mirror of
https://gitee.com/fastnlp/fastNLP.git
synced 2024-12-05 05:38:31 +08:00
Updates:
* fix losses的_fast_param_map的bug * Trainer添加sampelr初始化参数，并调整参数顺序 * refine codes
This commit is contained in:
FengZiYjun
parent
9c8ec45742
commit
513876d5db
@ -72,10 +72,9 @@ class LossBase(object):
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def _fast_param_map(self, pred_dict, target_dict):
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if len(self.param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1:
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return pred_dict.values[0], target_dict.values[0]
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return tuple(pred_dict.values())[0], tuple(target_dict.values())[0]
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return None
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def __call__(self, pred_dict, target_dict, check=False):
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"""
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:param pred_dict: A dict from forward function of the network.
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@ -1,4 +1,3 @@
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import inspect
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import warnings
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from collections import defaultdict
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@ -7,11 +6,12 @@ import numpy as np
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import torch
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from fastNLP.core.utils import CheckError
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from fastNLP.core.utils import CheckRes
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from fastNLP.core.utils import _build_args
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from fastNLP.core.utils import _check_arg_dict_list
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from fastNLP.core.utils import get_func_signature
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from fastNLP.core.utils import seq_lens_to_masks
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from fastNLP.core.utils import CheckRes
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class MetricBase(object):
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def __init__(self):
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@ -59,9 +59,10 @@ class MetricBase(object):
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func_args = [arg for arg in func_spect.args if arg != 'self']
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for func_param, input_param in self.param_map.items():
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if func_param not in func_args:
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raise NameError(f"Parameter `{func_param}` is not in {get_func_signature(self.evaluate)}. Please check the "
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f"initialization parameters, or change the signature of"
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f" {get_func_signature(self.evaluate)}.")
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raise NameError(
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f"Parameter `{func_param}` is not in {get_func_signature(self.evaluate)}. Please check the "
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f"initialization parameters, or change the signature of"
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f" {get_func_signature(self.evaluate)}.")
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# evaluate should not have varargs.
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if func_spect.varargs:
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@ -113,7 +114,7 @@ class MetricBase(object):
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if not self._checked:
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# 1. check consistence between signature and param_map
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func_spect = inspect.getfullargspec(self.evaluate)
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func_args = set([arg for arg in func_spect.args if arg!='self'])
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func_args = set([arg for arg in func_spect.args if arg != 'self'])
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for func_arg, input_arg in self.param_map.items():
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if func_arg not in func_args:
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raise NameError(f"`{func_arg}` not in {get_func_signature(self.evaluate)}.")
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@ -121,7 +122,7 @@ class MetricBase(object):
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# 2. only part of the param_map are passed, left are not
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for arg in func_args:
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if arg not in self.param_map:
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self.param_map[arg] = arg #This param does not need mapping.
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self.param_map[arg] = arg # This param does not need mapping.
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self._evaluate_args = func_args
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self._reverse_param_map = {input_arg: func_arg for func_arg, input_arg in self.param_map.items()}
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@ -153,14 +154,14 @@ class MetricBase(object):
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replaced_missing = list(missing)
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for idx, func_arg in enumerate(missing):
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replaced_missing[idx] = f"{self.param_map[func_arg]}" + f"(assign to `{func_arg}` " \
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f"in `{self.__class__.__name__}`)"
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f"in `{self.__class__.__name__}`)"
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check_res = CheckRes(missing=replaced_missing,
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unused=check_res.unused,
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duplicated=duplicated,
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required=check_res.required,
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all_needed=check_res.all_needed,
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varargs=check_res.varargs)
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unused=check_res.unused,
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duplicated=duplicated,
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required=check_res.required,
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all_needed=check_res.all_needed,
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varargs=check_res.varargs)
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if check_res.missing or check_res.duplicated or check_res.varargs:
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raise CheckError(check_res=check_res,
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@ -172,6 +173,7 @@ class MetricBase(object):
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return
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class AccuracyMetric(MetricBase):
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def __init__(self, pred=None, target=None, masks=None, seq_lens=None):
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super().__init__()
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@ -191,7 +193,7 @@ class AccuracyMetric(MetricBase):
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:param target_dict:
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:return: boolean, whether to go on codes in self.__call__(). When False, don't go on.
