vuron/adept: Платформа для обучения и вывода нейронных сетей, с API приближенным к PyTorch.

apps/python/resnet/resnet.py
175 строк5 KB
kolkir
Fix memroy acces in batchnorm, update resnet training script
08 мар 2025, 13:46
08 мар 2025, 13:461476fed
import adept.nn as nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):
        super(Bottleneck, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel=1, stride=1, padding=0)
        self.batch_norm1 = nn.BatchNorm2d(out_channels)

        self.conv2 = nn.Conv2d(
            out_channels, out_channels, kernel=3, stride=stride, padding=1
        )
        self.batch_norm2 = nn.BatchNorm2d(out_channels)

        self.conv3 = nn.Conv2d(
            out_channels,
            out_channels * self.expansion,
            kernel=1,
            stride=1,
            padding=0,
        )
        self.batch_norm3 = nn.BatchNorm2d(out_channels * self.expansion)

        self.i_downsample = i_downsample
        self.stride = stride

    def forward(self, x):
        identity = x.clone()

        x = self.conv1(x)
        x = self.batch_norm1(x)
        x = nn.relu(x)

        x = self.conv2(x)
        x = self.batch_norm2(x)
        x = nn.relu(x)

        x = self.conv3(x)
        x = self.batch_norm3(x)

        if self.i_downsample is not None:
            identity = self.i_downsample(identity)

        x += identity
        x = nn.relu(x)

        return x


class Block(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):
        super(Block, self).__init__()

        self.conv1 = nn.Conv2d(
            in_channels,
            out_channels,
            kernel=3,
            padding=1,
            stride=stride,
            bias=False,
        )
        self.batch_norm1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(
            out_channels,
            out_channels,
            kernel=3,
            padding=1,
            stride=1,
            bias=False,
        )
        self.batch_norm2 = nn.BatchNorm2d(out_channels)

        self.i_downsample = i_downsample
        self.stride = stride

    def forward(self, x):
        identity = x.clone()

        x = nn.relu(self.batch_norm1(self.conv1(x)))
        x = self.batch_norm2(self.conv2(x))

        if self.i_downsample is not None:
            identity = self.i_downsample(identity)

        x += identity
        x = nn.relu(x)
        return x


class ResNet(nn.Module):
    def __init__(self, ResBlock, layer_list, num_classes, num_channels=3):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(
            num_channels, 64, kernel=7, stride=2, padding=3, bias=False
        )
        self.batch_norm1 = nn.BatchNorm2d(64)
        self.max_pool = nn.MaxPool2d(kernel=3, stride=2, padding=1)

        self.layer1 = self._make_layer(ResBlock, layer_list[0], planes=64)
        self.layer2 = self._make_layer(ResBlock, layer_list[1], planes=128, stride=2)
        self.layer3 = self._make_layer(ResBlock, layer_list[2], planes=256, stride=2)
        self.layer4 = self._make_layer(ResBlock, layer_list[3], planes=512, stride=2)

        # TODO: implement AdaptiveAvgPool2d to make tensor dimensions N,C,1,1
        # self.avgpool = nn.AdaptiveAvgPool2d((1,1))

        self.fc = nn.Linear(512 * ResBlock.expansion, num_classes)

    def forward(self, x):
        x = nn.relu(self.batch_norm1(self.conv1(x)))
        x = self.max_pool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        # TODO: here must be adaptive avg pool to make tensor dimensions N,C,1,1 for all spatial dimensions
        # x = self.avgpool(x)

        # x = [N,2048,1,1]
        x = x.squeeze(3).squeeze(2)
        x = self.fc(x.unsqueeze(1))

        return x

    def _make_layer(self, ResBlock, blocks, planes, stride=1):
        ii_downsample = None
        layers = []

        if stride != 1 or self.in_channels != planes * ResBlock.expansion:
            ii_downsample = nn.Sequential(
                nn.Conv2d(
                    self.in_channels,
                    planes * ResBlock.expansion,
                    kernel=1,
                    stride=stride,
                ),
                nn.BatchNorm2d(planes * ResBlock.expansion),
            )

        layers.append(
            ResBlock(
                self.in_channels, planes, i_downsample=ii_downsample, stride=stride
            )
        )
        self.in_channels = planes * ResBlock.expansion

        for i in range(blocks - 1):
            layers.append(ResBlock(self.in_channels, planes))

        return nn.Sequential(*layers)


def ResNet18(num_classes, channels=3):
    return ResNet(Block, [2, 2, 2, 2], num_classes, channels)


def ResNet50(num_classes, channels=3):
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes, channels)


def ResNet101(num_classes, channels=3):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes, channels)


def ResNet152(num_classes, channels=3):
    return ResNet(Bottleneck, [3, 8, 36, 3], num_classes, channels)