File size: 4,314 Bytes
2a13495 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
from typing import Optional, Union
from segmentation_models_pytorch.base import (
SegmentationHead,
SegmentationModel,
ClassificationHead,
)
from segmentation_models_pytorch.encoders import get_encoder
from .decoder import LinknetDecoder
class Linknet(SegmentationModel):
"""Linknet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder*
and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial
resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *sum*
for fusing decoder blocks with skip connections.
Note:
This implementation by default has 4 skip connections (original - 3).
Args:
encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
to extract features of different spatial resolution
encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features
two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
Default is 5
encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and
other pretrained weights (see table with available weights for each encoder_name)
decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
Available options are **True, False, "inplace"**
in_channels: A number of input channels for the model, default is 3 (RGB images)
classes: A number of classes for output mask (or you can think as a number of channels of output mask)
activation: An activation function to apply after the final convolution layer.
Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**,
**callable** and **None**.
Default is **None**
aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
on top of encoder if **aux_params** is not **None** (default). Supported params:
- classes (int): A number of classes
- pooling (str): One of "max", "avg". Default is "avg"
- dropout (float): Dropout factor in [0, 1)
- activation (str): An activation function to apply "sigmoid"/"softmax"
(could be **None** to return logits)
Returns:
``torch.nn.Module``: **Linknet**
.. _Linknet:
https://arxiv.org/abs/1707.03718
"""
def __init__(
self,
encoder_name: str = "resnet34",
encoder_depth: int = 5,
encoder_weights: Optional[str] = "imagenet",
decoder_use_batchnorm: bool = True,
in_channels: int = 3,
classes: int = 1,
activation: Optional[Union[str, callable]] = None,
aux_params: Optional[dict] = None,
):
super().__init__()
if encoder_name.startswith("mit_b"):
raise ValueError(
"Encoder `{}` is not supported for Linknet".format(encoder_name)
)
self.encoder = get_encoder(
encoder_name,
in_channels=in_channels,
depth=encoder_depth,
weights=encoder_weights,
)
self.decoder = LinknetDecoder(
encoder_channels=self.encoder.out_channels,
n_blocks=encoder_depth,
prefinal_channels=32,
use_batchnorm=decoder_use_batchnorm,
)
self.segmentation_head = SegmentationHead(
in_channels=32, out_channels=classes, activation=activation, kernel_size=1
)
if aux_params is not None:
self.classification_head = ClassificationHead(
in_channels=self.encoder.out_channels[-1], **aux_params
)
else:
self.classification_head = None
self.name = "link-{}".format(encoder_name)
self.initialize()
|