Python tensorflow.python.ops.io_ops.restore_v2() Examples
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Example #1
Source File: saver.py From lingvo with Apache License 2.0 | 6 votes |
def ReadNpArrays(file_prefix, nmap): """Reads from a tf checkpoint to fill in values of a NesteMap. Args: file_prefix: A TF checkpoint filename prefix. nmap: A NestedMap of numpy dtypes. Returns: A NestedMap with numpy arrays compatible w/ nmap. """ g = tf.Graph() with g.as_default(): reads = [] for name, dtype in nmap.FlattenItems(): reads.append( io_ops.restore_v2( prefix=file_prefix, tensor_names=[name], shape_and_slices=[""], dtypes=[dtype])[0]) with tf.Session(graph=g) as sess: vals = sess.run(reads) return nmap.Pack(vals)
Example #2
Source File: checkpoint_utils.py From lambda-packs with MIT License | 6 votes |
def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name="checkpoint_initializer"): """Overrides given variable's initialization op. Sets variable initializer to assign op that initializes variable from tensor's value in the checkpoint. Args: variable: `tf.Variable` object. ckpt_file: string, full path of the checkpoint. tensor_name: Name of the tensor to load from the checkpoint. slice_spec: Slice specification for loading partitioned tensors. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op) # pylint:disable=protected-access
Example #3
Source File: checkpoint_utils.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name="checkpoint_initializer"): """Overrides given variable's initialization op. Sets variable initializer to assign op that initializes variable from tensor's value in the checkpoint. Args: variable: `tf.Variable` object. ckpt_file: string, full path of the checkpoint. tensor_name: Name of the tensor to load from the checkpoint. slice_spec: Slice specification for loading partitioned tensors. name: Name of the operation. """ base_type = variable.dtype.base_dtype with ops.colocate_with(variable): restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op) # pylint:disable=protected-access
Example #4
Source File: saver.py From TF_Face_Toolbox with Apache License 2.0 | 6 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): tensors = [] for spec in saveable.specs: # Ignore the moving_mean and moving_variance in other towers. if spec.name.startswith('replicated_'): if not spec.name.startswith('replicated_0') and 'BatchNorm/moving_' in spec.name: continue tensors.append( io_ops.restore_v2( filename_tensor, ['/'.join(spec.name.split('/')[1:])], [spec.slice_spec], [spec.tensor.dtype])[0]) else: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors
Example #5
Source File: saver.py From deep_image_model with Apache License 2.0 | 5 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): """Create ops to restore 'saveable'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveable: A BaseSaverBuilder.SaveableObject object. preferred_shard: Int. Shard to open first when loading a sharded file. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ # pylint: disable=protected-access tensors = [] for spec in saveable.specs: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors # pylint: enable=unused-argument
Example #6
Source File: checkpoint_utils.py From keras-lambda with MIT License | 5 votes |
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec, name="checkpoint_initializer"): """Sets variable initializer to assign op form value in checkpoint's tensor. Args: variable: `Variable` object. file_pattern: string, where to load checkpoints from. tensor_name: Name of the `Tensor` to load from checkpoint reader. slice_spec: Slice specification for loading partitioned variables. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( file_pattern, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op)
Example #7
Source File: saver.py From keras-lambda with MIT License | 5 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): """Create ops to restore 'saveable'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveable: A BaseSaverBuilder.SaveableObject object. preferred_shard: Int. Shard to open first when loading a sharded file. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ # pylint: disable=protected-access tensors = [] for spec in saveable.specs: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors # pylint: enable=unused-argument
Example #8
Source File: saver.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): """Create ops to restore 'saveable'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveable: A BaseSaverBuilder.SaveableObject object. preferred_shard: Int. Shard to open first when loading a sharded file. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ # pylint: disable=protected-access tensors = [] for spec in saveable.specs: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors # pylint: enable=unused-argument
Example #9
Source File: checkpoint_utils.py From human-rl with MIT License | 5 votes |
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec, name="checkpoint_initializer"): """Sets variable initializer to assign op form value in checkpoint's tensor. Args: variable: `Variable` object. file_pattern: string, where to load checkpoints from. tensor_name: Name of the `Tensor` to load from checkpoint reader. slice_spec: Slice specification for loading partitioned variables. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( file_pattern, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op)
Example #10
Source File: checkpoint_utils.py From human-rl with MIT License | 5 votes |
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec, name="checkpoint_initializer"): """Sets variable initializer to assign op form value in checkpoint's tensor. Args: variable: `Variable` object. file_pattern: string, where to load checkpoints from. tensor_name: Name of the `Tensor` to load from checkpoint reader. slice_spec: Slice specification for loading partitioned variables. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( file_pattern, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op)
Example #11
Source File: saver.py From TF_Face_Toolbox with Apache License 2.0 | 5 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): tensors = [] for spec in saveable.specs: print(spec.name) tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors
