update by comment

fluid/PaddleCV/image_classification/fast_resnet/README.md → fluid/PaddleCV/image_classification/fast_imagenet/README.md

-# PaddlePaddle Fast ResNet
+# PaddlePaddle Fast ImageNet Training
 
-PaddlePaddle Fast ResNet can train ImageNet with fewer epochs. We implemented the it according to the blog 
+PaddlePaddle Fast ImageNet can train ImageNet dataset with fewer epochs. We implemented the it according to the blog 
 [Now anyone can train Imagenet in 18 minutes](https://www.fast.ai/2018/08/10/fastai-diu-imagenet/) which published on the [fast.ai] website.
-PaddlePaddle Fast ResNet using the dynmiac batch size, dynamic image size, rectangular images validation and etc... so that the FastResNet can achieve the baseline
-(acc1: 75%, acc5: 93%) by 27 epochs on 8 GPUs.
+PaddlePaddle Fast ImageNet using the dynmiac batch size, dynamic image size, rectangular images validation and etc... so that the Fast ImageNet can achieve the baseline
+(acc1: 75%, acc5: 93%) by 27 epochs on 8 * V100 GPUs.
 
 ## Experiment
 
-1. Preparing the training data, resize the images to 160 and 352 by `resize.py`, the prepared data folder is as followed:
+1. Prepare the training data, resize the images to 160 and 352 using `resize.py`, the prepared data folder should look like:
     ``` text
     `-ImageNet
       |-train

fluid/PaddleCV/image_classification/fast_resnet/tools/resize.py → fluid/PaddleCV/image_classification/fast_imagenet/tools/resize.py

@@ -14,8 +14,7 @@ def mkdir(path):
         path.mkdir()
 
 mkdir(DEST)
-#szs = (160, 352)
-szs = (160,)
+szs = (160, 352)
 
 def resize_img(p, im, fn, sz):
     w,h = im.size
@@ -40,8 +39,6 @@ for sz in szs:
     for ds in ('validation','train'): mkdir((DEST/ssz/ds))
     for ds in ('train',): mkdir((DEST/ssz/ds))
 
-#for ds in ('val','train'): resize_imgs(PATH/ds)
-#for ds in ("validation", "train"):
-for ds in ("validation", ):
+for ds in ("validation", "train"):
     print(PATH/ds)
     resize_imgs(PATH/ds)
\ No newline at end of file

fluid/PaddleCV/image_classification/fast_resnet/train.py → fluid/PaddleCV/image_classification/fast_imagenet/train.py

@@ -31,7 +31,7 @@ import sys
 sys.path.append("..")
 from utility import add_arguments, print_arguments
 import functools
-from models.fast_resnet import FastResNet, lr_decay
+from models.fast_imagenet import FastImageNet, lr_decay
 import utils
 
 def parse_args():
@@ -46,7 +46,6 @@ def parse_args():
     add_arg('checkpoint',       str,   None,                 "Whether to resume checkpoint.")
     add_arg('lr',               float, 1.0,                  "set learning rate.")
     add_arg('lr_strategy',      str,   "piecewise_decay",    "Set the learning rate decay strategy.")
-    add_arg('model',            str,   "FastResNet",         "Set the network to use.")
     add_arg('data_dir',         str,   "./data/ILSVRC2012",  "The ImageNet dataset root dir.")
     add_arg('model_category',   str,   "models",             "Whether to use models_name or not, valid value:'models','models_name'" )
     add_arg('fp16',             bool,  False,                "Enable half precision training with fp16." )
@@ -123,7 +122,7 @@ def build_program(args, is_train, main_prog, startup_prog, py_reader_startup_pro
             t1 = fluid.layers.elementwise_sub(cast, img_mean, axis=1)
             t2 = fluid.layers.elementwise_div(t1, img_std, axis=1)
 
-            model = FastResNet(is_train=is_train)
+            model = FastImageNet(is_train=is_train)
             predict = model.net(t2, class_dim=class_dim, img_size=sz)
             cost, pred = fluid.layers.softmax_with_cross_entropy(predict, label, return_softmax=True)
             if args.scale_loss > 1:

fluid/PaddleCV/image_classification/models/__init__.py

@@ -9,4 +9,4 @@ from .inception_v4 import InceptionV4
 from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_32x4d
 from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131
 from .shufflenet_v2 import ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0
-from .fast_resnet import FastResNet
+from .fast_imagenet import FastImageNet 

fluid/PaddleCV/image_classification/models/fast_resnet.py → fluid/PaddleCV/image_classification/models/fast_imagenet.py

@@ -30,9 +30,9 @@ import paddle.fluid.core as core
 import paddle.fluid.profiler as profiler
 import utils
 
-__all__ = ["FastResNet"]
+__all__ = ["FastImageNet"]
 
-class FastResNet():
+class FastImageNet():
     def __init__(self, layers=50, is_train=True):
         self.layers = layers
         self.is_train = is_train