diff --git a/ai_ghost.py b/ai_ghost.py new file mode 100644 index 0000000..ba78ffe --- /dev/null +++ b/ai_ghost.py @@ -0,0 +1,290 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = ['g_ghost_regnetx_002', 'g_ghost_regnetx_004', 'g_ghost_regnetx_006', 'g_ghost_regnetx_008', 'g_ghost_regnetx_016', 'g_ghost_regnetx_032', + 'g_ghost_regnetx_040', 'g_ghost_regnetx_064', 'g_ghost_regnetx_080', 'g_ghost_regnetx_120', 'g_ghost_regnetx_160', 'g_ghost_regnetx_320'] + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + +class Bottleneck(nn.Module): + expansion = 1 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, stride=1, downsample=None, group_width=1, + dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = planes * self.expansion + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, width // min(width, group_width), dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes) + self.bn3 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + +class LambdaLayer(nn.Module): + def __init__(self, lambd): + super(LambdaLayer, self).__init__() + self.lambd = lambd + + def forward(self, x): + return self.lambd(x) + +class Stage(nn.Module): + def __init__(self, block, inplanes, planes, group_width, blocks, stride=1, dilate=False, cheap_ratio=0.5): + super(Stage, self).__init__() + norm_layer = nn.BatchNorm2d + downsample = None + self.dilation = 1 + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes: + downsample = nn.Sequential( + conv1x1(inplanes, planes, stride), + norm_layer(planes), + ) + + self.base = block(inplanes, planes, stride, downsample, group_width, + self.dilation, norm_layer) + self.end = block(planes, planes, group_width=group_width, + dilation=self.dilation, + norm_layer=norm_layer) + + group_width = int(group_width * 0.75) + raw_planes = int(planes * (1 - cheap_ratio) / group_width) * group_width + cheap_planes = planes - raw_planes + self.cheap_planes = cheap_planes + self.raw_planes = raw_planes + + self.merge = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(planes+raw_planes*(blocks-2), cheap_planes, + kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(cheap_planes), + nn.ReLU(inplace=True), + nn.Conv2d(cheap_planes, cheap_planes, kernel_size=1, bias=False), + nn.BatchNorm2d(cheap_planes), + ) + self.cheap = nn.Sequential( + nn.Conv2d(cheap_planes, cheap_planes, + kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(cheap_planes), + ) + self.cheap_relu = nn.ReLU(inplace=True) + + layers = [] + downsample = nn.Sequential( + LambdaLayer(lambda x: x[:, :raw_planes]) + ) + + layers.append(block(raw_planes, raw_planes, 1, downsample, group_width, + self.dilation, norm_layer)) + inplanes = raw_planes + for _ in range(2, blocks-1): + layers.append(block(inplanes, raw_planes, group_width=group_width, + dilation=self.dilation, + norm_layer=norm_layer)) + + self.layers = nn.Sequential(*layers) + + def forward(self, input): + x0 = self.base(input) + + m_list = [x0] + e = x0[:, :self.raw_planes] + for l in self.layers: + e = l(e) + m_list.append(e) + m = torch.cat(m_list, 1) + m = self.merge(m) + + c = x0[:, self.raw_planes:] + c = self.cheap_relu(self.cheap(c) + m) + + x = torch.cat((e, c), 1) + x = self.end(x) + return x + +class GGhostRegNet(nn.Module): + def __init__(self, block, layers, widths, num_classes=1000, zero_init_residual=True, + group_width=1, replace_stride_with_dilation=None, + norm_layer=None): + super(GGhostRegNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 32 + self.dilation = 1 + if replace_stride_with_dilation is None: + replace_stride_with_dilation = [False, False, False, False] + if len(replace_stride_with_dilation) != 4: + raise ValueError("replace_stride_with_dilation should be None " + "or a 4-element tuple, got {}".format(replace_stride_with_dilation)) + self.group_width = group_width + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + + self.layer1 = self._make_layer(block, widths[0], layers[0], stride=2, + dilate=replace_stride_with_dilation[0]) + + self.inplanes = widths[0] + if layers[1] > 2: + self.layer2 = Stage(block, self.inplanes, widths[1], group_width, layers[1], stride=2, + dilate=replace_stride_with_dilation[1], cheap_ratio=0.5) + else: + self.layer2 = self._make_layer(block, widths[1], layers[1], stride=2, + dilate=replace_stride_with_dilation[1]) + + self.inplanes = widths[1] + self.layer3 = Stage(block, self.inplanes, widths[2], group_width, layers[2], stride=2, + dilate=replace_stride_with_dilation[2], cheap_ratio=0.5) + + self.inplanes = widths[2] + if layers[3] > 2: + self.layer4 = Stage(block, self.inplanes, widths[3], group_width, layers[3], stride=2, + dilate=replace_stride_with_dilation[3], cheap_ratio=0.5) + else: + self.layer4 = self._make_layer(block, widths[3], layers[3], stride=2, + dilate=replace_stride_with_dilation[3]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(0.5) + self.fc = nn.Linear(self.inplanes * block.expansion, num_classes) + + # Initialize weights + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.group_width, + self.dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, group_width=self.group_width, + dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.dropout(x) + x = self.fc(x) + + return x + +def g_ghost_regnetx_002(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [1, 1, 1, 1], [32, 64, 128, 256], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_004(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [1, 1, 2, 2], [32, 64, 128, 256], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_006(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [1, 1, 3, 3], [32, 64, 128, 256], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_008(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [1, 1, 4, 4], [32, 64, 128, 256], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_016(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [2, 2, 4, 4], [64, 128, 256, 512], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_032(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [2, 2, 6, 6], [64, 128, 256, 512], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_040(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [3, 4, 6, 6], [64, 128, 256, 512], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_064(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [3, 4, 8, 8], [64, 128, 256, 512], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_080(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [3, 4, 12, 12], [64, 128, 256, 512], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_120(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [4, 6, 12, 12], [128, 256, 512, 1024], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_160(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [4, 6, 16, 16], [128, 256, 512, 1024], num_classes=num_classes, **kwargs) + return model + +def g_ghost_regnetx_320(pretrained=False, num_classes=1000, **kwargs): + model = GGhostRegNet(Bottleneck, [4, 6, 24, 24], [128, 256, 512, 1024], num_classes=num_classes, **kwargs) + return model + + + +# Add functions for other configurations like g_ghost_regnetx_004, g_ghost_regnetx_006, etc. +