forked from mindspore-Ecosystem/mindspore
unify Conv's attr pad_list
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RELEASE.md
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RELEASE.md
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# MindSpore 1.1.1 Release Notes
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## MindSpore
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### API Change
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#### Backwards Incompatible Change
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##### Python API
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###### `ops.AvgPool`, `ops.MaxPool`, `ops.MaxPoolWithArgmax` change attr name from 'ksize', 'padding' to 'kernel_size', 'pad_mode' ([!11350](https://gitee.com/mindspore/mindspore/pulls/11350))
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Previously the kernel size and pad mode attrs of pooling ops are named "ksize" and "padding", which is a little puzzling and inconsistent with convolution ops. So they are rename to "kernel_size" and "pad_mode".
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<table>
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<tr>
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<td style="text-align:center"> 1.1.0 </td> <td style="text-align:center"> 1.1.1 </td>
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</tr>
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<tr>
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<td>
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```python
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>>> from mindspore.ops import operations as P
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>>>
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>>> avg_pool = P.AvgPool(ksize=2, padding='same')
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>>> max_pool = P.MaxPool(ksize=2, padding='same')
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>>> max_pool_with_argmax = P.MaxPoolWithArgmax(ksize=2, padding='same')
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```
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</td>
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<td>
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```python
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>>> from mindspore.ops import operations as P
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>>>
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>>> avg_pool = P.AvgPool(kernel_size=2, pad_mode='same')
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>>> max_pool = P.MaxPool(kernel_size=2, pad_mode='same')
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>>> max_pool_with_argmax = P.MaxPoolWithArgmax(kernel_size=2, pad_mode='same')
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```
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</td>
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</tr>
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</table>
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# MindSpore 1.1.0 Release Notes
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## MindSpore
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@ -12,7 +56,7 @@
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- [STABLE] Openpose: proposes a bottom-up human attitude estimation algorithm using Part Affinity Fields on COCO2017 dataset.(Ascend)
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- [STABLE] CNN-CTC: proposes three major contributions to addresses scene text recognition (STR) on MJSynth and SynthText dataset.(Ascend)
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- [STABLE] CenterFace: a practical anchor-free face detection and alignment method for edge devices on WiderFace dataset.(Ascend)
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- [STABLE] ShuffleNetV2: a much faster and more accurate netowrk than the previous networks on ImageNet 2012 dataset.(GPU)
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- [STABLE] ShuffleNetV2: a much faster and more accurate network than the previous networks on ImageNet 2012 dataset.(GPU)
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- [STABLE] EfficientNet-B0: a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient on ImageNet 2012 dataset.(GPU)
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- [BETA] SSD-GhostNet: based on an Ghost module structure which generate more features from cheap operations on Oxford-IIIT Pet dataset.(Ascend)
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- [BETA] DS-CNN: Depthwise separable convolutional neural network on Speech commands dataset.(Ascend)
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@ -329,7 +373,7 @@ dtype is removed from GumbelCDF and is no longer an argument of the class.
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###### `nn.layer.combined.Conv2dBnAct`, `nn.layer.combined.DenseBnAct` move from nn.layer.quant to nn.layer.combined ([!8187](https://gitee.com/mindspore/mindspore/pulls/8187))
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Previously Conv2dBnAct and DenseBnAct are in nn.layer.quant, since they are not quant cells, now they are moved to nn.layer.combined. If you import Conv2dBnAct, DenseBnAct from mindspore.nn, then your code dosen't need any change.
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Previously Conv2dBnAct and DenseBnAct are in nn.layer.quant, since they are not quant cells, now they are moved to nn.layer.combined. If you import Conv2dBnAct, DenseBnAct from mindspore.nn, then your code doesn't need any change.
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<table>
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<tr>
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@ -414,7 +458,7 @@ In Ascend platform, if group > 1, the weight shape of Conv2D change from [in_cha
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3. Add Online Graph optimzation: by fusion Convolution/Matmul/Fullconnection and add/mul/pad/reshape, improve performance up to 50+% for some networks;
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4. Add auto tuning: by online tuning in the graph compilation phase, optimize performance up to 10%;
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5. Add weight quant: support weight quant
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6. Add opencl kernel binary cache: improve Initilization time .
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6. Add opencl kernel binary cache: improve Initialization time .
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#### Post quantization
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@ -461,7 +505,7 @@ The MindSpore Lite ToD framework is already in use in the newest Huawei Smart TV
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##### Java API
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- [Add] Implament JNI layer and add Java api for CPU and GPU backend
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- [Add] Implement JNI layer and add Java api for CPU and GPU backend
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#### Deprecations
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@ -686,7 +730,7 @@ Contributions of any kind are welcome!
