forked from mindspore-Ecosystem/mindspore
413 lines
12 KiB
Python
413 lines
12 KiB
Python
# 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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"""Generate vm_impl function for nn ops"""
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import numpy as np
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _grad_ops as G
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from mindspore.ops.vm_impl_registry import vm_impl_registry as vm_impl_getters
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from .vm_interface import vm
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# pylint: disable=unused-argument
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@vm_impl_getters.register(P.ScalarSummary)
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def vm_impl_scalar_summary(self):
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"""Generate vm_impl function for ScalarSummary"""
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def vm_impl(string_in, scalar):
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"""Implement by vm mode."""
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return scalar
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return vm_impl
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@vm_impl_getters.register(P.ReLU)
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def vm_impl_relu(self):
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"""Generate vm_impl function for ReLU"""
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def vm_impl(x):
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x = x.asnumpy()
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output = Tensor(vm.relu(x))
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return output
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return vm_impl
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@vm_impl_getters.register(P.Flatten)
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def vm_impl_flatten(self):
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"""Generate vm_impl function for Flatten"""
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def vm_impl(x):
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x = x.asnumpy()
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return Tensor(vm.flatten_batch(x))
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return vm_impl
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@vm_impl_getters.register(P.Softmax)
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def vm_impl_softmax(self):
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"""Generate vm_impl function for Softmax"""
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def vm_impl(x):
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x = x.asnumpy()
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return Tensor(vm.softmax(x))
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return vm_impl
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@vm_impl_getters.register(P.LogSoftmax)
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def vm_impl_log_softmax(self):
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"""Generate vm_impl function for LogSoftmax"""
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def vm_impl(x):
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x = x.asnumpy()
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return Tensor(vm.logsoftmax(x))
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return vm_impl
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@vm_impl_getters.register(P.Tanh)
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def vm_impl_tanh(self):
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"""Generate vm_impl function for Tanh"""
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def vm_impl(x):
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x = x.asnumpy()
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return Tensor(vm.tanh(x))
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return vm_impl
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@vm_impl_getters.register(P.FusedBatchNorm)
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def vm_impl_fused_batch_norm(self):
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"""Generate vm_impl function for FusedBatchNorm"""
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def vm_impl(x, scale, b, mean, variance):
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# pylint: disable=unused-argument
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x = x.asnumpy()
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scale = scale.asnumpy()
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b = b.asnumpy()
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mean = mean.asnumpy()
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variance = variance.asnumpy()
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out, x_mean, x_var, running_mean, running_var = vm.batch_norm(x, scale, b, mean, \
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variance, \
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eps=self.epsilon, \
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momentum=self.momentum)
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return Tensor(out), Tensor(x_mean), Tensor(x_var), \
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Tensor(running_mean), Tensor(running_var)
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return vm_impl
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@vm_impl_getters.register(P.BatchNorm)
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def vm_impl_batch_norm(self):
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"""Generate vm_impl function for BatchNorm"""
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def vm_impl(x, scale, b, mean, variance):
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# pylint: disable=unused-argument
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x = x.asnumpy()
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scale = scale.asnumpy()
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b = b.asnumpy()
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mean = mean.asnumpy()
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variance = variance.asnumpy()
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out, x_mean, x_var, running_mean, running_var = vm.batch_norm(x, scale, b, mean, \
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variance, \
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eps=self.epsilon)
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return Tensor(out), Tensor(x_mean), Tensor(x_var), \
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Tensor(running_mean), Tensor(running_var)
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return vm_impl
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@vm_impl_getters.register(P.Conv2D)
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def vm_impl_conv2d(self):
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"""Generate vm_impl function for Conv2D"""
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def vm_impl(x, w):
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x = x.asnumpy()
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weight = w.asnumpy()
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bias = None
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out = vm.conv2d(x, weight, bias, self.stride, self.pad, self.dilation)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(G.MaxPoolGradWithArgmax)
