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未验证 提交 7534af80 编写于 作者: R ruri 提交者： GitHub
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Merge pull request #1810 from shippingwang/fix_run_script_bug 

fix eval bug and refine run script
上级 a6fe23f7 8e42ab67
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Showing with 119 addition and 45 deletion
fluid/PaddleCV/image_classification/README.md
浏览文件 @ 7534af80
......@@ -81,7 +81,7 @@ python train.py \
* **lr**: initialized learning rate. Default: 0.1.
* **pretrained_model**: model path for pretraining. Default: None.
* **checkpoint**: the checkpoint path to resume. Default: None.
* **model_category**: the category of models, ("models"|"models_name"). Default: "models".
* **model_category**: the category of models, ("models"|"models_name"). Default: "models_name".
Or can start the training step by running the ```run.sh```.
......@@ -221,6 +221,8 @@ Models are trained by starting with learning rate ```0.1``` and decaying it by `
- Released models: not specify parameter names
**NOTE: These are trained by using model_category=models**
|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
|- |:-: |:-:|
|[ResNet152](http://paddle-imagenet-models.bj.bcebos.com/ResNet152_pretrained.zip) | 78.18%/93.93% | 78.11%/94.04% |
......
fluid/PaddleCV/image_classification/README_cn.md
浏览文件 @ 7534af80
......@@ -79,7 +79,7 @@ python train.py \
* **lr**: initialized learning rate. Default: 0.1.
* **pretrained_model**: model path for pretraining. Default: None.
* **checkpoint**: the checkpoint path to resume. Default: None.
* **model_category**: the category of models, ("models"|"models_name"). Default:"models".
* **model_category**: the category of models, ("models"|"models_name"). Default:"models_name".
**数据读取器说明：** 数据读取器定义在```reader.py```和```reader_cv2.py```中, 一般, CV2 reader可以提高数据读取速度, reader(PIL)可以得到相对更高的精度, 在[训练阶段](#training-a-model), 默认采用的增广方式是随机裁剪与水平翻转, 而在[评估](#inference)与[推断](#inference)阶段用的默认方式是中心裁剪。当前支持的数据增广方式有：
* 旋转
......@@ -213,6 +213,8 @@ Models包括两种模型：带有参数名字的模型，和不带有参数名
- Released models: not specify parameter names
**注意：这是model_category = models 的预训练模型**
|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
|- |:-: |:-:|
|[ResNet152](http://paddle-imagenet-models.bj.bcebos.com/ResNet152_pretrained.zip) | 78.18%/93.93% | 78.11%/94.04% |
......
fluid/PaddleCV/image_classification/eval.py
浏览文件 @ 7534af80
......@@ -7,8 +7,6 @@ import time
import sys
import paddle
import paddle.fluid as fluid
#import models
import models_name as models
#import reader_cv2 as reader
import reader as reader
import argparse
......@@ -27,9 +25,20 @@ add_arg('image_shape', str, "3,224,224", "Input image size")
add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.")
add_arg('pretrained_model', str, None, "Whether to use pretrained model.")
add_arg('model', str, "SE_ResNeXt50_32x4d", "Set the network to use.")
add_arg('model_category', str, "models_name", "Whether to use models_name or not, valid value:'models','models_name'." )
# yapf: enable
model_list = [m for m in dir(models) if "__" not in m]
def set_models(model_category):
global 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:
import models as models
def eval(args):
......@@ -40,6 +49,7 @@ def eval(args):
with_memory_optimization = args.with_mem_opt
image_shape = [int(m) for m in args.image_shape.split(",")]
model_list = [m for m in dir(models) if "__" not in m]
assert model_name in model_list, "{} is not in lists: {}".format(args.model,
model_list)
......@@ -63,11 +73,11 @@ def eval(args):
acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
else:
out = model.net(input=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label)
cost, pred = fluid.layers.softmax_with_cross_entropy(
out, label, return_softmax=True)
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
test_program = fluid.default_main_program().clone(for_test=True)
......@@ -125,6 +135,7 @@ def eval(args):
def main():
args = parser.parse_args()
print_arguments(args)
set_models(args.model_category)
eval(args)
......
fluid/PaddleCV/image_classification/infer.py
浏览文件 @ 7534af80
......@@ -7,7 +7,6 @@ import time
import sys
import paddle
import paddle.fluid as fluid
import models
import reader
import argparse
import functools
......@@ -23,9 +22,19 @@ add_arg('image_shape', str, "3,224,224", "Input image size")
add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.")
add_arg('pretrained_model', str, None, "Whether to use pretrained model.")
add_arg('model', str, "SE_ResNeXt50_32x4d", "Set the network to use.")
add_arg('model_category', str, "models_name", "Whether to use models_name or not, valid value:'models','models_name'." )
# yapf: enable
model_list = [m for m in dir(models) if "__" not in m]
def set_models(model_category):
global 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:
import models as models
def infer(args):
......@@ -35,7 +44,7 @@ def infer(args):
pretrained_model = args.pretrained_model
with_memory_optimization = args.with_mem_opt
image_shape = [int(m) for m in args.image_shape.split(",")]
model_list = [m for m in dir(models) if "__" not in m]
assert model_name in model_list, "{} is not in lists: {}".format(args.model,
model_list)
......@@ -85,6 +94,7 @@ def infer(args):
def main():
args = parser.parse_args()
print_arguments(args)
set_models(args.model_category)
infer(args)
......
