Merge branch 'trainer' of github.com:FengZiYjun/fastNLP into trainer

fastNLP/core/dataset.py

@@ -260,6 +260,12 @@ class DataSet(object):
             dev_set.append(self[idx])
         for idx in train_indices:
             train_set.append(self[idx])
+        for field_name in self.field_arrays:
+            train_set.field_arrays[field_name].is_input = self.field_arrays[field_name].is_input
+            train_set.field_arrays[field_name].is_target = self.field_arrays[field_name].is_target
+            dev_set.field_arrays[field_name].is_input = self.field_arrays[field_name].is_input
+            dev_set.field_arrays[field_name].is_target = self.field_arrays[field_name].is_target
+
         return train_set, dev_set
 
     @classmethod

fastNLP/core/fieldarray.py

@@ -11,7 +11,7 @@ class FieldArray(object):
         """
 
         :param str name: the name of the FieldArray
-        :param list content: a list of int, float, or other objects.
+        :param list content: a list of int, float, or a list of list.
         :param int padding_val: the integer for padding. Default: 0.
         :param bool is_target: If True, this FieldArray is used to compute loss.
         :param bool is_input: If True, this FieldArray is used to the model input.
@@ -26,7 +26,14 @@ class FieldArray(object):
 
     @staticmethod
     def _type_detection(content):
-        type_set = set([type(item) for item in content])
+
+        if isinstance(content, list) and len(content) > 0 and isinstance(content[0], list):
+            # 2-D list
+            # TODO: refactor
+            type_set = set([type(item) for item in content[0]])
+        else:
+            # 1-D list
+            type_set = set([type(item) for item in content])
         if len(type_set) == 1 and any(basic_type in type_set for basic_type in (str, int, float)):
             return type_set.pop()
         elif len(type_set) == 2 and float in type_set and int in type_set:
@@ -48,7 +55,7 @@ class FieldArray(object):
     def append(self, val):
         """Add a new item to the tail of FieldArray.
 
-        :param val: int, float, or str.
+        :param val: int, float, str, or a list of them.
         """
         val_type = type(val)
         if val_type is int and self.pytype is float:
@@ -60,9 +67,17 @@ class FieldArray(object):
                 self.content[idx] = float(self.content[idx])
             self.pytype = float
             self.dtype = self._map_to_np_type(self.pytype)
-
+        elif val_type is list:
+            if len(val) == 0:
+                raise ValueError("Cannot append an empty list.")
+            else:
+                if type(val[0]) != self.pytype:
+                    raise ValueError(
+                        "Cannot append a list of {}-type value into a {}-tpye FieldArray.".
+                            format(type(val[0]), self.pytype))
         elif val_type != self.pytype:
             raise ValueError("Cannot append a {}-type value into a {}-tpye FieldArray.".format(val_type, self.pytype))
+
         self.content.append(val)
 
     def __getitem__(self, indices):

fastNLP/core/losses.py

@@ -3,11 +3,11 @@ import torch.nn.functional as F
 
 from fastNLP.core.utils import CheckError
 from fastNLP.core.utils import CheckRes
+from fastNLP.core.utils import _build_args
+from fastNLP.core.utils import _check_function_or_method
 from fastNLP.core.utils import _get_arg_list
 from fastNLP.core.utils import _map_args
 from fastNLP.core.utils import get_func_signature
-from fastNLP.core.utils import _build_args
-from fastNLP.core.utils import _check_function_or_method
 
 
 class LossBase(object):
@@ -71,7 +71,8 @@ class LossBase(object):
 
         if len(duplicated) > 0 or len(missing) > 0:
             raise CheckError(
-                CheckRes(missing=missing, unused=[], duplicated=duplicated, required=[], all_needed=[]),
+                CheckRes(missing=missing, unused=[], duplicated=duplicated, required=[], all_needed=[],
+                         varargs=varargs),
                 func_signature=get_func_signature(self.get_loss)
             )
 
@@ -90,9 +91,9 @@ class LossBase(object):
         return loss
 
 
-class NewLoss(LossBase):
+class LossFunc(LossBase):
     def __init__(self, func, key_map=None, **kwargs):
-        super(NewLoss, self).__init__()
+        super(LossFunc, self).__init__()
         _check_function_or_method(func)
         if key_map is not None:
             if not isinstance(key_map, dict):

fastNLP/core/metrics.py

@@ -1,17 +1,18 @@
 
-import warnings
 import inspect
+import warnings
 from collections import defaultdict
 
 import numpy as np
 import torch
 
-from fastNLP.core.utils import get_func_signature
-from fastNLP.core.utils import _check_arg_dict_list
-from fastNLP.core.utils import _build_args
 from fastNLP.core.utils import CheckError
+from fastNLP.core.utils import _build_args
+from fastNLP.core.utils import _check_arg_dict_list
+from fastNLP.core.utils import get_func_signature
 from fastNLP.core.utils import seq_lens_to_masks
 
