add nll loss API for the paddlepaddle api2.0

* add nll loss API, update demo code of the comment

paddle/fluid/pybind/op_function_generator.cc

@@ -40,6 +40,7 @@ std::map<std::string, std::set<std::string>> op_ins_map = {
     {"assign", {"X"}},
     {"fake_quantize_dequantize_moving_average_abs_max",
      {"X", "InScale", "InAccum", "InState"}},
+    {"nll_loss", {"X", "Label", "Weight"}},
 };
 
 // NOTE(zhiqiu): Like op_ins_map.

python/paddle/fluid/tests/unittests/test_nll_loss.py

@@ -445,7 +445,6 @@ class TestNLLLoss(unittest.TestCase):
         startup_prog = fluid.Program()
         place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
         ) else fluid.CPUPlace()
-        #place = fluid.CPUPlace()
         with fluid.program_guard(prog, startup_prog):
             input = fluid.data(
                 name='input', shape=[5, 3, 5, 5], dtype='float64')
@@ -879,5 +878,105 @@ class TestNLLLossOp2DNoReduce(OpTest):
         self.label_shape = [5, 5, 5]
 
 
+class TestNLLLossName(unittest.TestCase):
+    def test_name(self):
+        prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        place = paddle.CPUPlace()
+        with paddle.static.program_guard(prog, startup_prog):
+            x = paddle.data(name='x', shape=[10, 10], dtype='float64')
+            label = paddle.data(name='label', shape=[10], dtype='int64')
+            nll_loss = paddle.nn.loss.NLLLoss(name='nll_loss')
+            res = nll_loss(x, label)
+            self.assertTrue(res.name.startswith('nll_loss'))
+
+
+class TestNLLLossInvalidArgs(unittest.TestCase):
+    def test_x_dim_value_error(self):
+        def test_x_dim_lt_2():
+            prog = paddle.static.Program()
+            startup_prog = paddle.static.Program()
+            place = paddle.CPUPlace()
+            with paddle.static.program_guard(prog, startup_prog):
+                x = paddle.data(name='x', shape=[10, ], dtype='float64')
+                label = paddle.data(name='label', shape=[10, ], dtype='float64')
+                nll_loss = paddle.nn.loss.NLLLoss()
+                res = nll_loss(x, label)
+
+        self.assertRaises(ValueError, test_x_dim_lt_2)
+
+        def test_x_dim_imperative_lt_2():
+            with fluid.dygraph.guard():
+                x_np = np.array(
+                    [0.88103855, 0.9908683, 0.6226845, 0.53331435,
+                     0.07999352]).astype(np.float32)
+                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
+                x = paddle.to_variable(x_np)
+                label = paddle.to_variable(label_np)
+                nll_loss = paddle.nn.loss.NLLLoss()
+                res = nll_loss(x, label)
+
+        self.assertRaises(ValueError, test_x_dim_imperative_lt_2)
+
+    def test_reduction_value_error(self):
+        def test_NLLLoss_reduction_not_sum_mean_none():
+            prog = paddle.static.Program()
+            startup_prog = paddle.static.Program()
+            place = paddle.CPUPlace()
+            with paddle.static.program_guard(prog, startup_prog):
+                x = paddle.data(name='x', shape=[10, 10], dtype='float64')
+                label = paddle.data(name='label', shape=[10], dtype='int64')
+                nll_loss = paddle.nn.loss.NLLLoss(reduction='')
+                res = nll_loss(x, label)
+
+        self.assertRaises(ValueError, test_NLLLoss_reduction_not_sum_mean_none)
+
+        def test_NLLLoss_reduction_imperative_not_sum_mean_none():
+            with fluid.dygraph.guard():
+                x_np = np.array(
+                    [[0.88103855, 0.9908683, 0.6226845],
+                     [0.53331435, 0.07999352, 0.8549948],
+                     [0.25879037, 0.39530203, 0.698465],
+                     [0.73427284, 0.63575995, 0.18827209],
+                     [0.05689114, 0.0862954, 0.6325046]]).astype(np.float32)
+                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
+                x = paddle.to_variable(x_np)
+                label = paddle.to_variable(label_np)
+                nll_loss = paddle.nn.loss.NLLLoss(reduction='')
+                res = nll_loss(x, label)
+
+        self.assertRaises(ValueError,
+                          test_NLLLoss_reduction_imperative_not_sum_mean_none)
+
+        def test_nll_loss_function_reduction_not_sum_mean_none():
+            prog = paddle.static.Program()
+            startup_prog = paddle.static.Program()
+            place = paddle.CPUPlace()
+            with paddle.static.program_guard(prog, startup_prog):
+                x = paddle.data(name='x', shape=[10, 10], dtype='float64')
+                label = paddle.data(name='label', shape=[10], dtype='int64')
+                res = paddle.nn.functional.nll_loss(x, label, reduction='')
+
+        self.assertRaises(ValueError,
+                          test_nll_loss_function_reduction_not_sum_mean_none)
+
+        def test_nll_loss_function_reduction_imperative_not_sum_mean_none():
+            with fluid.dygraph.guard():
+                x_np = np.array(
+                    [[0.88103855, 0.9908683, 0.6226845],
+                     [0.53331435, 0.07999352, 0.8549948],
+                     [0.25879037, 0.39530203, 0.698465],
+                     [0.73427284, 0.63575995, 0.18827209],
+                     [0.05689114, 0.0862954, 0.6325046]]).astype(np.float32)
+                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
+                x = paddle.to_variable(x_np)
+                label = paddle.to_variable(label_np)
+                res = paddle.nn.functional.nll_loss(x, label, reduction='')
+
+        self.assertRaises(
+            ValueError,
+            test_nll_loss_function_reduction_imperative_not_sum_mean_none)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/functional/__init__.py

