forked from mindspore-Ecosystem/mindspore
!1337 test(custom_op): delete conv2d custom op.
Merge pull request !1337 from gongchen/del_conv2d_custom_op
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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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from __future__ import absolute_import
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import te.lang.cce
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from te import platform as cce
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from te.platform.fusion_manager import fusion_manager
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from topi.cce import util
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from .conv_layer import conv_layer_cce
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from .conv_layer_fast import conv_layer_fast_cce
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Nonetype = type(None)
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# pylint: disable=unused-argument, no-value-for-parameter, too-many-branches
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@fusion_manager.register("conv2d")
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def conv2d_compute(inputs, weights, bias, outputs, strides, pad_list, dilations,
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kernel_name="conv2d"):
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"""
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conv2d compute
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Notice
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------
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only used by framework combine with IR
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Parameters
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----------
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inputs: tvm placeholder
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input 5hd feature map tensor
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weights: tvm placeholder
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input frac_z weight tensor
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outputs: tvm placeholder
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output tensor, dtype must be assigned
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bias: tvm placeholder or None
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input 1d bias tensor
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strides: integers
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stride on H/W, format sensitive
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pads: tuple/list of 4 integers
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[pad_top, pad_bottom, pad_left, pad_right]
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dilations: integers
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dilation on H/W, format sensitive
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kernel_name: string
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kernel name, default value is "conv2d"
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Returns
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-------
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tvm compute
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"""
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shape_w = []
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for i in weights.op.attrs['ori_shape']:
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shape_w.append(i.value)
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format_w = weights.op.attrs['ori_format']
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if format_w == "NCHW":
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weight_h = shape_w[2]
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weight_w = shape_w[3]
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elif format_w == "NHWC":
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weight_h = shape_w[1]
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weight_w = shape_w[2]
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elif format_w == "HWCN":
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weight_h = shape_w[0]
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weight_w = shape_w[1]
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else:
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raise RuntimeError("weights ori_format should be NCHW, NHWC or HWCN")
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format_x = inputs.op.attrs['ori_format']
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if format_x == "NCHW":
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strideh = strides[0]
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stridew = strides[0]
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dlt_h = dilations[0]
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dlt_w = dilations[0]
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elif format_x == "NHWC":
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strideh = strides[0]
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stridew = strides[0]
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dlt_h = dilations[0]
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dlt_w = dilations[0]
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else:
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raise RuntimeError("inputs ori_format should be NCHW or NHWC")
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if len(pad_list) == 4:
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padh = [pad_list[0], pad_list[1]]
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padw = [pad_list[2], pad_list[3]]
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else:
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raise RuntimeError("pads shape should be 4d.")
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para_dict = {"pad_h": padh, "pad_w": padw, "stride_h": strideh, "stride_w": stridew,
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"filter_h": weight_h, "filter_w": weight_w, "bias_tensor": bias}
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if cce.CceProductParams().cce_product == "5.10":
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para_dict["mad_dtype"] = "float16"
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res = te.lang.cce.conv(inputs, weights, para_dict)
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else:
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res = te.lang.cce.conv(inputs, weights, para_dict)
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return res
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@util.check_input_type(dict, dict, (dict, Nonetype), dict, (tuple, list), (tuple, list), (tuple, list),
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str)
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def conv2d(inputs, weights, bias, outputs, strides, pad_list, dilations,
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kernel_name="conv2d"):
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"""
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algorithm: conv2d
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Notice
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------
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only used by framework combine with IR
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Parameters
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----------
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inputs: dict with keys(shape and dtype)
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input 4d feature map tensor
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weights: dict with keys(shape and dtype)
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input 4d weight tensor
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outputs: dict with keys(shape and dtype)
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output tensor, dtype must be assigned
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bias: dict with keys(shape and dtype) or None
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input bias tensor
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strides: integers
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stride on H/W, format sensitive
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pads: integers
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[pad_top, pad_bottom, pad_left, pad_right]
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dilations: tuple/list of 4 integers
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dilation on H/W, format sensitive
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kernel_name: str
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kernel name, default value is "conv2d"
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Returns
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-------
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None
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"""
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shape_x = inputs.get("ori_shape")
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in_dtype = inputs.get("dtype")
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shape_w = weights.get("ori_shape")
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w_dtype = weights.get("dtype")
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res_dtype = outputs.get("dtype")
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if len(pad_list) == 4:
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padh = [pad_list[0], pad_list[1]]
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padw = [pad_list[2], pad_list[3]]
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else:
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raise RuntimeError("pads shape should be 4d.")
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if (not isinstance(shape_x, (tuple, list))) or len(shape_x) != 4:
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raise RuntimeError("inputs should be 4d list.")
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if (not isinstance(shape_w, (tuple, list))) or len(shape_w) != 4:
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raise RuntimeError("weights should be 4d list.")
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format_x = inputs.get("ori_format")
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if format_x == "NCHW":
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shape_fm = shape_x
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strideh = strides[0]
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stridew = strides[0]
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dlt_h = dilations[0]
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dlt_w = dilations[0]
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elif format_x == "NHWC":
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shape_fm = [shape_x[0], shape_x[3], shape_x[1], shape_x[2]]
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strideh = strides[0]
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stridew = strides[0]
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dlt_h = dilations[0]
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dlt_w = dilations[0]
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else:
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raise RuntimeError("inputs ori_format should be NCHW or NHWC.")
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format_w = weights.get("ori_format")
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if format_w == "NCHW":
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shape_filter = shape_w
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elif format_w == "NHWC":
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shape_filter = [shape_w[0], shape_w[3], shape_w[1], shape_w[2]]
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elif format_w == "HWCN":
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shape_filter = [shape_w[3], shape_w[2], shape_w[0], shape_w[1]]
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else:
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raise RuntimeError("weights ori_format should be NCHW, NHWC or HWCN.")