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"""
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if len(pred_dict)==1 and len(target_dict)==1:
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if len(pred_dict) == 1 and len(target_dict) == 1:
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pred = list(pred_dict.values())[0]
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target = list(target_dict.values())[0]
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self.evaluate(pred=pred, target=target)
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@ -211,7 +213,7 @@ class AccuracyMetric(MetricBase):
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None, None, torch.Size([B], torch.Size([B]). ignored if masks are provided.
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:return: dict({'acc': float})
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"""
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#TODO 这里报错需要更改，因为pred是啥用户并不知道。需要告知用户真实的value
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# TODO 这里报错需要更改，因为pred是啥用户并不知道。需要告知用户真实的value
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if not isinstance(pred, torch.Tensor):
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raise TypeError(f"`pred` in {get_func_signature(self.evaluate)} must be torch.Tensor,"
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f"got {type(pred)}.")
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@ -224,14 +226,14 @@ class AccuracyMetric(MetricBase):
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f"got {type(masks)}.")
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elif seq_lens is not None and not isinstance(seq_lens, torch.Tensor):
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raise TypeError(f"`seq_lens` in {get_func_signature(self.evaluate)} must be torch.Tensor,"
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f"got {type(seq_lens)}.")
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f"got {type(seq_lens)}.")
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if masks is None and seq_lens is not None:
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masks = seq_lens_to_masks(seq_lens=seq_lens, float=True)
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if pred.size()==target.size():
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if pred.size() == target.size():
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pass
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elif len(pred.size())==len(target.size())+1:
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elif len(pred.size()) == len(target.size()) + 1:
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pred = pred.argmax(dim=-1)
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else:
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raise RuntimeError(f"In {get_func_signature(self.evaluate)}, when pred have "
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@ -245,18 +247,17 @@ class AccuracyMetric(MetricBase):
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self.acc_count += torch.sum(torch.eq(pred, target).float() * masks.float()).item()
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self.total += torch.sum(masks.float()).item()
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else:
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self.acc_count += torch.sum(torch.eq(pred, target).float()).item()
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self.acc_count += torch.sum(torch.eq(pred, target).float()).item()
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self.total += np.prod(list(pred.size()))
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def get_metric(self, reset=True):
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evaluate_result = {'acc': round(self.acc_count/self.total, 6)}
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evaluate_result = {'acc': round(self.acc_count / self.total, 6)}
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if reset:
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self.acc_count = 0
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self.total = 0
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return evaluate_result
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def _prepare_metrics(metrics):
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"""
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@ -278,7 +279,8 @@ def _prepare_metrics(metrics):
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raise TypeError(f"{metric_name}.get_metric must be callable, got {type(metric.get_metric)}.")
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_metrics.append(metric)
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else:
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raise TypeError(f"The type of metric in metrics must be `fastNLP.MetricBase`, not `{type(metric)}`.")
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raise TypeError(
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f"The type of metric in metrics must be `fastNLP.MetricBase`, not `{type(metric)}`.")