@@ -131,6 +131,7 @@ from .loss import l1_loss  #DEFINE_ALIAS
 from .loss import log_loss  #DEFINE_ALIAS
 from .loss import margin_rank_loss  #DEFINE_ALIAS
 from .loss import mse_loss  #DEFINE_ALIAS
+from .loss import nll_loss  #DEFINE_ALIAS
 # from .loss import nce        #DEFINE_ALIAS
 from .loss import npair_loss  #DEFINE_ALIAS
 from .loss import rank_loss  #DEFINE_ALIAS

python/paddle/nn/functional/loss.py

@@ -27,6 +27,7 @@ from ...fluid.layers import log_loss  #DEFINE_ALIAS
 from ...fluid.layers import mse_loss  #DEFINE_ALIAS
 from ...fluid.layers import npair_loss  #DEFINE_ALIAS
 from ...fluid.layers import rank_loss  #DEFINE_ALIAS
+from ...fluid.layers import reshape
 from ...fluid.layers import sigmoid_cross_entropy_with_logits  #DEFINE_ALIAS
 from ...fluid.layers import sigmoid_focal_loss  #DEFINE_ALIAS
 from ...fluid.layers import smooth_l1  #DEFINE_ALIAS
@@ -39,6 +40,9 @@ from ...fluid.layers import edit_distance  #DEFINE_ALIAS
 from ...fluid.layers import huber_loss  #DEFINE_ALIAS
 from ...fluid.layers import margin_rank_loss  #DEFINE_ALIAS
 from ...fluid.layers import sampled_softmax_with_cross_entropy  #DEFINE_ALIAS
+from ...fluid.layer_helper import LayerHelper
+from ...fluid.framework import in_dygraph_mode
+from ...fluid.framework import Variable
 
