Merge pull request #40 from littletomatodonkey/add_dis_finetune

add finetune inferface based on distillation models

configs/finetune/ResNet50_vd_ssld_finetune.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+    params:
+        lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
+pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 40
+topk: 5
+image_shape: [3, 224, 224]
+
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.00375
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000001
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            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:

docs/zh_CN/faq.md

@@ -7,4 +7,9 @@
 
 >>
 * Q: 多卡评估时，为什么每张卡输出的精度指标不相同？
-* A: 目前PaddleClas基于fleet api使用多卡，在多卡评估时，每张卡都是单独读取各自part的数据，不同卡中计算的图片是不同的，因此最终指标也会有微量差异，如果希望得到准确的评估指标，可以使用单卡评估。
+* A: 目前PaddleClas基于fleet api使用多卡，在多卡评估时，每张卡都是单独读取各自part的数据，不同卡中计算的图片是不同的，因此最终指标也会有微量差异，如果希望得到准确的评估指标，可以使用单卡评估。
\ No newline at end of file
+
+
+>>
+* Q: 在配置文件的`TRAIN`字段中配置了`mix`的参数，为什么`mixup`的数据增广预处理没有生效呢？
+* A: 使用mixup时，数据预处理部分与模型输入部分均需要修改，因此还需要在配置文件中显式地配置`use_mix: True`，才能使得`mixup`生效。
\ No newline at end of file

ppcls/modeling/architectures/resnet_vd.py

@@ -29,9 +29,20 @@ __all__ = [
 
 
 class ResNet():
-    def __init__(self, layers=50, is_3x3=False):
+    def __init__(self,
+                 layers=50,
+                 is_3x3=False,
+                 postfix_name="",
+                 lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
         self.layers = layers
         self.is_3x3 = is_3x3
+        self.postfix_name = "" if postfix_name is None else postfix_name
+        self.lr_mult_list = lr_mult_list
+        assert len(
+            self.lr_mult_list
+        ) == 5, "lr_mult_list length in ResNet must be 5 but got {}!!".format(
+            len(self.lr_mult_list))
+        self.curr_stage = 0
 
     def net(self, input, class_dim=1000):
         is_3x3 = self.is_3x3
@@ -90,6 +101,7 @@ class ResNet():
 
         if layers >= 50:
             for block in range(len(depth)):
+                self.curr_stage += 1
                 for i in range(depth[block]):
                     if layers in [101, 152, 200] and block == 2:
                         if i == 0:
@@ -106,6 +118,7 @@ class ResNet():
                         name=conv_name)
         else:
             for block in range(len(depth)):
+                self.curr_stage += 1
                 for i in range(depth[block]):
                     conv_name = "res" + str(block + 2) + chr(97 + i)
                     conv = self.basic_block(
@@ -123,9 +136,9 @@ class ResNet():
             input=pool,
             size=class_dim,
             param_attr=fluid.param_attr.ParamAttr(
-                name="fc_0.w_0",
+                name="fc_0.w_0" + self.postfix_name,
                 initializer=fluid.initializer.Uniform(-stdv, stdv)),
-            bias_attr=ParamAttr(name="fc_0.b_0"))
+            bias_attr=ParamAttr(name="fc_0.b_0" + self.postfix_name))
 
         return out
 
@@ -137,6 +150,7 @@ class ResNet():
                       groups=1,
                       act=None,
                       name=None):
+        lr_mult = self.lr_mult_list[self.curr_stage]
         conv = fluid.layers.conv2d(
             input=input,
             num_filters=num_filters,
@@ -145,7 +159,7 @@ class ResNet():
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
+            param_attr=ParamAttr(name=name + "_weights" + self.postfix_name),
             bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
@@ -154,10 +168,10 @@ class ResNet():
         return fluid.layers.batch_norm(
             input=conv,
             act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
+            param_attr=ParamAttr(name=bn_name + '_scale' + self.postfix_name),
+            bias_attr=ParamAttr(bn_name + '_offset' + self.postfix_name),
+            moving_mean_name=bn_name + '_mean' + self.postfix_name,
+            moving_variance_name=bn_name + '_variance' + self.postfix_name)
 
     def conv_bn_layer_new(self,
                           input,
@@ -167,6 +181,7 @@ class ResNet():
                           groups=1,
                           act=None,
                           name=None):
+        lr_mult = self.lr_mult_list[self.curr_stage]
         pool = fluid.layers.pool2d(
             input=input,
             pool_size=2,
@@ -183,7 +198,9 @@ class ResNet():
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
+            param_attr=ParamAttr(
+                name=name + "_weights" + self.postfix_name,
+                learning_rate=lr_mult),
             bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
@@ -192,10 +209,14 @@ class ResNet():
         return fluid.layers.batch_norm(
             input=conv,
             act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
+            param_attr=ParamAttr(
+                name=bn_name + '_scale' + self.postfix_name,
+                learning_rate=lr_mult),
+            bias_attr=ParamAttr(
+                bn_name + '_offset' + self.postfix_name,
+                learning_rate=lr_mult),
+            moving_mean_name=bn_name + '_mean' + self.postfix_name,
+            moving_variance_name=bn_name + '_variance' + self.postfix_name)
 
     def shortcut(self, input, ch_out, stride, name, if_first=False):
         ch_in = input.shape[1]
@@ -273,8 +294,8 @@ def ResNet34_vd():
     return model
 
 
-def ResNet50_vd():
-    model = ResNet(layers=50, is_3x3=True)
+def ResNet50_vd(**args):
+    model = ResNet(layers=50, is_3x3=True, **args)
     return model
 
