Fix multi-loss

model.py

@@ -18,7 +18,6 @@ import inspect
 import os
 import pickle
 import numpy as np
-import itertools
 from collections import Iterable
 from collections import OrderedDict
 
@@ -742,14 +741,17 @@ class Model(fluid.dygraph.Layer):
                 else:
                     outs = self.eval(*data)
 
-                metrics = list(itertools.chain.from_iterable(outs))
-                metrics = [np.mean(metrics[0])]
+                # losses
+                loss = outs[0] if self._metrics else outs
+                metrics = [[l[0] for l in loss]]
+
+                # metrics
                 for metric in self._metrics:
                     res = metric.accumulate()
                     metrics.extend(to_list(res))
                 assert len(metrics_name) == len(metrics)
                 for k, v in zip(metrics_name, metrics):
-                    logs[k] = np.mean(v)
+                    logs[k] = v
 
                 logs['step'] = step
                 logs['batch_size'] = data[0].shape[0]
@@ -761,7 +763,7 @@ class Model(fluid.dygraph.Layer):
         cbks.on_begin('train')
         for epoch in range(epochs):
             cbks.on_epoch_begin(epoch)
-            # FIXME: adapte to DataLoader
+            # FIXME: adapt to DataLoader
             loader = train_loader
             if not isinstance(train_loader, Iterable):
                 loader = train_loader()
@@ -770,7 +772,7 @@ class Model(fluid.dygraph.Layer):
 
             if do_eval and epoch % eval_freq == 0:
                 cbks.on_begin('eval', logs)
-                # FIXME: adapte to DataLoader
+                # FIXME: adapt to DataLoader
                 loader = eval_loader
                 if not isinstance(eval_loader, Iterable):
                     loader = eval_loader()

progressbar.py

@@ -91,15 +91,17 @@ class ProgressBar(object):
             self._total_width = len(bar_chars)
             sys.stdout.write(bar_chars)
 
-            for k, v in values:
+            for k, val in values:
                 info += ' - %s:' % k
-                if isinstance(v, (float, np.float32, np.float64)):
-                    if abs(v) > 1e-3:
-                        info += ' %.4f' % v
+                val = val if isinstance(val, list) else [val]
+                for i, v in enumerate(val):
+                    if isinstance(v, (float, np.float32, np.float64)):
+                        if abs(v) > 1e-3:
+                            info += ' %.4f' % v
+                        else:
+                            info += ' %.4e' % v
                     else:
-                        info += ' %.4e' % v
-                else:
-                    info += ' %s' % v
+                        info += ' %s' % v
 
             if self._num is not None and current_num < self._num:
                 eta = time_per_unit * (self._num - current_num)
@@ -136,22 +138,24 @@ class ProgressBar(object):
                 count = 'step %3d' % current_num
             info = count + info
 
-            for k, v in values:
+            for k, val in values:
                 info += ' - %s:' % k
-                if isinstance(v, (float, np.float32, np.float64)):
-                    if abs(v) > 1e-3:
-                        info += ' %.4f' % v
-                    else:
-                        info += ' %.4e' % v
-                elif isinstance(v, np.ndarray) and \
-                     isinstance(v.size, 1) and \
-                     isinstance(v.dtype, (np.float32, np.float64)):
-                    if abs(v[0]) > 1e-3:
-                        info += ' %.4f' % v[0]
+                val = val if isinstance(val, list) else [val]
+                for v in val:
+                    if isinstance(v, (float, np.float32, np.float64)):
+                        if abs(v) > 1e-3:
+                            info += ' %.4f' % v
+                        else:
+                            info += ' %.4e' % v
+                    elif isinstance(v, np.ndarray) and \
+                         isinstance(v.size, 1) and \
+                         isinstance(v.dtype, (np.float32, np.float64)):
+                        if abs(v[0]) > 1e-3:
+                            info += ' %.4f' % v[0]
+                        else:
+                            info += ' %.4e' % v[0]
                     else:
-                        info += ' %.4e' % v[0]
-                else:
-                    info += ' %s' % v
+                        info += ' %s' % v
 
             info += fps
             info += '\n'

tests/test_model.py

@@ -24,7 +24,7 @@ import contextlib
 import paddle
 from paddle import fluid
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
-from model import Model, CrossEntropy, Input
+from model import Model, CrossEntropy, Input, Loss
 from metrics import Accuracy
 from callbacks import ProgBarLogger
 