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- Python API
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- improve interface '__bool__' for tensor([!4000](https://gitee.com/mindspore/mindspore/pulls/4000))
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- fix GPU-ResizeNearestNeighbor([!3760](https://gitee.com/mindspore/mindspore/pulls/3760))
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- fix topK multi dimention grad func([!3711](https://gitee.com/mindspore/mindspore/pulls/3711))
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- fix topK multi dimension grad func([!3711](https://gitee.com/mindspore/mindspore/pulls/3711))
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- fix scatterop error msg([!3699](https://gitee.com/mindspore/mindspore/pulls/3699))
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- fix bug of cast dtype when using mix_presion in pynative mode([!3730](https://gitee.com/mindspore/mindspore/pulls/3730))
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- Executor
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@ -773,7 +817,7 @@ Contributions of any kind are welcome!
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- Fixing type check mistakes of InplaceAdd and Inplace Sub ops([!2744](https://gitee.com/mindspore/mindspore/pulls/2744]))
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- Change order param only equal to group param([!2748](https://gitee.com/mindspore/mindspore/pulls/2748))
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- Executor
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- The performance of graph whith control flow is optimized([!2931](https://gitee.com/mindspore/mindspore/pulls/2931))
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- The performance of graph with control flow is optimized([!2931](https://gitee.com/mindspore/mindspore/pulls/2931))
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- Fix bug of wrong number of tuple layers([!3390](https://gitee.com/mindspore/mindspore/pulls/3390))
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- Fix cpu multi graph memory exception([!3631](https://gitee.com/mindspore/mindspore/pulls/3631))
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- Enable data sync when calling operator without defining a cell([!3081](https://gitee.com/mindspore/mindspore/pulls/3081))
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@ -968,7 +1012,7 @@ Contributions of any kind are welcome!
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- Fix dropout，topK and addn errors in PyNative mode ([!1285](https://gitee.com/mindspore/mindspore/pulls/1285), [!1138](https://gitee.com/mindspore/mindspore/pulls/1138), [!1033](https://gitee.com/mindspore/mindspore/pulls/1033)).
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- Fix memory leaks after execution in PyNatvie mode ([!1201](https://gitee.com/mindspore/mindspore/pulls/1201)).
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- Fix HCCL failure in some special scenes ([!1204](https://gitee.com/mindspore/mindspore/pulls/1204), [!1252](https://gitee.com/mindspore/mindspore/pulls/1252)).
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- Fix SSD network when Select failed, cann't find kernel info([!1449](https://gitee.com/mindspore/mindspore/pulls/1449)).
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- Fix SSD network when Select failed, can't find kernel info([!1449](https://gitee.com/mindspore/mindspore/pulls/1449)).
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- Fix Topk operator selection strategy bug between aicore and aicpu([!1367](https://gitee.com/mindspore/mindspore/pulls/1367)).
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- Fix input memory size of 'assign' op unequal in control sink mode when assigning a data from one child graph to another child graph([!802](https://gitee.com/mindspore/mindspore/pulls/802)).
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- Fix allreduce ir inconsistency([!989](https://gitee.com/mindspore/mindspore/pulls/989)).
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@ -301,9 +301,9 @@
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{"op_name": "Fill", "inputs": [{"index": 0, "name": "dims", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "value", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["float16", "DefaultFormat"], ["int16", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int64", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int64", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "fill.so", "compute_cost": 10, "kernel_name": "fill", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": true, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "Erf", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["float16", ""], ["float16", ""]], [["float32", ""], ["float32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "erf.so", "compute_cost": 10, "kernel_name": "erf", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "formatAgnostic"}
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{"op_name": "Erfc", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["float16", ""], ["float16", ""]], [["float32", ""], ["float32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "erfc.so", "compute_cost": 10, "kernel_name": "erfc", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "formatAgnostic"}
