Merge pull request #1705 from shippingwang/dev_sc

refine image classification train script

fluid/PaddleCV/image_classification/README.md

@@ -209,6 +209,7 @@ Models are trained by starting with learning rate ```0.1``` and decaying it by `
 |[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.zip) | 72.08%/90.63% | 71.65%/90.57% |
 |[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.zip) | 72.56%/90.83% | 72.32%/90.98% |
 |[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.91%/89.54% | 70.51%/89.35% |
+|[MobileNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.zip) | 71.90%/90.55% | 71.53%/90.41% |
 |[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.35%/92.80% | 76.22%/92.92% |
 |[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.49%/93.57% | 77.56%/93.64% |
 |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.zip) | 78.12%/93.93% | 77.92%/93.87% |

fluid/PaddleCV/image_classification/README_cn.md

@@ -204,6 +204,7 @@ Models包括两种模型：带有参数名字的模型，和不带有参数名
 |[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.zip) | 72.08%/90.63% | 71.65%/90.57% |
 |[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.zip) | 72.56%/90.83% | 72.32%/90.98% |
 |[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.91%/89.54% | 70.51%/89.35% |
+|[MobileNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.zip) | 71.90%/90.55% | 71.53%/90.41% |
 |[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.35%/92.80% | 76.22%/92.92% |
 |[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.49%/93.57% | 77.56%/93.64% |
 |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.zip) | 78.12%/93.93% | 77.92%/93.87% |

fluid/PaddleCV/image_classification/run.sh

@@ -6,7 +6,7 @@ python train.py \
        --class_dim=1000 \
        --image_shape=3,224,224 \
        --model_save_dir=output/ \
-       --with_mem_opt=False \
+       --with_mem_opt=True \
        --lr_strategy=piecewise_decay \
        --lr=0.1
 #      >log_SE_ResNeXt50_32x4d.txt 2>&1 &
@@ -19,7 +19,7 @@ python train.py \
 #       --class_dim=1000 \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
-#       --with_mem_opt=False \
+#       --with_mem_opt=True \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
 #       --lr=0.01
@@ -32,7 +32,7 @@ python train.py \
 #       --class_dim=1000 \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
-#       --with_mem_opt=False \
+#       --with_mem_opt=True \
 #       --lr_strategy=piecewise_decay \
 #       --num_epochs=120 \
 #       --lr=0.1
@@ -46,12 +46,22 @@ python train.py \
 #       --class_dim=1000 \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
-#       --with_mem_opt=False \
+#       --with_mem_opt=True \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
 #       --lr=0.1
 
-
+#python train.py \
+#	--model=MobileNetV2 \
+#	--batch_size=500 \
+#	--total_images=1281167 \
+#	--class_dim=1000 \
+#	--image_shape=3,224,224 \
+#	--model_save_dir=output/ \
+#	--with_mem_opt=True \
+#	--lr_strategy=cosine_decay \
+#	--num_epochs=200 \
+#	--lr=0.1
 #ResNet50:
 #python train.py \
 #       --model=ResNet50 \
@@ -60,7 +70,7 @@ python train.py \
 #       --class_dim=1000 \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
-#       --with_mem_opt=False \
+#       --with_mem_opt=True \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
 #       --lr=0.1

fluid/PaddleCV/image_classification/train.py

@@ -10,7 +10,6 @@ import math
 import paddle
 import paddle.fluid as fluid
 import paddle.dataset.flowers as flowers
-import models
 import reader
 import argparse
 import functools
@@ -19,8 +18,8 @@ import utils
 from utils.learning_rate import cosine_decay
 from utils.fp16_utils import create_master_params_grads, master_param_to_train_param
 from utility import add_arguments, print_arguments
-import models
-import models_name
+
+IMAGENET1000 = 1281167
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
@@ -40,25 +39,32 @@ add_arg('lr_strategy',      str,   "piecewise_decay",    "Set the learning rate
 add_arg('model',            str,   "SE_ResNeXt50_32x4d", "Set the network to use.")
 add_arg('enable_ce',        bool,  False,                "If set True, enable continuous evaluation job.")
 add_arg('data_dir',         str,   "./data/ILSVRC2012",  "The ImageNet dataset root dir.")
-add_arg('model_category',   str,   "models",             "Whether to use models_name or not, valid value:'models','models_name'" )
+add_arg('model_category',   str,   "models",        "Whether to use models_name or not, valid value:'models','models_name'." )
 add_arg('fp16',             bool,  False,                "Enable half precision training with fp16." )
 add_arg('scale_loss',       float, 1.0,                  "Scale loss for fp16." )
+add_arg('l2_decay',         float, 1e-4,                 "L2_decay parameter.")
+add_arg('momentum_rate',    float, 0.9,                  "momentum_rate.")
 # yapf: enable
 