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elif isinstance(metrics, MetricBase):
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_metrics = [metrics]
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else:
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@ -300,6 +302,7 @@ class Evaluator(object):
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"""
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raise NotImplementedError
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class ClassifyEvaluator(Evaluator):
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def __init__(self):
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super(ClassifyEvaluator, self).__init__()
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@ -335,6 +338,7 @@ class SeqLabelEvaluator(Evaluator):
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accuracy = total_correct / total_count
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return {"accuracy": float(accuracy)}
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class SeqLabelEvaluator2(Evaluator):
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# 上面的evaluator应该是错误的
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def __init__(self, seq_lens_field_name='word_seq_origin_len'):
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@ -367,7 +371,7 @@ class SeqLabelEvaluator2(Evaluator):
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if x_i in self.end_tagidx_set:
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truth_count += 1
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for j in range(start, idx_i + 1):
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if y_[j]!=x_[j]:
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if y_[j] != x_[j]:
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flag = False
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break
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if flag:
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@ -380,8 +384,7 @@ class SeqLabelEvaluator2(Evaluator):
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R = corr_count / (float(truth_count) + 1e-6)
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F = 2 * P * R / (P + R + 1e-6)
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return {"P": P, 'R':R, 'F': F}
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return {"P": P, 'R': R, 'F': F}
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class SNLIEvaluator(Evaluator):
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@ -563,10 +566,6 @@ def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary'):
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return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
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def classification_report(y_true, y_pred, labels=None, target_names=None, digits=2):
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raise NotImplementedError
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def accuracy_topk(y_true, y_prob, k=1):
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"""Compute accuracy of y_true matching top-k probable
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labels in y_prob.
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@ -28,11 +28,9 @@ class Trainer(object):
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"""
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def __init__(self, train_data, model, losser=None, metrics=None, n_epochs=3, batch_size=32, print_every=50,
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validate_every=-1,
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dev_data=None, use_cuda=False, save_path=None,
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optimizer=Adam(lr=0.01, weight_decay=0), check_code_level=0,
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metric_key=None):
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def __init__(self, train_data, model, losser=None, metrics=None, optimizer=Adam(lr=0.01, weight_decay=0),
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sampler=RandomSampler(), n_epochs=3, batch_size=32, print_every=50, validate_every=-1, dev_data=None,
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use_cuda=False, metric_key=None, save_path=None, check_code_level=0):
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"""
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:param DataSet train_data: the training data
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@ -54,7 +52,6 @@ class Trainer(object):
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::
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metric_key="-PPL" # language model gets better as perplexity gets smaller
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:param kwargs:
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"""
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super(Trainer, self).__init__()
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@ -105,6 +102,7 @@ class Trainer(object):
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self.print_every = int(print_every)
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self.validate_every = int(validate_every)
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self.best_metric_indicator = None
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self.sampler = sampler
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self._model_device = model.parameters().__next__().device
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@ -120,14 +118,9 @@ class Trainer(object):
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batch_size=self.batch_size,
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use_cuda=self.use_cuda)
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for k, v in kwargs.items():
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setattr(self, k, v)
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self.step = 0
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self.start_time = None # start timestamp
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# print(self.__dict__)
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def train(self):
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"""Start Training.
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@ -158,7 +151,7 @@ class Trainer(object):
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epoch = 1
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while epoch <= self.n_epochs:
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data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=RandomSampler(),
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data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler,
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as_numpy=False)
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self._train_epoch(data_iterator, self.model, epoch, start)
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@ -10,6 +10,8 @@ import torch
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CheckRes = namedtuple('CheckRes', ['missing', 'unused', 'duplicated', 'required', 'all_needed',
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'varargs'], verbose=False)
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def save_pickle(obj, pickle_path, file_name):
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"""Save an object into a pickle file.
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@ -53,6 +55,7 @@ def pickle_exist(pickle_path, pickle_name):
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else:
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return False
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def _build_args(func, **kwargs):
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spect = inspect.getfullargspec(func)
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if spect.varkw is not None:
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@ -108,7 +111,7 @@ def _check_arg_dict_list(func, args):
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assert callable(func) and isinstance(arg_dict_list, (list, tuple))
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assert len(arg_dict_list) > 0 and isinstance(arg_dict_list[0], dict)
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spect = inspect.getfullargspec(func)
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all_args = set([arg for arg in spect.args if arg!='self'])
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all_args = set([arg for arg in spect.args if arg != 'self'])
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defaults = []
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if spect.defaults is not None:
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defaults = [arg for arg in spect.defaults]
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@ -130,6 +133,7 @@ def _check_arg_dict_list(func, args):
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all_needed=list(all_args),
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varargs=varargs)
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def get_func_signature(func):
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"""
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@ -153,7 +157,7 @@ def get_func_signature(func):
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class_name = func.__self__.__class__.__name__
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signature = inspect.signature(func)
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signature_str = str(signature)
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if len(signature_str)>2:
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if len(signature_str) > 2:
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_self = '(self, '
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else:
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_self = '(self'
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@ -176,12 +180,13 @@ def _is_function_or_method(func):
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return False
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return True
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def _check_function_or_method(func):
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if not _is_function_or_method(func):
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raise TypeError(f"{type(func)} is not a method or function.")
