Merge branch 'dygraph' of https://github.com/wuyefeilin/PaddleSeg into dygraph

dygraph/infer.py

@@ -37,12 +37,8 @@ def parse_args():
     parser.add_argument(
         '--model_name',
         dest='model_name',
-        help=
-        'Model type for testing, which is one of ("UNet", "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", '
-        '"HRNet_W18", "HRNet_W30", "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", '
-        '"HRNet_W60", "HRNet_W64", "SE_HRNet_W18_Small_V1", "SE_HRNet_W18_Small_V2", "SE_HRNet_W18", '
-        '"SE_HRNet_W30", "SE_HRNet_W32", "SE_HRNet_W40","SE_HRNet_W44", "SE_HRNet_W48", '
-        '"SE_HRNet_W60", "SE_HRNet_W64")',
+        help='Model type for testing, which is one of {}'.format(
+            str(list(MODELS.keys()))),
         type=str,
         default='UNet')
 

dygraph/models/hrnet.py

@@ -18,7 +18,8 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
+from paddle.fluid.dygraph import SyncBatchNorm as BatchNorm
 
 __all__ = [
     "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30",

dygraph/models/unet.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-from paddle.fluid.dygraph import Conv2D, BatchNorm, Pool2D
+from paddle.fluid.dygraph import Conv2D, Pool2D
+from paddle.fluid.dygraph import SyncBatchNorm as BatchNorm
 
 
 class UNet(fluid.dygraph.Layer):

dygraph/train.py

@@ -38,12 +38,8 @@ def parse_args():
     parser.add_argument(
         '--model_name',
         dest='model_name',
-        help=
-        'Model type for training, which is one of ("UNet", "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", '
-        '"HRNet_W18", "HRNet_W30", "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", '
-        '"HRNet_W60", "HRNet_W64", "SE_HRNet_W18_Small_V1", "SE_HRNet_W18_Small_V2", "SE_HRNet_W18", '
-        '"SE_HRNet_W30", "SE_HRNet_W32", "SE_HRNet_W40","SE_HRNet_W44", "SE_HRNet_W48", '
-        '"SE_HRNet_W60", "SE_HRNet_W64")',
+        help='Model type for training, which is one of {}'.format(
+            str(list(MODELS.keys()))),
         type=str,
         default='UNet')
 
@@ -186,6 +182,7 @@ def train(model,
     total_steps = steps_per_epoch * (num_epochs - start_epoch)
     num_steps = 0
     best_mean_iou = -1.0
+    best_model_epoch = -1
     for epoch in range(start_epoch, num_epochs):
         for step, data in enumerate(loader):
             images = data[0]

dygraph/transforms/transforms.py

+# coding: utf8
 # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -33,6 +34,7 @@ class Compose:
         ValueError: transforms元素个数小于1。
 
     """
+
     def __init__(self, transforms, to_rgb=True):
         if not isinstance(transforms, list):
             raise TypeError('The transforms must be a list!')
@@ -86,6 +88,7 @@ class RandomHorizontalFlip:
         prob (float): 随机水平翻转的概率。默认值为0.5。
 
     """
+
     def __init__(self, prob=0.5):
         self.prob = prob
 
@@ -117,6 +120,7 @@ class RandomVerticalFlip:
     Args:
         prob (float): 随机垂直翻转的概率。默认值为0.1。
     """
+
     def __init__(self, prob=0.1):
         self.prob = prob
 
@@ -233,6 +237,7 @@ class ResizeByLong:
     Args:
         long_size (int): resize后图像的长边大小。
     """
+
     def __init__(self, long_size):
         self.long_size = long_size
 
@@ -274,6 +279,7 @@ class ResizeRangeScaling:
     Raises:
         ValueError: min_value大于max_value
     """
+
     def __init__(self, min_value=400, max_value=600):
         if min_value > max_value:
             raise ValueError('min_value must be less than max_value, '
@@ -321,6 +327,7 @@ class ResizeStepScaling:
     Raises:
         ValueError: min_scale_factor大于max_scale_factor
     """
+
     def __init__(self,
                  min_scale_factor=0.75,
                  max_scale_factor=1.25,
@@ -386,6 +393,7 @@ class Normalize:
     Raises:
         ValueError: mean或std不是list对象。std包含0。
     """
+
     def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
         self.mean = mean
         self.std = std
@@ -431,6 +439,7 @@ class Padding:
         TypeError: target_size不是int|list|tuple。
         ValueError:  target_size为list|tuple时元素个数不等于2。
     """
+
     def __init__(self,
                  target_size,
                  im_padding_value=[127.5, 127.5, 127.5],
@@ -483,7 +492,8 @@ class Padding:
                 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
                 .format(im_width, im_height, target_width, target_height))
         else:
-            im = cv2.copyMakeBorder(im,
+            im = cv2.copyMakeBorder(
+                im,
                 0,
                 pad_height,
                 0,
@@ -491,7 +501,8 @@ class Padding:
                 cv2.BORDER_CONSTANT,
                 value=self.im_padding_value)
             if label is not None:
-                label = cv2.copyMakeBorder(label,
+                label = cv2.copyMakeBorder(
+                    label,
                     0,
                     pad_height,
                     0,
@@ -516,6 +527,7 @@ class RandomPaddingCrop:
         TypeError: crop_size不是int/list/tuple。
         ValueError:  target_size为list/tuple时元素个数不等于2。
     """
+
     def __init__(self,
                  crop_size=512,
                  im_padding_value=[127.5, 127.5, 127.5],
@@ -564,7 +576,8 @@ class RandomPaddingCrop:
             pad_height = max(crop_height - img_height, 0)
             pad_width = max(crop_width - img_width, 0)
             if (pad_height > 0 or pad_width > 0):
-                im = cv2.copyMakeBorder(im,
+                im = cv2.copyMakeBorder(
+                    im,
                     0,
                     pad_height,
                     0,
@@ -572,7 +585,8 @@ class RandomPaddingCrop:
                     cv2.BORDER_CONSTANT,
                     value=self.im_padding_value)
                 if label is not None:
-                    label = cv2.copyMakeBorder(label,
+                    label = cv2.copyMakeBorder(
+                        label,
                         0,
                         pad_height,
                         0,
@@ -586,11 +600,11 @@ class RandomPaddingCrop:
                 h_off = np.random.randint(img_height - crop_height + 1)
                 w_off = np.random.randint(img_width - crop_width + 1)
 
