Merge pull request #14894 from velconia/add_huber_regression_loss_op

Add python interface for huber loss

paddle/fluid/API.spec

@@ -201,6 +201,7 @@ paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None
 paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
 paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)

paddle/fluid/operators/huber_loss_op.cc

@@ -124,8 +124,9 @@ REGISTER_OPERATOR(huber_loss, ops::HuberLossOp, ops::HuberLossOpMaker<float>,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(huber_loss_grad, ops::HuberLossGradOp);
 REGISTER_OP_CPU_KERNEL(
-    huber_loss,
-    ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>);
+    huber_loss, ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HuberLossKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     huber_loss_grad,
-    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, double>);

python/paddle/fluid/layers/nn.py

@@ -176,6 +176,7 @@ __all__ = [
     'get_tensor_from_selected_rows',
     'lstm',
     'psroi_pool',
+    'huber_loss',
 ]
 
 kIgnoreIndex = -100
@@ -497,7 +498,7 @@ def lstm(input,
     If Device is GPU, This op will use cudnn LSTM implementation
 
     A four-gate Long Short-Term Memory network with no peephole connections.
-    In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1, 
+    In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
     the cell input ct-1 and the previous layer input xt given matrices W, R and biases bW, bR from the following equations:
 
     $$ i_t = \\sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i) $$
@@ -524,19 +525,19 @@ def lstm(input,
     - $\tilde{c_t}$ is also called candidate hidden state,
       which is computed based on the current input and the previous hidden state.
 
-    Where sigmoid is the sigmoid operator: sigmoid(x) = 1 / (1 + e^-x), * represents a point-wise multiplication, 
+    Where sigmoid is the sigmoid operator: sigmoid(x) = 1 / (1 + e^-x), * represents a point-wise multiplication,
     X represensts a matrix multiplication
 
 
     Args:
         input (Variable): LSTM input tensor, shape MUST be ( seq_len x batch_size x input_size )
-        init_h(Variable): The initial hidden state of the LSTM                       
+        init_h(Variable): The initial hidden state of the LSTM
                        This is a tensor with shape ( num_layers x batch_size x hidden_size)
                        if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
         init_c(Variable): The initial cell state of the LSTM.
                        This is a tensor with shape ( num_layers x batch_size x hidden_size )
                        if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
-        max_len (int): max length of LSTM. the first dim of input tensor CAN NOT greater than max_len 
+        max_len (int): max length of LSTM. the first dim of input tensor CAN NOT greater than max_len
         hidden_size (int): hidden size of the LSTM
         num_layers (int): total layers number of the LSTM
         dropout_prob(float|0.0): dropout prob, dropout ONLY work between rnn layers, NOT between time steps
@@ -555,10 +556,10 @@ def lstm(input,
                          if is_bidirec set to True, shape will be ( seq_len x batch_sze x hidden_size*2)
         last_h(Tensor): the hidden state of the last step of LSTM
                         shape is ( num_layers x batch_size x hidden_size )
-                        if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)                     
+                        if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
         last_c(Tensor): the cell state of the last step of LSTM
                         shape is ( num_layers x batch_size x hidden_size )
-                        if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)                     
+                        if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
 
 
     Examples:
@@ -4658,7 +4659,7 @@ def ctc_greedy_decoder(input, blank, name=None):
                       [0.5, 0.1, 0.3, 0.1]]
 
         input.lod = [[4, 4]]
-      
+
         Computation:
 
         step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get:
@@ -4691,7 +4692,7 @@ def ctc_greedy_decoder(input, blank, name=None):
     Returns:
         Variable: CTC greedy decode result which is a 2-D tensor with shape [Lp, 1].
                   'Lp' is the sum if all output sequences' length. If all the sequences
-                  in result were empty, the result LoDTensor will be [-1] with 
+                  in result were empty, the result LoDTensor will be [-1] with
                   LoD [[]] and dims [1, 1].
 
