unify model and model_name (#386)

* unify model and model_name

* rm unsed archs fix bug of se_resnext

* fix distillation model for distributed.launch

* fix export model for googlenet

configs/CSPNet/CSPResNet50.yaml

-mode: 'train'
-ARCHITECTURE:
-    name: 'CSPResNet50_leaky'
-
-pretrained_model: ""
-model_save_dir: "./output/"
-classes_num: 1000
-total_images: 1281167
-save_interval: 1
-validate: True
-valid_interval: 1
-epochs: 120
-topk: 5
-image_shape: [3, 256, 256]
-
-use_mix: False
-ls_epsilon: -1
-
-LEARNING_RATE:
-    function: 'Piecewise'          
-    params:                   
-        lr: 0.1               
-        decay_epochs: [30, 60, 90] 
-        gamma: 0.1 
-
-OPTIMIZER:
-    function: 'Momentum'
-    params:
-        momentum: 0.9
-    regularizer:
-        function: 'L2'
-        factor: 0.000100
-
-TRAIN:
-    batch_size: 256
-    num_workers: 4
-    file_list: "./dataset/ILSVRC2012/train_list.txt"
-    data_dir: "./dataset/ILSVRC2012/"
-    shuffle_seed: 0
-    transforms:
-        - DecodeImage:
-            to_rgb: True
-            to_np: False
-            channel_first: False
-        - RandCropImage:
-            size: 256
-        - 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: 64
-    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:
-            resize_short: 256
-        - CropImage:
-            size: 256
-        - NormalizeImage:
-            scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
-            order: ''
-        - ToCHWImage:

configs/ResNet_ACNet/ResNet_ACNet.yaml

-mode: 'train'
-ARCHITECTURE:
-    name: "ResNet50_ACNet"
-
-pretrained_model: ""
-model_save_dir: "./output/"
-classes_num: 1000
-total_images: 1281167
-save_interval: 1
-validate: True
-valid_interval: 1
-epochs: 120
-topk: 5
-image_shape: [3, 224, 224]
-
-LEARNING_RATE:
-   function: 'Piecewise'
-   params:
-       lr: 0.1
-       decay_epochs: [30, 60, 90]
-       gamma: 0.1
-
-OPTIMIZER:
-    function: 'Momentum'
-    params:
-        momentum: 0.9
-    regularizer:
-        function: 'L2'
-        factor: 0.0001
-
-TRAIN:
-    batch_size: 256
-    num_workers: 4
-    file_list: "./dataset/ILSVRC2012/train_list.txt"
-    data_dir: "./dataset/ILSVRC2012/"
-    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: 64
-    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:
-            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/modeling/architectures/se_resnext.py

@@ -20,6 +20,7 @@ import numpy as np
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
+import paddle.nn.functional as F
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform

ppcls/utils/save_load.py

@@ -80,15 +80,19 @@ def load_distillation_model(model, pretrained_model, load_static_weights):
         load_static_weights
     ) == 2, "load_static_weights length should be 2 but got {}".format(
         len(load_static_weights))
+    teacher = model.teacher if hasattr(model,
+                                       "teacher") else model._layers.teacher
+    student = model.student if hasattr(model,
+                                       "student") else model._layers.student
     load_dygraph_pretrain(
-        model.teacher,
+        teacher,
         path=pretrained_model[0],
         load_static_weights=load_static_weights[0])
     logger.info(
         logger.coloring("Finish initing teacher model from {}".format(
             pretrained_model), "HEADER"))
     load_dygraph_pretrain(
-        model.student,
+        student,
         path=pretrained_model[1],
         load_static_weights=load_static_weights[1])
     logger.info(

tools/export_model.py

@@ -43,10 +43,11 @@ def parse_args():
 
 
 class Net(paddle.nn.Layer):
-    def __init__(self, net, to_static, class_dim):
+    def __init__(self, net, to_static, class_dim, model):
         super(Net, self).__init__()
         self.pre_net = net(class_dim=class_dim)
         self.to_static = to_static
+        self.model = model
 
     # Please modify the 'shape' according to actual needs
     @to_static(input_spec=[
@@ -55,6 +56,8 @@ class Net(paddle.nn.Layer):
     ])
     def forward(self, inputs):
         x = self.pre_net(inputs)
+        if self.model == "GoogLeNet":
+            x = x[0]
         x = F.softmax(x)
         return x
 
@@ -64,7 +67,7 @@ def main():
 
     net = architectures.__dict__[args.model]
 
-    model = Net(net, to_static, args.class_dim)
+    model = Net(net, to_static, args.class_dim, args.model)
     load_dygraph_pretrain(
         model.pre_net,
         path=args.pretrained_model,

tools/infer/predict.py

@@ -53,7 +53,7 @@ def main(args):
         assert args.use_fp16 is False
     else:
         assert args.use_gpu is True
-        assert args.model_name is not None
+        assert args.model is not None
     # HALF precission predict only work when using tensorrt
     if args.use_fp16 is True:
         assert args.use_tensorrt is True
@@ -105,8 +105,8 @@ def main(args):
         fp_message = "FP16" if args.use_fp16 else "FP32"
         trt_msg = "using tensorrt" if args.use_tensorrt else "not using tensorrt"
         print("{0}\t{1}\t{2}\tbatch size: {3}\ttime(ms): {4}".format(
-            args.model_name, trt_msg, fp_message, args.batch_size, 1000 *
-            test_time / test_num))
+            args.model, trt_msg, fp_message, args.batch_size, 1000 * test_time
+            / test_num))
 
 
 if __name__ == "__main__":

tools/infer/utils.py

@@ -40,7 +40,6 @@ def parse_args():
     parser.add_argument("--use_tensorrt", type=str2bool, default=False)
     parser.add_argument("--gpu_mem", type=int, default=8000)
     parser.add_argument("--enable_benchmark", type=str2bool, default=False)
-    parser.add_argument("--model_name", type=str)
     parser.add_argument("--top_k", type=int, default=1)
     parser.add_argument("--hubserving", type=str2bool, default=False)
 

tools/train.py

@@ -52,7 +52,7 @@ def parse_args():
 
 
 def main(args):
-    paddle.seed(123)
+    paddle.seed(12345)
 
     config = get_config(args.config, overrides=args.override, show=True)
     # assign the place
@@ -68,7 +68,6 @@ def main(args):
         strategy = paddle.distributed.init_parallel_env()
 
     net = program.create_model(config.ARCHITECTURE, config.classes_num)
-
     optimizer, lr_scheduler = program.create_optimizer(
         config, parameter_list=net.parameters())