external/cv/mmcv/cnn/alexnet.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import logging
from typing import Optional

import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint


class AlexNet(nn.Module):
    """AlexNet backbone.

    Args:
        num_classes (int): number of classes for classification.
    """

    def __init__(self, num_classes: int = -1):
        super().__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        if self.num_classes > 0:
            self.classifier = nn.Sequential(
                nn.Dropout(),
                nn.Linear(256 * 6 * 6, 4096),
                nn.ReLU(inplace=True),
                nn.Dropout(),
                nn.Linear(4096, 4096),
                nn.ReLU(inplace=True),
                nn.Linear(4096, num_classes),
            )

    def init_weights(self, pretrained: Optional[str] = None) -> None:
        if isinstance(pretrained, str):
            logger = logging.getLogger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif pretrained is None:
            # use default initializer
            pass
        else:
            raise TypeError('pretrained must be a str or None')

    def forward(self, x: torch.Tensor) -> torch.Tensor:

        x = self.features(x)
        if self.num_classes > 0:
            x = x.view(x.size(0), 256 * 6 * 6)
            x = self.classifier(x)

        return x