(losser), loss.size() )) loss.backward() model.zero_grad() @@ -272,26 +313,29 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No break if dev_data is not None: - if not hasattr(model, 'evaluate'): - raise AttributeError("{} has to have a 'evaluate' function to do evaluation. Or set" - "dev_data to 'None'." - .format(model_name)) outputs, truths = defaultdict(list), defaultdict(list) dev_batch = Batch(dataset=dataset, batch_size=batch_size, sampler=SequentialSampler()) + # TODO 这里修改为使用tester + + with torch.no_grad(): for batch_count, (batch_x, batch_y) in enumerate(dev_batch): _syn_model_data(model, batch_x, batch_y) if hasattr(model, 'predict'): + if not callable(model.predict): + raise TypeError(f"{get_func_signature(model.predict)} must be callable to be used " + f"for evaluation.") refined_batch_x = _build_args(model.predict, **batch_x) prev_func = model.predict output = prev_func(**refined_batch_x) - func_signature = get_func_signature(model.predict) - assert isinstance(output, dict), "The return value of {} should be dict.".format(func_signature) else: refined_batch_x = _build_args(model.forward, **batch_x) prev_func = model.forward output = prev_func(**refined_batch_x) + func_signature = get_func_signature(prev_func) + if not isinstance(output, dict): + raise TypeError(f"The return value of {func_signature} should be `dict`, not `{type(output)}`") for k, v in output.items(): outputs[k].append(v) for k, v in batch_y.items(): @@ -299,16 +343,15 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No if batch_count+1>DEFAULT_CHECK_NUM_BATCH: break for k, v in outputs.items(): - outputs[k] = itertools.chain(*v) + outputs[k] = tuple(itertools.chain(*v)) for k, v in truths.items(): - truths[k] = itertools.chain(*v) - _check_loss_evaluate(prev_func=prev_func, func=model.evaluate, check_level=check_level, - output=outputs, batch_y=truths) - refined_input = _build_args(model.evaluate, **outputs, **truths) - metrics = model.evaluate(**refined_input) - func_signature = get_func_signature(model.evaluate) - assert isinstance(metrics, dict), "The return value of {} should be dict.". \ - format(func_signature) + truths[k] = tuple(itertools.chain(*v)) + #TODO 这里需要根据新版的metrics做修改，另外这里需要捕获来自metric的报错，因为需要指导用户debug + + + + + def _check_forward_error(model_func, check_level, batch_x): diff --git a/fastNLP/core/utils.py b/fastNLP/core/utils.py index 84faaece..8ffcc7bb 100644 --- a/fastNLP/core/utils.py +++ b/fastNLP/core/utils.py @@ -3,6 +3,7 @@ import inspect import os from collections import Counter from collections import namedtuple +import torch CheckRes = namedtuple('CheckRes', ['missing', 'unused', 'duplicated', 'required', 'all_needed'], verbose=False) @@ -95,7 +96,24 @@ def _check_arg_dict_list(func, args): all_needed=list(all_args)) def get_func_signature(func): - # can only be used in function or class method + """ + + Given a function or method, return its signature. + For example: + (1) function + def func(a, b='a', *args): + xxxx + get_func_signature(func) # 'func(a, b='a', *args)' + (2) method + class Demo: + def __init__(self): + xxx + def forward(self, a, b='a', **args) + demo = Demo() + get_func_signature(demo.forward) # 'Demo.forward(self, a, b='a', **args)' + :param func: a function or a method + :return: str or None + """ if inspect.ismethod(func): class_name = func.__self__.__class__.__name__ signature = inspect.signature(func) @@ -113,10 +131,16 @@ def get_func_signature(func): return signature_str -# move data to model's device -import torch def _syn_model_data(model, *args): - assert len(model.state_dict())!=0, "This model has no parameter." + """ + + move data to model's device, element in *args should be dict. This is a inplace change. + :param model: + :param args: + :return: + """ + if len(model.state_dict())==0: + raise ValueError("model has no parameter.") device = model.parameters().__next__().device for arg in args: if isinstance(arg, dict): @@ -124,4 +148,33 @@ def _syn_model_data(model, *args): if isinstance(value, torch.Tensor): arg[key] = value.to(device) else: - raise ValueError("Only support dict type right now.") \ No newline at end of file + raise TypeError("Only support `dict` type right now.") + +def _prepare_metrics(metrics): + """ + + Prepare list of Metric based on input + :param metrics: + :return: + """ + _metrics = [] + if metrics: + if isinstance(metrics, list): + for metric in metrics: + if isinstance(metric, type): + metric = metric() + if isinstance(metric, None): + _metrics.append(metric) + else: + raise TypeError("The type of metric in metrics must be xxxx, not {}.".format( + type(), type(metric) + )) + elif isinstance(metrics, None): + _metrics = [metrics] + else: + raise TypeError("The type of metrics should be `list[xxx]` or `xxx`, got {}.".format( + type(metrics) + )) + + return _metrics +