 
-def set_models(model):
+def set_models(model_category):
     global models
-    if model == "models":
-        models = models
+    assert model_category in ["models", "models_name"
+                              ], "{} is not in lists: {}".format(
+                                  model_category, ["models", "models_name"])
+    if model_category == "models_name":
+        import models_name as models
     else:
-        models = models_name
+        import models as models
 
 
 def optimizer_setting(params):
     ls = params["learning_strategy"]
+    l2_decay = params["l2_decay"]
+    momentum_rate = params["momentum_rate"]
     if ls["name"] == "piecewise_decay":
         if "total_images" not in params:
-            total_images = 1281167
+            total_images = IMAGENET1000
         else:
             total_images = params["total_images"]
         batch_size = ls["batch_size"]
@@ -71,16 +77,17 @@ def optimizer_setting(params):
         optimizer = fluid.optimizer.Momentum(
             learning_rate=fluid.layers.piecewise_decay(
                 boundaries=bd, values=lr),
-            momentum=0.9,
-            regularization=fluid.regularizer.L2Decay(1e-4))
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
 
     elif ls["name"] == "cosine_decay":
         if "total_images" not in params:
-            total_images = 1281167
+            total_images = IMAGENET1000
         else:
             total_images = params["total_images"]
-
         batch_size = ls["batch_size"]
+        l2_decay = params["l2_decay"]
+        momentum_rate = params["momentum_rate"]
         step = int(total_images / batch_size + 1)
 
         lr = params["lr"]
@@ -89,43 +96,42 @@ def optimizer_setting(params):
         optimizer = fluid.optimizer.Momentum(
             learning_rate=cosine_decay(
                 learning_rate=lr, step_each_epoch=step, epochs=num_epochs),
-            momentum=0.9,
-            regularization=fluid.regularizer.L2Decay(4e-5))
-    elif ls["name"] == "exponential_decay":
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
+    elif ls["name"] == "linear_decay":
         if "total_images" not in params:
-            total_images = 1281167
+            total_images = IMAGENET1000
         else:
             total_images = params["total_images"]
         batch_size = ls["batch_size"]
-        step = int(total_images / batch_size +1)
-        lr = params["lr"]
         num_epochs = params["num_epochs"]
-        learning_decay_rate_factor=ls["learning_decay_rate_factor"]
-        num_epochs_per_decay = ls["num_epochs_per_decay"]
-        NUM_GPUS = 1
-
+        start_lr = params["lr"]
+        l2_decay = params["l2_decay"]
+        momentum_rate = params["momentum_rate"]
+        end_lr = 0
+        total_step = int((total_images / batch_size) * num_epochs)
+        lr = fluid.layers.polynomial_decay(
+            start_lr, total_step, end_lr, power=1)
         optimizer = fluid.optimizer.Momentum(
-            learning_rate=fluid.layers.exponential_decay(
-                learning_rate = lr * NUM_GPUS,
-                decay_steps = step * num_epochs_per_decay / NUM_GPUS,
-                decay_rate = learning_decay_rate_factor),
-            momentum=0.9,
-
-            regularization = fluid.regularizer.L2Decay(4e-5))
-
+            learning_rate=lr,
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
     else:
         lr = params["lr"]
+        l2_decay = params["l2_decay"]
+        momentum_rate = params["momentum_rate"]
         optimizer = fluid.optimizer.Momentum(
             learning_rate=lr,
-            momentum=0.9,
-            regularization=fluid.regularizer.L2Decay(1e-4))
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
 
     return optimizer
 
+
 def net_config(image, label, model, args):
     model_list = [m for m in dir(models) if "__" not in m]
-    assert args.model in model_list,"{} is not lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not lists: {}".format(args.model,
+                                                                  model_list)
 
     class_dim = args.class_dim
     model_name = args.model
@@ -149,7 +155,8 @@ def net_config(image, label, model, args):
         acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
     else:
         out = model.net(input=image, class_dim=class_dim)
-        cost, pred = fluid.layers.softmax_with_cross_entropy(out, label, return_softmax=True) 
+        cost, pred = fluid.layers.softmax_with_cross_entropy(
+            out, label, return_softmax=True)
         if args.scale_loss > 1:
             avg_cost = fluid.layers.mean(x=cost) * float(args.scale_loss)
         else:
@@ -190,18 +197,24 @@ def build_program(is_train, main_prog, startup_prog, args):
                 params["num_epochs"] = args.num_epochs
                 params["learning_strategy"]["batch_size"] = args.batch_size
                 params["learning_strategy"]["name"] = args.lr_strategy
+                params["l2_decay"] = args.l2_decay
+                params["momentum_rate"] = args.momentum_rate
 
