diff --git a/fastNLP/core/losses.py b/fastNLP/core/losses.py index f2fb16d0..76e9be0d 100644 --- a/fastNLP/core/losses.py +++ b/fastNLP/core/losses.py @@ -147,7 +147,7 @@ class LossBase(object): if not (isinstance(loss, torch.Tensor) and len(loss.size()) == 0): if not isinstance(loss, torch.Tensor): raise RuntimeError(f"loss ERROR: loss except a torch.Tensor but get {type(loss)}") - raise RuntimeError(f"loss ERROR: the size of loss except torch.Size([]) but got {loss.size}") + raise RuntimeError(f"loss ERROR: the size of loss except torch.Size([]) but got {loss.size()}") return loss @@ -219,8 +219,8 @@ class LossInForward(LossBase): if not (isinstance(loss, torch.Tensor) and len(loss.size()) == 0): if not isinstance(loss, torch.Tensor): - raise TypeError(f"loss ERROR: loss except a torch.Tensor but got {type(loss)}") - raise RuntimeError(f"loss ERROR: the size of loss except torch.Size([]) but got {loss.size}") + raise TypeError(f"loss excepts to be a torch.Tensor, got {type(loss)}") + raise RuntimeError(f"The size of loss excepts to be torch.Size([]), got {loss.size()}") return loss diff --git a/fastNLP/core/metrics.py b/fastNLP/core/metrics.py index ff40e4e4..c17d408b 100644 --- a/fastNLP/core/metrics.py +++ b/fastNLP/core/metrics.py @@ -202,12 +202,20 @@ class AccuracyMetric(MetricBase): pred2 = list(pred_dict.values())[1] if not (isinstance(pred1, torch.Tensor) and isinstance(pred2, torch.Tensor)): return fast_param - if len(pred1.size())>len(pred2.size()): - fast_param['pred'] = pred1 - fast_param['seq_lens'] = pred2 + if len(pred1.size())len(pred2.size()) and len(pred2.size())==1: + seq_lens = pred2 + pred = pred1 + else: + return fast_param + fast_param['pred'] = pred + fast_param['seq_lens'] = seq_lens else: return fast_param fast_param['target'] = targets[0] + # TODO need to make sure they all have same batch_size return fast_param def evaluate(self, pred, target, seq_lens=None): diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py index a0069571..13a3490a 100644 --- a/fastNLP/core/trainer.py +++ b/fastNLP/core/trainer.py @@ -48,6 +48,8 @@ class Trainer(object): :param str save_path: file path to save models :param Optimizer optimizer: an optimizer object :param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict. + `ignore` will not check unused field; `warning` when warn if some field are not used; `strict` means + it will raise error if some field are not used. :param str metric_key: a single indicator used to decide the best model based on metric results. It must be one of the keys returned by the FIRST metric in `metrics`. If the overall result gets better if the indicator gets smaller, add a `-` character in front of the string. For example diff --git a/fastNLP/core/utils.py b/fastNLP/core/utils.py index 4fd5eaec..0019b022 100644 --- a/fastNLP/core/utils.py +++ b/fastNLP/core/utils.py @@ -254,9 +254,9 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re else: _unused_param.append(_unused) if _unused_field: - unuseds.append([f"\tunused field: {_unused_field}"]) + unuseds.append(f"\tunused field: {_unused_field}") if _unused_param: - unuseds.append([f"\tunused param: {_unused_param}"]) # output from predict or forward + unuseds.append(f"\tunused param: {_unused_param}") # output from predict or forward if check_res.missing: errs.append(f"\tmissing param: {check_res.missing}") @@ -278,8 +278,8 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re _tmp = (f"You might need to provide {_miss_out_dataset} in DataSet and set it as target(Right now " f"target has {list(target_dict.keys())}) or output it " f"in {prev_func_signature}(Right now output has {list(pred_dict.keys())}).") - if _unused_field: - _tmp += f"You can use DataSet.rename_field() to rename the field in `unused field:`. " + # if _unused_field: + # _tmp += f"You can use DataSet.rename_field() to rename the field in `unused field:`. " suggestions.append(_tmp) if check_res.duplicated: @@ -287,7 +287,9 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re suggestions.append(f"Delete {check_res.duplicated} in the output of " f"{prev_func_signature} or do not set {check_res.duplicated} as targets. ") - if check_level == STRICT_CHECK_LEVEL: + if len(errs)>0: + errs.extend(unuseds) + elif check_level == STRICT_CHECK_LEVEL: errs.extend(unuseds) if len(errs) > 0: @@ -330,14 +332,16 @@ def _check_forward_error(forward_func, batch_x, dataset, check_level): suggestions.append(f"You might need to set {_miss_in_dataset} as input. ") if _miss_out_dataset: _tmp = f"You need to provide {_miss_out_dataset} in DataSet and set it as input. " - if check_res.unused: - _tmp += f"Or you might find it is in `unused field:`, you can use DataSet.rename_field() to " \ - f"rename the field in `unused field:`." + # if check_res.unused: + # _tmp += f"Or you might find it in `unused field:`, you can use DataSet.rename_field() to " \ + # f"rename the field in `unused field:`." suggestions.append(_tmp) if check_res.unused: _unused = [f"\tunused field: {check_res.unused}"] - if check_level == STRICT_CHECK_LEVEL: + if len(errs)>0: + errs.extend(_unused) + elif check_level == STRICT_CHECK_LEVEL: errs.extend(_unused) if len(errs) > 0: diff --git a/test/core/test_trainer.py b/test/core/test_trainer.py index ed4cc38d..fb6d02f8 100644 --- a/test/core/test_trainer.py +++ b/test/core/test_trainer.py @@ -7,6 +7,7 @@ import torch.nn.functional as F from fastNLP.core.dataset import DataSet from fastNLP.core.instance import Instance from fastNLP.core.losses import BCELoss +from fastNLP.core.losses import LossInForward from fastNLP.core.metrics import AccuracyMetric from fastNLP.core.optimizer import SGD from fastNLP.core.trainer import Trainer @@ -142,6 +143,84 @@ class TrainerTestGround(unittest.TestCase): # 应该正确运行 """ + def test_trainer_suggestion4(self): + # 检查报错提示能否正确提醒用户 + # 这里传入forward需要的数据，是否可以正确提示unused + dataset = prepare_fake_dataset2('x1', 'x_unused') + dataset.set_input('x1', 'x_unused', 'y', flag=True) + class Model(nn.Module): + def __init__(self): + super().__init__() + self.fc = nn.Linear(5, 4) + def forward(self, x1, x2, y): + x1 = self.fc(x1) + x2 = self.fc(x2) + x = x1 + x2 + loss = F.cross_entropy(x, y) + return {'loss': loss} + + model = Model() + trainer = Trainer( + train_data=dataset, + model=model, + use_tqdm=False, + print_every=2 + ) + + def test_trainer_suggestion5(self): + # 检查报错提示能否正确提醒用户 + # 这里传入多余参数，让其duplicate, 但这里因为y不会被调用，所以其实不会报错 + dataset = prepare_fake_dataset2('x1', 'x_unused') + dataset.rename_field('x_unused', 'x2') + dataset.set_input('x1', 'x2', 'y') + dataset.set_target('y') + class Model(nn.Module): + def __init__(self): + super().__init__() + self.fc = nn.Linear(5, 4) + def forward(self, x1, x2, y): + x1 = self.fc(x1) + x2 = self.fc(x2) + x = x1 + x2 + loss = F.cross_entropy(x, y) + return {'loss': loss} + + model = Model() + trainer = Trainer( + train_data=dataset, + model=model, + use_tqdm=False, + print_every=2 + ) + + def test_trainer_suggestion6(self): + # 检查报错提示能否正确提醒用户 + # 这里传入多余参数，让其duplicate + dataset = prepare_fake_dataset2('x1', 'x_unused') + dataset.rename_field('x_unused', 'x2') + dataset.set_input('x1', 'x2', 'y') + dataset.set_target('x1') + class Model(nn.Module): + def __init__(self): + super().__init__() + self.fc = nn.Linear(5, 4) + def forward(self, x1, x2, y): + x1 = self.fc(x1) + x2 = self.fc(x2) + x = x1 + x2 + loss = F.cross_entropy(x, y) + return {'pred': x} + + model = Model() + trainer = Trainer( + train_data=dataset, + model=model, + dev_data=dataset, + metrics=AccuracyMetric(), + use_tqdm=False, + print_every=2 + ) + def test_case2(self): # check metrics Wrong