forked from mindspore-Ecosystem/mindspore
Add Abs\AbsGrad\Sign\SmoothL1Loss\SmoothL1LossGrad and modify TopKV2->TopK for VM
This commit is contained in:
parent
0a9db34d5a
commit
5c9791a802
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@ -42,7 +42,6 @@ static std::map<string, string> tbe_func_adapter_map = {
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{"depthwise_conv2d_native", "depthwise_conv2d"},
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{"depthwise_conv2d_native_backprop_filter", "depthwise_conv2d_backprop_filter_d"},
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{"depthwise_conv2d_native_backprop_input", "depthwise_conv2d_backprop_input_d"},
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{"top_kv2", "top_k"},
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{"scatter_nd", "scatter_nd_d"},
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{"tile", "tile_d"},
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{"gather_v2", "gather_v2_d"},
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@ -14,6 +14,8 @@
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# ============================================================================
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"""tbe ops"""
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from .abs import _abs_tbe
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from .abs_grad import _abs_grad_tbe
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from .adam_apply_one_with_decay import _adam_apply_one_with_decay_tbe
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from .add import _add_tbe
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from .add_n import _add_n_tbe
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@ -49,7 +51,7 @@ from .sigmoid_cross_entropy_with_logits import _sigmoid_cross_entropy_with_logit
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from .sigmoid_cross_entropy_with_logits_grad import _sigmoid_cross_entropy_with_logits_grad_tbe
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from .tensor_add import _tensor_add_tbe
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from .trans_data import _trans_data_tbe
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from .topkv2 import _topk_v2_tbe
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from .top_k import _top_k_tbe
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from .matmul import _matmul_tbe
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from .sub import _sub_tbe
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from .reduce_mean_d import _reduce_mean_d_tbe
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@ -107,6 +109,7 @@ from .minimum_grad import _minimum_grad_tbe
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from .maximum_grad import _maximum_grad_tbe
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from .concat import _concat_tbe
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from .slice import _slice_tbe
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from .sign import _sign_tbe
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from .greater import _greater_tbe
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from .clip_by_norm_no_div_sum import _clip_by_norm_no_div_sum_tbe
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from .clip_by_value import _clip_by_value_tbe
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@ -130,6 +133,8 @@ from .resize_nearest_neighbor_grad_d import _resize_nearest_neighbor_grad_d_tbe
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from .pad_d import _pad_d_tbe
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from .arg_max_with_value import _arg_max_with_value_tbe
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from .arg_min_with_value import _arg_min_with_value_tbe
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from .smooth_l1_loss import _smooth_l1_loss_tbe
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from .smooth_l1_loss_grad import _smooth_l1_loss_grad_tbe
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from .fused_mul_add import _fused_mul_add_tbe
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from .fused_mul_add_n import _fused_mul_add_n_tbe
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from .fused_mul_apply_momentum import _fused_mul_apply_momentum_tbe
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@ -0,0 +1,41 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Abs op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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abs_op_info = TBERegOp("Abs") \
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.fusion_type("ELEMWISE") \
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.async_flag(False) \
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.binfile_name("abs.so") \
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.compute_cost(10) \
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.kernel_name("abs") \
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.partial_flag(True) \
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.op_pattern("formatAgnostic") \
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.input(0, "x", None, "required", None) \
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.output(0, "y", True, "required", "all") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default) \
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.dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
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.get_op_info()
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@op_info_register(abs_op_info)
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def _abs_tbe():
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"""Abs TBE register"""
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return
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@ -0,0 +1,44 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""AbsGrad op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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abs_grad_op_info = TBERegOp("AbsGrad") \
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.fusion_type("ELEMWISE") \
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.async_flag(False) \
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.binfile_name("abs_grad.so") \
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.compute_cost(10) \
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.kernel_name("abs_grad") \
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.partial_flag(True) \
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.op_pattern("formatAgnostic") \
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.input(0, "y", None, "required", None) \
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.input(1, "dy", None, "required", None) \
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.output(0, "z", False, "required", "all") \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
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.dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
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.dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
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.dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
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.dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
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.get_op_info()
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@op_info_register(abs_grad_op_info)
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def _abs_grad_tbe():
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"""AbsGrad TBE register"""
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return
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@ -0,0 +1,41 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Sign op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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sign_op_info = TBERegOp("Sign") \
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.fusion_type("ELEMWISE") \
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.async_flag(False) \
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.binfile_name("sign.so") \
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.compute_cost(10) \
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.kernel_name("sign") \
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.partial_flag(True) \
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.op_pattern("formatAgnostic") \
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.input(0, "x", None, "required", None) \
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.output(0, "y", True, "required", "all") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default) \
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.dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
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.get_op_info()
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@op_info_register(sign_op_info)
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def _sign_tbe():
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"""Sign TBE register"""
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return
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@ -0,0 +1,44 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""SmoothL1Loss op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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smooth_l1_loss_op_info = TBERegOp("SmoothL1Loss") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("smooth_l1_loss.so") \
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.compute_cost(10) \
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.kernel_name("smooth_l1_loss") \
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.partial_flag(True) \
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.attr("sigma", "required", "float", "all") \
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.input(0, "predict", False, "required", "all") \
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.input(1, "label", False, "required", "all") \
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.output(0, "loss", False, "required", "all") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
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.dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
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.dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
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.dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
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.dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
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.get_op_info()
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@op_info_register(smooth_l1_loss_op_info)
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def _smooth_l1_loss_tbe():
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"""SmoothL1Loss TBE register"""
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return
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@ -0,0 +1,45 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""SmoothL1LossGrad op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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smooth_l1_loss_grad_op_info = TBERegOp("SmoothL1LossGrad") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("smooth_l1_loss_grad.so") \
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.compute_cost(10) \
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.kernel_name("smooth_l1_loss_grad") \
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.partial_flag(True) \
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.attr("sigma", "required", "float", "all") \
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.input(0, "predict", False, "required", "all") \
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.input(1, "label", False, "required", "all") \
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.input(2, "dout", False, "required", "all") \
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.output(0, "loss", False, "required", "all") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
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.dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
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.dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
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.dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
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.dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
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.get_op_info()
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@op_info_register(smooth_l1_loss_grad_op_info)
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def _smooth_l1_loss_grad_tbe():
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"""SmoothL1LossGrad TBE register"""
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return
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@ -13,15 +13,15 @@
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# limitations under the License.
