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141 lines
4.3 KiB
141 lines
4.3 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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grad_all = C.GradOperation(get_all=True)
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class CropAndResizeNet(nn.Cell):
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def __init__(self, crop_size):
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super(CropAndResizeNet, self).__init__()
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self.crop_and_resize = P.CropAndResize()
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self.crop_size = crop_size
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def construct(self, x, boxes, box_indices):
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return self.crop_and_resize(x, boxes, box_indices, self.crop_size)
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def bprop(self, x, boxes, box_indices, out, dout):
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return x, boxes, box_indices
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class TestUserDefinedBpropNet(nn.Cell):
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def __init__(self, in_channel, out_channel):
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super(TestUserDefinedBpropNet, self).__init__()
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self.relu = nn.ReLU()
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self.conv = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=2, stride=1, has_bias=False,
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weight_init='ones', pad_mode='same')
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self.crop = CropAndResizeNet((10, 10))
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self.boxes = Tensor(np.ones((128, 4)).astype(np.float32))
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self.box_indices = Tensor(np.ones((128,)).astype(np.int32))
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def construct(self, x):
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x = self.relu(x)
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x = self.conv(x)
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x = self.crop(x, self.boxes, self.box_indices)
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return x
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class TestUserDefinedBpropGradNet(nn.Cell):
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def __init__(self, net):
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super(TestUserDefinedBpropGradNet, self).__init__()
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self.net = net
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def construct(self, x):
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return grad_all(self.net)(x)
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def test_user_defined_bprop():
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context.set_context(mode=context.GRAPH_MODE)
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net = TestUserDefinedBpropNet(3, 10)
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grad_net = TestUserDefinedBpropGradNet(net)
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x = Tensor(np.ones((128, 3, 12, 12)).astype(np.float32))
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grad_net(x)
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class SinNet(nn.Cell):
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def __init__(self):
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super(SinNet, self).__init__()
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self.sin = ops.Sin()
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def construct(self, x):
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out = self.sin(x)
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return out
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class SinGrad(nn.Cell):
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def __init__(self, network):
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super(SinGrad, self).__init__()
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self.grad = ops.GradOperation()
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self.network = network
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def construct(self, x):
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gout = self.grad(self.network)(x)
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return gout
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class SinGradSec(nn.Cell):
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def __init__(self, network):
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super(SinGradSec, self).__init__()
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self.grad = ops.GradOperation()
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self.network = network
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def construct(self, x):
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gout = self.grad(self.network)(x)
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return gout
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def test_second_grad_with_j_primitive():
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context.set_context(mode=context.GRAPH_MODE)
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net = SinNet()
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first_grad = SinGrad(net)
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second_grad = SinGradSec(first_grad)
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x = Tensor(np.array([1.0], dtype=np.float32))
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second_grad(x)
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# A CNode being used as FV is MapMorphism after MapMorphism of call-site CNode;
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def test_ad_fv_cnode_order():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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# cnode xay is not being MapMorphism when cnode second_level() is being MapMorphism and
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# BackPropagateFv as MapMorphism is started from output node and from left to right order.
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def construct(self, x, y):
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def first_level():
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xay = x + y
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def second_level():
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return xay
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return second_level() + xay
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return first_level()
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input_x = Tensor(np.array([1.0], dtype=np.float32))
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input_y = Tensor(np.array([2.0], dtype=np.float32))
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net = Net()
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net.add_flags_recursive(defer_inline=True)
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grad_net = grad_all(net)
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grad_net(input_x, input_y)
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