Removing print and adding a logger

python-package/README.md

@@ -19,31 +19,31 @@ You have to install ``onnxruntime-gpu`` manually to enable GPU inference, or ins
 ## Change Log
 
 ### [0.7.1] - 2022-12-14
-  
+
 #### Changed
-  
+
 - Change model downloading provider to cloudfront.
 
 ### [0.7] - 2022-11-28
-  
+
 #### Added
 
 - Add face swapping model and example.
- 
+
 #### Changed
-  
+
 - Set default ORT provider to CUDA and CPU.
- 
+
 ### [0.6] - 2022-01-29
-  
+
 #### Added
 
 - Add pose estimation in face-analysis app.
- 
+
 #### Changed
-  
+
 - Change model automated downloading url, to ucloud.
- 
+
 
 ## Quick Example
 
@@ -150,4 +150,6 @@ handler.prepare(ctx_id=0)
 
 ```
 
+### Enable debugging
 
+To enable the debug logger, export the environment variable DEBUG (set to any value) prior to starting your application.

python-package/insightface/__init__.py

@@ -2,6 +2,8 @@
 # pylint: disable=wrong-import-position
 """InsightFace: A Face Analysis Toolkit."""
 from __future__ import absolute_import
+from loguru import logger
+import os
 
 try:
     #import mxnet as mx
@@ -11,6 +13,10 @@
         "Unable to import dependency onnxruntime. "
     )
 
+if os.getenv("DEBUG") is None:
+    logger.remove()
+    logger.add(sys.stderr, level="INFO")
+
 __version__ = '0.7.3'
 
 from . import model_zoo

python-package/insightface/app/face_analysis.py

@@ -2,7 +2,7 @@
 # @Organization  : insightface.ai
 # @Author        : Jia Guo
 # @Time          : 2021-05-04
-# @Function      : 
+# @Function      :
 
 
 from __future__ import division
@@ -12,6 +12,7 @@
 
 import numpy as np
 import onnxruntime
+from loguru import logger
 from numpy.linalg import norm
 
 from ..model_zoo import model_zoo
@@ -30,15 +31,15 @@ def __init__(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=
         for onnx_file in onnx_files:
             model = model_zoo.get_model(onnx_file, **kwargs)
             if model is None:
-                print('model not recognized:', onnx_file)
+                logger.debug('model not recognized:', onnx_file)
             elif allowed_modules is not None and model.taskname not in allowed_modules:
-                print('model ignore:', onnx_file, model.taskname)
+                logger.debug('model ignore:', onnx_file, model.taskname)
                 del model
             elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules):
-                print('find model:', onnx_file, model.taskname, model.input_shape, model.input_mean, model.input_std)
+                logger.debug('find model:', onnx_file, model.taskname, model.input_shape, model.input_mean, model.input_std)
                 self.models[model.taskname] = model
             else:
-                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                logger.debug('duplicated model task type, ignore:', onnx_file, model.taskname)
                 del model
         assert 'detection' in self.models
         self.det_model = self.models['detection']
@@ -47,7 +48,7 @@ def __init__(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=
     def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)):
         self.det_thresh = det_thresh
         assert det_size is not None
-        print('set det-size:', det_size)
+        logger.debug('set det-size:', det_size)
         self.det_size = det_size
         for taskname, model in self.models.items():
             if taskname=='detection':

python-package/insightface/data/pickle_object.py

@@ -2,6 +2,7 @@
 import os
 import os.path as osp
 from pathlib import Path
+from loguru import logger
 import pickle
 
 def get_object(name):
@@ -16,9 +17,9 @@ def get_object(name):
         name = name + ".pkl"
 
     filepath = osp.join(objects_dir, name)
-    
+
     if not osp.exists(filepath):
-        print(f"[Error] File not found: {filepath}")
+        logger.error(f"[Error] File not found: {filepath}")
         return None
 
     with open(filepath, 'rb') as f:

python-package/insightface/data/rec_builder.py

@@ -4,6 +4,7 @@
 import os.path as osp
 import sys
 import mxnet as mx
+from loguru import logger
 
 
 class RecBuilder():
@@ -15,7 +16,7 @@ def __init__(self, path, image_size=(112, 112)):
         self.max_label = -1
         assert not osp.exists(path), '%s exists' % path
         os.makedirs(path)
-        self.writer = mx.recordio.MXIndexedRecordIO(os.path.join(path, 'train.idx'), 
+        self.writer = mx.recordio.MXIndexedRecordIO(os.path.join(path, 'train.idx'),
                                                     os.path.join(path, 'train.rec'),
                                                     'w')