-                im = im[h_off:(crop_height + h_off), w_off:(w_off +
-                                                            crop_width), :]
+                im = im[h_off:(crop_height + h_off), w_off:(
+                    w_off + crop_width), :]
                 if label is not None:
-                    label = label[h_off:(crop_height +
-                                         h_off), w_off:(w_off + crop_width)]
+                    label = label[h_off:(crop_height + h_off), w_off:(
+                        w_off + crop_width)]
         if label is None:
             return (im, im_info)
         else:
@@ -603,6 +617,7 @@ class RandomBlur:
     Args：
         prob (float): 图像模糊概率。默认为0.1。
     """
+
     def __init__(self, prob=0.1):
         self.prob = prob
 
@@ -650,6 +665,7 @@ class RandomRotation:
         label_padding_value (int): 标注图像padding的值。默认为255。
 
     """
+
     def __init__(self,
                  max_rotation=15,
                  im_padding_value=[127.5, 127.5, 127.5],
@@ -686,13 +702,15 @@ class RandomRotation:
             r[0, 2] += (nw / 2) - cx
             r[1, 2] += (nh / 2) - cy
             dsize = (nw, nh)
-            im = cv2.warpAffine(im,
+            im = cv2.warpAffine(
+                im,
                 r,
                 dsize=dsize,
                 flags=cv2.INTER_LINEAR,
                 borderMode=cv2.BORDER_CONSTANT,
                 borderValue=self.im_padding_value)
-            label = cv2.warpAffine(label,
+            label = cv2.warpAffine(
+                label,
                 r,
                 dsize=dsize,
                 flags=cv2.INTER_NEAREST,
@@ -713,6 +731,7 @@ class RandomScaleAspect:
         min_scale (float)：裁取图像占原始图像的面积比，取值[0，1]，为0时则返回原图。默认为0.5。
         aspect_ratio (float): 裁取图像的宽高比范围，非负值，为0时返回原图。默认为0.33。
     """
+
     def __init__(self, min_scale=0.5, aspect_ratio=0.33):
         self.min_scale = min_scale
         self.aspect_ratio = aspect_ratio
@@ -751,9 +770,11 @@ class RandomScaleAspect:
 
                     im = im[h1:(h1 + dh), w1:(w1 + dw), :]
                     label = label[h1:(h1 + dh), w1:(w1 + dw)]
-                    im = cv2.resize(im, (img_width, img_height),
+                    im = cv2.resize(
+                        im, (img_width, img_height),
                         interpolation=cv2.INTER_LINEAR)
-                    label = cv2.resize(label, (img_width, img_height),
+                    label = cv2.resize(
+                        label, (img_width, img_height),
                         interpolation=cv2.INTER_NEAREST)
                     break
         if label is None:
@@ -778,6 +799,7 @@ class RandomDistort:
         hue_range (int): 色调因子的范围。默认为18。
         hue_prob (float): 随机调整色调的概率。默认为0.5。
     """
+
     def __init__(self,
                  brightness_range=0.5,
                  brightness_prob=0.5,

dygraph/val.py

@@ -39,12 +39,8 @@ def parse_args():
     parser.add_argument(
         '--model_name',
         dest='model_name',
-        help=
-        'Model type for evaluation, which is one of ("UNet", "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", '
-        '"HRNet_W18", "HRNet_W30", "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", '
-        '"HRNet_W60", "HRNet_W64", "SE_HRNet_W18_Small_V1", "SE_HRNet_W18_Small_V2", "SE_HRNet_W18", '
-        '"SE_HRNet_W30", "SE_HRNet_W32", "SE_HRNet_W40","SE_HRNet_W44", "SE_HRNet_W48", '
-        '"SE_HRNet_W60", "SE_HRNet_W64")',
+        help='Model type for evaluation, which is one of {}'.format(
+            str(list(MODELS.keys()))),
         type=str,
         default='UNet')