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def _move_dict_value_to_device(*args, device:torch.device):
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def _move_dict_value_to_device(*args, device: torch.device):
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"""
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move data to model's device, element in *args should be dict. This is a inplace change.
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@ -206,7 +211,8 @@ class CheckError(Exception):
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CheckError. Used in losses.LossBase, metrics.MetricBase.
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"""
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def __init__(self, check_res:CheckRes, func_signature:str):
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def __init__(self, check_res: CheckRes, func_signature: str):
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errs = [f'The following problems occurred when calling `{func_signature}`']
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if check_res.varargs:
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@ -228,8 +234,9 @@ IGNORE_CHECK_LEVEL = 0
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WARNING_CHECK_LEVEL = 1
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STRICT_CHECK_LEVEL = 2
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def _check_loss_evaluate(prev_func_signature:str, func_signature:str, check_res:CheckRes,
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pred_dict:dict, target_dict:dict, dataset, check_level=0):
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def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_res: CheckRes,
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pred_dict: dict, target_dict: dict, dataset, check_level=0):
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errs = []
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unuseds = []
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_unused_field = []
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@ -268,8 +275,8 @@ def _check_loss_evaluate(prev_func_signature:str, func_signature:str, check_res:
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f"target is {list(target_dict.keys())}).")
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if _miss_out_dataset:
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_tmp = (f"You might need to provide {_miss_out_dataset} in DataSet and set it as target(Right now "
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f"target is {list(target_dict.keys())}) or output it "
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f"in {prev_func_signature}(Right now it outputs {list(pred_dict.keys())}).")
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f"target is {list(target_dict.keys())}) or output it "
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f"in {prev_func_signature}(Right now it outputs {list(pred_dict.keys())}).")
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if _unused_field:
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_tmp += f"You can use DataSet.rename_field() to rename the field in `unused field:`. "
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suggestions.append(_tmp)
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@ -277,15 +284,15 @@ def _check_loss_evaluate(prev_func_signature:str, func_signature:str, check_res:
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if check_res.duplicated:
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errs.append(f"\tduplicated param: {check_res.duplicated}.")
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suggestions.append(f"Delete {check_res.duplicated} in the output of "
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f"{prev_func_signature} or do not set {check_res.duplicated} as targets. ")
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f"{prev_func_signature} or do not set {check_res.duplicated} as targets. ")
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if check_level == STRICT_CHECK_LEVEL:
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errs.extend(unuseds)
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if len(errs)>0:
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if len(errs) > 0:
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errs.insert(0, f'The following problems occurred when calling {func_signature}')
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sugg_str = ""
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if len(suggestions)>1:
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if len(suggestions) > 1:
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for idx, sugg in enumerate(suggestions):
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sugg_str += f'({idx+1}). {sugg}'
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else:
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@ -332,10 +339,10 @@ def _check_forward_error(forward_func, batch_x, dataset, check_level):
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if check_level == STRICT_CHECK_LEVEL:
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errs.extend(_unused)
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if len(errs)>0:
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if len(errs) > 0:
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errs.insert(0, f'The following problems occurred when calling {func_signature}')
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sugg_str = ""
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if len(suggestions)>1:
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if len(suggestions) > 1:
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for idx, sugg in enumerate(suggestions):
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sugg_str += f'({idx+1}). {sugg}'
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else:
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@ -357,11 +364,11 @@ def seq_lens_to_masks(seq_lens, float=True):
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:return: list, np.ndarray or torch.Tensor, shape will be (B, max_length)
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"""
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if isinstance(seq_lens, np.ndarray):
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assert len(np.shape(seq_lens))==1, f"seq_lens can only have one dimension, got {len(np.shape(seq_lens))}."
