diff --git a/doc/RKNNToolKit2_OP_Support-v0.7.0.md b/doc/RKNNToolKit2_OP_Support_v1.0.0.md
similarity index 86%
rename from doc/RKNNToolKit2_OP_Support-v0.7.0.md
rename to doc/RKNNToolKit2_OP_Support_v1.0.0.md
index b353962..b66d998 100644
--- a/doc/RKNNToolKit2_OP_Support-v0.7.0.md
+++ b/doc/RKNNToolKit2_OP_Support_v1.0.0.md
@@ -38,25 +38,26 @@ Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 Version
| **Operators** | **Remarks** |
| -------------------- | ----------- |
-| BatchNorm |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
+| BatchNorm |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
| bn (BatchNorm + Scale) |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]
according to https://github.com/TimoSaemann/caffe-segnet-cudnn5|
-| BNLL ||
-| Concat |axis: 1,2,3|
-| Convolution |channel: [1, 8192]
kernel height/width: [1, 31]
stride height/width: [1, 7]
kernels: [1, 8184]
pad left/right/top/bottom: [0, 15]
group: 1, channel / N
|
-| ConvolutionDepthwise |channel:[1, 8192]
kernel height/width: [1, 8]
stride height/width: [1, 7]
kernels: 1
pad left/right/top/bottom: [0, 15]|
-| Crop ||
-| Deconvolution |channel: [1, 8192]
kernel height/width: [1, 31]
stride height/width: 2, 4, 8
kernels: [1, 8192]
pad left/right/top/bottom: [0, 15]|
+| BNLL ||
+| Concat |axis: 1,2,3|
+| Convolution |channel: [1, 8192]
kernel height/width: [1, 31]
stride height/width: [1, 7]
kernels: [1, 8184]
pad left/right/top/bottom: [0, 15]
group: 1, channel / N
|
+| ConvolutionDepthwise|channel:[1, 8192]
kernel height/width: [1, 8]
stride height/width: [1, 7]
kernels: 1
pad left/right/top/bottom: [0, 15]|
+| Crop ||
+| Deconvolution |channel: [1, 8192]
kernel height/width: [1, 31]
stride height/width: 2, 4, 8
kernels: [1, 8192]
pad left/right/top/bottom: [0, 15]|
| Dropout ||
| Eltwise |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]
support broadcast rule: per-layer/channel/element|
| Flatten ||
-| InnerProduct |channel: [1, 8192]|
-| LRN ||
-| Normalize |dims: 4|
+| InnerProduct |channel: [1, 8192]|
+| LRN ||
+| Normalize ||
| Permute ||
-| Pooling | **AveragePool**:
channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
**GlobalAveragePool**:
channel: [1, 8192]
kernel height/width: [1, 128]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
**MaxPool/GlobalMaxPool**:
channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
**MaxPool**:
auto_pad only support NOTSET，ceil_mode only support 0，unsupport dilations |
+| Power ||
+| Pooling | **AveragePool**:
channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
**GlobalAveragePool**:
channel: [1, 8192]
kernel height/width: [1, 128]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
**MaxPool/GlobalMaxPool**:
channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
**MaxPool**:
auto_pad only support NOTSET，ceil_mode only support 0，unsupport dilations |
| PRelu |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]
slope: per-layer/channel|
-| Proposal |batch: 1|
-| Reduction |output dims <= 4|
+| Proposal |batch: 1|
+| Reduction |output dims <= 4|
| Relu |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
| Relu6 |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
| Reorg ||
@@ -66,10 +67,11 @@ Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 Version
| ROIPooling |according to https://github.com/twmht/caffe-pva-faster-rcnn|
| Scale |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
| Sigmoid |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
-| Slice ||
+| Slice ||
| Softmax ||
| Split ||
| TanH |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
+| Tile ||
| Transpose ||
| Upsample |according to https://github.com/SeanQ88/caffe_upsample and https://github.com/TimoSaemann/caffe-segnet-cudnn5|
@@ -88,22 +90,25 @@ The list of ONNX OPs supported by RKNN Toolkit2 Version 0.6.0 is as follows:
| Conv |channel: [1, 8192]
kernel height/width: [1, 31]
stride height/width: [1, 7]
kernels: [1, 8184]
pad left/right/top/bottom: [0, 15]
dilation: [1, 31]
group: 1, channel / N|
| ConvTranspose |channel: [1, 8192]
kernel height/width: [1, 31]
stride height/width: 2, 4, 8
kernels: [1, 8192]
pad left/right/top/bottom: [0, 15]
dilation: [1, 31]
group: 1, channel / N|
| DepthToSpace ||
-| Div |support broadcast rule: per-element/other|
+| Div |support broadcast rule: per-element/other|
| Flatten ||
| Gemm |channel: [1, 8192]
One input should be Const|
| GlobalAveragePool |channel: [1, 8192]
kernel height/width: [1, 128]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]|
| GlobalMaxPool |channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]|
| Greater |support broadcast rule: per-element/other|
+| HardSigmoid ||
| LeakyRelu |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