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if bias is None:
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use_bias = False
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else:
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use_bias = True
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if cce.CceProductParams().cce_product == "5.10":
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conv_layer_fast_cce(shape_fm, shape_filter, in_dtype, w_dtype, res_dtype,
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padh, padw, strideh, stridew, bias=use_bias,
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kernel_name=kernel_name, need_build=True, need_print=False)
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else:
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conv_layer_cce(shape_fm, shape_filter, in_dtype, w_dtype, res_dtype,
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padh, padw, strideh, stridew,
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quantize_config=[0, 0, 0], scale_sqrt=[0, 0, 0],
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bias=use_bias, kernel_name=kernel_name,
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need_build=True, need_print=False)
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@ -1,40 +0,0 @@
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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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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from tests.st.ops.custom_ops_tbe.conv2d import conv2d
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cus_conv2D_op_info = TBERegOp("Cus_Conv2D") \
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.fusion_type("CONVLUTION") \
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.async_flag(False) \
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.binfile_name("conv2d.so") \
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.compute_cost(10) \
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.kernel_name("Cus_Conv2D") \
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.partial_flag(True) \
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.attr("stride", "required", "listInt", "all") \
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.attr("pad_list", "required", "listInt", "all") \
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.attr("dilation", "required", "listInt", "all") \
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.input(0, "x", False, "required", "all") \
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.input(1, "filter", False, "required", "all") \
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.input(2, "bias", False, "optional", "all") \
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.output(0, "y", True, "required", "all") \
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.dtype_format(DataType.F16_5HD, DataType.F16_FracZ, DataType.F32_Default, DataType.F16_5HD) \
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.get_op_info()
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@op_info_register(cus_conv2D_op_info)
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def Cus_Conv2D(inputs, weights, bias, outputs, strides, pads, dilations,
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kernel_name="conv2d"):
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conv2d(inputs, weights, bias, outputs, strides, pads, dilations,
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kernel_name)
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@ -1,520 +0,0 @@
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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 te.lang.cce
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from te import tvm
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from te.platform import CUBE_MKN
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from topi import generic
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from topi.cce import util
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from topi.cce.util import is_v200_version
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# pylint: disable=R0912,R0913,R0914,R0915,E1101
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# the dim of shape in conv must be 4
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PAD_SHAPE_DIM = 2
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NONETYPE = type(None)
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@util.check_input_type((list, tuple), (list, tuple), str, str, str, (list, int), (list, int),
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int, int, (list, tuple), (list, tuple),
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str, str, str,
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str, str, str,
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str, bool, str)
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def conv_layer_cce_para_check(shape_in, shape_w, in_dtype, w_dtype, res_dtype, padh, padw,
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strideh, stridew, quantize_config, scale_sqrt,
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scale_q_dtype, offset_q_dtype, scale_dq_dtype,
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scale_rq_dtype, offset_rq_dtype, offset_w_dtype,
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offset_pad_dtype, bias, kernel_name):
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# conv shape check
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util.check_kernel_name(kernel_name)
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# conv data type check
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util.check_dtype_rule(in_dtype, ['float16', 'int8', 'uint8'])
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util.check_dtype_rule(w_dtype, ['float16', 'int8', 'uint8'])
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res_dtype_list = ['float16', 'int8', 'uint8']
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if is_v200_version():
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res_dtype_list.append('int32')
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util.check_dtype_rule(res_dtype, res_dtype_list)
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util.check_dtype_rule(scale_q_dtype, ['float16'])
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util.check_dtype_rule(offset_q_dtype, ['float16'])
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util.check_dtype_rule(scale_dq_dtype, ['float16'])
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util.check_dtype_rule(scale_rq_dtype, ['float16'])
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util.check_dtype_rule(offset_rq_dtype, ['float16'])
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util.check_dtype_rule(offset_w_dtype, ['int32'])
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util.check_dtype_rule(offset_pad_dtype, ['uint8'])
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if not isinstance(bias, bool):
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raise RuntimeError("bias dtype should be bool.")
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if quantize_config[0] == 0:
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if is_v200_version():
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util.check_dtype_rule(in_dtype, ('int8',))
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util.check_dtype_rule(w_dtype, ('int8',))
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util.check_dtype_rule(res_dtype, ('int32',))
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else:
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util.check_dtype_rule(in_dtype, ['float16'])
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util.check_dtype_rule(w_dtype, ['float16'])
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util.check_dtype_rule(res_dtype, ['float16'])
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if quantize_config[0] == 1:
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util.check_dtype_rule(w_dtype, ['int8'])
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if quantize_config[1] == 0:
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util.check_dtype_rule(in_dtype, ['int8', 'float16'])
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util.check_dtype_rule(res_dtype, ['int8', 'float16'])
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elif quantize_config[1] == 1:
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util.check_dtype_rule(in_dtype, ['uint8', 'float16'])
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util.check_dtype_rule(res_dtype, ['uint8', 'float16'])
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elif quantize_config[1] == 2:
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raise RuntimeError("All Offset mode quantize not support.")
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else:
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raise RuntimeError("Invalid quantize algorithm.")
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# quantize switch on
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if quantize_config[0] == 1:
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quantize_turn_on = True
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# quantize -> DeQuantize dataflow
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if in_dtype == 'float16' and w_dtype == 'int8' and res_dtype == 'float16':
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pass
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# DeQuantize dataflow
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elif (in_dtype in ['int8', 'uint8'] and w_dtype == 'int8' and
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res_dtype == 'float16'):
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pass
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# quantize -> ReQuantize dataflow
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elif (in_dtype == 'float16' and w_dtype == 'int8' and res_dtype in
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['int8', 'uint8']):
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pass
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# ReQuantize dataflow
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elif (in_dtype in ['int8', 'uint8'] and w_dtype == 'int8' and res_dtype in
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['int8', 'uint8']):
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pass
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else:
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raise RuntimeError("Not support in/out data type for quantize.")
