Merge pull request #49 from littletomatodonkey/qs_code

add quick start demo

configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'MobileNetV3_large_x1_0'
+pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+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: 20
+    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:

configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
+
+pretrained_model:
+    - "./pretrain/flowers102_R50_vd_final/ppcls"
+    - "./pretrained/MobileNetV3_large_x1_0_pretrained/"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 7169
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+use_distillation: True
+
+LEARNING_RATE:
+    function: 'Cosine'
+    params:                   
+        lr: 0.0125
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.00007
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_test_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: 20
+    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:

configs/quick_start/ResNet50_vd.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.0125
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.00001
+
+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: 20
+    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:

configs/quick_start/ResNet50_vd_finetune.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+pretrained_model: "./pretrained/ResNet50_vd_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+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: 20
+    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:

configs/finetune/ResNet50_vd_ssld_finetune.yaml → configs/quick_start/ResNet50_vd_ssld_finetune.yaml

@@ -10,12 +10,10 @@ total_images: 1020
 save_interval: 1
 validate: True
 valid_interval: 1
-epochs: 40
+epochs: 20
 topk: 5
 image_shape: [3, 224, 224]
 
-ls_epsilon: 0.1
-
 LEARNING_RATE:
     function: 'Cosine'          
     params:                   
@@ -52,7 +50,7 @@ TRAIN:
         - ToCHWImage:
 
 VALID:
-    batch_size: 32
+    batch_size: 20
     num_workers: 4
     file_list: "./dataset/flowers102/val_list.txt"
     data_dir: "./dataset/flowers102/"

configs/quick_start/ResNet50_vd_ssld_random_erasing_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: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+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: ''
+        - RandomErasing:
+            EPSILON: 0.5
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    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:

ppcls/modeling/architectures/__init__.py

@@ -44,4 +44,4 @@ from .darts_gs import DARTS_GS_6M, DARTS_GS_4M
 from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet
 
 # distillation model
-from .distillation_models import ResNet50_vd_distill_MobileNetV3_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd
+from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd

ppcls/modeling/architectures/distillation_models.py

@@ -27,12 +27,12 @@ from .mobilenet_v3 import MobileNetV3_large_x1_0
 from .resnext101_wsl import ResNeXt101_32x16d_wsl
 
 __all__ = [
-    'ResNet50_vd_distill_MobileNetV3_x1_0',
+    'ResNet50_vd_distill_MobileNetV3_large_x1_0',
     'ResNeXt101_32x16d_wsl_distill_ResNet50_vd'
 ]
 
 
-class ResNet50_vd_distill_MobileNetV3_x1_0():
+class ResNet50_vd_distill_MobileNetV3_large_x1_0():
     def net(self, input, class_dim=1000):
         # student
         student = MobileNetV3_large_x1_0()

ppcls/utils/save_load.py

@@ -118,7 +118,10 @@ def init_model(config, program, exe):
 
     pretrained_model = config.get('pretrained_model')
     if pretrained_model:
-        load_params(exe, program, pretrained_model)
+        if not isinstance(pretrained_model, list):
+            pretrained_model = [pretrained_model]
+        for pretrain in pretrained_model:
+            load_params(exe, program, pretrain)
         logger.info("Finish initing model from {}".format(pretrained_model))