| Less |support broadcast rule: per-element/other|
-| LpNormalization |dims: 4|
+| LpNormalization ||
| LRN ||
| MatMul |channel: [1, 8192]
dims: 2|
-| MaxPool |channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
auto_pad only support NOTSET，ceil_mode only support 0，unsupport dilations|
+| Max |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]
dims=4|
+| MaxPool |channel: [1, 8192]
kernel height/width: [1, 7]
stride height/width: [1, 8]
pad left/right/top/bottom: [0, 7]
auto_pad only support NOTSET，ceil_mode only support 0，unsupport dilations|
| MaxRoiPool ||
| MaxUnpool |unsupport pad|
| Mul |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]
support broadcast rule: per-layer/channel/element|
| Pad |pad value should >= 0; pad dims must be 2 when mode is reflect or edge|
+| Pow ||
| PRelu |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]
slope support broadcast rule:: per-layer/channel|
| ReduceMean |output dims <= 4|
| ReduceSum |output dims <= 4|
@@ -118,6 +123,7 @@ The list of ONNX OPs supported by RKNN Toolkit2 Version 0.6.0 is as follows:
| Split ||
| Squeeze ||
| Tanh |channel: [1, 8192]
height: [1, 8192]
width: [1, 8176]|
+| Tile ||
| Transpose ||
| Upsample (resize) || |
diff --git a/doc/Rockchip_Quick_Start_RKNN_Toolkit2_CN_v0.7.0.pdf b/doc/Rockchip_Quick_Start_RKNN_Toolkit2_CN_v0.7.0.pdf
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deleted file mode 100644
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diff --git a/doc/Rockchip_User_Guide_RKNN_Toolkit2_EN_v1.0.0.pdf b/doc/Rockchip_User_Guide_RKNN_Toolkit2_EN_v1.0.0.pdf
new file mode 100644
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diff --git a/doc/changelog-v0.7.0.txt b/doc/changelog_v1.0.0.txt
similarity index 57%
rename from doc/changelog-v0.7.0.txt
rename to doc/changelog_v1.0.0.txt
index 64ef4d5..681a000 100644
--- a/doc/changelog-v0.7.0.txt
+++ b/doc/changelog_v1.0.0.txt
@@ -1,3 +1,24 @@
+2021-4-30
+版本：v1.0.0
+更新内容:
+1. 新功能:
+ 1）卷积类的per channel量化功能
+ 2）添加了config中custom_string的模型信息设置、img_quant_RGB2BGR设置
+ 3）添加了eval performance的性能测试接口
+ 4）添加了连板调试功能
+2. OP支持：
+ 1) 添加了Caffe新OP支持：Power/Tile/Eltwise(Max)/去除了normalize维度的限制
+ 2) 添加了onnx新OP支持:HardSigmoid/Pow/Tile
+3. 修复一些已知的bug：
+ 1) 修复了caffe FC的输出shape以及name的错误
+ 2) 优化了mmse的量化性能
+ 3）修复caffe的Pooling层的输出shape计算错误
+ 4）修复了caffe slice丢弃了其中一个输出的inference bug
+ 5）修复了caffe scale层的计算错误
+4. 弃置了reorder_channel的config设置，由用户自行保证inference输入数据的channel正确性
+
+
+
2021-4-2
版本：v0.7.0
更新内容:
diff --git a/doc/requirements-v0.7.0.txt b/doc/requirements_v1.0.0.txt
similarity index 100%
rename from doc/requirements-v0.7.0.txt
rename to doc/requirements_v1.0.0.txt
diff --git a/examples/caffe/mobilenet_v2/test.py b/examples/caffe/mobilenet_v2/test.py
index 4f8ad95..859d53e 100755
--- a/examples/caffe/mobilenet_v2/test.py
+++ b/examples/caffe/mobilenet_v2/test.py
@@ -26,7 +26,7 @@ if __name__ == '__main__':
# pre-process config
print('--> config model')
- rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], reorder_channel=True)
+ rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True)
print('done')
# Load tensorflow model
@@ -57,7 +57,6 @@ if __name__ == '__main__':
# Set inputs
img = cv2.imread('./goldfish_224x224.jpg')
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print('--> Init runtime environment')
ret = rknn.init_runtime()
diff --git a/examples/caffe/vgg-ssd/test.py b/examples/caffe/vgg-ssd/test.py
index 4cc3664..fd85ce6 100644
--- a/examples/caffe/vgg-ssd/test.py
+++ b/examples/caffe/vgg-ssd/test.py
@@ -160,7 +160,7 @@ if __name__ == '__main__':
# pre-process config
print('--> config model')
- rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[1, 1, 1], reorder_channel=True)
+ rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[1, 1, 1], quant_img_RGB2BGR=True)
print('done')
# Load tensorflow model
@@ -191,7 +191,6 @@ if __name__ == '__main__':
# Set inputs
img = cv2.imread('./road_300x300.jpg')
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print('--> Init runtime environment')
ret = rknn.init_runtime()
diff --git a/examples/darknet/yolov3_416x416/test.py b/examples/darknet/yolov3_416x416/test.py
index 79e17fb..a0d4587 100755
--- a/examples/darknet/yolov3_416x416/test.py
+++ b/examples/darknet/yolov3_416x416/test.py
@@ -26,7 +26,7 @@ if __name__ == '__main__':
# pre-process config
print('--> config model')
- rknn.config(mean_values=[0, 0, 0], std_values=[255, 255, 255], reorder_channel=False)
+ rknn.config(mean_values=[0, 0, 0], std_values=[255, 255, 255])
print('done')
# Load tensorflow model
@@ -91,8 +91,8 @@ if __name__ == '__main__':
if boxes is not None:
draw(image, boxes, scores, classes)
- cv2.imshow("results", image)
- cv2.waitKeyEx(0)
+ print('Save results to results.jpg!')