fluid/PaddleCV/image_classification/run.sh
浏览文件 @ 7534af80
#Hyperparameters config
#Example: SE_ResNext50_32x4d
python train.py \
--model=SE_ResNeXt50_32x4d \
--batch_size=32 \
--batch_size=400 \
--total_images=1281167 \
--class_dim=1000 \
--image_shape=3,224,224 \
--model_save_dir=output/ \
--with_mem_opt=True \
--lr_strategy=piecewise_decay \
--lr=0.1
--lr_strategy=cosine_decay \
--lr=0.1 \
--num_epochs=200 \
--l2_decay=1.2e-4 \
--model_category=models_name \
# >log_SE_ResNeXt50_32x4d.txt 2>&1 &
#AlexNet:
#python train.py \
# --model=AlexNet \
......@@ -20,23 +23,11 @@ python train.py \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --with_mem_opt=True \
# --model_category=models_name \
# --lr_strategy=piecewise_decay \
# --num_epochs=120 \
# --lr=0.01
#VGG11:
#python train.py \
# --model=VGG11 \
# --batch_size=512 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --with_mem_opt=True \
# --lr_strategy=piecewise_decay \
# --num_epochs=120 \
# --lr=0.1
# --lr=0.01 \
# --l2_decay=1e-4
#MobileNet v1:
#python train.py \
......@@ -47,9 +38,11 @@ python train.py \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --with_mem_opt=True \
# --model_category=models_name \
# --lr_strategy=piecewise_decay \
# --num_epochs=120 \
# --lr=0.1
# --lr=0.1 \
# --l2_decay=3e-5
#python train.py \
# --model=MobileNetV2 \
......@@ -58,10 +51,12 @@ python train.py \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --model_category=models_name \
# --with_mem_opt=True \
# --lr_strategy=cosine_decay \
# --num_epochs=200 \
# --lr=0.1
# --num_epochs=240 \
# --lr=0.1 \
# --l2_decay=4e-5
#ResNet50:
#python train.py \
# --model=ResNet50 \
......@@ -71,9 +66,11 @@ python train.py \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --with_mem_opt=True \
# --model_category=models_name \
# --lr_strategy=piecewise_decay \
# --num_epochs=120 \
# --lr=0.1
# --lr=0.1 \
# --l2_decay=1e-4
#ResNet101:
#python train.py \
......@@ -83,44 +80,58 @@ python train.py \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --with_mem_opt=False \
# --model_category=models_name \
# --with_mem_opt=True \
# --lr_strategy=piecewise_decay \
# --num_epochs=120 \
# --lr=0.1
# --lr=0.1 \
# --l2_decay=1e-4
#ResNet152:
#python train.py \
# --model=ResNet152 \
# --batch_size=256 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --lr_strategy=piecewise_decay \
# --model_category=models_name \
# --with_mem_opt=True \
# --lr=0.1 \
# --num_epochs=120 \
# --l2_decay=1e-4
#SE_ResNeXt50:
#SE_ResNeXt50_32x4d:
#python train.py \
# --model=SE_ResNeXt50 \
# --model=SE_ResNeXt50_32x4d \
# --batch_size=400 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --model_category=models_name \
# --model_save_dir=output/ \
# --lr=0.1 \
# --num_epochs=200 \
# --l2_decay=12e-5
# --with_mem_opt=True \
# --l2_decay=1.2e-4
#SE_ResNeXt101:
#SE_ResNeXt101_32x4d:
#python train.py \
# --model=SE_ResNeXt101 \
# --model=SE_ResNeXt101_32x4d \
# --batch_size=400 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --model_category=models_name \
# --model_save_dir=output/ \
# --lr=0.1 \
# --num_epochs=200 \
# --l2_decay=15e-5
# --with_mem_opt=True \
# --l2_decay=1.5e-5
#VGG11:
#python train.py \
......@@ -129,8 +140,12 @@ python train.py \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --class_dim=1000 \
# --model_category=models_name \
# --model_save_dir=output/ \
# --lr=0.1 \
# --num_epochs=90 \
# --with_mem_opt=True \
# --l2_decay=2e-4
#VGG13:
......@@ -138,8 +153,42 @@ python train.py \
# --model=VGG13 \
# --batch_size=256 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --lr=0.01 \
# --num_epochs=90 \
# --model_category=models_name \
# --model_save_dir=output/ \
# --with_mem_opt=True \
# --l2_decay=3e-4
#VGG16:
#python train.py
# --model=VGG16 \
# --batch_size=256 \
# --total_images=1281167 \
# --class_dim=1000 \
# --lr_strategy=cosine_decay \
# --image_shape=3,224,224 \
# --model_category=models_name \
# --model_save_dir=output/ \
# --lr=0.01 \
# --num_epochs=90 \
# --with_mem_opt=True \
# --l2_decay=3e-4
#VGG19:
#python train.py
# --model=VGG19 \
# --batch_size=256 \
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --lr=0.01 \
# --num_epochs=90 \
# --with_mem_opt=True \
# --model_category=models_name \
# --model_save_dir=output/ \
# --l2_decay=3e-4
fluid/PaddleCV/image_classification/train.py
浏览文件 @ 7534af80
......@@ -39,7 +39,7 @@ 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_name", "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.")
......
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