Example #12
Source File: checkpoint_utils.py From deep_image_model with Apache License 2.0 | 5 votes |
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec, name="checkpoint_initializer"): """Sets variable initializer to assign op form value in checkpoint's tensor. Args: variable: `Variable` object. file_pattern: string, where to load checkpoints from. tensor_name: Name of the `Tensor` to load from checkpoint reader. slice_spec: Slice specification for loading partitioned variables. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( file_pattern, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op)
Example #13
Source File: base_input_generator.py From lingvo with Apache License 2.0 | 5 votes |
def _InputBatch(self): p = self.params @tf.function def ReadData(): x, y = io_ops.restore_v2(p.ckpt, [p.data, p.label], [''] * 2, [p.data_dtype, p.label_dtype]) # Always convert to float32. return tf.cast(x, tf.float32), tf.cast(y, tf.float32) # Loads data and label into memory and keep it around. data, label = ops.cached_call( f=ReadData.get_concrete_function(), T=[tf.float32, tf.float32]) b, shape = self.InfeedBatchSize(), list(p.data_shape) data = tf.reshape(data, [-1] + shape) label = tf.reshape(label, [-1]) label = py_utils.HasShape(label, [tf.shape(data)[0]]) sample_ids = ops.random_permutation_sequence( num=p.num_samples, batch=b, repeat=p.repeat, seed=p.random_seed if p.random_seed else 0) n = tf.shape(sample_ids)[0] raw = py_utils.PadOrTrimTo(tf.gather(data, sample_ids), [b] + shape) ret = py_utils.NestedMap( raw=raw, data=self._Preprocess(raw), label=py_utils.PadOrTrimTo(tf.gather(label, sample_ids), [b]), weight=py_utils.PadOrTrimTo(tf.ones([n], dtype=tf.float32), [b])) if not py_utils.use_tpu(): ret['sample_ids'] = sample_ids return ret
Example #14
Source File: checkpoint_utils.py From tpu_models with Apache License 2.0 | 5 votes |
def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name="checkpoint_initializer"): """Overrides given variable's initialization op. Sets variable initializer to assign op that initializes variable from tensor's value in the checkpoint. Args: variable: `tf.Variable` object. ckpt_file: string, full path of the checkpoint. tensor_name: Name of the tensor to load from the checkpoint. slice_spec: Slice specification for loading partitioned tensors. name: Name of the operation. """ base_type = variable.dtype.base_dtype # Do not colocate with variable since RestoreV2 op only runs on CPU and # colocation will force variable (and other ops that colocate with variable) # to be on CPU as well. It is okay to place the variable's initializer op on # CPU since it will only be run once at the start. with ops.device(variable.device), ops.device("/cpu:0"): restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] names_to_saveables = saveable_object_util.op_list_to_dict([variable]) saveable_objects = [] for name, op in names_to_saveables.items(): for s in saveable_object_util.saveable_objects_for_op(op, name): saveable_objects.append(s) assert len(saveable_objects) == 1 # Should be only one variable. init_op = saveable_objects[0].restore([restore_op], restored_shapes=None) # pylint:disable=protected-access variable._initializer_op = init_op restore_op.set_shape(variable.shape) variable._initial_value = restore_op # pylint:enable=protected-access
Example #15
Source File: checkpoint_utils.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec, name="checkpoint_initializer"): """Sets variable initializer to assign op form value in checkpoint's tensor. Args: variable: `Variable` object. file_pattern: string, where to load checkpoints from. tensor_name: Name of the `Tensor` to load from checkpoint reader. slice_spec: Slice specification for loading partitioned variables. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( file_pattern, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op)
Example #16
Source File: saver.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): """Create ops to restore 'saveable'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveable: A BaseSaverBuilder.SaveableObject object. preferred_shard: Int. Shard to open first when loading a sharded file. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ # pylint: disable=protected-access tensors = [] for spec in saveable.specs: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors # pylint: enable=unused-argument
Example #17
Source File: checkpoint_utils.py From lambda-packs with MIT License | 5 votes |
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec, name="checkpoint_initializer"): """Sets variable initializer to assign op form value in checkpoint's tensor. Args: variable: `Variable` object. file_pattern: string, where to load checkpoints from. tensor_name: Name of the `Tensor` to load from checkpoint reader. slice_spec: Slice specification for loading partitioned variables. name: Name of the operation. """ base_type = variable.dtype.base_dtype restore_op = io_ops.restore_v2( file_pattern, [tensor_name], [slice_spec], [base_type], name=name)[0] variable._initializer_op = state_ops.assign(variable, restore_op)
Example #18
Source File: saver.py From lambda-packs with MIT License | 5 votes |
def restore_op(self, filename_tensor, saveable, preferred_shard): """Create ops to restore 'saveable'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveable: A BaseSaverBuilder.SaveableObject object. preferred_shard: Int. Shard to open first when loading a sharded file. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ # pylint: disable=protected-access tensors = [] for spec in saveable.specs: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors # pylint: enable=unused-argument
Example #19
Source File: saver.py From lingvo with Apache License 2.0 | 5 votes |
def _BuildRestore(self): """Builds restore ops.""" assign_ops = [] for var in self._vars: val, = io_ops.restore_v2( prefix=self._restore_prefix_ph, tensor_names=[_VarKey(var)], shape_and_slices=[""], dtypes=[var.dtype]) assign_ops.append(var.assign(val)) self._restore_op = tf.group(*assign_ops)