In this guide, you will learn how to apply multi-scale feature extraction to the models in MindCV.
In real deep learning model projects, we often exploit classic CV backbones, such as ResNet, VGG, for the purposes of better performance and fast development. Generally, using only the final output of backbones is not enough.
We need outputs from intermediate layers, which act as multi-scale abstractions of the input, to help further boost the performance of our downstream tasks.
To this end, we have designed a mechanism for extracting multi-scale features from backbones in MindCV. At the time of composing this guide, MindCV has supported extracting features with this mechanism from ResNet, MobileNetV3, ConvNeXt, ResNeST, EfficientNet, RepVGG, HRNet, and ReXNet. For more details of the feature extraction mechanism, please refer to FeatureExtractWrapper.
This guide will help you learn how to add pieces of code to extracting multi-scale features from the rest of backbones. There are mainly two steps to achieve this:
- In
__init__()of a model, register the intermediate layers whose outputted feature needs to be extracted inself.feature_info. - Add a wrapper function for model creation.
- Pass
feature_only=Trueandout_indicestocreate_model().
There are mainly three possible scenarios when implementing code for feature extraction in MindCV, i.e.,
- a model with separate sequential module for each layer,
- a model with one sequential module for all layers, and
- a model with nonsequential modules.
An example of scenario 1 is shown as follows.
class DummyNet1(nn.Cell):
def __init__(self, **kwargs):
super().__init__()
# separate sequential module for each layer
self.layer1 = Layer()
self.layer2 = Layer()
self.layer3 = Layer()
self.layer4 = Layer()
def forward_features(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def construct(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
To implement feature extraction for this scenario, we add a member variable self.feature_info into __init__() to register the extractable layers, e.g.,
class DummyNet1(nn.Cell):
def __init__(self, **kwargs):
super().__init__()
self.feature_info = [] # for layer registration
self.layer1 = Layer()
self.feature_info.append(dict(chs=, reduction=, name=”layer1”)) # register layer
self.layer2 = Layer()
self.feature_info.append(dict(chs=, reduction=, name=”layer2”)) # register layer
self.layer3 = Layer()
self.feature_info.append(dict(chs=, reduction=, name=”layer3”)) # register layer
self.layer4 = Layer()
self.feature_info.append(dict(chs=, reduction=, name=”layer4”)) # register layer
As we can see above, self.feature_info is a list of dictionaries, each of which contains three key-value pairs. Specifically, chs denotes the channel number of the produced feature, reduction denotes the total stride at the current layer, and name indicates the name of this layer stored in the model parameters which can be found using get_parameters().
For a real example of this scenario, please refer to ResNet.
For some models, the layers are in one sequential module. An example of scenario 2 is shown as follows.
class DummyNet2(nn.Cell):
def __init__(self, **kwargs):
super().__init__()
layers = []
for i in range(4):
layers.append(Layer())
# the layers are in one sequential module
self.layers = nn.SequentialCell(layers)
def forward_features(self, x):
x = self.layers(x)
return x
def construct(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
To implement feature extraction for this scenario, we also need to add self.feature_info into __init__() as in scenario 1, as well as create a member variable self.flatten_sequential = True to indicate that the sequential module in this model needs to be flattened before extracting features, e.g.,
class DummyNet2(nn.Cell):
def __init__(self, **kwargs):
super().__init__()
self.feature_info = [] # for layer registration
self.flatten_sequential = True # indication of flattening the sequential module
layers = []
for i in range(4):
layers.append(Layer())
self.feature_info.append(dict(chs=, reduction=, name=f”layer{i}”)) # register layer
self.layers = nn.SequentialCell(layers)
Please be reminded that the order of the module instantiations in __init__() is very important. The order must be kept as same as the order that these modules are called in forward_features() and construct(). Furthermore, only the modules called in forward_features() and construct() should be instantiated as member variables with the type of nn.Cell. Otherwise, the feature extraction mechanism will not work.
For a real example of this scenario, please refer to MobileNetV3.