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def vm_impl_max_pool_grad_with_argmax(self):
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"""Generate vm_impl function for MaxPoolGradWithArgmax"""
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def vm_impl(x, dout, argmax):
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x = x.asnumpy()
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dout = dout.asnumpy()
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arg_max = argmax.asnumpy()
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dx = vm.max_pool_grad_with_argmax(x, dout, arg_max,
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self.ksize[1], self.ksize[2], self.strides[1])
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return Tensor(dx)
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return vm_impl
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@vm_impl_getters.register(P.MaxPoolWithArgmax)
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def vm_impl_max_pool_with_argmax(self):
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"""Generate vm_impl function for MaxPoolWithArgmax"""
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def vm_impl(x):
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x = x.asnumpy()
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out, out_argmax = vm.max_pool_with_argmax(x, self.ksize[1], self.ksize[2], self.strides[1])
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return Tensor(out), Tensor(out_argmax)
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return vm_impl
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@vm_impl_getters.register(P.MaxPool)
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def vm_impl_max_pool(self):
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"""Generate vm_impl function for MaxPool"""
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def vm_impl(x):
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x = x.asnumpy()
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out = vm.max_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2])
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(G.MaxPoolGrad)
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def vm_impl_max_pool_grad(self):
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"""Generate vm_impl function for MaxPoolGrad"""
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def vm_impl(x, out, dout):
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x = x.asnumpy()
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dout = dout.asnumpy()
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out = vm.max_pool_grad(x, dout, self.ksize[-2], self.ksize[-1], self.strides[-2])
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.AvgPool)
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def vm_impl_avg_pool(self):
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"""Generate vm_impl function for AvgPool"""
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def vm_impl(x):
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x = x.asnumpy()
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out = vm.avg_pooling(x, self.ksize[-2], self.ksize[-1], self.strides[-2])
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(G.AvgPoolGrad)
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def vm_impl_avg_pool_grad(self):
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"""Generate vm_impl function for AvgPoolGrad"""
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def vm_impl(dout, origin_shape):
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dout = dout.asnumpy()
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out = vm.avg_pool_grad(dout, origin_shape, self.ksize[-2], self.ksize[-1], self.strides[-2])
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(G.FusedBatchNormGrad)
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def vm_impl_fused_batch_norm_grad(self):
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"""Generate vm_impl function for FusedBatchNormGrad"""
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def vm_impl(dy, x, scale, save_mean, save_inv_variance):
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dy = dy.asnumpy()
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x = x.asnumpy()
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scale = scale.asnumpy()
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save_mean = save_mean.asnumpy()
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save_inv_variance = save_inv_variance.asnumpy()
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dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
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return (Tensor(dx), Tensor(dscale), Tensor(dshift))
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return vm_impl
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@vm_impl_getters.register(G.BatchNormGrad)
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def vm_impl_fused_batch_norm_grad(self):
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"""Generate vm_impl function for BatchNormGrad"""
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def vm_impl(dy, x, scale, save_mean, save_inv_variance):
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dy = dy.asnumpy()
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x = x.asnumpy()
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scale = scale.asnumpy()
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save_mean = save_mean.asnumpy()
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save_inv_variance = save_inv_variance.asnumpy()
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dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
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return (Tensor(dx), Tensor(dscale), Tensor(dshift))
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return vm_impl
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@vm_impl_getters.register(G.ReluGrad)
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def vm_impl_relu_grad(self):
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"""Generate vm_impl function for ReluGrad"""
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def vm_impl(y_backprop, x):
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x = x.asnumpy()
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y_backprop = y_backprop.asnumpy()
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y_backprop = vm.relu_grad(x.copy()) * y_backprop
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return Tensor(y_backprop)
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return vm_impl
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@vm_impl_getters.register(P.Conv2DBackpropInput)
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def vm_impl_conv2d_backprop_input(self):
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"""Generate vm_impl function for Conv2DBackpropInput"""
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def vm_impl(dout, w, x_size):
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dout = dout.asnumpy()
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w = w.asnumpy()
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dx = vm.conv2d_backprop_input(dout, x_size, w, self.stride, self.pad)
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return Tensor(dx)
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return vm_impl
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@vm_impl_getters.register(G.Conv2DBackpropFilter)
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def vm_impl_conv2d_backprop_filter(self):
