Spaces:
Runtime error
Runtime error
File size: 5,178 Bytes
231edce |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
from torch import nn
from typing import Optional
from ...base import (
SegmentationModel,
SegmentationHead,
ClassificationHead,
)
from ...encoders.create import create_encoder
from .decoder import DeepLabV3PlusDecoder
class DeepLabV3Plus(SegmentationModel):
"""DeepLabV3+ implementation from "Encoder-Decoder with Atrous Separable
Convolution for Semantic Image Segmentation"
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)
encoder_output_stride: Downsampling factor for last encoder features (see original paper for explanation)
decoder_atrous_rates: Dilation rates for ASPP module (should be a tuple of 3 integer values)
decoder_channels: A number of convolution filters in ASPP module. Default is 256
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**
upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity
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``: **DeepLabV3Plus**
Reference:
https://arxiv.org/abs/1802.02611v3
"""
def __init__(
self,
encoder_name: str,
encoder_params: dict = {"pretrained": True, "output_stride": 16},
decoder_channels: int = 256,
decoder_atrous_rates: tuple = (12, 24, 36),
dropout: float = 0.2,
in_channels: int = 3,
classes: int = 1,
deep_supervision: bool = False,
activation: Optional[str] = None,
upsampling: int = 4,
aux_params: Optional[dict] = None,
):
super().__init__()
encoder_output_stride = encoder_params.pop("output_stride", None)
if encoder_output_stride not in [8, 16, 32]:
raise ValueError("Encoder output stride should be 8, 16, or 32; got {}".format(encoder_output_stride))
self.encoder = create_encoder(
name=encoder_name,
encoder_params=encoder_params,
encoder_output_stride=encoder_output_stride,
in_channels=in_channels
)
self.decoder = DeepLabV3PlusDecoder(
encoder_channels=self.encoder.out_channels,
out_channels=decoder_channels,
atrous_rates=decoder_atrous_rates,
output_stride=encoder_output_stride,
deep_supervision=deep_supervision
)
self.segmentation_head = SegmentationHead(
in_channels=self.decoder.out_channels,
out_channels=classes,
kernel_size=1,
dropout=dropout,
upsampling=upsampling,
)
self.deep_supervision = deep_supervision
if self.deep_supervision:
self.supervisor_heads = []
self.supervisor_heads.append(
SegmentationHead(
in_channels=48,
out_channels=classes,
dropout=dropout,
kernel_size=3,
upsampling=1,
)
)
self.supervisor_heads.append(
SegmentationHead(
in_channels=decoder_channels,
out_channels=classes,
dropout=dropout,
kernel_size=3,
upsampling=1,
)
)
self.supervisor_heads = nn.Sequential(*self.supervisor_heads)
if aux_params is not None:
self.classification_head = ClassificationHead(in_channels=self.encoder.out_channels[-1], **aux_params)
else:
self.classification_head = None
|