@@ -103,47 +103,65 @@ class MNIST(Model):
         return x
 
 
-def accuracy(pred, label, topk=(1, )):
-    maxk = max(topk)
-    pred = np.argsort(pred)[:, ::-1][:, :maxk]
-    correct = (pred == np.repeat(label, maxk, 1))
-
-    batch_size = label.shape[0]
-    res = []
-    for k in topk:
-        correct_k = correct[:, :k].sum()
-        res.append(100.0 * correct_k / batch_size)
-    return res
-
-
 @contextlib.contextmanager
 def null_guard():
     yield
 
 
+class MLP(Model):
+    def __init__(self):
+        super(MLP, self).__init__()
+        SIZE = 10
+        self._fc1 = Linear(784, 200, act="relu")
+        self._fc2 = Linear(200, 200, act="relu")
+        self._fc3 = Linear(200, 200, act="relu")
+        self._fc4 = Linear(200, 10, act="softmax")
+        self._fc5 = Linear(200, 10, act="softmax")
+
+    def forward(self, inputs):
+        x1 = self._fc1(inputs)
+        x2 = self._fc2(x1)
+        x3 = self._fc3(x2)
+        o1 = self._fc5(x3)
+        o2 = self._fc4(x2)
+        return o1, o2
+
+
+class MyCrossEntropy(Loss):
+    def __init__(self, average=True):
+        super(MyCrossEntropy, self).__init__()
+
+    def forward(self, outputs, labels):
+        loss1 = fluid.layers.cross_entropy(outputs[0], labels[0])
+        loss2 = fluid.layers.cross_entropy(outputs[1], labels[0])
+        return [loss1, loss2]
+
+
 class TestModel(unittest.TestCase):
-    def fit(self, dynamic):
+    def fit(self, dynamic, is_mlp=False):
+        im_shape = (-1, 784) if is_mlp else (-1, 1, 28, 28)
         guard = fluid.dygraph.guard() if dynamic else null_guard()
         batch_size = 128
         train_loader = fluid.io.xmap_readers(
-            lambda b: [np.array([x[0] for x in b]).reshape(-1, 1, 28, 28),
+            lambda b: [np.array([x[0] for x in b]).reshape(im_shape),
                        np.array([x[1] for x in b]).reshape(-1, 1)],
             paddle.batch(fluid.io.shuffle(paddle.dataset.mnist.train(), 6e4),
                          batch_size=batch_size, drop_last=True), 1, 1)
         val_loader = fluid.io.xmap_readers(
-            lambda b: [np.array([x[0] for x in b]).reshape(-1, 1, 28, 28),
+            lambda b: [np.array([x[0] for x in b]).reshape(im_shape),
                        np.array([x[1] for x in b]).reshape(-1, 1)],
             paddle.batch(paddle.dataset.mnist.test(),
                          batch_size=batch_size, drop_last=False), 1, 1)
         with guard:
-            inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
+            inputs = [Input(im_shape, 'float32', name='image')]
             labels = [Input([None, 1], 'int64', name='label')]
-            model = MNIST()
+            model = MNIST() if not is_mlp else MLP()
             optim = fluid.optimizer.Momentum(
                 learning_rate=0.01,
                 momentum=.9,
                 parameter_list=model.parameters())
-            model.prepare(optim, CrossEntropy(), Accuracy(), inputs, labels)
+            loss = CrossEntropy() if not is_mlp else MyCrossEntropy()
+            model.prepare(optim, loss, Accuracy(), inputs, labels)
             cbk = ProgBarLogger(50)
             model.fit(train_loader, val_loader, epochs=2, callbacks=cbk)
 
@@ -153,6 +171,12 @@ class TestModel(unittest.TestCase):
     def test_fit_dygraph(self):
         self.fit(True)
 
+    def test_fit_static_multi_loss(self):
+        self.fit(False, MyCrossEntropy())
+
+    def test_fit_dygraph_multi_loss(self):
+        self.fit(True, MyCrossEntropy())
+
 
 if __name__ == '__main__':
     unittest.main()