Revert "CNTK v2 library: API changes"
This reverts commit a0d6a0b661602a365b99bbf3fa2de5649431b26f.
This commit is contained in:
Amit Agarwal
Родитель
3499e05518
Коммит
60cf546948
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@ -623,7 +623,7 @@ namespace CNTK
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{
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assert(m_inputs.size() == 2);
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if (m_inputs[0].Shape().TotalSize() != m_inputs[1].Shape().TotalSize())
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InvalidArgument("ForwardBackward: Operands '%S' and '%S' shapes must have the same total size.", m_inputs[0].AsString().c_str(), m_inputs[1].AsString().c_str());
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InvalidArgument("ForwardBackward: The shapes of operands '%S' and '%S' must have the same total size.", m_inputs[0].AsString().c_str(), m_inputs[1].AsString().c_str());
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outputShape = {};
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break;
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@ -74,7 +74,7 @@ class MySgdFast(UserLearner):
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shape = result.shape
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static_tensor = result.data.slice_view([0]*len(shape),
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shape[2:])
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shape[1:])
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p.set_value(static_tensor)
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return True
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@ -418,3 +418,10 @@ def sanitize_dynamic_axes(axes):
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raise TypeError('type Axis expected, got %s instead' % type(ax))
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axes = tuple(reversed(axes))
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return axes
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def sanitize_variable_value_dict(var_value_dict):
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if len(var_value_dict) > 1:
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return var_value_dict
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else:
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return list(var_value_dict.values())[0]
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@ -9,7 +9,7 @@ import numpy as np
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import numbers
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from numbers import Number
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from . import sequence
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from .functions import CloneMethod, Function, load_model, UserFunction
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from .functions import CloneMethod, Function, load_model
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from .variables import Variable, Parameter, Constant
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from ..utils import get_data_type
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from cntk.internal import sanitize_input, sanitize_shape, sanitize_axis, sanitize_dynamic_axes
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@ -2506,8 +2506,8 @@ from cntk.device import use_default_device
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from cntk.axis import Axis
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@typemap
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def input(shape, dtype=np.float32, needs_gradient=False, is_sparse=False,
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dynamic_axes=[Axis.default_batch_axis()], name=''):
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def input_variable(shape, dtype=np.float32, needs_gradient=False, is_sparse=False,
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dynamic_axes=Axis.default_input_variable_dynamic_axes(), name=''):
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'''
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It creates an input in the network: a place where data,
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such as features and labels, should be provided.
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@ -2538,34 +2538,6 @@ def input(shape, dtype=np.float32, needs_gradient=False, is_sparse=False,
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return input_variable(shape, is_sparse, dtype, needs_gradient, name, dynamic_axes)
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@typemap
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def input_variable(shape, dtype=np.float32, needs_gradient=False, is_sparse=False,
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dynamic_axes=Axis.default_input_variable_dynamic_axes(), name=''):
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'''
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DEPRECATED.
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It creates an input in the network: a place where data,
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such as features and labels, should be provided.
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Args:
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shape (tuple or int): the shape of the input tensor
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dtype (type, optional): np.float32 (default) or np.float64
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needs_gradients (bool, optional): whether to back-propagates to it or not. False by default.
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is_sparse (bool, optional): whether the variable is sparse (`False` by default)
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dynamic_axes (list or tuple, default): a list of dynamic axis (e.g., batch axis, time axis)
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name (str, optional): the name of the Function instance in the network
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Returns:
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:class:`~cntk.ops.variables.Variable`
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'''
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import warnings
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warnings.warn('This will be removed in future versions. Please use '
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'input() or sequence.input() instead.', DeprecationWarning)
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if (type(dynamic_axes) in (list, tuple)) and (len(dynamic_axes) == 2):
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return sequence.input(shape, dtype, needs_gradient, is_sparse, dynamic_axes[1], name)
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else:
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return input(shape, dtype, needs_gradient, is_sparse, dynamic_axes, name)
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@typemap
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def output_variable(shape, dtype, dynamic_axes, name=''):
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'''
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@ -2595,16 +2567,16 @@ def output_variable(shape, dtype, dynamic_axes, name=''):
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return output_variable(shape, dtype, dynamic_axes, name)
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@typemap
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def forward_declaration(shape=None, dynamic_axes=None, name=''):
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def placeholder_variable(shape=None, dynamic_axes=None, name=''):
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'''
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It creates a forward declaration of an output variable to be used as a placeholder
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for a later output variable in the recurrent network. The forward declaration should be later
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replaced with the actual output variable it represents by calling bind_forward_declaration(s).