         self.meta = []
@@ -64,7 +65,7 @@ def add_image(self, img, label):
     def close(self):
         with open(osp.join(self.path, 'train.meta'), 'wb') as pfile:
             pickle.dump(self.meta, pfile, protocol=pickle.HIGHEST_PROTOCOL)
-        print('stat:', self.widx, self.wlabel)
+        logger.debug('stat:', self.widx, self.wlabel)
         with open(os.path.join(self.path, 'property'), 'w') as f:
             f.write("%d,%d,%d\n" % (self.max_label+1, self.image_size[0], self.image_size[1]))
             f.write("%d\n" % (self.widx))

python-package/insightface/model_zoo/inswapper.py

@@ -3,6 +3,7 @@
 import onnxruntime
 import cv2
 import onnx
+from loguru import logger
 from onnx import numpy_helper
 from ..utils import face_align
 
@@ -35,7 +36,7 @@ def __init__(self, model_file=None, session=None):
         input_cfg = inputs[0]
         input_shape = input_cfg.shape
         self.input_shape = input_shape
-        print('inswapper-shape:', self.input_shape)
+        logger.debug('inswapper-shape:', self.input_shape)
         self.input_size = tuple(input_shape[2:4][::-1])
 
     def forward(self, img, latent):

python-package/insightface/model_zoo/model_store.py

@@ -8,6 +8,7 @@
 import zipfile
 import glob
 
+from loguru import logger
 from ..utils import download, check_sha1
 
 _model_sha1 = {
@@ -72,11 +73,11 @@ def get_model_file(name, root=os.path.join('~', '.insightface', 'models')):
         if check_sha1(file_path, sha1_hash):
             return file_path
         else:
-            print(
+            logger.debug(
                 'Mismatch in the content of model file detected. Downloading again.'
             )
     else:
-        print('Model file is not found. Downloading.')
+        logger.debug('Model file is not found. Downloading.')
 
     if not os.path.exists(root):
         os.makedirs(root)

python-package/insightface/model_zoo/model_zoo.py

@@ -2,12 +2,13 @@
 # @Organization  : insightface.ai
 # @Author        : Jia Guo
 # @Time          : 2021-05-04
-# @Function      : 
+# @Function      :
 
 import os
 import os.path as osp
 import glob
 import onnxruntime
+from loguru import logger
 from .arcface_onnx import *
 from .retinaface import *
 #from .scrfd import *
@@ -19,7 +20,7 @@
 __all__ = ['get_model']
 
 
-class PickableInferenceSession(onnxruntime.InferenceSession): 
+class PickableInferenceSession(onnxruntime.InferenceSession):
     # This is a wrapper to make the current InferenceSession class pickable.
     def __init__(self, model_path, **kwargs):
         super().__init__(model_path, **kwargs)
@@ -38,7 +39,7 @@ def __init__(self, onnx_file):
 
     def get_model(self, **kwargs):
         session = PickableInferenceSession(self.onnx_file, **kwargs)
-        print(f'Applied providers: {session._providers}, with options: {session._provider_options}')
+        logger.debug(f'Applied providers: {session._providers}, with options: {session._provider_options}')
         inputs = session.get_inputs()
         input_cfg = inputs[0]
         input_shape = input_cfg.shape

python-package/insightface/model_zoo/retinaface.py

@@ -2,7 +2,7 @@
 # @Organization  : insightface.ai
 # @Author        : Jia Guo
 # @Time          : 2021-09-18
-# @Function      : 
+# @Function      :
 
 from __future__ import division
 import datetime
@@ -13,6 +13,7 @@
 import os.path as osp
 import cv2
 import sys
+from loguru import logger
 
 def softmax(z):
     assert len(z.shape) == 2
@@ -139,7 +140,7 @@ def prepare(self, ctx_id, **kwargs):
         input_size = kwargs.get('input_size', None)
         if input_size is not None:
             if self.input_size is not None:
-                print('warning: det_size is already set in detection model, ignore')
+                logger.warning('warning: det_size is already set in detection model, ignore')
             else:
                 self.input_size = input_size
 
@@ -207,7 +208,7 @@ def forward(self, img, threshold):
     def detect(self, img, input_size = None, max_num=0, metric='default'):
         assert input_size is not None or self.input_size is not None
         input_size = self.input_size if input_size is None else input_size
-            
+
         im_ratio = float(img.shape[0]) / img.shape[1]
         model_ratio = float(input_size[1]) / input_size[0]
         if im_ratio>model_ratio:

python-package/insightface/model_zoo/scrfd.py

@@ -2,7 +2,7 @@
 # @Organization  : insightface.ai
 # @Author        : Jia Guo
 # @Time          : 2021-05-04
-# @Function      : 
+# @Function      :
 
 from __future__ import division
 import datetime
@@ -13,6 +13,7 @@
 import os.path as osp
 import cv2
