add pos_weight for SigmoidBinaryCrossEntropyLoss (#13612)

* add pos_weight for SigmoidBinaryCrossEntropyLoss in gluon.loss * Update loss.py * add test add test * set the default value of pos_weight to be 1 * fix unittest * set N be a random number * fix issues * test without random number * test with random N * fix * fix errors * fix errors * fix order * Update loss.py * Update loss.py * fix pylint * default pos_weight=None * add broadcast_mul and fix pylint * fix unittest * Update loss.py * Update loss.py * Update loss.py
apache · Mar 8, 2019 · ce9e3cf · ce9e3cf
1 parent 8668db7
commit ce9e3cf
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diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
@@ -217,6 +217,7 @@ List of Contributors
 * [Ming Yang](http://ufoym.com)
 * [Satya Krishna Gorti](https://github.com/satyakrishnagorti)
 * [Neo Chien](https://github.com/cchung100m)
+* [Wujie Zhou](https://github.com/eureka7mt)
 
 Label Bot
 ---------

diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
@@ -30,6 +30,7 @@
 from ..base import numeric_types
 from .block import HybridBlock
 
+
 def _apply_weighting(F, loss, weight=None, sample_weight=None):
     """Apply weighting to loss.
 
@@ -60,10 +61,12 @@ def _apply_weighting(F, loss, weight=None, sample_weight=None):
 
     return loss
 
+
 def _reshape_like(F, x, y):
     """Reshapes x to the same shape as y."""
     return x.reshape(y.shape) if F is ndarray else F.reshape_like(x, y)
 
+
 class Loss(HybridBlock):
     """Base class for loss.
 
@@ -74,6 +77,7 @@ class Loss(HybridBlock):
     batch_axis : int, default 0
         The axis that represents mini-batch.
     """
+
     def __init__(self, weight, batch_axis, **kwargs):
         super(Loss, self).__init__(**kwargs)
         self._weight = weight
@@ -126,13 +130,14 @@ class L2Loss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, weight=1., batch_axis=0, **kwargs):
         super(L2Loss, self).__init__(weight, batch_axis, **kwargs)
 
     def hybrid_forward(self, F, pred, label, sample_weight=None):
         label = _reshape_like(F, label, pred)
         loss = F.square(label - pred)
-        loss = _apply_weighting(F, loss, self._weight/2, sample_weight)
+        loss = _apply_weighting(F, loss, self._weight / 2, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)
 
 
@@ -164,6 +169,7 @@ class L1Loss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, weight=None, batch_axis=0, **kwargs):
         super(L1Loss, self).__init__(weight, batch_axis, **kwargs)
 
@@ -184,18 +190,22 @@ class SigmoidBinaryCrossEntropyLoss(Loss):
 
         prob = \frac{1}{1 + \exp(-{pred})}
 
-        L = - \sum_i {label}_i * \log({prob}_i) +
+        L = - \sum_i {label}_i * \log({prob}_i) * pos\_weight +
             (1 - {label}_i) * \log(1 - {prob}_i)
 
     If `from_sigmoid` is True, this loss computes:
 
     .. math::
 
-        L = - \sum_i {label}_i * \log({pred}_i) +
+        L = - \sum_i {label}_i * \log({pred}_i) * pos\_weight +
             (1 - {label}_i) * \log(1 - {pred}_i)
 
+    A tensor `pos_weight > 1` decreases the false negative count, hence increasing
+    the recall.
+    Conversely setting `pos_weight < 1` decreases the false positive count and
+    increases the precision.
 
-    `label` and `pred` can have arbitrary shape as long as they have the same
+    `pred` and `label` can have arbitrary shape as long as they have the same
     number of elements.
 
     Parameters
@@ -218,25 +228,45 @@ class SigmoidBinaryCrossEntropyLoss(Loss):
           to the same shape as pred. For example, if pred has shape (64, 10)
           and you want to weigh each sample in the batch separately,
           sample_weight should have shape (64, 1).
+        - **pos_weight**: a weighting tensor of positive examples. Must be a vector with length
+          equal to the number of classes.For example, if pred has shape (64, 10),
+          pos_weight should have shape (1, 10).
 