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It creates a placeholder variable that has to be later bound to an actual variable.
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A common use of this is to serve as a placeholder for a later output variable in a
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recurrent network, which is replaced with the actual output variable by calling
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replace_placeholder(s).
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Args:
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shape (tuple or int): the shape of the variable tensor
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dynamic_axes (list): the list of dynamic axes that the variable uses
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name (str, optional): the name of the forward declaration variable in the network
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dynamic_axes (list): the list of dynamic axes that the actual variable uses
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Returns:
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:class:`~cntk.ops.variables.Variable`
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@ -2622,29 +2594,6 @@ def forward_declaration(shape=None, dynamic_axes=None, name=''):
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dynamic_axes = sanitize_dynamic_axes(dynamic_axes)
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return placeholder_variable(shape, name, dynamic_axes)
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@typemap
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def placeholder_variable(shape=None, dynamic_axes=None, name=''):
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'''
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DEPRECATED.
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It creates a variable place holder for recurrence networks, when the network's dynamic axes
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are unfolded, the place holder will get assigned a variable along the correspondent dynamic axis.
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Args:
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shape (tuple or int): the shape of the variable tensor
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dynamic_axes (list): the list of dynamic axes that the actual variable uses
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name (str, optional): the name of the placeholder variable in the network
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Returns:
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:class:`~cntk.ops.variables.Variable`
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'''
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import warnings
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warnings.warn('This will be removed in future versions. Please use '
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'forward_declaration() instead.', DeprecationWarning)
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return forward_declaration(shape, dynamic_axes, name)
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@typemap
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def parameter(shape=None, init=None, dtype=None, device=None, name=''):
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'''
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@ -2,7 +2,7 @@ from cntk import cntk_py
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from cntk.device import DeviceDescriptor, cpu
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from cntk.utils import variable_value_to_seq, Record, \
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get_python_function_arguments, map_function_arguments
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from cntk.internal import map_if_possible, typemap, sanitize_var_map, sanitize_batch, sanitize_dtype_cntk, _as_tuple
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from cntk.internal import map_if_possible, typemap, sanitize_var_map, sanitize_batch, sanitize_dtype_cntk, _as_tuple, sanitize_variable_value_dict
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from cntk.ops.variables import Variable
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from enum import Enum, unique
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@ -511,11 +511,7 @@ class Function(cntk_py.Function):
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'''
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_, output_map = self.forward(arguments, self.outputs, device=device, as_numpy=as_numpy)
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if len(output_map) > 1:
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return output_map
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else:
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return list(output_map.values())[0]
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return sanitize_variable_value_dict(output_map)
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@typemap
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@ -723,17 +719,26 @@ class Function(cntk_py.Function):
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Args:
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at (dict) : mapping of the Function's arguments to values
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wrt (list optional): list of Variables with respect to which the
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wrt (list, default `None`): list of Variables with respect to which the
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gradient will be computed. If omitted, the gradients with
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respect to all arguments that need gradient will be computed.
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as_numpy (bool): whether to return the gradients as a NumPy array. Default True.
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respect to all arguments of this Function that need gradient will be computed.
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outputs (iterable, optional): outputs (including intermediate outputs in the graph)
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to fetch values for. If not specified, values for none of the outputs are fetched.
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device (:class:`~cntk.device.DeviceDescriptor`, default `None`): the device
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descriptor that contains the type and id of the device on which the
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computation is performed. If `None`, the default device is used.
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as_numpy (bool, default `True`): whether to return the gradients as a NumPy array. Default True.
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Specifying this as False returns a CNTK Value which avoids a
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costly conversion but returns a somewhat opaque object.
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Returns:
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dict or NumPy Array: Dict with keys of ``wrt`` variables and gradient values of
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``wrt`` variables. A single NumPy array if there is only one gradient value.
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Each element has the same shape as ``wrt`` including dynamic axes (such as the batch axis).
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dict or NumPy Array or a tuple of these: Dict with keys of ``wrt`` variables and gradient values of
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``wrt`` variables. A single NumPy array if there is only one gradient value.
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If ``outputs`` were specified (to fetch values for), this method returns a tuple where the 2nd element
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of the tuple is the ``outputs`` values; a dict with keys of specified ``outputs`` variables and
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values of computed ``outputs``, or a single NumPy array if there is only one output value.