+
 class MetricBase(object):
     def __init__(self):
         self.param_map = {} # key is param in function, value is input param.

fastNLP/core/tester.py

@@ -1,18 +1,18 @@
-import itertools
 from collections import defaultdict
 
 import torch
 from torch import nn
 
 from fastNLP.core.batch import Batch
-from fastNLP.core.sampler import SequentialSampler
 from fastNLP.core.dataset import DataSet
+from fastNLP.core.metrics import _prepare_metrics
+from fastNLP.core.sampler import SequentialSampler
 from fastNLP.core.utils import CheckError
 from fastNLP.core.utils import _build_args
-from fastNLP.core.utils import get_func_signature
-from fastNLP.core.utils import _move_dict_value_to_device
-from fastNLP.core.metrics import _prepare_metrics
 from fastNLP.core.utils import _check_loss_evaluate
+from fastNLP.core.utils import _move_dict_value_to_device
+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. """
@@ -27,16 +27,6 @@ class Tester(object):
 
         self.metrics = _prepare_metrics(metrics)
 
-        # check predict
-        if hasattr(self._model, 'predict'):
-            self._predict_func = self._model.predict
-            if not callable(self._predict_func):
-                _model_name = model.__class__.__name__
-                raise TypeError(f"`{_model_name}.predict` must be callable to be used "
-                                f"for evaluation, not `{type(self._predict_func)}`.")
-        else:
-            self._predict_func = self._model.forward
-
         self.data = data
         if torch.cuda.is_available() and self.use_cuda:
             self._model = model.cuda()
@@ -45,9 +35,18 @@ class Tester(object):
         self.use_cuda = use_cuda
         self.batch_size = batch_size
         self.verbose = verbose
-
         self._model_device = model.parameters().__next__().device
 
+        # check predict
+        if hasattr(self._model, 'predict'):
+            self._predict_func = self._model.predict
+            if not callable(self._predict_func):
+                _model_name = model.__class__.__name__
+                raise TypeError(f"`{_model_name}.predict` must be callable to be used "
+                                f"for evaluation, not `{type(self._predict_func)}`.")
+        else:
+            self._predict_func = self._model.forward
+
     def test(self):
         # turn on the testing mode; clean up the history
         network = self._model

fastNLP/core/trainer.py

@@ -80,8 +80,9 @@ class Trainer(object):
         # parse metric_key
         # increase_better is True. It means the exp result gets better if the indicator increases.
         # It is true by default.
-        self.increase_better = False if metric_key[0] == "-" else True
+        self.increase_better = True
         if metric_key is not None:
+            self.increase_better = False if metric_key[0] == "-" else True
             self.metric_key = metric_key[1:] if metric_key[0] == "+" or metric_key[0] == "-" else metric_key
         else:
             self.metric_key = None
@@ -208,10 +209,12 @@ class Trainer(object):
     def _do_validation(self):
         res = self.tester.test()
         for name, num in res.items():
-            self._summary_writer.add_scalar("valid_{}".format(name), num, global_step=self.step)
+            pass
+            # self._summary_writer.add_scalar("valid_{}".format(name), num, global_step=self.step)
         if self.save_path is not None and self._better_eval_result(res):
+            metric_key = self.metric_key if self.metric_key is not None else "None"
             self._save_model(self.model,
-                            "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time]))
+                             "best_" + "_".join([self.model.__class__.__name__, metric_key, self.start_time]))
 
     def _mode(self, model, is_test=False):
         """Train mode or Test mode. This is for PyTorch currently.

fastNLP/core/utils.py

@@ -5,9 +5,8 @@ import warnings
 from collections import Counter
 from collections import namedtuple
 
-import torch
 import numpy as np
-
+import torch
 
 CheckRes = namedtuple('CheckRes', ['missing', 'unused', 'duplicated', 'required', 'all_needed',
                                    'varargs'], verbose=False)
@@ -266,7 +265,8 @@ def _check_forward_error(forward_func, batch_x, check_level):
     if check_res.varargs:
         errs.append(f"\tvarargs: {check_res.varargs}(Does not support pass positional arguments, please delete it)")
     if check_res.missing:
-        errs.append(f"\tmissing param: {check_res.missing}, provided with {list(batch_x.keys())}.")
+        errs.append(f"\tmissing param: {check_res.missing}, provided with {list(batch_x.keys())}. "
+                    f"Please set {check_res.missing} as input.")
     if check_res.unused:
         _unused = [f"\tunused param: {check_res.unused}"]
         if check_level == STRICT_CHECK_LEVEL:

fastNLP/models/base_model.py

@@ -1,7 +1,5 @@
 import torch
 
-from fastNLP.core.trainer import Trainer
-
 
 class BaseModel(torch.nn.Module):
     """Base PyTorch model for all models.
@@ -11,8 +9,20 @@ class BaseModel(torch.nn.Module):
         super(BaseModel, self).__init__()
 
