forked from mindspore-Ecosystem/mindspore
add test case of Bprop and tester
add test case for grad concat fix usage in test framework fix testcase format code
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268d358a1d
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@ -65,32 +65,11 @@ class IthOutputCell(nn.Cell):
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self.output_index = output_index
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def construct(self, *inputs):
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raise NotImplementedError
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def construct1(self, x1):
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predict = self.network(x1)[self.output_index]
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return predict
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def construct2(self, x1, x2):
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predict = self.network(x1, x2)[self.output_index]
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return predict
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def construct3(self, x1, x2, x3):
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predict = self.network(x1, x2, x3)[self.output_index]
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return predict
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def construct4(self, x1, x2, x3, x4):
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predict = self.network(x1, x2, x3, x4)[self.output_index]
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return predict
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def construct5(self, x1, x2, x3, x4, x5):
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predict = self.network(x1, x2, x3, x4, x5)[self.output_index]
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predict = self.network(*inputs)[self.output_index]
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return predict
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def get_output_cell(network, num_input, output_index, training=True):
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net = IthOutputCell(network, output_index)
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f = getattr(net, 'construct%d' % num_input)
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setattr(net, "construct", f)
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set_block_training(net, training)
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return net
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@ -24,11 +24,14 @@ from mindspore.common import dtype as mstype
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context.set_context(mode=context.GRAPH_MODE)
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def test_net_vargs_expand():
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class AddNet(Cell):
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def __init__(self):
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super(AddNet, self).__init__()
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self.w = Parameter(Tensor(np.ones((3, 4, 5), np.float32)), "w2", requires_grad=True)
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self.w = Parameter(
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Tensor(np.ones((3, 4, 5), np.float32)), "w2", requires_grad=True)
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def construct(self, x, y):
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return x + y
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x = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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@ -37,22 +40,59 @@ def test_net_vargs_expand():
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net = AddNet()
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out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
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class VarNet(Cell):
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def __init__(self, net):
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super(VarNet, self).__init__()
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self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
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self.w = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
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self.b = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
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self.w = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
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self.net = net
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def construct(self, *args):
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return self.net(*args)*self.w + self.b
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class SecondNet(Cell):
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def __init__(self):
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super(SecondNet, self).__init__()
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self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
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self.b2 = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
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def construct(self, *args):
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res = args[0] + args[1]
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return res + self.b2
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class Bprop(Cell):
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def __init__(self, func, wrt_params, params, grad_op, sens=None):
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super(Bprop, self).__init__(auto_prefix=False)
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self.func = func
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self.wrt_params = wrt_params
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self.params = None
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if self.wrt_params and params:
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self.params = ParameterTuple(params)
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self.grad = grad_op
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self.with_sens = False
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self.sens = sens
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if sens:
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self.sens = Tensor(sens, dtype=mstype.float32)
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self.with_sens = True
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def construct(self, *inputs):
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# pylint: disable=no-else-return
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if self.wrt_params:
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if self.with_sens:
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return self.grad(self.func, self.params)(*inputs, self.sens)
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else:
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return self.grad(self.func, self.params)(*inputs)
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elif self.with_sens:
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return self.grad(self.func)(*inputs, self.sens)
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else:
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return self.grad(self.func)(*inputs)
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def test_all_var_args_grad_with_sens():
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""""test grad_by_list_with_sens with all var args input"""
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class GradNet(Cell):
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@ -60,6 +100,7 @@ def test_all_var_args_grad_with_sens():
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_by_list_with_sens(self.net, self.weights)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -69,12 +110,14 @@ def test_all_var_args_grad_with_sens():
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grad_net = GradNet(net)
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out = grad_net(x, y, sens)
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def test_grad_list_var_args():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_by_list(self.net, self.weights)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -83,12 +126,14 @@ def test_grad_list_var_args():
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grad_net = GradNet(net)
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out = grad_net(x, y)
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def test_grad_all_var_args():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_all(self.net)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -97,12 +142,14 @@ def test_grad_all_var_args():
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grad_net = GradNet(net)
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out = grad_net(x, y)
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def test_grad_all_var_args_with_sens():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_all_with_sens(self.net)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -112,12 +159,14 @@ def test_grad_all_var_args_with_sens():
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grad_net = GradNet(net)
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out = grad_net(x, y, sens)
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def test_grad_var_args_with_sens():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_with_sens(self.net)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -127,27 +176,34 @@ def test_grad_var_args_with_sens():
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grad_net = GradNet(net)
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out = grad_net(x, y, sens)
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def test_var_args_grad():
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class VarNet(Cell):
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def __init__(self, net):
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super(VarNet, self).__init__()
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self.b = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
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self.b = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
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self.net = net
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def construct(self, *args):
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return self.net(*args) + self.b
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class SecondNet(Cell):
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def __init__(self):
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super(SecondNet, self).__init__()
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self.b2 = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
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self.b2 = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
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def construct(self, *args):
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res = args[0] + args[1]
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return res + self.b2
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class GradNet(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, sens):
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return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -164,12 +220,14 @@ def test_var_args_positional():
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def __init__(self, net):
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super(VarNet, self).__init__()
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self.net = net
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def construct(self, x, y):
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return self.net(x, y)*x
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class SecondNet(Cell):
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def __init__(self):
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super(SecondNet, self).__init__()
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def construct(self, *args):
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return args[0] + args[1]
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@ -178,6 +236,7 @@ def test_var_args_positional():
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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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return C.grad_all(self.net)(x, y)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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@ -185,3 +244,71 @@ def test_var_args_positional():
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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out = grad_net(x, y)
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def test_grad_within_if_else():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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grad_op = C.GradOperation(
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name='grad', get_all=False, get_by_list=True, sens_param=True)
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self.grad = Bprop(self.net, True, self.weights, grad_op, 1.0)
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def construct(self, *inputs):
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return self.grad(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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sens = Tensor(1.0, dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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out = grad_net(x, y)
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print("test_grad_var_args_with_sens out=", out)
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def test_grad_for_concat():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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grad_op = C.GradOperation(
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name='grad', get_all=True, get_by_list=False, sens_param=True)
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self.grad = Bprop(self.net, False, self.weights, grad_op)
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def construct(self, *inputs):
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return self.grad(*inputs)
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class Concat(Cell):
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def __init__(self, axis):
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super().__init__()
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self.concat = P.Concat(axis=axis)
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def construct(self, *input1):
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return self.concat(input1)
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class ConcatFactory:
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def __init__(self, input_shape, axis, dtype=np.float32):
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super(ConcatFactory, self).__init__()
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self.inputs_np = []
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for s in input_shape:
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self.inputs_np.append(np.random.randn(*s).astype(dtype))
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self.axis = axis
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self.out_numpy = np.concatenate(self.inputs_np, axis=self.axis)
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self.out_grad_np = self.out_numpy
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def grad_mindspore_impl(self):
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inputs = []
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for i in self.inputs_np:
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inputs.append(Tensor(i))
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net = Concat(axis=self.axis)
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grad_net = GradNet(net)
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grad_net.set_train()
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input_grad = grad_net(*inputs, Tensor(self.out_grad_np))
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def grad_cmp(self):
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input_grad_mindspore = self.grad_mindspore_impl()
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fact = ConcatFactory(input_shape=(
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(2, 184320, 1), (2, 46080, 1), (2, 11520, 1), (2, 2880, 1), (2, 720, 1)), axis=1)
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fact.grad_cmp()
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