Update arguments used to finetune pruned model. (#3485)

PaddleSlim/classification/pruning/README.md

@@ -130,7 +130,13 @@ fc10_weights (1280L, 1000L)
 |-30%|- |- |- |-|
 |-50%|- |- |- |-|
 
->训练超参：
+>训练超参
+batch size: 256
+lr_strategy: piecewise_decay
+step_epochs: 30, 60, 90
+num_epochs: 120
+l2_decay: 3e-5
+lr: 0.1
 
 ### MobileNetV2
 
@@ -142,6 +148,12 @@ fc10_weights (1280L, 1000L)
 |-50%|- |- |- |-|
 
 >训练超参：
+batch size: 500
+lr_strategy: cosine_decay
+num_epochs: 240
+l2_decay: 4e-5
+lr: 0.1
+
 
 ### ResNet50
 
@@ -152,6 +164,11 @@ fc10_weights (1280L, 1000L)
 |-30%|- |- |- |-|
 |-50%|- |- |- |-|
 
->训练超参：
+>训练超参
+batch size: 256
+lr_strategy: cosine_decay
+num_epochs: 120
+l2_decay: 1e-4
+lr: 0.1
 
 ## FAQ

PaddleSlim/classification/pruning/compress.py

@@ -4,6 +4,7 @@ import logging
 import paddle
 import argparse
 import functools
+import math
 import paddle.fluid as fluid
 sys.path.append("..")
 import imagenet_reader as reader
@@ -24,12 +25,48 @@ add_arg('batch_size',       int,  64*4,                 "Minibatch size.")
 add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
 add_arg('model',            str,  None,                "The target model.")
 add_arg('pretrained_model', str,  None,                "Whether to use pretrained model.")
+add_arg('lr',               float,  0.1,               "The learning rate used to fine-tune pruned model.")
+add_arg('lr_strategy',      str,  "piecewise_decay",   "The learning rate decay strategy.")
+add_arg('l2_decay',         float,  3e-5,               "The l2_decay parameter.")
+add_arg('momentum_rate',    float,  0.9,               "The value of momentum_rate.")
+add_arg('num_epochs',       int,  120,               "The number of total epochs.")
+add_arg('total_images',     int,  1281167,               "The number of total training images.")
+parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
 add_arg('config_file',      str, None,                 "The config file for compression with yaml format.")
 # yapf: enable
 
 
 model_list = [m for m in dir(models) if "__" not in m]
 
+def piecewise_decay(args):
+    step = int(math.ceil(float(args.total_images) / args.batch_size))
+    bd = [step * e for e in args.step_epochs]
+    lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)]
+    learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate,
+        momentum=args.momentum_rate,
+        regularization=fluid.regularizer.L2Decay(args.l2_decay))
+    return optimizer
+
+def cosine_decay(args):
+    step = int(math.ceil(float(args.total_images) / args.batch_size))
+    learning_rate = fluid.layers.cosine_decay(
+        learning_rate=args.lr,
+        step_each_epoch=step,
+        epochs=args.num_epochs)
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate,
+        momentum=args.momentum_rate,
+        regularization=fluid.regularizer.L2Decay(args.l2_decay))
+    return optimizer
+
+def create_optimizer(args):
+    if args.lr_strategy == "piecewise_decay":
+        return piecewise_decay(args)
+    elif args.lr_strategy == "cosine_decay":
+        return cosine_decay(args)
+
 def compress(args):
     class_dim=1000
     image_shape="3,224,224"
@@ -45,25 +82,14 @@ def compress(args):
     acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
     acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
     val_program = fluid.default_main_program().clone()
-#    for param in fluid.default_main_program().global_block().all_parameters():
-#        print param.name, param.shape
-#    return
-    opt = fluid.optimizer.Momentum(
-        momentum=0.9,
-        learning_rate=fluid.layers.piecewise_decay(
-            boundaries=[5000 * 30, 5000 * 60, 5000 * 90],
-            values=[0.1, 0.01, 0.001, 0.0001]),
-        regularization=fluid.regularizer.L2Decay(4e-5))
-
+    opt = create_optimizer(args)
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
     if args.pretrained_model:
-
         def if_exist(var):
             return os.path.exists(os.path.join(args.pretrained_model, var.name))
-
         fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
 
     val_reader = paddle.batch(reader.val(), batch_size=args.batch_size)