     Examples:
@@ -5045,7 +5046,7 @@ def hsigmoid(input,
     """
     The hierarchical sigmoid operator is used to accelerate the training
     process of language model. This operator organizes the classes into a
-    complete binary tree, or you can use is_custom to pass your own tree to 
+    complete binary tree, or you can use is_custom to pass your own tree to
     implement hierarchical. Each leaf node represents a class(a word) and each
     internal node acts as a binary classifier. For each word there's a unique
     path from root to it's leaf node, hsigmoid calculate the cost for each
@@ -5061,7 +5062,7 @@ def hsigmoid(input,
         2. build a dict to store word_id -> word's leaf to root path, we call it path_table.
         3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code
          means label of each binary classification, using 1 indicate true, 0 indicate false.
-        4. now, each word should has its path and code along the path, you can pass a batch of path and code 
+        4. now, each word should has its path and code along the path, you can pass a batch of path and code
         related to the same batch of inputs.
 
 
@@ -5071,8 +5072,8 @@ def hsigmoid(input,
             and :math:`D` is the feature size.
         label (Variable): The tensor variable contains labels of training data.
             It's a tensor with shape is :math:`[N \\times 1]`.
-        num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set, 
-            it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num 
+        num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set,
+            it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num
             which indicates the num of classes using by binary classify.
         param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
              of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid
@@ -5085,15 +5086,15 @@ def hsigmoid(input,
              is not set, the bias is initialized zero. Default: None.
         name (str|None): A name for this layer(optional). If set None, the layer
              will be named automatically. Default: None.
-        path_table: (Variable|None) this variable can store each batch of samples' path to root, 
+        path_table: (Variable|None) this variable can store each batch of samples' path to root,
             it should be in leaf -> root order
-            path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like 
-            structure and each element in this array is indexes in parent nodes' Weight Matrix. 
-        path_code:  (Variable|None) this variable can store each batch of samples' code, 
+            path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like
+            structure and each element in this array is indexes in parent nodes' Weight Matrix.
+        path_code:  (Variable|None) this variable can store each batch of samples' code,
             each code consist with every code of parent nodes. it should be in leaf -> root order
-        is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is 
+        is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is
              set you need to set path_table/path_code/num_classes, otherwise num_classes should be set
-        is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient 
+        is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient
              of W and input will be sparse.
 
     Returns:
@@ -9377,3 +9378,51 @@ def psroi_pool(input,
             'pooled_width': pooled_width
         })
     return out
+
+
+def huber_loss(input, label, delta):
+    """
+    Huber loss is a loss function used in robust.
+    Huber loss can evaluate the fitness of input to label.
+    Different from MSE loss, Huber loss is more robust for outliers.
+
+    When the difference between input and label is large than delta
+    .. math::
+
+        huber\_loss = delta * (label - input) - 0.5 * delta * delta
+
+    When the difference between input and label is less than delta
+    .. math::
+
+        huber\_loss = 0.5 * (label - input) * (label - input)
+
+
+    Args:
+        input (Variable): This input is a probability computed by the previous operator.
+                          The first dimension is batch size, and the last dimension is 1.
+        label (Variable): The groud truth whose first dimension is batch size
+                          and last dimension is 1.
+        delta (float): The parameter of huber loss, which controls
+                       the range of outliers
+
+    Returns:
+        huber\_loss (Variable): The huber loss with shape [batch_size, 1].
+
+    Examples:
+        .. code-block:: python
+
+            predictions = fluid.layers.softmax(x)
+            loss = fluid.layers.huber_loss(input=predictions, label=label, 1.0)
+    """
+    helper = LayerHelper('huber_loss', **locals())
+    residual = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    helper.append_op(
+        type='huber_loss',
+        inputs={'X': input,
+                'Y': label},
+        outputs={'Out': out,
+                 'Residual': residual},
+        attrs={'delta': delta})
+    return out