                 optimizer = optimizer_setting(params)
-
                 if args.fp16:
                     params_grads = optimizer.backward(avg_cost)
                     master_params_grads = create_master_params_grads(
                         params_grads, main_prog, startup_prog, args.scale_loss)
                     optimizer.apply_gradients(master_params_grads)
-                    master_param_to_train_param(master_params_grads, params_grads, main_prog)
+                    master_param_to_train_param(master_params_grads,
+                                                params_grads, main_prog)
                 else:
                     optimizer.minimize(avg_cost)
+                global_lr = optimizer._global_learning_rate()
 
+    if is_train:
+        return py_reader, avg_cost, acc_top1, acc_top5, global_lr
+    else:
         return py_reader, avg_cost, acc_top1, acc_top5
 
 
@@ -220,7 +233,7 @@ def train(args):
         startup_prog.random_seed = 1000
         train_prog.random_seed = 1000
 
-    train_py_reader, train_cost, train_acc1, train_acc5 = build_program(
+    train_py_reader, train_cost, train_acc1, train_acc5, global_lr = build_program(
         is_train=True,
         main_prog=train_prog,
         startup_prog=startup_prog,
@@ -255,7 +268,8 @@ def train(args):
     if visible_device:
         device_num = len(visible_device.split(','))
     else:
-        device_num = subprocess.check_output(['nvidia-smi', '-L']).decode().count('\n')
+        device_num = subprocess.check_output(
+            ['nvidia-smi', '-L']).decode().count('\n')
 
     train_batch_size = args.batch_size / device_num
     test_batch_size = 16
@@ -283,11 +297,12 @@ def train(args):
         use_cuda=bool(args.use_gpu),
         loss_name=train_cost.name)
 
-    train_fetch_list = [train_cost.name, train_acc1.name, train_acc5.name]
+    train_fetch_list = [
+        train_cost.name, train_acc1.name, train_acc5.name, global_lr.name
+    ]
     test_fetch_list = [test_cost.name, test_acc1.name, test_acc5.name]
 
     params = models.__dict__[args.model]().params
-
     for pass_id in range(params["num_epochs"]):
 
         train_py_reader.start()
@@ -299,7 +314,9 @@ def train(args):
         try:
             while True:
                 t1 = time.time()
-                loss, acc1, acc5 = train_exe.run(fetch_list=train_fetch_list)
+                loss, acc1, acc5, lr = train_exe.run(
+                    fetch_list=train_fetch_list)
+
                 t2 = time.time()
                 period = t2 - t1
                 loss = np.mean(np.array(loss))
@@ -308,21 +325,27 @@ def train(args):
                 train_info[0].append(loss)
                 train_info[1].append(acc1)
                 train_info[2].append(acc5)
+                lr = np.mean(np.array(lr))
                 train_time.append(period)
+
                 if batch_id % 10 == 0:
                     print("Pass {0}, trainbatch {1}, loss {2}, \
-                        acc1 {3}, acc5 {4} time {5}"
-                          .format(pass_id, batch_id, loss, acc1, acc5,
-                                  "%2.2f sec" % period))
+                        acc1 {3}, acc5 {4}, lr{5}, time {6}"
+                          .format(pass_id, batch_id, loss, acc1, acc5, "%.5f" %
+                                  lr, "%2.2f sec" % period))
                     sys.stdout.flush()
                 batch_id += 1
+                if batch_id == 31:
+                    exit(0)
+
         except fluid.core.EOFException:
             train_py_reader.reset()
 
         train_loss = np.array(train_info[0]).mean()
         train_acc1 = np.array(train_info[1]).mean()
         train_acc5 = np.array(train_info[2]).mean()
-        train_speed = np.array(train_time).mean() / (train_batch_size * device_num)
+        train_speed = np.array(train_time).mean() / (train_batch_size *
+                                                     device_num)
 
         test_py_reader.start()
 
@@ -394,10 +417,7 @@ def train(args):
 
 def main():
     args = parser.parse_args()
-    models_now = args.model_category
-    assert models_now in ["models", "models_name"], "{} is not in lists: {}".format(
-            models_now, ["models", "models_name"])
-    set_models(models_now)
+    set_models(args.model_category)
     print_arguments(args)
     train(args)