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Each element has the same shape as the ``wrt`` or ``outputs`` variables including dynamic axes
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(such as the batch axis).
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'''
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if device is None:
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device = DeviceDescriptor.use_default_device()
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@ -757,17 +762,10 @@ class Function(cntk_py.Function):
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for k in wrt_map:
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wrt_map[k] = variable_value_to_seq(wrt_map[k], k)
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if len(wrt_map) > 1:
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ret_grad = wrt_map
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else:
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ret_grad = list(wrt_map.values())[0]
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if len(output_map) == 0:
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return ret_grad
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elif len(output_map) == 1:
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return ret_grad, list(output_map.values())[0]
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return sanitize_variable_value_dict(wrt_map)
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else:
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return ret_grad, output_map
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return sanitize_variable_value_dict(wrt_map), sanitize_variable_value_dict(output_map)
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@property
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@typemap
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@ -897,45 +895,9 @@ class Function(cntk_py.Function):
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'''
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return super(Function, self).uid()
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@typemap
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def bind_forward_declarations(self, bindings):
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'''
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In-place bind the specified forward declarations in the Function graph to the
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specified bindings in the map.
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Args:
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bindings (dict): map from forward declarations to binding variables
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Returns:
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:class:`Function`: itself
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'''
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bindings = bindings or {}
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if not isinstance(bindings, dict):
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raise TypeError("forward declaration bindings map must be a dictionary")
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return super(Function, self).replace_placeholders(bindings)
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@typemap
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def bind_forward_declaration(self, binding):
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'''
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In-place bind the only forward declaration in the Function graph with the
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specified binding.
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Args:
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binding (:class:`~cntk.ops.variables.Variable`): the variable
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that the forward declaration will be bound to
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Returns:
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:class:`Function`: itself
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:raises ExceptionType: when the function has multiple forward declarations.
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'''
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return super(Function, self).replace_placeholder(binding)
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@typemap
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def replace_placeholders(self, substitutions):
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'''
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DEPRECATED.
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In-place replace specified placeholders in the Function graph with the
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specified replacements in the map.
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@ -945,16 +907,14 @@ class Function(cntk_py.Function):
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Returns:
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:class:`Function`: itself
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'''
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import warnings
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warnings.warn('This will be removed in future versions. Please use '
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'bind_forward_declarations() instead.', DeprecationWarning)
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return self.bind_forward_declarations(substitutions)
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substitutions = substitutions or {}
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if not isinstance(substitutions, dict):
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raise TypeError("Variable substitution map must be a dictionary")
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return super(Function, self).replace_placeholders(substitutions)
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@typemap
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def replace_placeholder(self, substitution):
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'''
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DEPRECATED.
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In-place replace the only placeholder in the function graph with the
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specified substitution.
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@ -967,10 +927,7 @@ class Function(cntk_py.Function):
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:raises ExceptionType: when the function has multiple placeholders.
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'''
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import warnings
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warnings.warn('This will be removed in future versions. Please use '
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'bind_forward_declaration() instead.', DeprecationWarning)
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return self.bind_forward_declaration(substitution)
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return super(Function, self).replace_placeholder(substitution)
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@typemap
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def find_all_with_name(self, name):
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@ -3,35 +3,8 @@
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# for full license information.
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# ==============================================================================
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import numpy as np
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from ...utils import get_data_type
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from cntk.internal import typemap, sanitize_input
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from ...axis import Axis
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##########################################################################
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# variable ops
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##########################################################################
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@typemap
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def input(shape, dtype=np.float32, needs_gradient=False, is_sparse=False,
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sequence_axis=Axis.default_dynamic_axis(), name=''):
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'''
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It creates an input in the network: a place where data,
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such as features and labels, should be provided.
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Args:
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shape (tuple or int): the shape of the input tensor
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dtype (type, optional): np.float32 (default) or np.float64
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needs_gradients (bool, optional): whether to back-propagates to it or not. False by default.
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is_sparse (bool, optional): whether the variable is sparse (`False` by default)
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dynamic_axes (list or tuple, default): a list of dynamic axis (e.g., batch axis, time axis)
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name (str, optional): the name of the Function instance in the network
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Returns:
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:class:`~cntk.ops.variables.Variable`
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'''
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from ... import input
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return input(shape, dtype, needs_gradient, is_sparse, [Axis.default_batch_axis(), sequence_axis], name)
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##########################################################################
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# sequence ops
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