add autoargument

configs/EfficientNet/EfficientNetB0.yaml

@@ -0,0 +1,84 @@
+mode: 'train'
+ARCHITECTURE:
+    name: "EfficientNetB0"
+    drop_connect_rate: 0.1
+    padding_type : "SAME"
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 360
+topk: 5
+image_shape: [3, 224, 224]
+use_ema: True
+ema_decay: 0.9999
+use_aa: True
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+    function: 'ExponentialWarmup'
+    params:
+        lr: 0.032
+
+OPTIMIZER:
+    function: 'RMSProp'
+    params:
+        momentum: 0.9
+        rho: 0.9
+        epsilon: 0.001
+    regularizer:
+        function: 'L2'
+        factor: 0.00001
+
+TRAIN:
+    batch_size: 512
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/train_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: Fals
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - AA:
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+
+
+VALID:
+    batch_size: 128
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/val_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            interpolation: 2
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+

ppcls/data/imaug/operators.py

@@ -25,6 +25,7 @@ import random
 import cv2
 import numpy as np
 
+from autoargument import ImageNetPolicy
 
 class OperatorParamError(ValueError):
     """ OperatorParamError
@@ -171,6 +172,18 @@ class RandFlipImage(object):
         else:
             return img
 
+class AA(object):
+     
+    def __init__(self):
+        self.policy = ImageNetPolicy()
+
+    def __call__(self,img):
+        from PIL import Image
+        img = np.ascontiguousarray(img)
+        img = Image.fromarray(img)
+        img = self.policy(img)
+        img = np.asarray(img)
+
 
 class NormalizeImage(object):
     """ normalize image such as substract mean, divide std

ppcls/optimizer/learning_rate.py

@@ -145,6 +145,59 @@ class CosineWarmup(object):
         return learning_rate
 
 
+class ExponentialWarmup(object):
+    
+    """
+    Exponential learning rate decay with warmup
+    [0, warmup_epoch): linear warmup
+    [warmup_epoch, epochs): Exponential decay
+
+    Args:
+        lr(float): initial learning rate
+        step_each_epoch(int): steps each epoch
+        decay_epochs(float): decay epochs
+        decay_rate(float): decay rate
+        warmup_epoch(int): epoch num of warmup
+    """
+
+    def __init__(self, lr, step_each_epoch, decay_epochs=2.4, decay_rate=0.97, warmup_epoch=5, **kwargs):
+        super(CosineWarmup, self).__init__()
+        self.lr = lr
+        self.step_each_epoch = step_each_epoch
+        self.decay_epochs = decay_epochs * self.step_each_epoch
+        self.decay_rate = decay_rate
+        self.warmup_epoch = fluid.layers.fill_constant(
+            shape=[1],
+            value=float(warmup_epoch),
+            dtype='float32',
+            force_cpu=True)
+
+    def __call__(self):
+        global_step = _decay_step_counter()
+        learning_rate = fluid.layers.tensor.create_global_var(
+            shape=[1],
+            value=0.0,
+            dtype='float32',
+            persistable=True,
+            name="learning_rate")
+
+        epoch = ops.floor(global_step / self.step_each_epoch)
+        with fluid.layers.control_flow.Switch() as switch:
+            with switch.case(epoch < self.warmup_epoch):
+                decayed_lr = self.lr * \
+                        (global_step / (self.step_each_epoch * self.warmup_epoch))
+                fluid.layers.tensor.assign(
+                    input=decayed_lr, output=learning_rate)
+            with switch.default():
+                rest_step = global_step - self.warmup_epoch * self.step_each_epoch
+                div_res = ops.floor(rest_step / self.decay_epochs)
+
+                decayed_lr = self.lr*(self.decay_rate**div_res)
+                fluid.layers.tensor.assign(
+                    input=decayed_lr, output=learning_rate)
+
+        return learning_rate
+
 class LearningRateBuilder():
     """
     Build learning rate variable