PaddleSlim/classification/pruning/configs/mobilenet_v1.yaml

@@ -14,7 +14,7 @@ strategies:
         target_ratio: 0.5
         pruned_params: '.*_sep_weights'
 compressor:
-    epoch: 3
+    epoch: 121
     checkpoint_path: './checkpoints/mobilenet_v1/'
     strategies:
         - uniform_pruning_strategy

PaddleSlim/classification/pruning/configs/mobilenet_v2.yaml

@@ -16,7 +16,7 @@ strategies:
 #        pruned_params: '.*linear_weights'
 #        pruned_params: '.*expand_weights'
 compressor:
-    epoch: 2
+    epoch: 241
     checkpoint_path: './checkpoints/'
     strategies:
         - uniform_pruning_strategy

PaddleSlim/classification/pruning/configs/resnet50.yaml → PaddleSlim/classification/pruning/configs/resnet34.yaml

@@ -14,7 +14,7 @@ strategies:
         target_ratio: 0.5
         pruned_params: '.*branch.*_weights'
 compressor:
-    epoch: 4
-    checkpoint_path: './checkpoints/resnet50/'
+    epoch: 121
+    checkpoint_path: './checkpoints/resnet34/'
     strategies:
         - uniform_pruning_strategy

PaddleSlim/classification/pruning/run.sh

@@ -6,7 +6,7 @@ export CUDA_VISIBLE_DEVICES=0
 root_url="http://paddle-imagenet-models-name.bj.bcebos.com"
 MobileNetV1="MobileNetV1_pretrained.tar"
 MobileNetV2="MobileNetV2_pretrained.tar"
-ResNet50="ResNet50_pretrained.tar"
+ResNet34="ResNet34_pretrained.tar"
 pretrain_dir='../pretrain'
 
 if [ ! -d ${pretrain_dir} ]; then
@@ -25,9 +25,9 @@ if [ ! -f ${MobileNetV2} ]; then
     tar xf ${MobileNetV2}
 fi
 
-if [ ! -f ${ResNet50} ]; then
-    wget ${root_url}/${ResNet50}
-    tar xf ${ResNet50}
+if [ ! -f ${ResNet34} ]; then
+    wget ${root_url}/${ResNet34}
+    tar xf ${ResNet34}
 fi
 
 cd -
@@ -36,6 +36,11 @@ nohup python -u compress.py \
 --model "MobileNet" \
 --use_gpu 1 \
 --batch_size 256 \
+--total_images 1281167 \
+--lr_strategy "piecewise_decay" \
+--num_epochs 120 \
+--lr 0.1 \
+--l2_decay 3e-5 \
 --pretrained_model ../pretrain/MobileNetV1_pretrained \
 --config_file "./configs/mobilenet_v1.yaml" \
 > mobilenet_v1.log 2>&1 &
@@ -46,18 +51,28 @@ tailf mobilenet_v1.log
 #--model "MobileNetV2" \
 #--use_gpu 1 \
 #--batch_size 256 \
+#--total_images 1281167 \
+#--lr_strategy "cosine_decay" \
+#--num_epochs 240 \
+#--lr 0.1 \
+#--l2_decay 4e-5 \
 #--pretrained_model ../pretrain/MobileNetV2_pretrained \
 #--config_file "./configs/mobilenet_v2.yaml" \
 #> mobilenet_v2.log 2>&1 &
 #tailf mobilenet_v2.log
 
 
-## for compression of resnet50
+## for compression of resnet34
 #python -u compress.py \
-#--model "ResNet50" \
+#--model "ResNet34" \
 #--use_gpu 1 \
 #--batch_size 256 \
-#--pretrained_model ../pretrain/ResNet50_pretrained \
-#--config_file "./configs/resnet50.yaml" \
-#> resnet50.log 2>&1 &
-#tailf resnet50.log
+#--total_images 1281167 \
+#--lr_strategy "cosine_decay" \
+#--lr 0.1 \
+#--num_epochs 120 \
+#--l2_decay 1e-4 \
+#--pretrained_model ../pretrain/ResNet34_pretrained \
+#--config_file "./configs/resnet34.yaml" \
+#> resnet34.log 2>&1 &
+#tailf resnet34.log