 

ppcls/utils/check.py

@@ -59,15 +59,18 @@ def check_architecture(architecture):
     """
     check architecture and recommend similar architectures
     """
-    assert isinstance(architecture, str), \
-            ("the type of architecture({}) should be str". format(architecture))
-    similar_names = similar_architectures(architecture, get_architectures())
+    assert isinstance(architecture, dict), \
+            ("the type of architecture({}) should be dict". format(architecture))
+    assert "name" in architecture, \
+            ("name must be in the architecture keys, just contains: {}". format(architecture.keys()))
+
+    similar_names = similar_architectures(architecture["name"],
+                                          get_architectures())
     model_list = ', '.join(similar_names)
     err = "{} is not exist! Maybe you want: [{}]" \
-          "".format(architecture, model_list)
-
+          "".format(architecture["name"], model_list)
     try:
-        assert architecture in similar_names
+        assert architecture["name"] in similar_names
     except AssertionError:
         logger.error(err)
         sys.exit(1)
@@ -80,7 +83,7 @@ def check_mix(architecture, use_mix=False):
     err = "Cannot use mix processing in GoogLeNet, " \
           "please set use_mix = False."
     try:
-        if architecture == "GoogLeNet": assert use_mix == False
+        if architecture["name"] == "GoogLeNet": assert use_mix == False
     except AssertionError:
         logger.error(err)
         sys.exit(1)

ppcls/utils/config.py

@@ -19,7 +19,7 @@ from ppcls.utils import logger
 
 __all__ = ['get_config']
 
-CONFIG_SECS = ['TRAIN', 'VALID', 'OPTIMIZER', 'LEARNING_RATE']
+CONFIG_SECS = ['ARCHITECTURE', 'TRAIN', 'VALID', 'OPTIMIZER', 'LEARNING_RATE']
 
 
 class AttrDict(dict):
@@ -110,7 +110,7 @@ def check_config(config):
     mode = config.get('mode', 'train')
     check.check_gpu()
 
-    architecture = config.get('architecture')
+    architecture = config.get('ARCHITECTURE')
     check.check_architecture(architecture)
 
     use_mix = config.get('use_mix')

ppcls/utils/save_load.py

@@ -106,7 +106,7 @@ def load_params(exe, prog, path, ignore_params=[]):
     fluid.io.set_program_state(prog, state)
 
 
-def init_model(config, program, exe, prefix="ppcls"):
+def init_model(config, program, exe, prefix=""):
     """
     load model from checkpoint or pretrained_model
     """

tools/program.py

@@ -88,19 +88,21 @@ def create_dataloader(feeds):
     return dataloader
 
 
-def create_model(name, image, classes_num):
+def create_model(architecture, image, classes_num):
     """
     Create a model
 
     Args:
-        name(str): model name, such as ResNet50
+        architecture(dict): architecture information, name(such as ResNet50) is needed
         image(variable): model input variable
         classes_num(int): num of classes
 
     Returns:
         out(variable): model output variable
     """
-    model = architectures.__dict__[name]()
+    name = architecture["name"]
+    params = architecture.get("params", {})
+    model = architectures.__dict__[name](**params)
     out = model.net(input=image, class_dim=classes_num)
     return out
 
@@ -122,7 +124,7 @@ def create_loss(out,
     Args:
         out(variable): model output variable
         feeds(dict): dict of model input variables
-        architecture(str): model name, such as ResNet50
+        architecture(dict): architecture information, name(such as ResNet50) is needed
         classes_num(int): num of classes
         epsilon(float): parameter for label smoothing, 0.0 <= epsilon <= 1.0
         mix(bool): whether to use mix(include mixup, cutmix, fmix)
@@ -130,7 +132,7 @@ def create_loss(out,
     Returns:
         loss(variable): loss variable
     """
-    if architecture == "GoogLeNet":
+    if architecture["name"] == "GoogLeNet":
         assert len(out) == 3, "GoogLeNet should have 3 outputs"
         loss = GoogLeNetLoss(class_dim=classes_num, epsilon=epsilon)
         target = feeds['label']
@@ -188,7 +190,7 @@ def create_fetchs(out,
     Args:
         out(variable): model output variable
         feeds(dict): dict of model input variables(included label)
-        architecture(str): model name, such as ResNet50
+        architecture(dict): architecture information, name(such as ResNet50) is needed
         topk(int): usually top5
         classes_num(int): num of classes
         epsilon(float): parameter for label smoothing, 0.0 <= epsilon <= 1.0
@@ -293,12 +295,12 @@ def build(config, main_prog, startup_prog, is_train=True):
             use_mix = config.get('use_mix') and is_train
             feeds = create_feeds(config.image_shape, mix=use_mix)
             dataloader = create_dataloader(feeds.values())
-            out = create_model(config.architecture, feeds['image'],
+            out = create_model(config.ARCHITECTURE, feeds['image'],
                                config.classes_num)
             fetchs = create_fetchs(
                 out,
                 feeds,
-                config.architecture,
+                config.ARCHITECTURE,
                 config.topk,
                 config.classes_num,
                 epsilon=config.get('ls_epsilon'),

tools/train.py

@@ -96,7 +96,7 @@ def main(args):
 
         if epoch_id % config.save_interval == 0:
             model_path = os.path.join(config.model_save_dir,
-                                      config.architecture)
+                                      config.ARCHITECTURE["name"])
             save_model(train_prog, model_path, epoch_id)