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{"op_name": "DepthwiseConv2dNative", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "stride", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilation", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "format", "param_type": "required", "type": "str", "value": "all"}, {"name": "offset_a", "param_type": "optional", "type": "int", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NC1HWC0"], ["float16", "C1HWNCoC0"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "NC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "depthwise_conv2d.so", "compute_cost": 10, "kernel_name": "depthwise_conv2d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "DepthwiseConv2dNativeBackpropFilter", "inputs": [{"index": 0, "name": "input", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "filter_grad", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "filter_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "stride", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilation", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "format", "param_type": "required", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NC1HWC0"], ["float16", "NC1HWC0"], ["float32", "C1HWNCoC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "depthwise_conv2d_backprop_filter_d.so", "compute_cost": 10, "kernel_name": "depthwise_conv2d_backprop_filter_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "DepthwiseConv2dNativeBackpropInput", "inputs": [{"index": 0, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "input_grad", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "stride", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilation", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "format", "param_type": "required", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "C1HWNCoC0"], ["float16", "NC1HWC0"], ["float16", "NC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "depthwise_conv2d_backprop_input_d.so", "compute_cost": 10, "kernel_name": "depthwise_conv2d_backprop_input_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "DepthwiseConv2dNative", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "stride", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilation", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "format", "param_type": "required", "type": "str", "value": "all"}, {"name": "offset_a", "param_type": "optional", "type": "int", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NC1HWC0"], ["float16", "C1HWNCoC0"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "NC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "depthwise_conv2d.so", "compute_cost": 10, "kernel_name": "depthwise_conv2d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "DepthwiseConv2dNativeBackpropFilter", "inputs": [{"index": 0, "name": "input", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "filter_grad", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "filter_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "stride", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilation", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "format", "param_type": "required", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NC1HWC0"], ["float16", "NC1HWC0"], ["float32", "C1HWNCoC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "depthwise_conv2d_backprop_filter_d.so", "compute_cost": 10, "kernel_name": "depthwise_conv2d_backprop_filter_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "DepthwiseConv2dNativeBackpropInput", "inputs": [{"index": 0, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "input_grad", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "stride", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilation", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "format", "param_type": "required", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "C1HWNCoC0"], ["float16", "NC1HWC0"], ["float16", "NC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "depthwise_conv2d_backprop_input_d.so", "compute_cost": 10, "kernel_name": "depthwise_conv2d_backprop_input_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
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{"op_name": "GreaterEqual", "inputs": [{"index": 0, "name": "x1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x2", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int8", ""], ["int8", ""], ["bool", ""]], [["uint8", ""], ["uint8", ""], ["bool", ""]], [["int32", ""], ["int32", ""], ["bool", ""]], [["float16", ""], ["float16", ""], ["bool", ""]], [["float32", ""], ["float32", ""], ["bool", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "greater_equal.so", "compute_cost": 10, "kernel_name": "greater_equal", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "broadcast"}
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{"op_name": "NotEqual", "inputs": [{"index": 0, "name": "x1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x2", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["int8", ""], ["int8", ""], ["bool", ""]], [["uint8", ""], ["uint8", ""], ["bool", ""]], [["int32", ""], ["int32", ""], ["bool", ""]], [["float16", ""], ["float16", ""], ["bool", ""]], [["float32", ""], ["float32", ""], ["bool", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "not_equal.so", "compute_cost": 10, "kernel_name": "not_equal", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "broadcast"}
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{"op_name": "FloorMod", "inputs": [{"index": 0, "name": "x1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x2", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["float16", ""], ["float16", ""], ["float16", ""]], [["float32", ""], ["float32", ""], ["float32", ""]], [["int32", ""], ["int32", ""], ["int32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "floor_mod.so", "compute_cost": 10, "kernel_name": "floor_mod", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "broadcast"}
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|
|||
