Merge pull request #53 from shippingwang/refine_0

Refine code

configs/eval.yaml

 mode: 'valid'
 ARCHITECTURE:
-    name: ""
+    name: "ResNet50_vd"
 
-pretrained_model: ""
+pretrained_model: "./pretrained_model/ResNet50_vd_pretrained"
 classes_num: 1000
 total_images: 1281167
 topk: 5
@@ -11,8 +11,8 @@ image_shape: [3, 224, 224]
 VALID:
     batch_size: 16
     num_workers: 4
-    file_list: "../dataset/ILSVRC2012/val_list.txt"
-    data_dir: "../dataset/ILSVRC2012/"
+    file_list: "./dataset/ILSVRC2012/val_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
     shuffle_seed: 0
     transforms:
         - DecodeImage:

configs/flower.yaml

 mode: 'train'
-architecture: 'ResNet50_vd'
+ARCHITECTURE: 
+    name: 'ResNet50_vd'
 pretrained_model:
 model_save_dir: "./output/"
 classes_num: 102
@@ -29,8 +30,8 @@ OPTIMIZER:
 TRAIN:
     batch_size: 32
     num_workers: 1
-    file_list: "./dataset/flower102/train_list.txt"
-    data_dir: "./dataset/flower102"
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102"
     shuffle_seed: 0
     transforms:
         - DecodeImage:
@@ -54,8 +55,8 @@ TRAIN:
 VALID:
     batch_size: 64
     num_workers: 1
-    file_list: "./dataset/flower102/val_list.txt"
-    data_dir: "./dataset/flower102/"
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
     shuffle_seed: 0
     transforms:
         - DecodeImage:

dataset/README.md

-#mannual

dataset/download_imagenet2012.sh

-set -e
-if [ "x${IMAGENET_USERNAME}" == x -o "x${IMAGENET_ACCESS_KEY}" == x ];then
-  echo "Please create an account on image-net.org."
-  echo "It will provide you a pair of username and accesskey to download imagenet data."
-  read -p "Username: " IMAGENET_USERNAME
-  read -p "Accesskey: " IMAGENET_ACCESS_KEY
-fi
-
-root_url=http://www.image-net.org/challenges/LSVRC/2012/nnoupb
-valid_tar=ILSVRC2012_img_val.tar
-train_tar=ILSVRC2012_img_train.tar
-train_folder=train/
-valid_folder=val/
-
-echo "Download imagenet training data..."
-mkdir -p ${train_folder}
-wget -nd -c ${root_url}/${train_tar}
-tar xf ${train_tar} -C ${train_folder}
-
-cd ${train_folder}
-for x in `ls *.tar`
-do
-  filename=`basename $x .tar`
-  mkdir -p $filename
-  tar -xf $x -C $filename
-  rm -rf $x
-done
-cd -
-
-echo "Download imagenet validation data..."
-mkdir -p ${valid_folder}
-wget -nd -c ${root_url}/${valid_tar}
-tar xf ${valid_tar} -C ${valid_folder}
-
-echo "Download imagenet label file: val_list.txt & train_list.txt"
-label_file=ImageNet_label.tgz
-label_url=http://paddle-imagenet-models.bj.bcebos.com/${label_file}
-wget -nd -c ${label_url}
-tar zxf ${label_file}
-

ppcls/utils/check.py

@@ -67,7 +67,7 @@ def check_architecture(architecture):
     similar_names = similar_architectures(architecture["name"],
                                           get_architectures())
     model_list = ', '.join(similar_names)
-    err = "{} is not exist! Maybe you want: [{}]" \
+    err = "Architecture [{}] is not exist! Maybe you want: [{}]" \
           "".format(architecture["name"], model_list)
     try:
         assert architecture["name"] in similar_names

tools/eval.py

@@ -71,8 +71,9 @@ def main(args):
 
     valid_reader = Reader(config, 'valid')()
     valid_dataloader.set_sample_list_generator(valid_reader, place)
-    compiled_valid_prog = program.compile(config, valid_prog)
-
+    
+    #compiled_valid_prog = program.compile(config, valid_prog)
+    compiled_valid_prog = valid_prog
     program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, 0,
                 'valid')
 

tools/eval.sh

+export PYTHONPATH=$PWD:$PYTHONPATH
+
+python -m paddle.distributed.launch \
+    --selected_gpus="0" \
+    tools/eval.py \
+        -c ./configs/eval.yaml

tools/program.py

@@ -389,3 +389,4 @@ def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
         fetchs_str = ''.join([str(m) for m in metric_list] + [str(batch_time)])
         logger.info("[epoch:%3d][%s][step:%4d]%s" %
                     (epoch, mode, idx, fetchs_str))
+    logger.info("END [epoch:%3d][%s]%s"%(epoch, mode, fetchs_str))

tools/run.sh

@@ -4,6 +4,5 @@ export PYTHONPATH=$PWD:$PYTHONPATH
 
 python -m paddle.distributed.launch \
     --selected_gpus="0,1,2,3" \
-    --log_dir=log_ResNet50 \
     tools/train.py \
         -c ./configs/ResNet/ResNet50.yaml

tools/train.py

@@ -87,8 +87,8 @@ def main(args):
     if config.validate:
         valid_reader = Reader(config, 'valid')()
         valid_dataloader.set_sample_list_generator(valid_reader, place)
-        compiled_valid_prog = program.compile(config, valid_prog)
-
+        #compiled_valid_prog = program.compile(config, valid_prog)
+        compiled_valid_prog = valid_prog
     compiled_train_prog = fleet.main_program
     for epoch_id in range(config.epochs):
         # 1. train with train dataset