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"""Generate vm_impl function for Conv2DBackpropFilter"""
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def vm_impl(dout, x, w_size):
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x = x.asnumpy()
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dout = dout.asnumpy()
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dw = vm.conv2d_backprop_filter(dout, x, w_size, self.stride, self.pad)
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return Tensor(dw)
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return vm_impl
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@vm_impl_getters.register(G.FlattenGrad)
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def vm_impl_flatten_grad(self):
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"""Generate vm_impl function for FlattenGrad"""
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def vm_impl(dout, x):
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dout = dout.asnumpy()
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dout = vm.flatten_grad(dout, x)
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return Tensor(dout)
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return vm_impl
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@vm_impl_getters.register(P.BiasAdd)
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def vm_impl_bias_add(self):
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"""Generate vm_impl function for BiasAdd"""
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def vm_impl(wx, bias):
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wx = wx.asnumpy()
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bias = bias.asnumpy()
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out = wx + bias
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(G.BiasAddGrad)
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def vm_impl_bias_add_grad(self):
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"""Generate vm_impl function for BiasAddGrad"""
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def vm_impl(dout):
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dout = dout.asnumpy()
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shape = np.shape(dout)
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return Tensor(np.add.reduce(dout, axis=tuple(range(len(shape) - 1))))
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return vm_impl
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@vm_impl_getters.register(P.SoftmaxCrossEntropyWithLogits)
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def vm_impl_softmax_cross_entropy_with_logits(self):
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"""Generate vm_impl function for SoftmaxCrossEntropyWithLogits"""
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def vm_impl(logits, labels):
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logits = logits.asnumpy()
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labels = labels.asnumpy()
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loss, dx = vm.softmax_cross_entropy_with_logits(logits, labels)
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return (Tensor(np.array(loss)), Tensor(dx))
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return vm_impl
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@vm_impl_getters.register(P.SparseSoftmaxCrossEntropyWithLogits)
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def vm_impl_sparse_softmax_cross_entropy_with_logits(self):
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"""Generate vm_impl function for SparseSoftmaxCrossEntropyWithLogits"""
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def vm_impl(logits, labels):
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logits = logits.asnumpy()
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labels = labels.asnumpy()
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n_class = labels.max() + 1
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n_sample = labels.shape[0]
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one_hot_label = np.zeros((n_sample, n_class)) # 3个样本，4个类别
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one_hot_label[:, labels] = 1 # 非零列赋值为1
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loss, dx = vm.softmax_cross_entropy_with_logits(logits, one_hot_label)
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if self.is_grad:
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return (Tensor(dx),)
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return (Tensor(np.array(loss)),)
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return vm_impl
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@vm_impl_getters.register(P.ApplyMomentum)
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def vm_impl_momentum(self):
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"""Generate vm_impl function for Momentum"""
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def vm_impl(variable,
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accumulation,
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learning_rate,
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gradient,
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momentum,
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use_nesterov=False):
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gradient = gradient.asnumpy()
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accumulation = accumulation.asnumpy()
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variable = variable.asnumpy()
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shape = accumulation.shape
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learning_rate = np.full(shape, learning_rate.asnumpy())
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momentum = np.full(shape, momentum.asnumpy())
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accumulation = accumulation * momentum + gradient
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if use_nesterov is True:
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variable -= gradient * learning_rate + accumulation * momentum * learning_rate
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else:
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variable -= accumulation * learning_rate
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return Tensor(variable)
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return vm_impl
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@vm_impl_getters.register(P.ResizeBilinear)
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def vm_impl_resize_bilinear(self):
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"""Generate vm_impl function for ResizeBilinear"""
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def vm_impl(x):
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out = vm.ResizeBilinear(x)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(G.ResizeBilinearGrad)
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def vm_impl_resize_bilinear_grad(self):
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"""Generate vm_impl function for ResizeBilinearGrad"""
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def vm_impl(dout, original_image):
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out = vm.ResizeBilinearGrad(dout, original_image)
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return Tensor(out)
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return vm_impl
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