{"op_name": "FakeQuantWithMinMaxVarsGradient", "inputs": [{"index": 0, "name": "gradients", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "min", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "max", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "backprops_wrt_x", "need_compile": true, "param_type": "required", "shape": "all"}, {"index": 1, "name": "backprops_wrt_min", "need_compile": true, "param_type": "required", "shape": "all"}, {"index": 2, "name": "backprops_wrt_max", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "num_bits", "param_type": "optional", "type": "int", "value": "all"}, {"name": "narrow_range", "param_type": "optional", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "fake_quant_with_min_max_vars_gradient.so", "compute_cost": 10, "kernel_name": "fake_quant_with_min_max_vars_gradient", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "FakeQuantWithMinMaxVarsPerChannel", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "min", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "max", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "num_bits", "param_type": "optional", "type": "int", "value": "all"}, {"name": "narrow_range", "param_type": "optional", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "fake_quant_with_min_max_vars_per_channel.so", "compute_cost": 10, "kernel_name": "fake_quant_with_min_max_vars_per_channel", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "FakeQuantWithMinMaxVarsPerChannelGradient", "inputs": [{"index": 0, "name": "gradients", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "min", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "max", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "backprops_wrt_x", "need_compile": true, "param_type": "required", "shape": "all"}, {"index": 1, "name": "backprops_wrt_min", "need_compile": true, "param_type": "required", "shape": "all"}, {"index": 2, "name": "backprops_wrt_max", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "num_bits", "param_type": "optional", "type": "int", "value": "all"}, {"name": "narrow_range", "param_type": "optional", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "fake_quant_with_min_max_vars_per_channel_gradient.so", "compute_cost": 10, "kernel_name": "fake_quant_with_min_max_vars_per_channel_gradient", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3D", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "offset_x", "param_type": "optional", "type": "int", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "FRACTAL_Z_3D"], ["float16", "DefaultFormat"], ["int8", "DefaultFormat"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d.so", "compute_cost": 10, "kernel_name": "conv3d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3DBackpropInput", "inputs": [{"index": 0, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "FRACTAL_Z_3D"], ["float16", "NDC1HWC0"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_backprop_input_d.so", "compute_cost": 10, "kernel_name": "conv3d_backprop_input_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3DBackpropFilter", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "filter_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "NDC1HWC0"], ["float32", "FRACTAL_Z_3D"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_backprop_filter_d.so", "compute_cost": 10, "kernel_name": "conv3d_backprop_filter_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3DTranspose", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "optional", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "output_padding", "param_type": "optional", "type": "listInt", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "FRACTAL_Z_3D"], ["float16", "DefaultFormat"], ["int8", "DefaultFormat"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_transpose_d.so", "compute_cost": 10, "kernel_name": "conv3d_transpose_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3D", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "offset_x", "param_type": "optional", "type": "int", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "FRACTAL_Z_3D"], ["float16", "DefaultFormat"], ["int8", "DefaultFormat"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d.so", "compute_cost": 10, "kernel_name": "conv3d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3DBackpropInput", "inputs": [{"index": 0, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "FRACTAL_Z_3D"], ["float16", "NDC1HWC0"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_backprop_input_d.so", "compute_cost": 10, "kernel_name": "conv3d_backprop_input_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3DBackpropFilter", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "filter_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "NDC1HWC0"], ["float32", "FRACTAL_Z_3D"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_backprop_filter_d.so", "compute_cost": 10, "kernel_name": "conv3d_backprop_filter_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "Conv3DTranspose", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pad_list", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "optional", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "output_padding", "param_type": "optional", "type": "listInt", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "FRACTAL_Z_3D"], ["float16", "DefaultFormat"], ["int8", "DefaultFormat"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_transpose_d.so", "compute_cost": 10, "kernel_name": "conv3d_transpose_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "LambApplyOptimizerAssign", "inputs": [{"index": 0, "name": "grad", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "inputv", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "inputm", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "input3", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 4, "name": "mul0_x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 5, "name": "mul1_x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 6, "name": "mul2_x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 7, "name": "mul3_x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 8, "name": "add2_y", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 9, "name": "steps", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 10, "name": "do_use_weight", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 11, "name": "weight_decay_rate", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "output0", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 