fix padding bug and add resize_short_size args (#2428)

* fix padding bug and add resize_short_size args

* fix typo

* pass args:settings in eval and infer

PaddleCV/image_classification/eval.py

@@ -7,7 +7,7 @@ import time
 import sys
 import paddle
 import paddle.fluid as fluid
-import reader as reader
+import reader_cv2 as reader
 import argparse
 import functools
 import models
@@ -25,7 +25,7 @@ add_arg('image_shape',      str,  "3,224,224",         "Input image size")
 add_arg('with_mem_opt',     bool, True,                "Whether to use memory optimization or not.")
 add_arg('pretrained_model', str,  None,                "Whether to use pretrained model.")
 add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
-
+add_arg('resize_short_size', int, 256,                "Set resize short size")
 # yapf: enable
 
 def eval(args):
@@ -84,7 +84,7 @@ def eval(args):
 
         fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
 
-    val_reader = paddle.batch(reader.val(), batch_size=args.batch_size)
+    val_reader = paddle.batch(reader.val(settings=args), batch_size=args.batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
 
     test_info = [[], [], []]

PaddleCV/image_classification/infer.py

@@ -7,7 +7,7 @@ import time
 import sys
 import paddle
 import paddle.fluid as fluid
-import reader
+import reader_cv2 as reader
 import argparse
 import functools
 import models
@@ -25,6 +25,7 @@ add_arg('with_mem_opt',     bool, True,                 "Whether to use memory o
 add_arg('pretrained_model', str,  None,                 "Whether to use pretrained model.")
 add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
 add_arg('save_inference',   bool, False,                 "Whether to save inference model or not")
+add_arg('resize_short_size', int, 256,                  "Set resize short size")
 # yapf: enable
 
 def infer(args):
@@ -78,7 +79,7 @@ def infer(args):
         print("model: ",model_name," is already saved")
         exit(0)
     test_batch_size = 1
-    test_reader = paddle.batch(reader.test(), batch_size=test_batch_size)
+    test_reader = paddle.batch(reader.test(settings=args), batch_size=test_batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image])
 
     TOPK = 1

PaddleCV/image_classification/models/resnet_vc.py

@@ -92,7 +92,7 @@ class ResNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),

PaddleCV/image_classification/models/resnet_vd.py

@@ -104,7 +104,7 @@ class ResNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),
@@ -140,7 +140,7 @@ class ResNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=1,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),

PaddleCV/image_classification/models/resnext_vd.py

@@ -101,7 +101,7 @@ class ResNeXt():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),
@@ -137,7 +137,7 @@ class ResNeXt():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=1,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),

PaddleCV/image_classification/models/se_resnext_vd.py

@@ -173,7 +173,7 @@ class SE_ResNeXt():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             bias_attr=False,
@@ -205,7 +205,7 @@ class SE_ResNeXt():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=1,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),

PaddleCV/image_classification/reader_cv2.py

@@ -162,7 +162,7 @@ def process_image(
     else:
         if crop_size > 0:
             target_size = settings.resize_short_size
-            img = resize_short(img, 256)
+            img = resize_short(img, target_size)
 
             img = crop_image(img, target_size=crop_size, center=True)
 
@@ -222,14 +222,14 @@ def _reader_creator(settings,
                     img_path = os.path.join(data_dir, img_path)
  
                     yield [img_path]
-
+    crop_size = int(settings.image_shape.split(",")[2])
     image_mapper = functools.partial(
         process_image,
         settings=settings,
         mode=mode,
         color_jitter=color_jitter,
         rotate=rotate,
-        crop_size=224)
+        crop_size=crop_size)
     reader = paddle.reader.xmap_readers(
         image_mapper, reader, THREAD, BUF_SIZE, order=False)
     return reader

PaddleCV/image_classification/train.py

@@ -38,7 +38,7 @@ add_arg('pretrained_model', str,   None,                 "Whether to use pretrai
 add_arg('checkpoint',       str,   None,                 "Whether to resume checkpoint.")
 add_arg('lr',               float, 0.1,                  "set learning rate.")
 add_arg('lr_strategy',      str,   "piecewise_decay",    "Set the learning rate decay strategy.")
-add_arg('model',            str,   "ResNet50",          "Set the network to use.")
+add_arg('model',            str,   "SE_ResNeXt50_32x4d", "Set the network to use.")
 add_arg('enable_ce',        bool,  False,                "If set True, enable continuous evaluation job.")
 add_arg('data_dir',         str,   "./data/ILSVRC2012/",  "The ImageNet dataset root dir.")
 add_arg('fp16',             bool,  False,                "Enable half precision training with fp16." )