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if quantize_config not in ([1, 0, 0], [1, 1, 0], [1, 0, 1], [1, 1, 1]):
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raise RuntimeError("Invalid Quantize Config.")
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if scale_sqrt not in ([0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1],
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[1, 0, 1], [0, 1, 1], [1, 1, 1]):
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raise RuntimeError("Invalid Quantize Config.")
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# quantize switch off
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elif quantize_config[0] == 0:
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if quantize_config != [0, 0, 0]:
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raise RuntimeError("Invalid Quantize Config.")
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if scale_sqrt != [0, 0, 0]:
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raise RuntimeError("Invalid Quantize Config.")
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else:
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raise RuntimeError("Invalid Quantize Config.")
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if isinstance(padh, list):
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if len(padh) != PAD_SHAPE_DIM:
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raise RuntimeError("Dimension must be %d when padh is a list." % PAD_SHAPE_DIM)
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pad_top = padh[0]
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pad_bottom = padh[1]
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else:
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pad_top = padh
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pad_bottom = padh
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if isinstance(padw, list):
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if len(padw) != PAD_SHAPE_DIM:
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raise RuntimeError("Dimension must be %d when padw is a list." % PAD_SHAPE_DIM)
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pad_left = padw[0]
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pad_right = padw[1]
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else:
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pad_left = padw
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pad_right = padw
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shape_in, shape_w = te.lang.cce.check_conv_shape(shape_in, shape_w, pad_top, pad_bottom, \
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pad_left, pad_right, strideh, \
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stridew, in_dtype, w_dtype, res_dtype)
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return shape_in, shape_w
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@util.check_input_type((list, tuple), (list, tuple), str, str, str, \
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(list, int), (list, int), int, int,
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(list, NONETYPE), (list, NONETYPE),
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str, str, str,
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str, str, str, str,
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bool, str, bool, bool)
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def conv_layer_cce(shape_in, shape_w, in_dtype, w_dtype, res_dtype, padh, padw, strideh, stridew,
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quantize_config=None, scale_sqrt=None,
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scale_q_dtype='float16', offset_q_dtype='float16', scale_dq_dtype='float16',
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scale_rq_dtype='float16', offset_rq_dtype='float16', offset_w_dtype='int32',
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offset_pad_dtype='uint8', bias=False, kernel_name="cce_conv", need_build=False,
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need_print=False):
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"""
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||||
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||||
Parameters
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||||
----------
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||||
shape_in : shape of data_in
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||||
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||||
shape_w : shape of filter
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||||
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||||
in_dtype : the feature map data type
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||||
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w_dtype : the weight data type
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||||
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||||
res_dtype : the result data type