0, "name": "inputv", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 0, "name": "inputm", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "lamb_apply_optimizer_assign.so", "compute_cost": 10, "kernel_name": "lamb_apply_optimizer_assign", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "LambApplyWeightAssign", "inputs": [{"index": 0, "name": "input0", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "input1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "input2", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "input3", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 4, "name": "input_param", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "input_param", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "lamb_apply_weight_assign.so", "compute_cost": 10, "kernel_name": "lamb_apply_weight_assign", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
{"op_name": "NLLLoss", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "target", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "weight", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "total_weight", "need_compile": false, "param_type": "optional", "shape": "all"}], "attr": [{"name": "reduction", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "nll_loss.so", "compute_cost": 10, "kernel_name": "nll_loss", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
|
||||
|
|
|
|||
|
|
@ -34,7 +34,6 @@ constexpr size_t kConv2DBackpropInputNum = 4;
|
|||
constexpr size_t kConv2DAxisNum = 4;
|
||||
constexpr auto kAttrOffsetA = "offset_a";
|
||||
constexpr auto kAttrPadList = "pad_list";
|
||||
constexpr auto kAttrPads = "pads";
|
||||
constexpr auto kAttrMode = "mode";
|
||||
constexpr auto kAttrChannelMultiplier = "channel_multiplier";
|
||||
constexpr auto kAttrPerm = "perm";
|
||||
|
|
@ -200,9 +199,8 @@ void SetCommonAttrs(const CNodePtr &conv2d, const CNodePtr &depth_conv) {
|
|||
AnfAlgo::CopyNodeAttr(kAttrKernelSize, conv2d, depth_conv);
|
||||
AnfAlgo::CopyNodeAttr(kAttrDilation, conv2d, depth_conv);
|
||||
AnfAlgo::CopyNodeAttr(kAttrFormat, conv2d, depth_conv);
|
||||
AnfAlgo::CopyNodeAttr(kAttrPadList, kAttrPads, conv2d, depth_conv);
|
||||
AnfAlgo::CopyNodeAttr(kAttrPadList, conv2d, depth_conv);
|
||||
AnfAlgo::CopyNodeAttr(kAttrPadMode, conv2d, depth_conv);
|
||||
AnfAlgo::CopyNodeAttr(kAttrPad, conv2d, depth_conv);
|
||||
AnfAlgo::SetNodeAttr(kAttrMode, MakeValue(3), depth_conv);
|
||||
AnfAlgo::SetNodeAttr(kAttrChannelMultiplier, MakeValue(1), depth_conv);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -843,7 +843,7 @@ void OnnxExporter::ExportPrimDepthwiseConv2d(const FuncGraphPtr & /*func_graph*/
|
|||
onnx_attr_proto->set_s("SAME_UPPER");
|
||||
} else {
|
||||
onnx_attr_proto->set_name("pads");
|
||||
SetAttrTupleValueToProto(prim->GetAttr("pads"), onnx::AttributeProto_AttributeType_INTS, onnx_attr_proto, prim);
|
||||
SetAttrTupleValueToProto(prim->GetAttr("pad_list"), onnx::AttributeProto_AttributeType_INTS, onnx_attr_proto, prim);
|
||||
}
|
||||
// set strides
|
||||
onnx_attr_proto = node_proto->add_attribute();
|
||||
|
|
|
|||
|
|
@ -68,7 +68,7 @@ REG_ADPT_DESC(Conv2DBackpropFilterD, prim::kPrimConv2DBackpropFilter->name(), AD
|
|||
INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}, {3, INPUT_DESC(bias)}};
|
||||
ATTR_MAP(DepthwiseConv2D) = {
|
||||
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
|
||||
};
|
||||
|
|
@ -81,7 +81,7 @@ INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
|
|||
{1, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
|
||||
ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
|
||||
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
};
|
||||
OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}};
|
||||
|
|
@ -94,7 +94,7 @@ INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
|
|||
{2, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
|
||||
ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
|
||||
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
|
||||
};
|
||||
OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}};
|
||||
|
|
|
|||
|
|
@ -320,7 +320,7 @@ AbstractBasePtr InferImplConv2D(const AnalysisEnginePtr &, const PrimitivePtr &p
|
|||
int64_t c_axis = 1;
|
||||
int64_t h_axis = 2;
|
||||
int64_t w_axis = 3;
|
||||
auto data_format_ptr = primitive->GetAttr("data_format");
|
||||
auto data_format_ptr = primitive->GetAttr("format");
|
||||
std::string data_format = "NCHW";
|
||||
if ((data_format_ptr != nullptr) && data_format_ptr->isa<StringImm>()) {
|
||||
data_format = data_format_ptr->cast<StringImmPtr>()->value();
|
||||
|
|
|
|||
|
|
@ -166,11 +166,11 @@ def get_bprop_extract_image_patches(self):
|
|||
def get_bprop_depthwise_conv2d_native(self):
|
||||
"""Grad definition for `DepthwiseConv2dNative` operation."""
|
||||
input_grad = G.DepthwiseConv2dNativeBackpropInput(
|
||||
self.channel_multiplier, self.kernel_size, self.pad_mode, self.pad, self.pads, self.mode, self.stride,
|
||||
self.channel_multiplier, self.kernel_size, self.pad_mode, self.pad, self.pad_list, self.mode, self.stride,
|
||||
self.dilation, self.group
|
||||
)
|
||||
filter_grad = G.DepthwiseConv2dNativeBackpropFilter(
|
||||
self.channel_multiplier, self.kernel_size, self.pad_mode, self.pad, self.pads, self.mode, self.stride,
|
||||
self.channel_multiplier, self.kernel_size, self.pad_mode, self.pad, self.pad_list, self.mode, self.stride,
|
||||
self.dilation, self.group
|
||||
)
|
||||
get_shape = P.Shape()
|
||||
|
|
@ -280,7 +280,7 @@ def _get_mean_matrix(x_shape, ksize, stride, pad_mode, x_dtype):
|
|||
the value of element which is padded is 0, else are 1.
|
||||
For each element of output, it is mapped for slide window: `[h*h_stride : h*h_stride + h_ksize,
|
||||
w*w_stride : w*w_stride + w_ksize]` of `assist_input_matrix`, so the sum of slide window is the
|
||||
number of input that assosiate with output element.
|
||||
number of input that associate with output element.