 __all__ = [
     'bpr_loss',
@@ -54,6 +58,7 @@ __all__ = [
     'margin_rank_loss',
     'mse_loss',
     #       'nce',
+    'nll_loss',
     'npair_loss',
     'rank_loss',
     'sampled_softmax_with_cross_entropy',
@@ -154,3 +159,112 @@ def l1_loss(x, label, reduction='mean', name=None):
         return paddle.mean(unreduced, name=name)
     else:
         return paddle.elementwise_sub(x, label, act='abs', name=name)
+
+
+def nll_loss(input,
+             label,
+             weight=None,
+             ignore_index=-100,
+             reduction='mean',
+             name=None):
+    """
+    This api returns negative log likelihood.
+    See more detail in :ref:`api_nn_loss_NLLLoss` .
+
+    Parameters:
+         input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes.
+             But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`.
+             The data type is float32, float64.
+         label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`.
+             The data type is int64.
+         weight (Tensor, optional): Weight tensor, a manual rescaling weight given
+             to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise,
+             it treated as if having all ones. the data type is
+             float32, float64, Default is ``'None'``.
+         ignore_index (int64, optional): Specifies a target value that is ignored
+             and does not contribute to the input gradient.
+         reduction (str, optional): Indicate how to average the loss,
+             the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
+             If `reduction` is ``'mean'``, the reduced mean loss is returned;
+             if `reduction` is ``'sum'``, the reduced sum loss is returned;
+             if `reduction` is ``'none'``, no reduction will be apllied.
+             Default is ``'mean'``.
+         name (str, optional): Name for the operation (optional, default is None).
+             For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+         `Tensor`, the value of negative log likelihood loss.
+
+    Examples:
+        .. code-block:: python
+                import paddle
+                import numpy as np
+                from paddle.nn.functional import nll_loss
+                log_softmax = paddle.nn.LogSoftmax(axis=1)
+
+                input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
+                                     [0.53331435, 0.07999352, 0.8549948 ],
+                                     [0.25879037, 0.39530203, 0.698465  ],
+                                     [0.73427284, 0.63575995, 0.18827209],
+                                     [0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
+                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
+
+                place = paddle.CPUPlace()
+                paddle.disable_static(place)
+                input = paddle.to_variable(input_np)
+                log_out = log_softmax(input)
+                label = paddle.to_variable(label_np)
+                result = nll_loss(log_out, label)
+                print(result.numpy()) # [1.0720209]
+    """
+    if reduction not in ['sum', 'mean', 'none']:
+        raise ValueError(
+            "The value of 'reduction' in nll_loss should be 'sum', 'mean' or "
+            "'none', but received %s, which is not allowed." % reduction)
+
+    input_shape = list(input.shape)
+    input_dims = len(input_shape)
+    if input_dims < 2:
+        raise ValueError('Expected 2 or more dimensions (got {})'.format(
+            input_dims))
+    n = input_shape[0]
+    c = input_shape[1]
+    if in_dygraph_mode():
+        if input_dims != 2 and input_dims != 4:
+            input, _ = core.ops.reshape2(input, 'shape', [n, c, 1, -1])
+            label, _ = core.ops.reshape2(label, 'shape', [n, 1, -1])
+            out_shape = [n] + input_shape[2:]
+        out, total_weight = core.ops.nll_loss(input, label, weight,
+                                              'ignore_index', ignore_index,
+                                              'reduction', reduction)
+        if input_dims != 2 and input_dims != 4 and reduction == 'none':
+            out, _ = core.ops.reshape2(out, 'shape', out_shape)
+        return out
+
+    helper = LayerHelper('nll_loss', **locals())
+
+    if input_dims != 2 and input_dims != 4:
+        input = reshape(input, shape=[n, c, 1, -1])
+        label = reshape(label, shape=[n, 1, -1])
+        out_shape = [n] + input_shape[2:]
+
+    fluid.data_feeder.check_variable_and_dtype(
+        input, 'input', ['float32', 'float64'], 'nll_loss')
+    fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
+                                               'nll_loss')
+    inputs = {'X': input, 'Label': label}
+    attrs = {'reduction': reduction, 'ignore_index': ignore_index}
+    if weight is not None:
+        if isinstance(weight, Variable):
+            inputs['Weight'] = weight
+
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    total_weight = helper.create_variable_for_type_inference(dtype=input.dtype)
+    outputs = {'Out': out, 'Total_weight': total_weight}
+
+    helper.append_op(
+        type='nll_loss', inputs=inputs, outputs=outputs, attrs=attrs)
+    if input_dims != 2 and input_dims != 4 and reduction == 'none':
+        out = reshape(out, shape=out_shape)
+
+    return out

python/paddle/nn/layer/loss.py

@@ -15,6 +15,7 @@
 # TODO: define loss functions of neural network  
 import paddle.fluid as fluid
 import paddle
+from .. import functional as F
 
