Refine Model of high level API (#25559)
* Refine Model 1. Take the network (instance of Layer) as the input of Model. 2. Refine set_dict/load_dict of Layer. 3. Refine Input interface, so update code sample about Inputfix_copy_if_different
qingqing01
5 years ago
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4152d39962
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b5f8784cab
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import six
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.parallel import ParallelEnv
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__all__ = ['set_device', ]
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# TODO(qingqing01): remove or refine _global_device, set_device and get_device
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# after core framework supporting these function.
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_global_device = None
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def set_device(device):
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"""
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Args:
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device (str): specify device type, 'cpu' or 'gpu'.
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Returns:
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fluid.CUDAPlace or fluid.CPUPlace: Created GPU or CPU place.
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Examples:
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.. code-block:: python
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import paddle.incubate.hapi as hapi
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input = hapi.set_device('gpu')
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"""
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assert isinstance(device, six.string_types) and device.lower() in ['cpu', 'gpu'], \
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"Expected device in ['cpu', 'gpu'], but got {}".format(device)
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device = fluid.CUDAPlace(ParallelEnv().dev_id) \
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if device.lower() == 'gpu' and fluid.is_compiled_with_cuda() \
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else fluid.CPUPlace()
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global _global_device
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_global_device = device
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return device
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def _get_device():
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"""
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Return global device.
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"""
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if _global_device is not None:
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device = _global_device
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else:
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if fluid.is_compiled_with_cuda():
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device = fluid.CUDAPlace(ParallelEnv().dev_id)
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else:
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device = fluid.CPUPlace()
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return device
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import warnings
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import paddle.fluid as fluid
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from paddle.fluid.framework import in_dygraph_mode
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from .device import _get_device
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def monkey_patch_layer():
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def load_dict(self,
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stat_dict,
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include_sublayers=True,
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use_structured_name=True):
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'''
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Set parameters from stat_dict. All the parameters will be reset by the
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tensor in the stat_dict
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This api will be Deprecated. Please use set_dict
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Parameters:
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state_dict(dict) : Dict contains all the parameters
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include_sublayers(bool, optional) : If true, also include the
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parameters from sublayers. Default: True
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use_structured_name(bool, optional) : If true, use structured name
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as key, otherwise, use parameter name as key. Default: True
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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with fluid.dygraph.guard():
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emb = fluid.dygraph.Embedding([10, 10])
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state_dict = emb.state_dict()
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fluid.save_dygraph( state_dict, "paddle_dy")
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para_state_dict, _ = fluid.load_dygraph( "paddle_dy")
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emb.load_dict( para_state_dict )
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'''
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def _check_match(key, param):
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state = stat_dict.get(key, None)
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if state is None:
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raise ValueError(
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"{} is not found in the providing file.".format(key))
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if list(state.shape) != list(param.shape):
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raise ValueError(
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"{} receives a shape {}, but the expected shape is {}.".
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format(key, list(state.shape), list(param.shape)))
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return param, state
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matched_param_state = []
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for key, param in self.state_dict().items():
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key_name = key if use_structured_name else param.name
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try:
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match_res = _check_match(key_name, param)
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except ValueError as err:
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warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
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matched_param_state.append(match_res)
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if in_dygraph_mode():
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for param, state in matched_param_state:
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param.set_value(state)
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else:
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def _set_var(var, ndarray):
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t = fluid.global_scope().find_var(var.name).get_tensor()
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p = t._place()
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if p.is_cpu_place():
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place = fluid.CPUPlace()
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elif p.is_cuda_pinned_place():
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place = fluid.CUDAPinnedPlace()
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else:
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p = fluid.core.Place()
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p.set_place(t._place())
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place = fluid.CUDAPlace(p.gpu_device_id())
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t.set(ndarray, place)
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executor = fluid.Executor(_get_device())._default_executor
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# restore parameter states
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fluid.core._create_loaded_parameter(
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[param for param, state in matched_param_state],
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fluid.global_scope(), executor)
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for param, state in matched_param_state:
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_set_var(param, state)
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setattr(fluid.dygraph.Layer, 'load_dict', load_dict)
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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import paddle.fluid as fluid
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import paddle.incubate.hapi.vision.models as models
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from paddle.incubate.hapi import Model, Input
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# test the predicted resutls of static graph and dynamic graph are equal
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# when used pretrained model
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class TestPretrainedModel(unittest.TestCase):
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def infer(self, x, arch, dygraph=True):
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if dygraph:
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fluid.enable_dygraph()
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net = models.__dict__[arch](pretrained=True, classifier_activation=None)
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inputs = [Input('image', [None, 3, 224, 224], 'float32')]
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model = Model(network=net, inputs=inputs)
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model.prepare()
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res = model.test_batch(x)
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if dygraph:
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fluid.disable_dygraph()
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return res
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def test_models(self):
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arches = ['mobilenet_v1', 'mobilenet_v2', 'resnet18']
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for arch in arches:
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x = np.array(np.random.random((2, 3, 224, 224)), dtype=np.float32)
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y_dygraph = self.infer(x, arch)
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y_static = self.infer(x, arch, dygraph=False)
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np.testing.assert_allclose(y_dygraph, y_static)
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if __name__ == '__main__':
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unittest.main()
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