|
||||
"""
|
||||
|
||||
n_input, c_input, h_input, w_input = x_shape
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ conv3d_op_info = TBERegOp("Conv3D") \
|
|||
.kernel_name("conv3d") \
|
||||
.partial_flag(True) \
|
||||
.attr("strides", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("dilations", "required", "listInt", "all") \
|
||||
.attr("groups", "optional", "int", "all") \
|
||||
.attr("format", "optional", "str", "all") \
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ conv3d_backprop_filter_op_info = TBERegOp("Conv3DBackpropFilter") \
|
|||
.partial_flag(True) \
|
||||
.attr("filter_size", "required", "listInt", "all") \
|
||||
.attr("strides", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("dilations", "required", "listInt", "all") \
|
||||
.attr("groups", "optional", "int", "all") \
|
||||
.attr("format", "optional", "str", "all") \
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ conv3d_backprop_input_op_info = TBERegOp("Conv3DBackpropInput") \
|
|||
.partial_flag(True) \
|
||||
.attr("input_size", "required", "listInt", "all") \
|
||||
.attr("strides", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("dilations", "required", "listInt", "all") \
|
||||
.attr("groups", "optional", "int", "all") \
|
||||
.attr("format", "optional", "str", "all") \
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ conv3d_transpose_op_info = TBERegOp("Conv3DTranspose") \
|
|||
.partial_flag(True) \
|
||||
.attr("input_size", "required", "listInt", "all") \
|
||||
.attr("strides", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("dilations", "optional", "listInt", "all") \
|
||||
.attr("groups", "optional", "int", "all") \
|
||||
.attr("format", "optional", "str", "all") \
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ depthwise_conv2d_op_info = TBERegOp("DepthwiseConv2dNative") \
|
|||
.partial_flag(True) \
|
||||
.attr("stride", "required", "listInt", "all") \
|
||||
.attr("dilation", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("format", "required", "str", "all") \
|
||||
.attr("offset_a", "optional", "int", "all") \
|
||||
.input(0, "x", False, "required", "all") \
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ depthwise_conv2d_backprop_filter_op_info = TBERegOp("DepthwiseConv2dNativeBackpr
|
|||
.attr("filter_size", "required", "listInt", "all") \
|
||||
.attr("stride", "required", "listInt", "all") \
|
||||
.attr("dilation", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("format", "required", "str", "all") \
|
||||
.input(0, "input", False, "required", "all") \
|
||||
.input(1, "out_backprop", False, "required", "all") \
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ depthwise_conv2d_backprop_input_op_info = TBERegOp("DepthwiseConv2dNativeBackpro
|
|||
.attr("input_size", "required", "listInt", "all") \
|
||||
.attr("stride", "required", "listInt", "all") \
|
||||
.attr("dilation", "required", "listInt", "all") \
|
||||
.attr("pads", "required", "listInt", "all") \
|
||||
.attr("pad_list", "required", "listInt", "all") \
|
||||
.attr("format", "required", "str", "all") \
|
||||
.input(0, "filter", False, "required", "all") \
|
||||
.input(1, "out_backprop", False, "required", "all") \
|
||||
|
|
|
|||
|
|
@ -354,8 +354,9 @@ class Conv3DBackpropFilter(PrimitiveWithInfer):
|
|||
if isinstance(pad, int):
|
||||
pad = (pad,) * 6
|
||||
validator.check_equal_int(len(pad), 6, 'pad size', self.name)
|
||||
self.add_prim_attr('pad', self.pad)
|
||||
self.pad_list = pad
|
||||
self.add_prim_attr('pads', self.pad_list)
|
||||
self.add_prim_attr('pad_list', self.pad_list)
|
||||
|
||||
self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name)
|
||||
if self.pad_mode != 'pad' and self.pad_list != (0, 0, 0, 0, 0, 0):
|
||||
|
|
@ -415,7 +416,7 @@ class Conv3DBackpropFilter(PrimitiveWithInfer):
|
|||
pad_right = pad_needed_w - pad_left
|
||||
self.pad_list = (pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right)
|
||||
|
||||
self.add_prim_attr('pads', self.pad_list)
|
||||
self.add_prim_attr('pad_list', self.pad_list)
|
||||
out = {
|
||||
'value': None,
|
||||
'shape': w_size_v,
|
||||
|
|
@ -432,7 +433,10 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
|
|||
out_channel (int): The dimensionality of the output space.
|
||||
kernel_size (Union[int, tuple[int]]): The size of the convolution window.
|
||||
pad_mode (str): Modes to fill padding. It could be "valid", "same", or "pad". Default: "valid".
|
||||
pad (int): The pad value to be filled. Default: 0.
|
||||
pad (Union(int, tuple[int])): The pad value to be filled. Default: 0. If `pad` is an integer, the paddings of
|
||||
top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of four integers, the
|
||||
padding of top, bottom, left and right equal to pad[0], pad[1], pad[2], and pad[3] correspondingly.
