未验证 提交 39adb22a 编写于 作者: R Ryan 提交者: GitHub

Add IntermediateLayerGetter (#47908)

上级 3125733a
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import paddle
from paddle.vision.models._utils import IntermediateLayerGetter
class TestBase:
def setUp(self):
self.init_model()
self.model.eval()
self.layer_names = [
(order, name)
for order, (name, _) in enumerate(self.model.named_children())
]
# choose two layer children of model randomly
self.start, self.end = sorted(
random.sample(self.layer_names, 2), key=lambda x: x[0]
)
self.return_layers_dic = {self.start[1]: "feat1", self.end[1]: "feat2"}
self.new_model = IntermediateLayerGetter(
self.model, self.return_layers_dic
)
def init_model(self):
self.model = None
@paddle.no_grad()
def test_inter_result(self):
inp = paddle.randn([1, 3, 80, 80])
inter_oup = self.new_model(inp)
for layer_name, layer in self.model.named_children():
if (isinstance(layer, paddle.nn.Linear) and inp.ndim == 4) or (
len(layer.sublayers()) > 0
and isinstance(layer.sublayers()[0], paddle.nn.Linear)
and inp.ndim == 4
):
inp = paddle.flatten(inp, 1)
inp = layer(inp)
if layer_name in self.return_layers_dic:
feat_name = self.return_layers_dic[layer_name]
self.assertTrue((inter_oup[feat_name] == inp).all())
class TestIntermediateLayerGetterResNet18(TestBase, unittest.TestCase):
def init_model(self):
self.model = paddle.vision.models.resnet18(pretrained=False)
class TestIntermediateLayerGetterDenseNet121(TestBase, unittest.TestCase):
def init_model(self):
self.model = paddle.vision.models.densenet121(pretrained=False)
class TestIntermediateLayerGetterVGG11(TestBase, unittest.TestCase):
def init_model(self):
self.model = paddle.vision.models.vgg11(pretrained=False)
class TestIntermediateLayerGetterMobileNetV3Small(TestBase, unittest.TestCase):
def init_model(self):
self.model = paddle.vision.models.MobileNetV3Small()
class TestIntermediateLayerGetterShuffleNetV2(TestBase, unittest.TestCase):
def init_model(self):
self.model = paddle.vision.models.shufflenet_v2_x0_25()
if __name__ == "__main__":
unittest.main()
......@@ -12,6 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from typing import Dict
import paddle
import paddle.nn as nn
def _make_divisible(v, divisor=8, min_value=None):
"""
......@@ -30,3 +36,73 @@ def _make_divisible(v, divisor=8, min_value=None):
if new_v < 0.9 * v:
new_v += divisor
return new_v
class IntermediateLayerGetter(nn.LayerDict):
"""
Layer wrapper that returns intermediate layers from a model.
It has a strong assumption that the layers have been registered into the model in the
same order as they are used. This means that one should **not** reuse the same nn.Layer
twice in the forward if you want this to work.
Additionally, it is only able to query sublayer that are directly assigned to the model.
So if `model` is passed, `model.feature1` can be returned, but not `model.feature1.layer2`.
Args:
model (nn.Layer): model on which we will extract the features
return_layers (Dict[name, new_name]): a dict containing the names of the layers for
which the activations will be returned as the key of the dict, and the value of the
dict is the name of the returned activation (which the user can specify).
Examples:
.. code-block:: python
import paddle
m = paddle.vision.models.resnet18(pretrained=False)
# extract layer1 and layer3, giving as names `feat1` and feat2`
new_m = paddle.vision.models._utils.IntermediateLayerGetter(m,
{'layer1': 'feat1', 'layer3': 'feat2'})
out = new_m(paddle.rand([1, 3, 224, 224]))
print([(k, v.shape) for k, v in out.items()])
# [('feat1', [1, 64, 56, 56]), ('feat2', [1, 256, 14, 14])]
"""
__annotations__ = {
"return_layers": Dict[str, str],
}
def __init__(self, model: nn.Layer, return_layers: Dict[str, str]) -> None:
if not set(return_layers).issubset(
[name for name, _ in model.named_children()]
):
raise ValueError("return_layers are not present in model")
orig_return_layers = return_layers
return_layers = {str(k): str(v) for k, v in return_layers.items()}
layers = OrderedDict()
for name, module in model.named_children():
layers[name] = module
if name in return_layers:
del return_layers[name]
if not return_layers:
break
super(IntermediateLayerGetter, self).__init__(layers)
self.return_layers = orig_return_layers
def forward(self, x):
out = OrderedDict()
for name, module in self.items():
if (isinstance(module, nn.Linear) and x.ndim == 4) or (
len(module.sublayers()) > 0
and isinstance(module.sublayers()[0], nn.Linear)
and x.ndim == 4
):
x = paddle.flatten(x, 1)
x = module(x)
if name in self.return_layers:
out_name = self.return_layers[name]
out[out_name] = x
return out
......@@ -17,7 +17,7 @@ import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
from ..ops import ConvNormActivation
from .utils import _make_divisible
from ._utils import _make_divisible
__all__ = []
......
......@@ -19,7 +19,7 @@ import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
from ..ops import ConvNormActivation
from .utils import _make_divisible
from ._utils import _make_divisible
__all__ = []
......
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