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||||
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padh: the padding shape in H
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padw: the padding shape in weight
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||||
strideh: the stride value in H
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||||
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||||
stridew: the stride value in weight
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||||
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quantize_config: quantize config table, default [0, 0, 0]
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||||
quantize_config[0] - quantize function switch
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||||
0: quantize off
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||||
1: quantize on
|
||||
quantize_config[1] - quantize_algorithm
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||||
0: non offset
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||||
1: half offset
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||||
2: all offset ( Not supported now )
|
||||
quantize_config[2] - QuantizeScaleType (for Dequantize/Requantize, quantize always scalar)
|
||||
0: scalar
|
||||
1: vector
|
||||
|
||||
scale_sqrt: scale mode
|
||||
scale_sqrt[0] - Quantize scale mode
|
||||
0: non sqrt
|
||||
1: sqrt
|
||||
scale_sqrt[1] - DeQuantize scale mode
|
||||
0: non sqrt
|
||||
1: sqrt
|
||||
scale_sqrt[2] - ReQuantize scale mode
|
||||
0: non sqrt
|
||||
1: sqrt
|
||||
|
||||
scale_q_dtype: Quantize scale data type, default 'float16'
|
||||
|
||||
offset_q_dtype: Quantize offset data type, default 'float16'
|
||||
|
||||
scale_dq_dtype: DeQuantize scale data type, default 'float16'
|
||||
|
||||
scale_rq_dtype: ReQuantize scale data type, default 'float16'
|
||||
|
||||
offset_rq_dtype: ReQuantize offset data type, default 'float16'
|
||||
|
||||
offset_w_dtype: weight offset data type, default 'int32'
|
||||
|
||||
offset_pad_dtype: Quantize Cube offset data type, default 'uint8'
|
||||
|
||||
bias: the tag for bias or not
|
||||
|
||||
kernel_name : cce kernel name, default value is "cce_conv"
|
||||
|
||||
need_build : if need to build CCEC kernel, default value is False
|
||||
|
||||
need_print : if need to print the ir, default value is False
|
||||
|
||||
Returns
|
||||
-------
|
||||
wrapped_tensor
|
||||
|
||||
"""
|
||||
# for pylint, otherwise "Dangerous default value [] as argument"
|
||||
if quantize_config is None:
|
||||
quantize_config = [0, 0, 0]
|
||||
if scale_sqrt is None:
|
||||
scale_sqrt = [0, 0, 0]
|
||||
|
||||
in_dtype = in_dtype.lower()
|
||||
w_dtype = w_dtype.lower()
|
||||
res_dtype = res_dtype.lower()
|
||||
scale_q_dtype = scale_q_dtype.lower()
|
||||
offset_q_dtype = offset_q_dtype.lower()
|
||||
scale_dq_dtype = scale_dq_dtype.lower()
|
||||
scale_rq_dtype = scale_rq_dtype.lower()
|
||||
offset_rq_dtype = offset_rq_dtype.lower()
|
||||
offset_w_dtype = offset_w_dtype.lower()
|
||||
offset_pad_dtype = offset_pad_dtype.lower()
|
||||
|
||||
mad_dtype = 'float32'
|
||||
if w_dtype == 'int8':
|
||||
mad_dtype = 'int32'
|
||||
|
||||
shape_in = list(shape_in)
|
||||
shape_w = list(shape_w)
|
||||
|
||||
shape_in, shape_w = conv_layer_cce_para_check(shape_in, shape_w, in_dtype, w_dtype, res_dtype, padh, padw, strideh,
|
||||
stridew,
|
||||
quantize_config, scale_sqrt, scale_q_dtype, offset_q_dtype,
|
||||
scale_dq_dtype,
|
||||
scale_rq_dtype, offset_rq_dtype, offset_w_dtype, offset_pad_dtype,
|
||||
bias, kernel_name)
|
||||
|
||||
# quantize switch on
|
||||
if quantize_config[0] == 1:
|
||||
quantize_turn_on = True
|
||||
# quantize -> DeQuantize dataflow
|
||||
if in_dtype == 'float16' and w_dtype == 'int8' and res_dtype == 'float16':
|
||||
is_quantize = True
|
||||
is_dequantize = True
|
||||
is_requantize = False
|
||||
# DeQuantize dataflow
|
||||
elif (in_dtype in ['int8', 'uint8'] and w_dtype == 'int8' and
|
||||
res_dtype == 'float16'):
|
||||
is_quantize = False
|
||||
is_dequantize = True
|
||||
is_requantize = False
|
||||
# quantize -> ReQuantize dataflow
|
||||
elif (in_dtype == 'float16' and w_dtype == 'int8' and res_dtype in
|
||||
['int8', 'uint8']):
|
||||
is_quantize = True
|
||||
is_dequantize = False
|
||||
is_requantize = True
|
||||
# ReQuantize dataflow
|
||||
elif (in_dtype in ['int8', 'uint8'] and w_dtype == 'int8' and res_dtype in
|
||||
['int8', 'uint8']):
|
||||
is_quantize = False
|
||||
is_dequantize = False
|
||||
is_requantize = True
|
||||
else:
|
||||
raise RuntimeError("Not support in/out data type for quantize.")
|
||||
|
||||
# quantize switch off
|
||||
elif quantize_config[0] == 0:
|
||||
quantize_turn_on = False
|
||||
is_quantize = False
|
||||
is_dequantize = False
|
||||
is_requantize = False
|
||||
|
||||
if quantize_config != [0, 0, 0]:
|
||||
raise RuntimeError("Invalid Quantize Config.")
|
||||
if scale_sqrt != [0, 0, 0]:
|
||||
raise RuntimeError("Invalid Quantize Config.")
|
||||
else:
|
||||
raise RuntimeError("Invalid Quantize Config.")
|
||||
|