 import sys
+from loguru import logger
 
 def softmax(z):
     assert len(z.shape) == 2
@@ -142,7 +143,7 @@ def prepare(self, ctx_id, **kwargs):
         input_size = kwargs.get('input_size', None)
         if input_size is not None:
             if self.input_size is not None:
-                print('warning: det_size is already set in scrfd model, ignore')
+                logger.warning('warning: det_size is already set in scrfd model, ignore')
             else:
                 self.input_size = input_size
 
@@ -220,7 +221,7 @@ def forward(self, img, threshold):
     def detect(self, img, input_size = None, max_num=0, metric='default'):
         assert input_size is not None or self.input_size is not None
         input_size = self.input_size if input_size is None else input_size
-            
+
         im_ratio = float(img.shape[0]) / img.shape[1]
         model_ratio = float(input_size[1]) / input_size[0]
         if im_ratio>model_ratio:
@@ -329,10 +330,10 @@ def scrfd_2p5gkps(**kwargs):
             #bboxes, kpss = detector.detect(img, 0.5, input_size = (640, 640))
             bboxes, kpss = detector.detect(img, 0.5)
             tb = datetime.datetime.now()
-            print('all cost:', (tb-ta).total_seconds()*1000)
-        print(img_path, bboxes.shape)
+            logger.debug('all cost:', (tb-ta).total_seconds()*1000)
+        logger.debug(img_path, bboxes.shape)
         if kpss is not None:
-            print(kpss.shape)
+            logger.debug(kpss.shape)
         for i in range(bboxes.shape[0]):
             bbox = bboxes[i]
             x1,y1,x2,y2,score = bbox.astype(np.int)
@@ -343,6 +344,6 @@ def scrfd_2p5gkps(**kwargs):
                     kp = kp.astype(np.int)
                     cv2.circle(img, tuple(kp) , 1, (0,0,255) , 2)
         filename = img_path.split('/')[-1]
-        print('output:', filename)
+        logger.debug('output:', filename)
         cv2.imwrite('./outputs/%s'%filename, img)
 

python-package/insightface/utils/download.py

@@ -4,6 +4,7 @@
 import os
 import hashlib
 import requests
+from loguru import logger
 from tqdm import tqdm
 
 
@@ -67,7 +68,7 @@ def download_file(url, path=None, overwrite=False, sha1_hash=None):
         if not os.path.exists(dirname):
             os.makedirs(dirname)
 
-        print('Downloading %s from %s...' % (fname, url))
+        logger.debug('Downloading %s from %s...' % (fname, url))
         r = requests.get(url, stream=True)
         if r.status_code != 200:
             raise RuntimeError("Failed downloading url %s" % url)

python-package/insightface/utils/storage.py

@@ -2,6 +2,7 @@
 import os
 import os.path as osp
 import zipfile
+from loguru import logger
 from .download import download_file
 
 BASE_REPO_URL = 'https://github.com/deepinsight/insightface/releases/download/v0.7'
@@ -11,7 +12,7 @@ def download(sub_dir, name, force=False, root='~/.insightface'):
     dir_path = os.path.join(_root, sub_dir, name)
     if osp.exists(dir_path) and not force:
         return dir_path
-    print('download_path:', dir_path)
+    logger.debug('download_path:', dir_path)
     zip_file_path = os.path.join(_root, sub_dir, name + '.zip')
     model_url = "%s/%s.zip"%(BASE_REPO_URL, name)
     download_file(model_url,
@@ -35,7 +36,7 @@ def download_onnx(sub_dir, model_file, force=False, root='~/.insightface', downl
         return new_model_file
     if not osp.exists(model_root):
         os.makedirs(model_root)
-    print('download_path:', new_model_file)
+    logger.debug('download_path:', new_model_file)
     if not download_zip:
         model_url = "%s/%s"%(BASE_REPO_URL, model_file)
         download_file(model_url,

python-package/setup.py

@@ -56,10 +56,11 @@ def find_version(*file_paths):
     'cython',
     'albumentations',
     'prettytable',
+    'loguru',
 ]
 
 extensions = [
-        Extension("insightface.thirdparty.face3d.mesh.cython.mesh_core_cython", 
+        Extension("insightface.thirdparty.face3d.mesh.cython.mesh_core_cython",
             ["insightface/thirdparty/face3d/mesh/cython/mesh_core_cython.pyx", "insightface/thirdparty/face3d/mesh/cython/mesh_core.cpp"], language='c++'),
         ]
 data_images = list(glob.glob('insightface/data/images/*.jpg'))
@@ -91,7 +92,7 @@ def find_version(*file_paths):
         logging.warning("   Install Homebrew: https://brew.sh/")
         logging.warning("   Then, run: brew install llvm libomp")
         logging.info("Proceeding without setting the compiler.")
-    
+
     else:
         # Check if LLVM is installed
         llvm_check = subprocess.run(["brew", "--prefix", "llvm"], capture_output=True, text=True)