|
||||
pad_list (tuple): The pad list like (top, bottom, left, right). Default: (0, 0, 0, 0).
|
||||
mode (int): Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution ,
|
||||
2 deconvolution, 3 depthwise convolution. Default: 1.
|
||||
stride (tuple): The stride to be applied to the convolution filter. Default: (1, 1).
|
||||
|
|
@ -464,7 +468,11 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
|
|||
self.mode = mode
|
||||
pad_mode = pad_mode.upper()
|
||||
self.add_prim_attr('pad_mode', pad_mode)
|
||||
self.pad = pad
|
||||
if isinstance(pad, int):
|
||||
pad = (pad,) * 4
|
||||
else:
|
||||
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
if isinstance(stride, tuple) and len(stride) == 4:
|
||||
self.stride = (stride[2], stride[3])
|
||||
self.add_prim_attr('stride', self.stride)
|
||||
|
|
@ -506,8 +514,10 @@ class DepthwiseConv2dNativeBackpropFilter(PrimitiveWithInfer):
|
|||
mode (int): Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution,
|
||||
2 deconvolution,3 depthwise convolution. Default: 3.
|
||||
pad_mode (str): The mode to fill padding which can be: "valid", "same" or "pad". Default: "valid".
|
||||
pad (int): The pad value to be filled. Default: 0.
|
||||
pads (tuple): The pad list like (top, bottom, left, right). Default: (0, 0, 0, 0).
|
||||
pad (Union(int, tuple[int])): The pad value to be filled. Default: 0. If `pad` is an integer, the paddings of
|
||||
top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of four integers, the
|
||||
padding of top, bottom, left and right equal to pad[0], pad[1], pad[2], and pad[3] correspondingly.
|
||||
pad_list (tuple): The pad list like (top, bottom, left, right). Default: (0, 0, 0, 0).
|
||||
stride (int): The stride to be applied to the convolution filter. Default: 1.
|
||||
dilation (int): Specifies the space to use between kernel elements. Default: 1.
|
||||
group (int): Splits input into groups. Default: 1.
|
||||
|
|
@ -522,7 +532,7 @@ class DepthwiseConv2dNativeBackpropFilter(PrimitiveWithInfer):
|
|||
kernel_size,
|
||||
pad_mode="valid",
|
||||
pad=0,
|
||||
pads=(0, 0, 0, 0),
|
||||
pad_list=(0, 0, 0, 0),
|
||||
mode=3,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
|
|
@ -533,8 +543,12 @@ class DepthwiseConv2dNativeBackpropFilter(PrimitiveWithInfer):
|
|||
self.kernel_size = kernel_size
|
||||
self.mode = mode
|
||||
self.pad_mode = pad_mode
|
||||
self.pad = pad
|
||||
self.pads = pads
|
||||
if isinstance(pad, int):
|
||||
pad = (pad,) * 4
|
||||
else:
|
||||
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
self.pad_list = pad_list
|
||||
self.stride = stride
|
||||
self.dilation = dilation
|
||||
self.group = group
|
||||
|
|
@ -567,8 +581,10 @@ class DepthwiseConv2dNativeBackpropInput(PrimitiveWithInfer):
|
|||
mode (int): Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution ,
|
||||
2 deconvolution,3 depthwise convolution. Default: 3.
|
||||
pad_mode (str): Modes to fill padding. It could be "valid", "same", or "pad". Default: "valid".
|
||||
pad (int): The pad value to be filled. Default: 0.
|
||||
pads (tuple): The pad list like (top, bottom, left, right). Default: (0, 0, 0, 0).
|
||||
pad (Union(int, tuple[int])): The pad value to be filled. Default: 0. If `pad` is an integer, the paddings of
|
||||
top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of four integers, the
|
||||
padding of top, bottom, left and right equal to pad[0], pad[1], pad[2], and pad[3] correspondingly.
|
||||
pad_list (tuple): The pad list like (top, bottom, left, right). Default: (0, 0, 0, 0).
|
||||
stride (int): The stride to be applied to the convolution filter. Default: 1.
|
||||
dilation (int): Specifies the space to use between kernel elements. Default: 1.
|
||||
group (int): Splits input into groups. Default: 1.