||||
batch_size = shape_in[0]
|
||||
in_channel = shape_in[1]
|
||||
feature_map_h = shape_in[2]
|
||||
feature_map_w = shape_in[3]
|
||||
block_size_k = CUBE_MKN[in_dtype]['mac'][1]
|
||||
fmap_shape_nc1hwc0 = (batch_size, (in_channel + block_size_k - 1) // block_size_k,
|
||||
feature_map_h, feature_map_w, block_size_k)
|
||||
|
||||
out_channel = shape_w[0]
|
||||
in_channel_weight = shape_w[1]
|
||||
filter_h = shape_w[2]
|
||||
filter_w = shape_w[3]
|
||||
block_size_k = CUBE_MKN[w_dtype]['mac'][1]
|
||||
block_size_n = CUBE_MKN[w_dtype]['mac'][2]
|
||||
filter_shape_frac_z = (in_channel_weight * filter_h * filter_w // block_size_k,
|
||||
out_channel // block_size_n, block_size_n, block_size_k)
|
||||
|
||||
with tvm.target.cce():
|
||||
data = tvm.placeholder(
|
||||
fmap_shape_nc1hwc0, name='Fmap', dtype=in_dtype)
|
||||
weight = tvm.placeholder(
|
||||
filter_shape_frac_z, name='Filter', dtype=w_dtype)
|
||||
bias_tensor = None
|
||||
scale_q = None
|
||||
scale_dq = None
|
||||
scale_rq = None
|
||||
offset_pad = None
|
||||
offset_rq = None
|
||||
offset_q = None
|
||||
scale_drq = None
|
||||
|
||||
# bias or fusion_bias(half offset)
|
||||
if bias or (quantize_config[1] == 1 and quantize_turn_on):
|
||||
bias_tensor = tvm.placeholder(
|
||||
(out_channel,), name='bias_tensor', \
|
||||
dtype="int32" if quantize_turn_on else res_dtype)
|
||||
|
||||
# quantize on
|
||||
if quantize_turn_on:
|
||||
quantize_algorithm = quantize_config[1]
|
||||
if is_quantize:
|
||||
scale_q = tvm.placeholder(
|
||||
(CUBE_MKN[scale_q_dtype]['mac'][1],), name='scaleQ', dtype=scale_q_dtype)
|
||||
if quantize_algorithm == 1:
|
||||
offset_q = tvm.placeholder(
|
||||
(CUBE_MKN[offset_q_dtype]['mac'][1],), name='offsetQ', dtype=offset_q_dtype)
|
||||
|
||||
if is_dequantize:
|
||||
scale_dq_shape = (CUBE_MKN[scale_dq_dtype]['mac'][1],) if quantize_config[2] == 0 \
|
||||
else (out_channel,)
|
||||
scale_dq = tvm.placeholder(
|
||||
scale_dq_shape, name='scaleDq', dtype=scale_dq_dtype)
|
||||
|
||||
if is_requantize:
|
||||
scale_rq_shape = (CUBE_MKN[scale_rq_dtype]['mac'][1],) if quantize_config[2] == 0 \
|
||||
else (out_channel,)
|
||||
scale_rq = tvm.placeholder(
|
||||
scale_rq_shape, name='scaleRq', dtype=scale_rq_dtype)
|
||||
if quantize_algorithm == 1:
|
||||
offset_rq_shape = (CUBE_MKN[offset_rq_dtype]['mac'][1],)
|
||||
offset_rq = tvm.placeholder(
|
||||
offset_rq_shape, name='offsetRq', dtype=offset_rq_dtype)
|
||||
|
||||
# need offset_pad , for half offset
|
||||
if quantize_algorithm == 1:
|
||||
offset_pad = tvm.placeholder(
|
||||
(CUBE_MKN[offset_pad_dtype]['mac'][1],), name='offset_pad',
|
||||
dtype=offset_pad_dtype)
|
||||
|
||||
if quantize_algorithm == 0:
|
||||
if is_quantize:
|
||||
if is_dequantize:
|
||||
scale_drq = scale_dq
|
||||
else:
|
||||
scale_drq = scale_rq
|
||||
|
||||
conv_res = te.lang.cce.conv(
|
||||
data, weight, {"bias_tensor": bias_tensor,
|
||||
"scale_q": scale_q,
|
||||
"offset_q": offset_q,
|
||||
"scale_drq": scale_drq,
|
||||
"offset_pad": offset_pad,
|
||||
"offset_rq": offset_rq,
|
||||
"quantize_config": quantize_config,
|
||||
"is_quantize": is_quantize,
|
||||
"is_dequantize": is_dequantize,
|
||||
"is_requantize": is_requantize,
|
||||
"scale_sqrt": scale_sqrt,
|
||||
"pad_h": padh, "pad_w": padw,
|
||||
"stride_h": strideh, "stride_w": stridew,
|
||||
"filter_h": filter_h, "filter_w": filter_w,
|
||||
"res_dtype": res_dtype, "mad_dtype": mad_dtype},
|
||||
dsl_flag=False)
|
||||
if bias:
|
||||
tensor_list = [data, weight, bias_tensor, scale_q,
|
||||
scale_drq, conv_res]
|
||||
else:
|
||||
tensor_list = [data, weight, scale_q,
|
||||
scale_drq, conv_res]
|
||||
else:
|
||||
if is_dequantize:
|
||||
scale_drq = scale_dq
|
||||
else:
|
||||
scale_drq = scale_rq
|
||||
conv_res = te.lang.cce.conv(
|
||||
data, weight, {"bias_tensor": bias_tensor,
|
||||
"scale_q": scale_q,
|
||||
"offset_q": offset_q,
|
||||
"scale_drq": scale_drq,
|
||||
"offset_pad": offset_pad,
|
||||
"offset_rq": offset_rq,
|
||||
"quantize_config": quantize_config,
|
||||
"is_quantize": is_quantize,
|
||||
"is_dequantize": is_dequantize,
|
||||
"is_requantize": is_requantize,
|
||||
"scale_sqrt": scale_sqrt,
|
||||
"pad_h": padh, "pad_w": padw,
|
||||
"stride_h": strideh, "stride_w": stridew,
|
||||
"filter_h": filter_h, "filter_w": filter_w,
|
||||
"res_dtype": res_dtype, "mad_dtype": mad_dtype},
|
||||
dsl_flag=False)
|
||||
if bias:
|
||||
tensor_list = [data, weight, bias_tensor,
|
||||
scale_drq, conv_res]
|
||||
else:
|
||||
tensor_list = [data, weight,
|
||||
scale_drq, conv_res]
|
||||
|
||||
# half offset
|
||||
else:
|
||||
if is_quantize:
|
||||
if is_dequantize:
|
||||
scale_drq = scale_dq
|
||||
else:
|
||||
scale_drq = scale_rq
|
||||
conv_res = te.lang.cce.conv(
|
||||
data, weight, {"bias_tensor": bias_tensor,
|
||||
"scale_q": scale_q,
|
||||
"offset_q": offset_q,
|
||||
"scale_drq": scale_drq,
|
||||
"offset_pad": offset_pad,
|
||||
"offset_rq": offset_rq,
|
||||
"quantize_config": quantize_config,
|
||||
"is_quantize": is_quantize,
|
||||
"is_dequantize": is_dequantize,