 __all__ = [
     #       'NCELoss',
@@ -460,11 +461,11 @@ class NLLLoss(fluid.dygraph.Layer):
 	:alias_main: paddle.nn.NLLLoss
 	:alias: paddle.nn.NLLLoss,paddle.nn.layer.NLLLoss,paddle.nn.layer.loss.NLLLoss
 
-    This op accepts input and target label and returns negative log likelihood 
+    This class accepts input and target label and returns negative log likelihood
     cross error. It is useful to train a classification problem with C classes.
      
     The input for the loss is epected to contain log-probabilities of
-    each classes. It hs to be a Tensor of size either (batch_size, C) or 
+    each classes. It has to be a Tensor of size either (batch_size, C) or
     (batch_size, C, d1, d2, ..., dK) with K >= 1 for the K-dimensional case.
     The label for the loss should be a class index in the range [0, C-1]
     where C is the number of classes. If ignore_index is specified, the
@@ -494,106 +495,77 @@ class NLLLoss(fluid.dygraph.Layer):
         \\end{cases}
 
     Parameters:
-        input (Variable): Input tensor, the data type is float32, float64. 
-        label (Variable): Label tensor, the data type is int64_t.
-        weight (Variable, optional): Weight tensor, a manual rescaling weight given
-            to each class. If given, it has to be a Tensor of size `C`. Otherwise,
+        weight (Tensor, optional): Weight tensor, a manual rescaling weight given
+            to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise,
             it treated as if having all ones. the data type is 
             float32, float64, Default is ``'None'``.
+        ignore_index (int64, optional): Specifies a target value that is ignored
+            and does not contribute to the input gradient.
         reduction (str, optional): Indicate how to average the loss, 
             the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
-            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; 
+            If `reduction` is ``'mean'``, the reduced mean loss is returned;
+            if `reduction` is ``'sum'``, the reduced sum loss is returned;
+            if `reduction` is ``'none'``, no reduction will be apllied.
             Default is ``'mean'``.
-        ignore_index (int64, optional): Specifies a target value that is ignored
-            and does not contribute to the input gradient.
+         name (str, optional): Name for the operation (optional, default is None).
+             For more information, please refer to :ref:`api_guide_Name`.
 
-    Returns:
-        The tensor variable storing the nll_loss.
-
-    Return type: Variable.
+    Shape:
+        input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes.
+            But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`.
+            The data type is float32, float64.
+        label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`.
+            The data type is int64.
+        output (Tensor): the `negative log likelihood loss` between input `x` and `label`.
+            If `reduction` is `'none'`, the shape is `[N, *]`.
+            If `reduction` is `'sum'` or `'mean'`, the shape is `[1]`.
 
     Examples:
-
         .. code-block:: python
 
-            # declarative mode
-            import paddle.fluid as fluid
-            import numpy as np
                 import paddle
+                import numpy as np
 
-            input_np = np.random.random(size=(10, 10)).astype(np.float32)
-            label_np = np.random.randint(0, 10, size=(10,)).astype(np.int64)
-            prog = fluid.Program()
-            startup_prog = fluid.Program()
-            place = fluid.CPUPlace()
-            with fluid.program_guard(prog, startup_prog):
-                input = fluid.data(name='input', shape=[10, 10], dtype='float32')
-                label = fluid.data(name='label', shape=[10], dtype='int64')
-                nll_loss = paddle.nn.loss.NLLLoss()
-                res = nll_loss(input, label)
+                nll_loss = paddle.nn.layer.NLLLoss()
+                log_softmax = paddle.nn.LogSoftmax(axis=1)
 
