forked from mindspore-Ecosystem/mindspore
255 lines
8.1 KiB
Python
255 lines
8.1 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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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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from mindspore.common.parameter import Parameter, ParameterTuple
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grad_all = C.GradOperation(get_all=True)
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grad_by_list = C.GradOperation(get_by_list=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 TwoInputBPropOperator(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.Mul()
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self.add = P.Add()
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def construct(self, x, y):
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return self.op(x, y)
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def bprop(self, x, y, out, dout):
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return self.add(5, x), self.add(y, 9)
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class BPropOperatatorNet(nn.Cell):
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def __init__(self, mul_size):
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super().__init__()
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mul_np = np.full(mul_size, 0.1, dtype=np.float32)
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floordiv_np = np.full(mul_size, 0.1, dtype=np.float32)
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self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
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self.floordiv_weight = Parameter(Tensor(floordiv_np), name="floordiv_weight")
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self.mul = TwoInputBPropOperator()
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self.floor_div = P.FloorDiv()
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self.bn = nn.BatchNorm1d(num_features=96)
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def construct(self, inputs):
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x = self.mul(inputs, self.mul_weight)
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x = self.floor_div(x, self.floordiv_weight)
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x = self.bn(x)
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return x
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def test_user_defined_bprop_with_u():
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net = BPropOperatatorNet(mul_size=(128, 96))
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grad_net = TestUserDefinedBpropGradNet(net)
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x = Tensor(np.random.randn(128, 96).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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# True and False branch of switch have different number of parameters.
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def test_if_branch_with_different_params():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
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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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self.weight1 = Parameter(Tensor(np.array([1.0], dtype=np.float32)), name="weight1")
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self.weight2 = Parameter(Tensor(np.array([2.0], dtype=np.float32)), name="weight2")
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def construct(self, idx, end, x):
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out = x
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if idx < end:
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out = out + self.weight1 * self.weight2
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else:
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out = out + self.weight1
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return out
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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self.weights = ParameterTuple(net.trainable_params())
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def construct(self, idx, end, x):
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return grad_by_list(self.net, self.weights)(idx, end, x)
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idx = Tensor(np.array((0), dtype=np.int32))
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end = Tensor(np.array((3), dtype=np.int32))
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x = Tensor(np.array([2.0], dtype=np.float32))
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net = Net()
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grad_net = GradNet(net)
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grad_net(idx, end, x)
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# Only lift fv in scope of lift_top_func_graph other than all func_graphs inside manager.
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# Otherwise, "Illegal AnfNode for evaluating" may be reported
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# because weight1 in Net may use old_parameter other than replicated one.
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def test_limit_lift_fv_scope():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
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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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self.weight1 = Parameter(Tensor(np.array([1.0], dtype=np.float32)), name="weight1")
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def construct(self, x, y):
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def inner_add(a, b):
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return a + b
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out = inner_add(x, y) + self.weight1
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return out
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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self.weights = ParameterTuple(net.trainable_params())
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def construct(self, x, y):
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def inner_grad_add(a, b):
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return a + b
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d_weight = grad_by_list(self.net, self.weights)(x, y)[0]
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d_out = inner_grad_add(d_weight, y)
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return d_out
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x = Tensor(np.array([2.0], dtype=np.float32))
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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 = GradNet(net)
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grad_net.add_flags_recursive(defer_inline=True)
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grad_net(x, y)
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