|
||||
"is_requantize": is_requantize,
|
||||
"scale_sqrt": scale_sqrt,
|
||||
"pad_h": padh, "pad_w": padw,
|
||||
"stride_h": strideh, "stride_w": stridew,
|
||||
"filter_h": filter_h, "filter_w": filter_w,
|
||||
"res_dtype": res_dtype, "mad_dtype": mad_dtype},
|
||||
dsl_flag=False)
|
||||
if is_dequantize:
|
||||
tensor_list = [data, weight, bias_tensor, scale_q, offset_q,
|
||||
scale_drq, offset_pad, conv_res]
|
||||
else:
|
||||
tensor_list = [data, weight, bias_tensor, scale_q, offset_q,
|
||||
scale_drq, offset_rq, offset_pad, conv_res]
|
||||
else:
|
||||
if is_dequantize:
|
||||
scale_drq = scale_dq
|
||||
else:
|
||||
scale_drq = scale_rq
|
||||
conv_res = te.lang.cce.conv(
|
||||
data, weight, {"bias_tensor": bias_tensor,
|
||||
"scale_q": scale_q,
|
||||
"offset_q": offset_q,
|
||||
"scale_drq": scale_drq,
|
||||
"offset_pad": offset_pad,
|
||||
"offset_rq": offset_rq,
|
||||
"quantize_config": quantize_config,
|
||||
"is_quantize": is_quantize,
|
||||
"is_dequantize": is_dequantize,
|
||||
"is_requantize": is_requantize,
|
||||
"scale_sqrt": scale_sqrt,
|
||||
"pad_h": padh, "pad_w": padw,
|
||||
"stride_h": strideh, "stride_w": stridew,
|
||||
"filter_h": filter_h, "filter_w": filter_w,
|
||||
"res_dtype": res_dtype, "mad_dtype": mad_dtype},
|
||||
dsl_flag=False)
|
||||
if is_dequantize:
|
||||
tensor_list = [data, weight, bias_tensor,
|
||||
scale_drq, offset_pad, conv_res]
|
||||
else:
|
||||
tensor_list = [data, weight, bias_tensor,
|
||||
scale_drq, offset_rq, offset_pad, conv_res]
|
||||
else:
|
||||
conv_res = te.lang.cce.conv(
|
||||
data, weight, {"bias_tensor": bias_tensor,
|
||||
"scale_q": scale_q,
|
||||
"offset_q": offset_q,
|
||||
"scale_drq": scale_drq,
|
||||
"offset_pad": offset_pad,
|
||||
"offset_rq": offset_rq,
|
||||
"quantize_config": quantize_config,
|
||||
"is_quantize": is_quantize,
|
||||
"is_dequantize": is_dequantize,
|
||||
"is_requantize": is_requantize,
|
||||
"scale_sqrt": scale_sqrt,
|
||||
"pad_h": padh, "pad_w": padw,
|
||||
"stride_h": strideh, "stride_w": stridew,
|
||||
"filter_h": filter_h, "filter_w": filter_w,
|
||||
"res_dtype": res_dtype, "mad_dtype": mad_dtype},
|
||||
dsl_flag=False)
|
||||
if bias:
|
||||
tensor_list = [data, weight, bias_tensor, conv_res]
|
||||
else:
|
||||
tensor_list = [data, weight, conv_res]
|
||||
sch = generic.auto_schedule(conv_res)
|
||||
|
||||
config = {
|
||||
"print_ir": need_print,
|
||||
"need_build": need_build,
|
||||
"name": kernel_name,
|
||||
"tensor_list": tensor_list
|
||||
}
|
||||
|
||||
te.lang.cce.cce_build_code(sch, config)
|
||||
|
|
@ -1,180 +0,0 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
import te.lang.cce
|
||||
from te import tvm
|
||||
from te.platform import CUBE_MKN
|
||||
from topi import generic
|
||||
from topi.cce import util
|
||||
|
||||
# pylint: disable=R0913,R0914,R0915,E1101
|
||||
# the dim of shape in conv must be 4
|
||||
PAD_SHAPE_DIM = 2
|
||||
|
||||
NoneType = type(None)
|
||||
|
||||
|
||||
@util.check_input_type((list, tuple), (list, tuple), str, str, str,
|
||||
(list, int), (list, int), int, int, bool, str)
|
||||
def conv_layer_fast_cce_para_check(shape_in, shape_w, in_dtype, w_dtype, res_dtype,
|
||||
padh, padw, strideh, stridew, bias, kernel_name):
|
||||
# conv shape check
|
||||
util.check_kernel_name(kernel_name)
|
||||
|
||||
# conv data type check
|
||||
util.check_dtype_rule(in_dtype, ['float16'])
|
||||
util.check_dtype_rule(w_dtype, ['float16'])
|
||||
util.check_dtype_rule(res_dtype, ['float16'])
|
||||
|
||||
if not isinstance(bias, bool):
|
||||
raise RuntimeError("bias dtype should be bool.")
|
||||
|
||||
if isinstance(padh, list):
|
||||
if len(padh) != PAD_SHAPE_DIM:
|
||||
raise RuntimeError("Dimension must be %d when padh is a list." % PAD_SHAPE_DIM)
|
||||
pad_top = padh[0]
|
||||
pad_bottom = padh[1]
|
||||
else:
|
||||
pad_top = padh
|
||||
pad_bottom = padh
|
||||
|
||||
if isinstance(padw, list):
|
||||
if len(padw) != PAD_SHAPE_DIM:
|
||||
raise RuntimeError("Dimension must be %d when padw is a list." % PAD_SHAPE_DIM)
|
||||
pad_left = padw[0]
|
||||
pad_right = padw[1]
|
||||
else:
|
||||
pad_left = padw
|
||||
pad_right = padw
|
||||
|
||||
shape_in, shape_w = te.lang.cce.check_conv_shape(shape_in, shape_w, pad_top, pad_bottom,
|
||||
pad_left, pad_right, strideh, stridew,
|
||||
in_dtype, w_dtype, res_dtype)
|
||||
|
||||
return shape_in, shape_w
|
||||
|
||||
|
||||
@util.check_input_type((list, tuple), (list, tuple), str, str, str,
|
||||
(list, int), (list, int), int, int,
|
||||
bool, str, bool, bool)
|
||||
def conv_layer_fast_cce(shape_in, shape_w, in_dtype, w_dtype, res_dtype,
|
||||
padh, padw, strideh, stridew, bias=False,
|
||||
kernel_name="cce_conv",
|
||||
need_build=False, need_print=False):
|
||||
"""
|
||||
|
||||
Parameters
|
||||
----------
|
||||
shape_in : shape of data_in
|
||||
|
||||
shape_w : shape of filter
|
||||
|
||||
in_dtype : the feature map data type
|
||||
|
||||
w_dtype : the weight data type
|
||||
|
||||
res_dtype : the result data type
|
||||
|
||||
padh: the padding shape in H
