feature_extractor_models/base/heads.py

import torch.nn as nn
from .modules import Activation


class SegmentationHead(nn.Sequential):
    def __init__(
        self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1
    ):
        conv2d = nn.Conv2d(
            in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2
        )
        upsampling = (
            nn.UpsamplingBilinear2d(scale_factor=upsampling)
            if upsampling > 1
            else nn.Identity()
        )
        activation = Activation(activation)
        super().__init__(conv2d, upsampling, activation)


class ClassificationHead(nn.Sequential):
    def __init__(
        self, in_channels, classes, pooling="avg", dropout=0.2, activation=None
    ):
        if pooling not in ("max", "avg"):
            raise ValueError(
                "Pooling should be one of ('max', 'avg'), got {}.".format(pooling)
            )
        pool = nn.AdaptiveAvgPool2d(1) if pooling == "avg" else nn.AdaptiveMaxPool2d(1)
        flatten = nn.Flatten()
        dropout = nn.Dropout(p=dropout, inplace=True) if dropout else nn.Identity()
        linear = nn.Linear(in_channels, classes, bias=True)
        activation = Activation(activation)
        super().__init__(pool, flatten, dropout, linear, activation)