未验证 提交 8be488a5 编写于 作者: J Jason 提交者: GitHub

Merge pull request #74 from SunAhong1993/syf0519

intrepret
...@@ -113,7 +113,8 @@ def get_normlime_interpreter(img, model, dataset, num_samples=3000, batch_size=5 ...@@ -113,7 +113,8 @@ def get_normlime_interpreter(img, model, dataset, num_samples=3000, batch_size=5
root_path = osp.join(root_path, '.paddlex') root_path = osp.join(root_path, '.paddlex')
pre_models_path = osp.join(root_path, "pre_models") pre_models_path = osp.join(root_path, "pre_models")
if not osp.exists(pre_models_path): if not osp.exists(pre_models_path):
os.makedirs(root_path) if not osp.exists(root_path):
os.makedirs(root_path)
url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz" url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
pdx.utils.download_and_decompress(url, path=root_path) pdx.utils.download_and_decompress(url, path=root_path)
npy_dir = precompute_for_normlime(precompute_predict_func, npy_dir = precompute_for_normlime(precompute_predict_func,
......
...@@ -4,7 +4,6 @@ os.environ['CUDA_VISIBLE_DEVICES'] = '0' ...@@ -4,7 +4,6 @@ os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import os.path as osp import os.path as osp
import paddlex as pdx import paddlex as pdx
from paddlex.cls import transforms
# 下载和解压Imagenet果蔬分类数据集 # 下载和解压Imagenet果蔬分类数据集
veg_dataset = 'https://bj.bcebos.com/paddlex/interpret/mini_imagenet_veg.tar.gz' veg_dataset = 'https://bj.bcebos.com/paddlex/interpret/mini_imagenet_veg.tar.gz'
...@@ -17,7 +16,6 @@ pdx.utils.download_and_decompress(model_file, path='./') ...@@ -17,7 +16,6 @@ pdx.utils.download_and_decompress(model_file, path='./')
# 加载模型 # 加载模型
model = pdx.load_model('mini_imagenet_veg_mobilenetv2') model = pdx.load_model('mini_imagenet_veg_mobilenetv2')
# 定义测试所用的数据集 # 定义测试所用的数据集
test_dataset = pdx.datasets.ImageNet( test_dataset = pdx.datasets.ImageNet(
data_dir='mini_imagenet_veg', data_dir='mini_imagenet_veg',
...@@ -37,4 +35,4 @@ pdx.interpret.visualize( ...@@ -37,4 +35,4 @@ pdx.interpret.visualize(
model, model,
test_dataset, test_dataset,
algo='normlime', algo='normlime',
save_dir='./') save_dir='./')
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