|
||||
|
||||
padw: the padding shape in weight
|
||||
|
||||
strideh: the stride value in H
|
||||
|
||||
stridew: the stride value in weight
|
||||
|
||||
bias: the tag for bias or not
|
||||
|
||||
kernel_name : cce kernel name, default value is "cce_conv"
|
||||
|
||||
need_buid : if need to build CCEC kernel, default value is False
|
||||
|
||||
need_print : if need to print the ir, default value is False
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
|
||||
"""
|
||||
in_dtype = in_dtype.lower()
|
||||
w_dtype = w_dtype.lower()
|
||||
res_dtype = res_dtype.lower()
|
||||
|
||||
shape_in = list(shape_in)
|
||||
shape_w = list(shape_w)
|
||||
|
||||
shape_in, shape_w = conv_layer_fast_cce_para_check(shape_in, shape_w, in_dtype, w_dtype, res_dtype,
|
||||
padh, padw, strideh, stridew, bias, kernel_name)
|
||||
|
||||
batch_size = shape_in[0]
|
||||
in_channel = shape_in[1]
|
||||
feature_map_h = shape_in[2]
|
||||
feature_map_w = shape_in[3]
|
||||
block_size_k = CUBE_MKN[in_dtype]['mac'][1]
|
||||
fmap_shape_nc1hwc0 = (batch_size, (in_channel + block_size_k - 1) // block_size_k,
|
||||
feature_map_h, feature_map_w, block_size_k)
|
||||
|
||||
out_channel = shape_w[0]
|
||||
in_channel_weight = shape_w[1]
|
||||
filter_h = shape_w[2]
|
||||
filter_w = shape_w[3]
|
||||
block_size_k = CUBE_MKN[w_dtype]['mac'][1]
|
||||
block_size_n = CUBE_MKN[w_dtype]['mac'][2]
|
||||
filter_shape_frac_z = (in_channel_weight * filter_h * filter_w // block_size_k,
|
||||
out_channel // block_size_n, block_size_n, block_size_k)
|
||||
|
||||
with tvm.target.cce():
|
||||
data = tvm.placeholder(
|
||||
fmap_shape_nc1hwc0, name='Fmap', dtype=in_dtype)
|
||||
weight = tvm.placeholder(
|
||||
filter_shape_frac_z, name='Filter', dtype=w_dtype)
|
||||
bias_tensor = None
|
||||
|
||||
if bias:
|
||||
bias_tensor = tvm.placeholder(
|
||||
(out_channel,), name='bias_tensor', dtype=res_dtype)
|
||||
|
||||
mad_dtype = "float16"
|
||||
|
||||
conv_res = te.lang.cce.conv(
|
||||
data, weight, {"bias_tensor": bias_tensor,
|
||||
"scale_q": None,
|
||||
"offset_q": None,
|
||||
"scale_drq": None,
|
||||
"offset_pad": None,
|
||||
"offset_rq": None,
|
||||
"quantize_config": [0, 0, 0],
|
||||
"is_quantize": False,
|
||||
"is_dequantize": False,
|
||||
"is_requantize": False,
|
||||
"scale_sqrt": [0, 0, 0],
|
||||
"pad_h": padh, "pad_w": padw,
|
||||
"stride_h": strideh, "stride_w": stridew,
|
||||
"filter_h": filter_h, "filter_w": filter_w,
|
||||
"res_dtype": res_dtype, "mad_dtype": mad_dtype},
|
||||
dsl_flag=False)
|
||||
if bias:
|
||||
tensor_list = [data, weight, bias_tensor, conv_res]
|
||||
else:
|
||||
tensor_list = [data, weight, conv_res]
|
||||
sch = generic.auto_schedule(conv_res)
|
||||
|
||||
config = {
|
||||
"print_ir": need_print,
|
||||
"need_build": need_build,
|
||||
"name": kernel_name,
|
||||
"tensor_list": tensor_list
|
||||
}
|
||||
|
||||
te.lang.cce.cce_build_code(sch, config)
|
||||
|
|
@ -1,153 +0,0 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
import math
|
||||
import numpy as np
|
||||
from functools import reduce
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore._checkparam import ParamValidator as validator
|
||||
from mindspore._checkparam import Rel, check_bool, check_int_positive, twice
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
|
||||
|
||||
|
||||
class Cus_Conv2D(PrimitiveWithInfer):
|
||||
r"""
|
||||
Applies 2D convolution for the input.
|
||||
|
||||
Input is typically of shape :math:`(N, C, H, W)`, where :math:`N` is batch size and :math:`C` is channel number.
|
||||
For each batch of shape :math:`(C, H, W)` the formula (given mode 1) is defined as:
|
||||
|
||||
.. math::
|
||||
out_j = \sum_{i=0}^{C_{in} - 1} ccor(W_{ij}, X_i) + b_j,
|
||||
|
||||
where :math:`ccor` is cross correlation operator, :math:`C_{in}` is the input channel number, :math:`j` ranges
|
||||
from :math:`0` to :math:`C_{out} - 1`, :math:`W_{ij}` corresponds to i-th channel of the j-th filter and
|
||||
:math:`out_{j}` corresponds to the j-th channel of the output.
|
||||
|
||||
The first introduction can be found in paper `Gradient Based Learning Applied to Document Recognition
|
||||
<http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf>`_.
|
||||
|
||||
More detailed introduction can be found here: http://cs231n.github.io/convolutional-networks/.
|
||||
|
||||
Args:
|
||||
out_channel (int): The dimensionality of the output space.
|
||||
kernel_size (Union[int, tuple[int]]): The kernel size of the 2D convolution.
|
||||
mode (int): 0 Math convolutiuon, 1 cross-correlation convolution ,
|
||||
2 deconvolution, 3 depthwise convolution. Default: 1.
|
||||
pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
|
||||
pad (int): The pad value to fill. Default: 0.
|
||||
stride (int): The stride to apply conv filter. Default: 1.
|
||||
dilation (int): Specifying the dilation rate to use for dilated convolution. Default: 1.
|
||||
group (int): Split input into groups. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, the value that applied 2D convolution.