+ cv2.imwrite('results.jpg', image)
rknn.release()
diff --git a/examples/common_function_demos/load_quantized_model/README.md b/examples/load_quantized_model/README.md
similarity index 100%
rename from examples/common_function_demos/load_quantized_model/README.md
rename to examples/load_quantized_model/README.md
diff --git a/examples/common_function_demos/load_quantized_model/goldfish_299x299.jpg b/examples/load_quantized_model/goldfish_299x299.jpg
similarity index 100%
rename from examples/common_function_demos/load_quantized_model/goldfish_299x299.jpg
rename to examples/load_quantized_model/goldfish_299x299.jpg
diff --git a/examples/common_function_demos/load_quantized_model/test.py b/examples/load_quantized_model/test.py
similarity index 99%
rename from examples/common_function_demos/load_quantized_model/test.py
rename to examples/load_quantized_model/test.py
index f07f70d..5ebbd6d 100755
--- a/examples/common_function_demos/load_quantized_model/test.py
+++ b/examples/load_quantized_model/test.py
@@ -101,7 +101,7 @@ if __name__ == '__main__':
print('done')
# pre-process config
print('--> Config model')
- rknn.config(reorder_channel=False)
+ rknn.config()
print('done')
# Load tensorflow model
diff --git a/examples/onnx/resnet50v2/test.py b/examples/onnx/resnet50v2/test.py
index 71de9f3..008f152 100644
--- a/examples/onnx/resnet50v2/test.py
+++ b/examples/onnx/resnet50v2/test.py
@@ -80,7 +80,7 @@ if __name__ == '__main__':
print('done')
# print('--> config model')
- rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.82, 58.82, 58.82], reorder_channel=False)
+ rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.82, 58.82, 58.82])
print('done')
# Load model
diff --git a/examples/pytorch/resnet18/test.py b/examples/pytorch/resnet18/test.py
index bd851c0..3fe6016 100644
--- a/examples/pytorch/resnet18/test.py
+++ b/examples/pytorch/resnet18/test.py
@@ -50,7 +50,7 @@ if __name__ == '__main__':
# pre-process config
print('--> config model')
- rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395], reorder_channel=False)
+ rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395])
print('done')
# Load pytorch model
diff --git a/examples/tensorflow/ssd_mobilenet_v1/test.py b/examples/tensorflow/ssd_mobilenet_v1/test.py
index 323b071..f3c7a05 100755
--- a/examples/tensorflow/ssd_mobilenet_v1/test.py
+++ b/examples/tensorflow/ssd_mobilenet_v1/test.py
@@ -60,7 +60,7 @@ if __name__ == '__main__':
rknn = RKNN()
# Config for Model Input PreProcess
- rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5], reorder_channel=False)
+ rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5])
# Load TensorFlow Model
print('--> Loading model')
diff --git a/examples/tflite/mobilenet_v1/test.py b/examples/tflite/mobilenet_v1/test.py
index b71b218..891a379 100755
--- a/examples/tflite/mobilenet_v1/test.py
+++ b/examples/tflite/mobilenet_v1/test.py
@@ -24,11 +24,11 @@ def show_outputs(outputs):
if __name__ == '__main__':
# Create RKNN object
- rknn = RKNN(verbose=True)
+ rknn = RKNN()
# pre-process config
print('--> config model')
- rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], reorder_channel=False)
+ rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128])
print('done')
# Load tensorflow model
diff --git a/examples/tflite/mobilenet_v1_quant/test.py b/examples/tflite/mobilenet_v1_quant/test.py
index 16e5b66..a782e11 100755
--- a/examples/tflite/mobilenet_v1_quant/test.py
+++ b/examples/tflite/mobilenet_v1_quant/test.py
@@ -24,11 +24,11 @@ def show_outputs(outputs):
if __name__ == '__main__':
# Create RKNN object
- rknn = RKNN(verbose=True)
+ rknn = RKNN()
# pre-process config
print('--> config model')
- rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], reorder_channel=False)
+ rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128])
print('done')
# Load tensorflow model
diff --git a/packages/rknn_toolkit2-0.7.0-cp36-cp36m-linux_x86_64.whl b/packages/rknn_toolkit2-1.0.0-cp36-cp36m-linux_x86_64.whl
similarity index 62%
rename from packages/rknn_toolkit2-0.7.0-cp36-cp36m-linux_x86_64.whl
rename to packages/rknn_toolkit2-1.0.0-cp36-cp36m-linux_x86_64.whl
index 9b8d0de..47b9819 100644
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