     Outputs:
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, **kwargs):
-        super(SigmoidBinaryCrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
+        super(SigmoidBinaryCrossEntropyLoss, self).__init__(
+            weight, batch_axis, **kwargs)
         self._from_sigmoid = from_sigmoid
 
-    def hybrid_forward(self, F, pred, label, sample_weight=None):
+    def hybrid_forward(self, F, pred, label, sample_weight=None, pos_weight=None):
         label = _reshape_like(F, label, pred)
         if not self._from_sigmoid:
-            # We use the stable formula: max(x, 0) - x * z + log(1 + exp(-abs(x)))
-            loss = F.relu(pred) - pred * label + F.Activation(-F.abs(pred), act_type='softrelu')
+            if pos_weight is None:
+                # We use the stable formula: max(x, 0) - x * z + log(1 + exp(-abs(x)))
+                loss = F.relu(pred) - pred * label + \
+                    F.Activation(-F.abs(pred), act_type='softrelu')
+            else:
+                # We use the stable formula: x - x * z + (1 + z * pos_weight - z) * \
+                #    (log(1 + exp(-abs(x))) + max(-x, 0))
+                log_weight = 1 + F.broadcast_mul(pos_weight - 1, label)
+                loss = pred - pred * label + log_weight * \
+                       (F.Activation(-F.abs(pred), act_type='softrelu') + F.relu(-pred))
         else:
-            loss = -(F.log(pred+1e-12)*label + F.log(1.-pred+1e-12)*(1.-label))
+            eps = 1e-12
+            if pos_weight is None:
+                loss = -(F.log(pred + eps) * label
+                         + F.log(1. - pred + eps) * (1. - label))
+            else:
+                loss = -(F.broadcast_mul(F.log(pred + eps) * label, pos_weight)
+                         + F.log(1. - pred + eps) * (1. - label))
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)
 
+
 SigmoidBCELoss = SigmoidBinaryCrossEntropyLoss
 
 
@@ -301,9 +331,11 @@ class SoftmaxCrossEntropyLoss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, axis=-1, sparse_label=True, from_logits=False, weight=None,
                  batch_axis=0, **kwargs):
-        super(SoftmaxCrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
+        super(SoftmaxCrossEntropyLoss, self).__init__(
+            weight, batch_axis, **kwargs)
         self._axis = axis
         self._sparse_label = sparse_label
         self._from_logits = from_logits
@@ -315,10 +347,11 @@ def hybrid_forward(self, F, pred, label, sample_weight=None):
             loss = -F.pick(pred, label, axis=self._axis, keepdims=True)
         else:
             label = _reshape_like(F, label, pred)
-            loss = -F.sum(pred*label, axis=self._axis, keepdims=True)
+            loss = -F.sum(pred * label, axis=self._axis, keepdims=True)
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)
 
+
 SoftmaxCELoss = SoftmaxCrossEntropyLoss
 
 
@@ -382,6 +415,7 @@ class KLDivLoss(Loss):
         `Kullback-Leibler divergence
         <https://en.wikipedia.org/wiki/Kullback-Leibler_divergence>`_
     """
+
     def __init__(self, from_logits=True, axis=-1, weight=None, batch_axis=0,
                  **kwargs):
         super(KLDivLoss, self).__init__(weight, batch_axis, **kwargs)
@@ -391,7 +425,7 @@ def __init__(self, from_logits=True, axis=-1, weight=None, batch_axis=0,
     def hybrid_forward(self, F, pred, label, sample_weight=None):
         if not self._from_logits:
             pred = F.log_softmax(pred, self._axis)
-        loss = label * (F.log(label+1e-12) - pred)
+        loss = label * (F.log(label + 1e-12) - pred)
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)
 
@@ -453,11 +487,12 @@ class CTCLoss(Loss):
         Sequence Data with Recurrent Neural Networks
         <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_
     """
+
     def __init__(self, layout='NTC', label_layout='NT', weight=None, **kwargs):
         assert layout in ['NTC', 'TNC'],\
-               "Only 'NTC' and 'TNC' layouts for pred are supported. Got: %s"%layout
+            "Only 'NTC' and 'TNC' layouts for pred are supported. Got: %s" % layout
         assert label_layout in ['NT', 'TN'],\
-               "Only 'NT' and 'TN' layouts for label are supported. Got: %s"%label_layout
+            "Only 'NT' and 'TN' layouts for label are supported. Got: %s" % label_layout
         self._layout = layout
         self._label_layout = label_layout
         batch_axis = label_layout.find('N')
@@ -512,6 +547,7 @@ class HuberLoss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, rho=1, weight=None, batch_axis=0, **kwargs):
         super(HuberLoss, self).__init__(weight, batch_axis, **kwargs)
         self._rho = rho
@@ -520,7 +556,7 @@ def hybrid_forward(self, F, pred, label, sample_weight=None):
         label = _reshape_like(F, label, pred)
         loss = F.abs(label - pred)
         loss = F.where(loss > self._rho, loss - 0.5 * self._rho,
-                       (0.5/self._rho) * F.square(loss))
+                       (0.5 / self._rho) * F.square(loss))
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)
 