|
||||
|
||||
Inputs:
|
||||
- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
|
||||
|
||||
Outputs:
|
||||
Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
|
||||
"""
|
||||
|
||||
@prim_attr_register
|
||||
def __init__(self,
|
||||
out_channel,
|
||||
kernel_size,
|
||||
mode=1,
|
||||
pad_mode="valid",
|
||||
pad=0,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
group=1):
|
||||
"""init Conv2D"""
|
||||
self.init_prim_io_names(inputs=['x', 'w'], outputs=['output'])
|
||||
self.kernel_size = kernel_size
|
||||
self.kernel_size = validator.check_type('kernel_size', kernel_size, (int, tuple))
|
||||
if isinstance(self.kernel_size, int):
|
||||
self.kernel_size = (self.kernel_size, self.kernel_size)
|
||||
validator.check_integer('length of kernel_size', len(self.kernel_size), 2, Rel.GE)
|
||||
validator.equal('type of pad', type(pad), 'not bool', not isinstance(pad, bool))
|
||||
validator.equal('type of pad', type(pad), 'int', isinstance(pad, int))
|
||||
self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'])
|
||||
self.pad = validator.check_pad_value_by_mode(self.__class__.__name__, pad_mode, pad)
|
||||
if self.pad_mode == 'pad':
|
||||
validator.check_integer('pad', self.pad, 0, Rel.GE)
|
||||
|
||||
self.mode = validator.check_integer('mode', mode, 1, Rel.EQ)
|
||||
self.add_prim_attr('data_format', "NCHW")
|
||||
self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT)
|
||||
self.group = validator.check_integer('group', group, 0, Rel.GT)
|
||||
self.dilation = validator.check_integer('dilation', dilation, 1, Rel.GE)
|
||||
validator.check_type('kernel_size', kernel_size, [int, tuple])
|
||||
if isinstance(kernel_size, int) and kernel_size < 1:
|
||||
raise ValueError('Attr \'kernel_size\' of \'Conv2D\' Op passed '
|
||||
+ str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
|
||||
if isinstance(kernel_size, tuple) and (len(kernel_size) != 2 or
|
||||
(not isinstance(kernel_size[0], int)) or
|
||||
(not isinstance(kernel_size[1], int)) or
|
||||
kernel_size[0] < 1 or kernel_size[1] < 1):
|
||||
raise ValueError('Attr \'kernel_size\' of \'Conv2D\' Op passed '
|
||||
+ str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
|
||||
self.stride = validator.check_integer('stride', stride, 1, Rel.GE)
|
||||
from conv2d_impl import Cus_Conv2D
|
||||
|
||||
def infer_shape(self, x_shape, w_shape):
|
||||
validator.check_integer("weight_shape", len(w_shape), 4, Rel.EQ)
|
||||
validator.check_integer("x_shape", len(x_shape), 4, Rel.EQ)
|
||||
validator.check_param_equal("x_shape[1]", x_shape[1] // self.group, "w_shape[1]", w_shape[1])
|
||||
validator.check_param_equal('out_channel', self.out_channel, 'w_shape[0]', w_shape[0])
|
||||
validator.check_param_equal('kernel_size', self.kernel_size, 'w_shape[2:4]', tuple(w_shape[2:4]))
|
||||
|
||||
kernel_size_h = w_shape[2]
|
||||
kernel_size_w = w_shape[3]
|
||||
|
||||
if self.pad_mode == "valid":
|
||||
h_out = math.ceil((x_shape[2] - kernel_size_h + 1) / self.stride)
|
||||
w_out = math.ceil((x_shape[3] - kernel_size_w + 1) / self.stride)
|
||||
pad_top, pad_bottom, pad_left, pad_right = 0, 0, 0, 0
|
||||
elif self.pad_mode == "same":
|
||||
h_out = math.ceil(x_shape[2] / self.stride)
|
||||
w_out = math.ceil(x_shape[3] / self.stride)
|
||||
|
||||
pad_needed_h = max(0, (h_out - 1) * self.stride + kernel_size_h - x_shape[2])
|
||||
pad_top = math.floor(pad_needed_h / 2)
|
||||
pad_bottom = pad_needed_h - pad_top
|
||||
|
||||
pad_needed_w = max(0, (w_out - 1) * self.stride + kernel_size_w - x_shape[3])
|
||||
pad_left = math.floor(pad_needed_w / 2)
|
||||
pad_right = pad_needed_w - pad_left
|
||||
elif self.pad_mode == 'pad':
|
||||
pad_top, pad_bottom, pad_left, pad_right = self.pad, self.pad, self.pad, self.pad
|
||||
|
||||
h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (self.dilation - 1)) \
|
||||
/ self.stride
|
||||
w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (self.dilation - 1)) \
|
||||
/ self.stride
|
||||
h_out = math.floor(h_out)
|
||||
w_out = math.floor(w_out)
|
||||
|
||||
self.pad_list = [pad_top, pad_bottom, pad_left, pad_right]
|
||||
self.add_prim_attr('pad_list', (pad_top, pad_bottom, pad_left, pad_right))
|
||||
|
||||
out_channel = self.out_channel
|
||||
out_shape = [x_shape[0], out_channel, h_out, w_out]
|
||||
return out_shape
|
||||
|
||||
def infer_dtype(self, x_dtype, w_dtype):
|
||||
args = {'x_dtype': x_dtype, 'w_dtype': w_dtype}
|
||||
validator.check_type_same(args, [mstype.int8, mstype.int32, mstype.float16, mstype.float32])
|
||||
return x_dtype
|
||||
|
|
@ -1,54 +0,0 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
import numpy as np
|
||||
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common.api import ms_function
|
||||
from mindspore.common.initializer import initializer
|
||||
from mindspore.common.parameter import Parameter
|
||||
from .cus_conv2d import Cus_Conv2D
|
||||
|
||||
context.set_context(device_target="Ascend")
|
||||
|
||||
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
out_channel = 64
|
||||
kernel_size = 7
|
||||
self.conv = Cus_Conv2D(out_channel,
|
||||
kernel_size,
|
||||
mode=1,
|
||||
pad_mode="valid",
|
||||
pad=0,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
group=1)
|
||||
self.w = Parameter(initializer(
|
||||
'normal', [64, 3, 7, 7]), name='w')
|
||||
|
||||
@ms_function
|
||||
def construct(self, x):
|
||||
return self.conv(x, self.w)
|
||||
|
||||
|
||||
def test_net():
|
||||
np.random.seed(3800)
|
||||
x = np.random.randn(32, 3, 224, 224).astype(np.float32)
|
||||
conv = Net()
|
||||
output = conv(Tensor(x))
|
||||
print(output.asnumpy())
|
||||
Loading…
Reference in New Issue