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Improve EfficientNetSim docstring and type hints #254

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merged 3 commits into from
May 3, 2022
Merged

Improve EfficientNetSim docstring and type hints #254

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ca3003e
Fix typos in docstring
emla2805 Apr 28, 2022
2bd55a1
Remove reference to image augmentation
emla2805 Apr 28, 2022
eeeac36
Correct input image type annotation
emla2805 Apr 28, 2022
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20 changes: 9 additions & 11 deletions tensorflow_similarity/architectures/efficientnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,9 +44,8 @@
}


# Create an image augmentation pipeline.
def EfficientNetSim(
input_shape: Tuple[int],
input_shape: Tuple[int, int, int],
embedding_size: int = 128,
variant: str = "B0",
weights: str = "imagenet",
Expand All @@ -56,14 +55,13 @@ def EfficientNetSim(
pooling: str = "gem",
gem_p=3.0,
) -> SimilarityModel:
"""Build an EffecientNet Model backbone for similarity learning
"""Build an EfficientNet Model backbone for similarity learning

Architecture from [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)

Args:
input_shape: Size of the image input prior to augmentation,
must be bigger than the size of Effnet version you use. See below for
min input size.
input_shape: Size of the input image. Must match size of EfficientNet version you use.
See below for version input size.

embedding_size: Size of the output embedding. Usually between 64
and 512. Defaults to 128.
Expand All @@ -73,7 +71,7 @@ def EfficientNetSim(
weights: Use pre-trained weights - the only available currently being
imagenet. Defaults to "imagenet".

trainable: Make the EfficienNet backbone fully trainable or partially
trainable: Make the EfficientNet backbone fully trainable or partially
trainable.
- "full" to make the entire backbone trainable,
- "partial" to only make the last 3 block trainable
Expand All @@ -99,9 +97,9 @@ def EfficientNetSim(
The gem_p param sets the contrast amount on the pooling.

gem_p: Sets the power in the GeneralizedMeanPooling2D layer. A value
of 1.0 is equivelent to GlobalMeanPooling2D, while larger values
of 1.0 is equivalent to GlobalMeanPooling2D, while larger values
will increase the contrast between activations within each feature
map, and a value of math.inf will be equivelent to MaxPool2d.
map, and a value of math.inf will be equivalent to MaxPool2d.

Note:
EfficientNet expects images at the following size:
Expand Down Expand Up @@ -156,14 +154,14 @@ def build_effnet(
weights: Use pre-trained weights - the only available currently being
imagenet.

trainable: Make the EfficienNet backbone fully trainable or partially
trainable: Make the EfficientNet backbone fully trainable or partially
trainable.
- "full" to make the entire backbone trainable,
- "partial" to only make the last 3 block trainable
- "frozen" to make it not trainable.

Returns:
The ouptut layer of the efficientnet model
The output layer of the efficientnet model
"""

# init
Expand Down