From 289879c8dde53c448fd84e3e30c253fa5fa9a954 Mon Sep 17 00:00:00 2001
From: yayoimizuha <poko.peko.tomo@icloud.com>
Date: Tue, 24 Oct 2023 00:16:37 +0900
Subject: [PATCH] update

---
 .drone.yml                 |  1 +
 test_script/onnx_cacher.py | 10 +++++++---
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/.drone.yml b/.drone.yml
index 2a26d66..ff0ccec 100644
--- a/.drone.yml
+++ b/.drone.yml
@@ -16,6 +16,7 @@ steps:
       PYTHONPATH: /drone/src/
       DATA_DIR: /drone/src/data/
     commands:
+      - git diff --name-only `git merge-base refs/remotes/origin/master HEAD`..HEAD | grep finetune/facenet_transfer_learning.py > /dev/null || exit 78
       - python -m pip install -q -U pip
       - pip install -q torchsummary matplotlib pytorch-metric-learning torchinfo torch torchvision tqdm Pillow facenet-pytorch
       - apt update -qq
diff --git a/test_script/onnx_cacher.py b/test_script/onnx_cacher.py
index a9cc4ab..5097e56 100644
--- a/test_script/onnx_cacher.py
+++ b/test_script/onnx_cacher.py
@@ -1,11 +1,14 @@
 from os import getcwd
 from os.path import join
-from onnxruntime import InferenceSession, SessionOptions, __version__
+from onnxruntime import InferenceSession, SessionOptions, __version__, get_all_providers, get_available_providers
 from PIL import Image
 import numpy
 
+print(get_all_providers())
+print(get_available_providers())
+
 onnx_session = InferenceSession(
-    path_or_bytes="test_script/retinaface.onnx",
+    path_or_bytes=r"retinaface.onnx",
     providers=[
         'CUDAExecutionProvider',
         ('TensorrtExecutionProvider', {
@@ -13,6 +16,7 @@ onnx_session = InferenceSession(
             'trt_engine_cache_path': join(getcwd(), 'onnx_cache'),
             'trt_fp16_enable': True,
         }),
+        'DmlExecutionProvider',
         'CPUExecutionProvider'
     ]
 )
@@ -20,7 +24,7 @@ print(__version__)
 image_arr = numpy.expand_dims(numpy.array(
     Image.open(r'C:\Users\tomokazu\CLionProjects\ameba_blog_downloader\manaka_test.jpg').convert('RGB')), 0).transpose(
     0, 3, 1, 2).astype(numpy.float32)
-image_arr /= 255.0
+# image_arr /= 255.0
 print(image_arr)
 print(image_arr.shape)
 res = onnx_session.run(input_feed={'input': image_arr}, output_names=["bbox", "confidence", "landmark"])