|
||||
|
|
@ -583,7 +599,7 @@ class DepthwiseConv2dNativeBackpropInput(PrimitiveWithInfer):
|
|||
kernel_size,
|
||||
pad_mode="valid",
|
||||
pad=0,
|
||||
pads=(0, 0, 0, 0),
|
||||
pad_list=(0, 0, 0, 0),
|
||||
mode=3,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
|
|
@ -594,8 +610,12 @@ class DepthwiseConv2dNativeBackpropInput(PrimitiveWithInfer):
|
|||
self.kernel_size = kernel_size
|
||||
self.mode = mode
|
||||
self.pad_mode = pad_mode
|
||||
self.pad = pad
|
||||
self.pads = pads
|
||||
if isinstance(pad, int):
|
||||
pad = (pad,) * 4
|
||||
else:
|
||||
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
self.pad_list = pad_list
|
||||
self.stride = stride
|
||||
self.dilation = dilation
|
||||
self.group = group
|
||||
|
|
|
|||
|
|
@ -1295,6 +1295,7 @@ class Conv2D(PrimitiveWithCheck):
|
|||
pad = (pad,) * 4
|
||||
else:
|
||||
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
self.padding = pad
|
||||
self.pad_mode = validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.name)
|
||||
|
||||
|
|
@ -1400,6 +1401,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
|
|||
pad = (pad,) * 4
|
||||
else:
|
||||
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
self.padding = pad
|
||||
self.pad_mode = validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.name)
|
||||
if pad_mode != 'pad' and pad != (0, 0, 0, 0):
|
||||
|
|
@ -1450,7 +1452,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
|
|||
w_out = math.floor(w_out)
|
||||
|
||||
self.pad_list = (pad_top, pad_bottom, pad_left, pad_right)
|
||||
self.add_prim_attr('pads', self.pad_list)
|
||||
self.add_prim_attr('pad_list', self.pad_list)
|
||||
|
||||
out_channel = self.channel_multiplier * x_shape[1]
|
||||
out_shape = [x_shape[0], out_channel, h_out, w_out]
|
||||
|
|
@ -1817,12 +1819,12 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
|
|||
self.add_prim_attr('stride', self.stride)
|
||||
self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True)
|
||||
self.add_prim_attr('dilation', self.dilation)
|
||||
|
||||
validator.check_value_type('pad', pad, (int, tuple), self.name)
|
||||
if isinstance(pad, int):
|
||||
pad = (pad,) * 4
|
||||
else:
|
||||
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
self.padding = pad
|
||||
self.pad_mode = validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.name)
|
||||
if pad_mode != 'pad' and pad != (0, 0, 0, 0):
|
||||
|
|
@ -7039,6 +7041,7 @@ class Conv3D(PrimitiveWithInfer):
|
|||
if isinstance(pad, int):
|
||||
pad = (pad,) * 6
|
||||
validator.check_equal_int(len(pad), 6, 'pad size', self.name)
|
||||
self.add_prim_attr("pad", pad)
|
||||
self.padding = pad
|
||||
validator.check_int_range(self.padding[0], 0, kernel_size[0], Rel.INC_LEFT,
|
||||
'pad_d belonging [0, kernel_size_d)', self.name)
|
||||
|
|
@ -7128,7 +7131,7 @@ class Conv3D(PrimitiveWithInfer):
|
|||
w_out = math.floor(w_out)
|
||||
|
||||
self.pad_list = [pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right]
|
||||
self.add_prim_attr('pads', (pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right))
|
||||
self.add_prim_attr('pad_list', (pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right))
|
||||
out_channel = self.out_channel
|
||||
out_shape = [x_shape[0], out_channel, d_out, h_out, w_out]
|
||||
_check_shape('output', out_shape, self.name)
|
||||
|
|
@ -7206,6 +7209,7 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
|
|||
if isinstance(pad, int):
|
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pad = (pad,) * 6
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validator.check_equal_int(len(pad), 6, 'pad size', self.name)
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||||
self.add_prim_attr("pad", pad)
|
||||
self.pad_list = pad
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self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name)
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|
|
@ -7264,7 +7268,7 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
|
|||
pad_right = pad_needed_w - pad_left
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||||
self.pad_list = (pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right)
|
||||
|
||||
self.add_prim_attr('pads', self.pad_list)
|
||||
self.add_prim_attr('pad_list', self.pad_list)
|
||||
out = {
|
||||
'value': None,
|
||||
'shape': x_size_v,
|
||||
|
|
@ -7385,7 +7389,7 @@ class Conv3DTranspose(PrimitiveWithInfer):
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|||
# infer shape
|
||||
x_shape = x['shape']
|
||||
w_shape = w['shape']
|
||||
self.add_prim_attr('pads', self.pad_list)
|
||||
self.add_prim_attr('pad_list', self.pad_list)
|
||||
pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right = self.pad_list
|
||||
d_out = (x_shape[2] - 1) * self.stride[2] - (pad_head + pad_tail) + self.dilation[2] * \
|
||||
(self.kernel_size[0] - 1) + self.output_padding[2] + 1
|
||||
|
|
|
|||
Loading…
Reference in New Issue