Merge branch 'master' of https://github.com/PaddlePaddle/hapi into seq_tag

examples/tsm/infer.py

@@ -20,6 +20,7 @@ import argparse
 import numpy as np
 
 from hapi.model import Input, set_device
+from hapi.vision.transforms import Compose
 
 from check import check_gpu, check_version
 from modeling import tsm_resnet50

examples/tsm/main.py

@@ -24,6 +24,7 @@ from paddle.fluid.dygraph.parallel import ParallelEnv
 
 from hapi.model import Model, CrossEntropy, Input, set_device
 from hapi.metrics import Accuracy
+from hapi.vision.transforms import Compose
 
 from modeling import tsm_resnet50
 from check import check_gpu, check_version

examples/tsm/transforms.py

@@ -21,24 +21,7 @@ import logging
 logger = logging.getLogger(__name__)
 
 __all__ = ['GroupScale', 'GroupMultiScaleCrop', 'GroupRandomCrop',
-           'GroupRandomFlip', 'GroupCenterCrop', 'NormalizeImage',
-           'Compose']
-
-
-class Compose(object):
-    def __init__(self, transforms=[]):
-        self.transforms = transforms
-
-    def __call__(self, *data):
-        for f in self.transforms:
-            try:
-                data = f(*data)
-            except Exception as e:
-                stack_info = traceback.format_exc()
-                logger.info("fail to perform transform [{}] with error: "
-                        "{} and stack:\n{}".format(f, e, str(stack_info)))
-                raise e
-        return data
+           'GroupRandomFlip', 'GroupCenterCrop', 'NormalizeImage']
 
 
 class GroupScale(object):

examples/yolov3/main.py

@@ -27,7 +27,7 @@ from paddle.io import DataLoader
 
 from hapi.model import Model, Input, set_device
 from hapi.distributed import DistributedBatchSampler
-from hapi.vision.transforms import BatchCompose
+from hapi.vision.transforms import Compose, BatchCompose
 
 from modeling import yolov3_darknet53, YoloLoss
 from coco import COCODataset

examples/yolov3/transforms.py

@@ -20,7 +20,6 @@ import traceback
 import numpy as np
 
 __all__ = [
-    "Compose",
     'ColorDistort',
     'RandomExpand',
     'RandomCrop',
@@ -34,37 +33,6 @@ __all__ = [
 ]
 
 
-class Compose(object):
-    """Composes several transforms together.
-
-    Args:
-        transforms (list of ``Transform`` objects): list of transforms to compose.
-
-    """
-
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, *data):
-        for f in self.transforms:
-            try:
-                data = f(*data)
-            except Exception as e:
-                stack_info = traceback.format_exc()
-                print("fail to perform transform [{}] with error: "
-                      "{} and stack:\n{}".format(f, e, str(stack_info)))
-                raise e
-        return data
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        for t in self.transforms:
-            format_string += '\n'
-            format_string += '    {0}'.format(t)
-        format_string += '\n)'
-        return format_string
-
-
 class ColorDistort(object):
     """Random color distortion.
 

hapi/datasets/flowers.py

@@ -121,7 +121,7 @@ class Flowers(Dataset):
         image = np.array(Image.open(io.BytesIO(image)))
 
         if self.transform is not None:
-            image, label = self.transform(image, label)
+            image = self.transform(image)
 
         return image, label
 

hapi/datasets/mnist.py

@@ -149,7 +149,7 @@ class MNIST(Dataset):
     def __getitem__(self, idx):
         image, label = self.images[idx], self.labels[idx]
         if self.transform is not None:
-            image, label = self.transform(image, label)
+            image = self.transform(image)
         return image, label
 
     def __len__(self):

hapi/model.py

@@ -874,8 +874,16 @@ class Model(fluid.dygraph.Layer):
             global _parallel_context_initialized
             if ParallelEnv().nranks > 1 and not _parallel_context_initialized:
                 if fluid.in_dygraph_mode():
+                    main_prog_seed = fluid.default_main_program().random_seed
+                    startup_prog_seed = fluid.default_startup_program(
+                    ).random_seed
                     fluid.disable_dygraph()
                     fluid.enable_dygraph(self._place)
+                    # enable_dygraph would create and switch to a new program,
+                    # thus also copy seed to the new program
+                    fluid.default_main_program().random_seed = main_prog_seed
+                    fluid.default_startup_program(
+                    ).random_seed = startup_prog_seed
                     fluid.dygraph.parallel.prepare_context()
                 else:
                     prepare_distributed_context(self._place)

hapi/vision/transforms/transforms.py

@@ -29,8 +29,10 @@ import traceback
 from . import functional as F
 
 if sys.version_info < (3, 3):
+    Sequence = collections.Sequence
     Iterable = collections.Iterable
 else:
+    Sequence = collections.abc.Sequence
     Iterable = collections.abc.Iterable
 
 __all__ = [
@@ -54,19 +56,44 @@ __all__ = [
 
 
 class Compose(object):
-    """Composes several transforms together.
+    """
+    Composes several transforms together use for composing list of transforms
+    together for a dataset transform.
 
     Args:
         transforms (list of ``Transform`` objects): list of transforms to compose.
 
+    Returns:
+        A compose object which is callable, __call__ for this Compose
+        object will call each given :attr:`transforms` sequencely.
+
+    Examples:
+    
+        .. code-block:: python
+
+            from hapi.datasets import Flowers
+            from hapi.vision.transforms import Compose, ColorJitter, Resize
+
+            transform = Compose([ColorJitter(), Resize(size=608)])
+            flowers = Flowers(mode='test', transform=transform)
+
+            for i in range(10):
+                sample = flowers[i]
+                print(sample[0].shape, sample[1])
+
     """
 
     def __init__(self, transforms):
         self.transforms = transforms
 
-    def __call__(self, data):
+    def __call__(self, *data):
         for f in self.transforms:
             try:
+                # multi-fileds in a sample
+                if isinstance(data, Sequence):
+                    data = f(*data)
+                # single field in a sample, call transform directly
+                else:
                     data = f(data)
             except Exception as e:
                 stack_info = traceback.format_exc()