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# ============================================================================
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"""TopKV2 op"""
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"""TopK op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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top_k_v2_op_info = TBERegOp("TopKV2") \
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top_k_op_info = TBERegOp("TopK") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("top_k_v2.so") \
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.binfile_name("top_k.so") \
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.compute_cost(10) \
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.kernel_name("top_k_v2") \
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.kernel_name("top_k") \
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.partial_flag(True) \
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.attr("k", "required", "int", "all")\
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.attr("sorted", "required", "bool", "all")\
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@ -33,7 +33,7 @@ top_k_v2_op_info = TBERegOp("TopKV2") \
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.get_op_info()
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@op_info_register(top_k_v2_op_info)
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def _topk_v2_tbe():
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"""TopKV2 TBE register"""
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@op_info_register(top_k_op_info)
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def _top_k_tbe():
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"""TopK TBE register"""
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return
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@ -599,3 +599,4 @@ class DataType:
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F32_NCHW = ("float32", "NCHW")
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F32_NHWC = ("float32", "NHWC")
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F32_HWCN = ("float32", "HWCN")
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@ -0,0 +1,42 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self, sigma=1.0):
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super(Net, self).__init__()
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self.SmoothL1Loss = P.SmoothL1Loss(sigma)
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def construct(self, pred, gt):
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return self.SmoothL1Loss(pred, gt)
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def test_net():
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pred = np.random.randn(2, 4).astype(np.float32)
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gt = np.random.randn(2, 4).astype(np.float32)
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smooth_l1_loss = Net()
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loss = smooth_l1_loss(Tensor(pred), Tensor(gt))
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print("------------- input ---------------")
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print("predict:\n", pred)
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print("grount truth:\n", gt)
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print("------------- output ---------------")
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print("loss:\n", loss.asnumpy())
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@ -0,0 +1,55 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.ops.composite import GradOperation
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self, sigma=1.0):
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super(Net, self).__init__()
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self.SmoothL1Loss = P.SmoothL1Loss(sigma)
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def construct(self, pred, gt):
|
||||
return self.SmoothL1Loss(pred, gt)
|
||||
|
||||
class Grad(nn.Cell):
|
||||
def __init__(self, network):
|
||||
super(Grad, self).__init__()
|
||||
self.grad = GradOperation(name="get_all", get_all=True, sens_param=True)
|
||||
self.network = network
|
||||
|
||||
def construct(self, pred, gt, dout):
|
||||
return self.grad(self.network)(pred, gt, dout)
|
||||
|
||||
|
||||
def test_net():
|
||||
pred = np.random.randn(2, 4).astype(np.float32)
|
||||
gt = np.random.randn(2, 4).astype(np.float32)
|
||||
dout = np.random.randn(2, 4).astype(np.float32)
|
||||
smooth_l1_loss_grad = Grad(Net())
|
||||
output = smooth_l1_loss_grad(Tensor(pred), Tensor(gt), Tensor(dout))
|
||||
print("------------- input ---------------")
|
||||
print("predict:\n", pred)
|
||||
print("grount truth:\n", gt)
|
||||
print("dout:\n", dout)
|
||||
print("------------- output ---------------")
|
||||
print("predict grad:\n", output[0].asnumpy())
|
|
@ -24,7 +24,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|||
class Net(nn.Cell):
|
||||
def __init__(self, k):
|
||||
super(Net, self).__init__()
|
||||
self.topk = P.TopK()
|
||||
self.topk = P.TopK(True)
|
||||
self.k = k
|
||||
|
||||
def construct(self, x):
|
Loading…
Reference in New Issue