     def fit(self, train_data, dev_data=None, **train_args):
-        trainer = Trainer(**train_args)
-        trainer.train(self, train_data, dev_data)
+        raise NotImplementedError
 
     def predict(self, *args, **kwargs):
         raise NotImplementedError
+
+
+class LinearClassifier(BaseModel):
+    def __init__(self, in_feature_dim, out_feature_dim):
+        super(LinearClassifier, self).__init__()
+        self.linear = torch.nn.Linear(in_feature_dim, out_feature_dim)
+        self.softmax = torch.nn.Softmax()
+
+    def forward(self, x):
+        return {"predict": self.softmax(self.linear(x))}
+
+    def predict(self, x):
+        return {"predict": self.softmax(self.linear(x))}

test/core/test_loss.py

@@ -16,7 +16,8 @@ class TestLoss(unittest.TestCase):
 
 		# loss_func = loss.Loss("nll")
 		print(callable(tc.nn.NLLLoss))
-		loss_func = loss.NewLoss(F.nll_loss)
+
+	loss_func = loss.LossFunc(F.nll_loss)
 
 		nll_loss = loss.NLLLoss()
 
@@ -330,36 +331,36 @@ class TestLoss(unittest.TestCase):
 			c = kwargs['c']
 			return (a + b) * c
 
-		import torch
-		from fastNLP.core.losses import LossBase, NewLoss
 
-		get_loss = NewLoss(func, {'a': 'predict', 'b': 'truth'})
+from fastNLP.core.losses import LossFunc
+
+get_loss = LossFunc(func, {'a': 'predict', 'b': 'truth'})
 		predict = torch.randn(5, 3)
 		truth = torch.LongTensor([1, 0, 1, 2, 1])
 		loss1 = get_loss({'predict': predict}, {'truth': truth})
-		get_loss_2 = NewLoss(func2, {'a': 'predict'})
+get_loss_2 = LossFunc(func2, {'a': 'predict'})
 		loss2 = get_loss_2({'predict': predict}, {'truth': truth})
-		get_loss_3 = NewLoss(func3)
+get_loss_3 = LossFunc(func3)
 		loss3 = get_loss_3({'predict': predict}, {'truth': truth})
 		print(loss1, loss2, loss3)
 		assert loss1 == loss2 and loss1 == loss3
 
-		get_loss_4 = NewLoss(func4)
+get_loss_4 = LossFunc(func4)
 		loss4 = get_loss_4({'a': 1, 'b': 3}, {})
 		print(loss4)
 		assert loss4 == (1 + 3) * 2
 
-		get_loss_5 = NewLoss(func4)
+get_loss_5 = LossFunc(func4)
 		loss5 = get_loss_5({'a': 1, 'b': 3}, {'c': 4})
 		print(loss5)
 		assert loss5 == (1 + 3) * 4
 
-		get_loss_6 = NewLoss(func6)
+get_loss_6 = LossFunc(func6)
 		loss6 = get_loss_6({'a': 1, 'b': 3}, {'c': 4})
 		print(loss6)
 		assert loss6 == (1 + 3) * 4
 
-		get_loss_7 = NewLoss(func6, c='cc')
+get_loss_7 = LossFunc(func6, c='cc')
 		loss7 = get_loss_7({'a': 1, 'b': 3}, {'cc': 4})
 		print(loss7)
 		assert loss7 == (1 + 3) * 4

test/core/test_trainer.py

@@ -1,7 +1,47 @@
 import unittest
 
+import numpy as np
+import torch
 
-class TestTrainer(unittest.TestCase):
-    def test_case_1(self):
-        pass
+from fastNLP.core.dataset import DataSet
+from fastNLP.core.instance import Instance
+from fastNLP.core.losses import LossFunc
+from fastNLP.core.metrics import AccuracyMetric
+from fastNLP.core.optimizer import SGD
+from fastNLP.core.trainer import Trainer
+from fastNLP.models.base_model import LinearClassifier
 
+
+class TrainerTestGround(unittest.TestCase):
+    def test_case(self):
+        mean = np.array([-3, -3])
+        cov = np.array([[1, 0], [0, 1]])
+        class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+        mean = np.array([3, 3])
+        cov = np.array([[1, 0], [0, 1]])
+        class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+        data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
+                           [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
+
+        data_set.set_input("x", flag=True)
+        data_set.set_target("y", flag=True)
+
+        train_set, dev_set = data_set.split(0.3)
+
+        model = LinearClassifier(2, 1)
+
+        trainer = Trainer(train_set, model,
+                          losser=LossFunc(torch.nn.functional.binary_cross_entropy,
+                                          key_map={"target": "y", "input": "predict"}),
+                          metrics=AccuracyMetric(pred="predict", target="y"),
+                          n_epochs=10,
+                          batch_size=32,
+                          print_every=10,
+                          validate_every=-1,
+                          dev_data=dev_set,
+                          optimizer=SGD(0.001),
+                          check_code_level=2
+                          )
+        trainer.train()