Layers in models sometimes are nonsequential modules. An example of scenario 3 is shown as follows.
class DummyNet3(nn.Cell):
def __init__(self, **kwargs):
super().__init__()
layer1 = []
for i in range(3):
layer1.append(Layer())
# layers in self.layer1 are not sequential
self.layer1 = nn.CellList(layer1)
self.stage1 = Stage()
layer2 = []
for i in range(3):
layer2.append(Layer())
# layers in self.layer2 are not sequential
self.layer2 = nn.CellList(layer2)
self.stage2 = Stage()
def forward_features(self, x):
x_list = []
# layers are parallel instead of sequential
for i in range(3):
x_list.append(self.layer1[i](x))
x = self.stage1(x_list)
x_list = []
# layers are parallel instead of sequential
for i in range(3):
x_list.append(self.layer2[i](x))
x = self.stage2(x_list)
return x
def construct(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
To implement feature extraction for this scenario, we need to first create a new model feature class by inheriting the original model class. Then, we add the self.feature_info and a member variable self.is_rewritten = True to indicate that this class is rewritten for feature extraction. Finally, we reimplement forward_features() and construct() with feature extraction logic. Here is an example.
class DummyFeatureNet3(DummyNet3):
def __init__(self, **kwargs):
super(DummyFeatureNet3, self).__init__(**kwargs)
self.feature_info = [] # for layer registration
self.is_rewritten = True # indication of rewriting for feature extraction
self.feature_info.append(dict(chs=, reduction=, name=”stage1”) # register layer
self.feature_info.append(dict(chs=, reduction=, name=”stage2”) # register layer
def forward_features(self, x): # reimplement feature extraction logic
out = []
x_list = []
for i in range(3):
x_list.append(self.layer1[i](x))
x = self.stage1(x_list)
out.append(x)
x_list = []
for i in range(3):
x_list.append(self.layer2[i](x))
x = self.stage2(x_list)
out.append(x)
return out
def construct(self, x):
x = self.forward_features(x)
return x
For a real example of this scenario, please refer to HRNet.
After adding layer registration, we need to add one more simple wrapper function for model creation, so that the model instance can be passed to build_model_with_cfg() for feature extraction.
Usually, the original creation function of a model in MindCV looks like this,
@register_model
def dummynet18(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> DummyNet:
default_cfg = default_cfgs["dummynet18"]
model = DummyNet(..., num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
As for the models falling into scenarios 1 & 2, in the wrapper function of model creation, simply pass the arguements to build_model_with_cfg(), e.g.,
def _create_dummynet(pretrained=False, **kwargs):
return build_model_with_cfg(DummyNet, pretrained, **kwargs)
@register_model
def dummynet18(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> DummyNet:
default_cfg = default_cfgs["dummynet18"]
model_args = dict(..., num_classes=num_classes, in_channels=in_channels, **kwargs)
return _create_dummynet(pretrained, **dict(default_cfg=default_cfg, **model_args))
As for the models falling into scenario 3, most part of the wrapper function is the same as the ones for scenarios 1 & 2. The difference lies in the part of deciding which model class to be instantiated. This is conditioned on feature_only, e.g.,
def _create_dummynet(pretrained=False, **kwargs):
if not kwargs.get("features_only", False):
return build_model_with_cfg(DummyNet3, pretrained, **kwargs)
else:
return build_model_with_cfg(DummyFeatureNet3, pretrained, **kwargs)
@register_model
def dummynet18(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> DummyNet:
default_cfg = default_cfgs["dummynet18"]
model_args = dict(..., num_classes=num_classes, in_channels=in_channels, **kwargs)
return _create_dummynet(pretrained, **dict(default_cfg=default_cfg, **model_args))
For real examples, please refer to ResNet and HRNet.
After the previous two steps are done, we can simply create the backbone that outputs the desired features by passing feature_only=True and out_indices to create_model(), e.g.,
from mindcv.models import create_model
backbone = create_model(
model_name="dummynet18",
features_only=True, # set features_only to be True
out_indices=[0, 1, 2], # specify the feature_info indices of the desired layers
)
In addtion, if we want to load a checkpoint into the backbone for feature extraction and this backbone falls into scenarios 2, we need to also set auto_mapping=True, e.g.,
from mindcv.models import create_model
backbone = create_model(
model_name="dummynet18",
checkpoint_path="/path/to/dummynet18.ckpt",
auto_mapping=True, # set auto_mapping to be True when loading a checkpoint for scenarios 2 models
features_only=True, # set features_only to be True
out_indices=[0, 1, 2], # specify the feature_info indices of the desired layers
)
Congradulations! Now you have learnt how to apply multi-scale feature extraction to the models in MindCV.