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@ -9,7 +9,7 @@ def grad_var_name(var_name):
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return var_name + "@GRAD"
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def create_op(scope, op_type, inputs, outputs, attrs=None):
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def create_op(scope, op_type, inputs, outputs, attrs):
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kwargs = dict()
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for in_name, in_dup in Operator.get_op_inputs(op_type):
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@ -17,7 +17,7 @@ def create_op(scope, op_type, inputs, outputs, attrs=None):
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kwargs[in_name] = []
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if in_dup:
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sub_in = inputs[in_name]
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for sub_in_name in sub_in:
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for sub_in_name, arr in sub_in:
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var = scope.new_var(sub_in_name)
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kwargs[in_name].append(sub_in_name)
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else:
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@ -29,15 +29,16 @@ def create_op(scope, op_type, inputs, outputs, attrs=None):
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kwargs[out_name] = []
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if out_dup:
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sub_in = outputs[out_name]
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for sun_in_name in sub_in:
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var = scope.new_var(sun_in_name)
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kwargs[out_name].append(sun_in_name)
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for sub_in_name, arr in sub_in:
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var = scope.new_var(sub_in_name)
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kwargs[out_name].append(sub_in_name)
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else:
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var = scope.new_var(out_name)
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kwargs[out_name].append(out_name)
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for attr_name in Operator.get_op_attr_names(op_type):
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kwargs[attr_name] = attrs[attr_name]
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if attr_name in attrs:
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kwargs[attr_name] = attrs[attr_name]
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return Operator(op_type, **kwargs)
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@ -46,10 +47,9 @@ def set_input(scope, op, inputs, place):
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if in_name in inputs:
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if in_dup:
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sub_in = inputs[in_name]
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for sub_in_name in sub_in:
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for sub_in_name, arr in sub_in:
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var = scope.find_var(sub_in_name)
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tensor = var.get_tensor()
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arr = sub_in[sub_in_name]
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tensor.set_dims(arr.shape)
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tensor.set(arr, place)
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else:
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@ -65,7 +65,7 @@ def set_output_grad(scope, op, outputs, place):
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if out_name in outputs:
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if out_dup:
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sub_out = outputs[out_name]
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for sub_out_name in sub_out:
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for sub_out_name, arr in sub_out:
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out_tensor = scope.find_var(sub_out_name).get_tensor()
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grad_tensor = scope.new_var(grad_var_name(
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sub_out_name)).get_tensor()
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@ -110,7 +110,7 @@ def get_numeric_gradient(scope,
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# we use a for loop to compute the gradient of every element.
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for i in xrange(tensor_size):
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if in_place:
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set_input(op, inputs, core.CPUPlace())
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set_input(scope, op, inputs, core.CPUPlace())
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# get one input element throw it's index i.
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origin = tensor_to_check.get_float_element(i)
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@ -120,7 +120,7 @@ def get_numeric_gradient(scope,
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y_pos = get_output()
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if in_place:
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set_input(op, inputs, core.CPUPlace())
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set_input(scope, op, inputs, core.CPUPlace())
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x_neg = origin - delta
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tensor_to_check.set_float_element(i, x_neg)
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@ -168,7 +168,11 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
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class OpTest(unittest.TestCase):
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def check_output_with_place(self, place):
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self.scope = core.Scope()
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self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
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op_inputs = self.inputs if hasattr(self, "inputs") else dict()
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op_outputs = self.outputs if hasattr(self, "outputs") else dict()
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op_attrs = self.attrs if hasattr(self, "attrs") else dict()
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self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
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op_attrs)
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if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
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return
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set_input(self.scope, self.op, self.inputs, place)
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@ -227,7 +231,11 @@ class OpTest(unittest.TestCase):
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in_place=False,
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max_relative_error=0.005):
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self.scope = core.Scope()
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self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
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op_inputs = self.inputs if hasattr(self, "inputs") else dict()
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op_outputs = self.outputs if hasattr(self, "outputs") else dict()
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op_attrs = self.attrs if hasattr(self, "attrs") else dict()
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self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
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op_attrs)
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if no_grad_set is None:
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no_grad_set = set()
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