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assert len(np.shape(seq_lens)) == 1, f"seq_lens can only have one dimension, got {len(np.shape(seq_lens))}."
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assert seq_lens.dtype in (int, np.int32, np.int64), f"seq_lens can only be integer, not {seq_lens.dtype}."
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raise NotImplemented
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elif isinstance(seq_lens, torch.LongTensor):
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assert len(seq_lens.size())==1, f"seq_lens can only have one dimension, got {len(seq_lens.size())==1}."
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assert len(seq_lens.size()) == 1, f"seq_lens can only have one dimension, got {len(seq_lens.size())==1}."
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batch_size = seq_lens.size(0)
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max_len = seq_lens.max()
|
||||
indexes = torch.arange(max_len).view(1, -1).repeat(batch_size, 1).to(seq_lens.device)
|
||||
@ -375,4 +382,3 @@ def seq_lens_to_masks(seq_lens, float=True):
|
||||
raise NotImplemented
|
||||
else:
|
||||
raise NotImplemented
|
||||
|
||||
|
||||
@ -31,15 +31,7 @@ class TrainerTestGround(unittest.TestCase):
|
||||
|
||||
model = NaiveClassifier(2, 1)
|
||||
|
||||
trainer = Trainer(train_set, model,
|
||||
losser=BCELoss(pred="predict", target="y"),
|
||||
metrics=AccuracyMetric(pred="predict", target="y"),
|
||||
n_epochs=10,
|
||||
batch_size=32,
|
||||
print_every=10,
|
||||
validate_every=-1,
|
||||
dev_data=dev_set,
|
||||
optimizer=SGD(0.1),
|
||||
check_code_level=2
|
||||
)
|
||||
trainer = Trainer(train_set, model, losser=BCELoss(pred="predict", target="y"),
|
||||
metrics=AccuracyMetric(pred="predict", target="y"), optimizer=SGD(), n_epochs=10,
|
||||
batch_size=32, print_every=10, validate_every=-1, dev_data=dev_set, check_code_level=2)
|
||||
trainer.train()
|
||||
|
||||
@ -71,20 +71,16 @@ class TestTutorial(unittest.TestCase):
|
||||
|
||||
# 实例化Trainer，传入模型和数据，进行训练
|
||||
copy_model = deepcopy(model)
|
||||
overfit_trainer = Trainer(model=copy_model, train_data=test_data, dev_data=test_data,
|
||||
overfit_trainer = Trainer(train_data=test_data, model=copy_model,
|
||||
losser=CrossEntropyLoss(pred="output", target="label_seq"),
|
||||
metrics=AccuracyMetric(pred="predict", target="label_seq"),
|
||||
save_path="./save",
|
||||
batch_size=4,
|
||||
n_epochs=10)
|
||||
metrics=AccuracyMetric(pred="predict", target="label_seq"), n_epochs=10, batch_size=4,
|
||||
dev_data=test_data, save_path="./save")
|
||||
overfit_trainer.train()
|
||||
|
||||
trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,
|
||||
trainer = Trainer(train_data=train_data, model=model,
|
||||
losser=CrossEntropyLoss(pred="output", target="label_seq"),
|
||||
metrics=AccuracyMetric(pred="predict", target="label_seq"),
|
||||
save_path="./save",
|
||||
batch_size=4,
|
||||
n_epochs=10)
|
||||
metrics=AccuracyMetric(pred="predict", target="label_seq"), n_epochs=10, batch_size=4,
|
||||
dev_data=test_data, save_path="./save")
|
||||
trainer.train()
|
||||
print('Train finished!')
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user