!732 dock AcoshGrad for GE and AvgPool AvgPoolGrad for VM
Merge pull request !732 from zhangbuxue/dock_AcoshGrad_AvgPool_AvgPoolGrad_for_vmpull/732/MERGE
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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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"""AvgPool op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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avg_pool_op_info = TBERegOp("AvgPool") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("avg_pool.so") \
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.compute_cost(10) \
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.kernel_name("avg_pool") \
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.partial_flag(True) \
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.attr("ksize", "required", "listInt", "all") \
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.attr("strides", "required", "listInt", "all") \
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.attr("padding", "required", "str", "all") \
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.attr("data_format", "optional", "str", "all") \
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.input(0, "x", False, "required", "all") \
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.output(0, "y", False, "required", "all") \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
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.get_op_info()
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@op_info_register(avg_pool_op_info)
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def _avg_pool_tbe():
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"""AvgPool TBE register"""
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return
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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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"""AvgPoolGrad op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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avg_pool_grad_op_info = TBERegOp("AvgPoolGrad") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("avg_pool_grad_d.so") \
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.compute_cost(10) \
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.kernel_name("avg_pool_grad_d") \
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.partial_flag(True) \
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.attr("x_origin", "required", "listInt", "all") \
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.attr("ksize", "required", "listInt", "all") \
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.attr("strides", "required", "listInt", "all") \
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.attr("padding", "required", "str", "all") \
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.attr("data_format", "optional", "str", "all") \
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.input(0, "input_grad", False, "required", "all") \
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.input(1, "mean_matrix", False, "optional", "all") \
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.input(2, "kernel_matrix", False, "optional", "all") \
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.output(0, "out_grad", True, "required", "all") \
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.dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_C1HWNCoC0, DataType.F16_5HD) \
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.get_op_info()
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@op_info_register(avg_pool_grad_op_info)
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def _avg_pool_grad_tbe():
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"""AvgPoolGrad TBE register"""
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return
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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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"""Inner operators."""
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from ..._checkparam import Validator as validator
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from ...common import dtype as mstype
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from ..primitive import PrimitiveWithInfer, prim_attr_register
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class ExtractImagePatches(PrimitiveWithInfer):
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"""
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Extract patches from images.
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The input tensor must be a 4-D tensor and the data format is NHWC.
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Args:
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ksizes (Union[tuple[int], list[int]]): The size of sliding window, should be a tuple or list of int,
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and the format is [1, ksize_row, ksize_col, 1].
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strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
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should be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
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rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dim
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pixel positions, should be a tuple or list of int, and the format is [1, rate_row, rate_col, 1].
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padding (str): The type of padding algorithm, is a string whose value is "same" or "valid",
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not case sensitive. Default: "valid".
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- same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
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- valid: Means that the patch area taken must be completely contained in the original image.
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Inputs:
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- **input_x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_row, in_col, in_depth] and
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data type is int8, float16, uint8.
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Outputs:
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Tensor, a 4-D tensor whose data type is same as 'input_x',
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and the shape is [out_batch, out_row, out_col, out_depth], the out_batch is same as the in_batch.
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"""
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@prim_attr_register
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def __init__(self, ksizes, strides, rates, padding="valid"):
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"""init"""
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def _check_tuple_or_list(arg_name, arg_val, prim_name):
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validator.check_value_type(f"{arg_name}s", ksizes, [tuple, list], self.name)
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if len(arg_val) != 4 or arg_val[0] != 1 or arg_val[3] != 1:
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raise ValueError(f"For \'{prim_name}\' the format of {arg_name}s should be [1, {arg_name}_row, "
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f"{arg_name}_col, 1], but got {arg_val}.")
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if not isinstance(arg_val[1], int) or not isinstance(arg_val[2], int) or arg_val[1] < 1 or arg_val[2] < 1:
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raise ValueError(f"For '{prim_name}' the {arg_name}_row and {arg_name}_col in {arg_name}s should be an "
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f"positive integer number, but got {arg_name}_row is {arg_val[1]}, {arg_name}_col "
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f"is {arg_val[2]}")
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_check_tuple_or_list("ksize", ksizes, self.name)
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_check_tuple_or_list("stride", strides, self.name)
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_check_tuple_or_list("rate", rates, self.name)
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self.padding = validator.check_string('padding', padding.upper(), ['VALID', 'SAME'], self.name)
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self.add_prim_attr("padding", self.padding)
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def infer_shape(self, input_x):
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"""infer shape"""
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in_batch, in_row, in_col, in_depth = input_x
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_, ksize_row, ksize_col, _ = self.ksizes
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_, stride_row, stride_col, _ = self.strides
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_, rate_row, rate_col, _ = self.rates
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if len(input_x) != 4:
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raise ValueError("The `input_x` should be a 4-D tensor, "
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f"but got a {len(input_x)}-D tensor whose shape is {input_x}")
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out_batch = in_batch
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out_depth = ksize_row * ksize_col * in_depth
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if self.padding == "VALID":
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out_row = \
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(in_row - (ksize_row + (ksize_row - 1) * (rate_row - 1))) // stride_row + 1
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out_col = \
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(in_col - (ksize_col + (ksize_col - 1) * (rate_col - 1))) // stride_col + 1
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else:
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out_row = (in_row - 1) // stride_row + 1
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out_col = (in_col - 1) // stride_col + 1
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out_shape = [out_batch, out_row, out_col, out_depth]
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return out_shape
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def infer_dtype(self, input_x):
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"""infer dtype"""
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validator.check_tensor_type_same({"input_x": input_x}, (mstype.int8, mstype.float16, mstype.float32), self.name)
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return input_x
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