@@ -558,6 +594,7 @@ class HingeLoss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs):
         super(HingeLoss, self).__init__(weight, batch_axis, **kwargs)
         self._margin = margin
@@ -602,6 +639,7 @@ class SquaredHingeLoss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs):
         super(SquaredHingeLoss, self).__init__(weight, batch_axis, **kwargs)
         self._margin = margin
@@ -647,6 +685,7 @@ class LogisticLoss(Loss):
         - **loss**: loss tensor with shape (batch_size,). Dimenions other than
           batch_axis are averaged out.
     """
+
     def __init__(self, weight=None, batch_axis=0, label_format='signed', **kwargs):
         super(LogisticLoss, self).__init__(weight, batch_axis, **kwargs)
         self._label_format = label_format
@@ -659,7 +698,8 @@ def hybrid_forward(self, F, pred, label, sample_weight=None):
         if self._label_format == 'signed':
             label = (label + 1.0) / 2.0  # Transform label to be either 0 or 1
         # Use a stable formula in computation
-        loss = F.relu(pred) - pred * label + F.Activation(-F.abs(pred), act_type='softrelu')
+        loss = F.relu(pred) - pred * label + \
+            F.Activation(-F.abs(pred), act_type='softrelu')
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return F.mean(loss, axis=self._batch_axis, exclude=True)
 
@@ -696,14 +736,15 @@ class TripletLoss(Loss):
     Outputs:
         - **loss**: loss tensor with shape (batch_size,).
     """
+
     def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs):
         super(TripletLoss, self).__init__(weight, batch_axis, **kwargs)
         self._margin = margin
 
     def hybrid_forward(self, F, pred, positive, negative):
         positive = _reshape_like(F, positive, pred)
         negative = _reshape_like(F, negative, pred)
-        loss = F.sum(F.square(positive-pred) - F.square(negative-pred),
+        loss = F.sum(F.square(positive - pred) - F.square(negative - pred),
                      axis=self._batch_axis, exclude=True)
         loss = F.relu(loss + self._margin)
         return _apply_weighting(F, loss, self._weight, None)
@@ -748,6 +789,7 @@ class PoissonNLLLoss(Loss):
     Outputs:
         - **loss**: Average loss (shape=(1,1)) of the loss tensor with shape (batch_size,).
     """
+
     def __init__(self, weight=None, from_logits=True, batch_axis=0, compute_full=False, **kwargs):
         super(PoissonNLLLoss, self).__init__(weight, batch_axis, **kwargs)
         self._from_logits = from_logits
@@ -761,7 +803,8 @@ def hybrid_forward(self, F, pred, target, sample_weight=None, epsilon=1e-08):
             loss = pred - target * F.log(pred + epsilon)
         if self._compute_full:
             # Using numpy's pi value
-            stirling_factor = target * F.log(target)- target + 0.5 * F.log(2 * target * np.pi)
+            stirling_factor = target * \
+                F.log(target) - target + 0.5 * F.log(2 * target * np.pi)
             target_gt_1 = target > 1
             stirling_factor *= target_gt_1
             loss += stirling_factor
@@ -804,6 +847,7 @@ class CosineEmbeddingLoss(Loss):
     Outputs:
         - **loss**: The loss tensor with shape (batch_size,).
     """
+
     def __init__(self, weight=None, batch_axis=0, margin=0, **kwargs):
         super(CosineEmbeddingLoss, self).__init__(weight, batch_axis, **kwargs)
         self._margin = margin
@@ -820,7 +864,8 @@ def hybrid_forward(self, F, input1, input2, label, sample_weight=None):
             z_array = F.array([0])
         else:
             z_array = F.zeros((1, 1))
-        cos_sim_b = F.broadcast_maximum(z_array, y_minus_1 * (cos_sim - self._margin), axis=1)
+        cos_sim_b = F.broadcast_maximum(
+            z_array, y_minus_1 * (cos_sim - self._margin), axis=1)
         loss = cos_sim_a + cos_sim_b
         loss = _apply_weighting(F, loss, self._weight, sample_weight)
         return loss
@@ -829,7 +874,7 @@ def _cosine_similarity(self, F, x, y, axis=-1):
         # Calculates the cosine similarity between 2 vectors
         x_norm = F.norm(x, axis=axis).reshape(-1, 1)
         y_norm = F.norm(y, axis=axis).reshape(-1, 1)
-        x_dot_y = F.sum(x*y, axis=axis).reshape(-1, 1)
+        x_dot_y = F.sum(x * y, axis=axis).reshape(-1, 1)
         if F is ndarray:
             eps_arr = F.array([1e-12])
         else:

diff --git a/tests/python/unittest/test_loss.py b/tests/python/unittest/test_loss.py
@@ -126,8 +126,8 @@ def test_logistic_loss_equal_bce():
     loss_bce = gluon.loss.SigmoidBCELoss(from_sigmoid=False)
     data = mx.random.uniform(-10, 10, shape=(N, 1))
     label = mx.nd.round(mx.random.uniform(0, 1, shape=(N, 1)))
-    assert_almost_equal(loss_binary(data, label).asnumpy(), loss_bce(data, label).asnumpy())
-    assert_almost_equal(loss_signed(data, 2 * label - 1).asnumpy(), loss_bce(data, label).asnumpy())
+    assert_almost_equal(loss_binary(data, label).asnumpy(), loss_bce(data, label).asnumpy(), atol=1e-6)
+    assert_almost_equal(loss_signed(data, 2 * label - 1).asnumpy(), loss_bce(data, label).asnumpy(), atol=1e-6)
 
 @with_seed()
 def test_kl_loss():
@@ -421,6 +421,37 @@ def test_poisson_nllloss_mod():
             optimizer='adam')
     assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.05
 
+@with_seed()
+def test_bce_loss_with_pos_weight():
+    # Suppose it's a multi-label classification
+    N = np.random.randint(5, 30)
+    data = mx.nd.random.uniform(-1, 1, shape=(N, 20))
+    label = mx.nd.array(np.random.randint(2, size=(N, 5)), dtype='float32')
+    pos_weight = mx.nd.random.uniform(0, 10, shape=(1, 5))
+    pos_weight = mx.nd.repeat(pos_weight, repeats=N, axis=0)
+    data_iter = mx.io.NDArrayIter(data, {'label': label, 'pos_w': pos_weight}, batch_size=10, label_name='label')
+    output = get_net(5)
+    l = mx.symbol.Variable('label')
+    pos_w = mx.symbol.Variable('pos_w')
+    Loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
+    loss = Loss(output, l, None, pos_w)
+    loss = mx.sym.make_loss(loss)
+    mod = mx.mod.Module(loss, data_names=('data',), label_names=('label', 'pos_w'))
+    mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.01},
+            eval_metric=mx.metric.Loss(), optimizer='adam',
+            initializer=mx.init.Xavier(magnitude=2))
+    assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.01
+    # Test against npy
+    data = mx.nd.random.uniform(-5, 5, shape=(N, 5))
+    label = mx.nd.array(np.random.randint(2, size=(N, 5)), dtype='float32')
+    pos_weight = mx.nd.random.uniform(0, 10, shape=(1, 5))
+    mx_bce_loss = Loss(data, label, None, pos_weight).asnumpy()
+    prob_npy = 1.0 / (1.0 + np.exp(-data.asnumpy()))
+    label_npy = label.asnumpy()
+    pos_weight_npy = pos_weight.asnumpy()
+    npy_bce_loss = (- label_npy * np.log(prob_npy)*pos_weight_npy - (1 - label_npy) * np.log(1 - prob_npy)).mean(axis=1)
+    assert_almost_equal(mx_bce_loss, npy_bce_loss, rtol=1e-4, atol=1e-5)
+
 
 if __name__ == '__main__':
     import nose