-                exe = fluid.Executor(place)
-                static_result = exe.run(
-                    prog,
-                    feed={"input": input_np,
-                          "label": label_np},
-                    fetch_list=[res])
-            print(static_result)
+                input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
+                                 [0.53331435, 0.07999352, 0.8549948 ],
+                                 [0.25879037, 0.39530203, 0.698465  ],
+                                 [0.73427284, 0.63575995, 0.18827209],
+                                 [0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
+                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
+
+                place = paddle.CPUPlace()
+                paddle.disable_static(place)
+                input = paddle.to_variable(input_np)
+                log_out = log_softmax(input)
+                label = paddle.to_variable(label_np)
+                result = nll_loss(log_out, label)
+                print(result.numpy()) # [1.0720209]
 
-            # imperative mode
-            import paddle.fluid.dygraph as dg
-            with dg.guard(place) as g:
-                input = dg.to_variable(input_np)
-                label = dg.to_variable(label_np)
-                output = nll_loss(input, label)
-                print(output.numpy())
     """
 
-    def __init__(self, weight=None, reduction='mean', ignore_index=-100):
+    def __init__(self,
+                 weight=None,
+                 ignore_index=-100,
+                 reduction='mean',
+                 name=None):
+        if reduction not in ['sum', 'mean', 'none']:
+            raise ValueError(
+                "The value of 'reduction' in nll_loss should be 'sum', 'mean' or "
+                "'none', but received %s, which is not allowed." % reduction)
         super(NLLLoss, self).__init__()
-        self.weight = weight
-        self.reduction = reduction
-        self.ignore_index = ignore_index
+        self._weight = weight
+        self._ignore_index = ignore_index
+        self._reduction = reduction
+        self._name = name
 
     def forward(self, input, label):
-        dtype = self._helper.input_dtype(input)
-
-        fluid.data_feeder.check_variable_and_dtype(
-            input, 'input', ['float32', 'float64'], 'nll_loss')
-        fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
-                                                   'nll_loss')
-
-        if self.reduction not in ['sum', 'mean', 'none']:
-            raise ValueError(
-                "The value of 'reduction' in nll_loss should be 'sum', 'mean' or 'none', but "
-                "received %s, which is not allowed." % self.reduction)
-
-        x_shape = list(input.shape)
-        n = x_shape[0]
-        c = x_shape[1]
-        x_dims = len(x_shape)
-        if x_dims < 2:
-            raise ValueError('Expected 2 or more dimensions (got {})'.format(
-                x_dims))
-        if x_dims != 2 and x_dims != 4:
-            input = fluid.layers.reshape(input, shape=[n, c, 1, -1])
-            label = fluid.layers.reshape(label, shape=[n, 1, -1])
-            out_shape = [n] + x_shape[2:]
-
-        inputs = {'X': input, 'Label': label}
-        attrs = {'reduction': self.reduction, 'ignore_index': self.ignore_index}
-        if self.weight is not None:
-            if isinstance(self.weight, fluid.framework.Variable):
-                inputs['Weight'] = self.weight
-
-        out = self._helper.create_variable_for_type_inference(dtype=input.dtype)
-        total_weight = self._helper.create_variable_for_type_inference(
-            dtype=input.dtype)
-        outputs = {'Out': out, 'Total_weight': total_weight}
-
-        self._helper.append_op(
-            type='nll_loss', inputs=inputs, outputs=outputs, attrs=attrs)
-        if x_dims != 2 and x_dims != 4 and self.reduction == 'none':
-            out = fluid.layers.reshape(out, shape=out_shape)
-
-        return out
+        return F.nll_loss(
+            input,
+            label,
+            weight=self._weight,
+            ignore_index=self._ignore_index,
+            reduction=self._reduction,
+            name=self._name)