Merge pull request #15 from guoshengCS/fix-data-train

Reorganize data from data_loader into inputs and labels.

model.py

@@ -29,6 +29,7 @@ from paddle.fluid.executor import global_scope
 from paddle.fluid.io import is_belong_to_optimizer
 from paddle.fluid.dygraph.base import to_variable
 from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.fluid.layers.utils import flatten
 from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
 from paddle.fluid.incubate.fleet.base import role_maker
 from paddle.fluid.io import DataLoader, Dataset
@@ -414,13 +415,7 @@ class StaticGraphAdapter(object):
         losses = []
         metrics = []
         with fluid.program_guard(prog, self._startup_prog):
-            if isinstance(self.model._inputs, dict):
-                ins = [
-                    self.model._inputs[n]
-                    for n in extract_args(self.model.forward) if n != 'self'
-                ]
-            else:
-                ins = self.model._inputs
+            ins = self.model._inputs
             lbls = self.model._labels if self.model._labels else []
             inputs = [k.forward() for k in to_list(ins)]
             labels = [k.forward() for k in to_list(lbls)]
@@ -867,8 +862,10 @@ class Model(fluid.dygraph.Layer):
                     metric.__class__.__name__)
         self._metrics = to_list(metrics)
 
-        self._inputs = inputs
-        self._labels = labels
+        self._inputs = to_list(inputs) if not isinstance(inputs, dict) else [
+            inputs[n] for n in extract_args(self.forward) if n != 'self'
+        ]
+        self._labels = to_list(labels)
 
         if not in_dygraph_mode():
             self._adapter.prepare()
@@ -1174,17 +1171,30 @@ class Model(fluid.dygraph.Layer):
             callbacks.on_epoch_begin(epoch)
 
         for step, data in enumerate(data_loader):
-            if not fluid.in_dygraph_mode():
-                data = data[0]
-                batch_size = data[0].shape()[0]
-            else:
-                batch_size = data[0].shape[0]
+            # data might come from different types of data_loader and have
+            # different format, as following:
+            # 1. DataLoader in static graph:
+            #    [[input1, input2, ..., label1, lable2, ...]]
+            # 2. DataLoader in dygraph
+            #    [input1, input2, ..., label1, lable2, ...]
+            # 3. custumed iterator yield concated inputs and labels:
+            #   [input1, input2, ..., label1, lable2, ...]
+            # 4. custumed iterator yield seperated inputs and labels:
+            #   ([input1, input2, ...], [label1, lable2, ...])
+            # To handle all of these, flatten (nested) list to list.
+            data = flatten(data)
+            # LoDTensor.shape is callable, where LoDTensor comes from
+            # DataLoader in static graph
+            batch_size = data[0].shape()[0] if callable(data[
+                0].shape) else data[0].shape[0]
 
             callbacks.on_batch_begin(mode, step, logs)
             if mode == 'train':
-                outs = self.train(*data)
+                outs = self.train(data[:len(self._inputs)],
+                                  data[len(self._inputs):])
             else:
-                outs = self.eval(*data)
+                outs = self.eval(data[:len(self._inputs)],
+                                 data[len(self._inputs):])
 
             # losses
             loss = outs[0] if self._metrics else outs

progressbar.py

@@ -107,7 +107,7 @@ class ProgressBar(object):
                 eta = time_per_unit * (self._num - current_num)
                 if eta > 3600:
                     eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) //
-                                                60, eta % 60)
+                                                   60, eta % 60)
                 elif eta > 60:
                     eta_format = '%d:%02d' % (eta // 60, eta % 60)
                 else:
@@ -148,7 +148,7 @@ class ProgressBar(object):
                         else:
                             info += ' %.4e' % v
                     elif isinstance(v, np.ndarray) and \
-                        isinstance(v.size, 1) and \
+                        v.size == 1 and \
                         isinstance(v.dtype, (np.float32, np.float64)):
                         if abs(v[0]) > 1e-3:
                             info += ' %.4f' % v[0]