def __init__(self, builder: ConvBuilder, num_blocks, num_classes=1000, deps=None): super(SBottleneckResNet, self).__init__() # self.mean_tensor = torch.from_numpy(np.array([0.485, 0.456, 0.406])).reshape(1, 3, 1, 1).cuda().type(torch.cuda.FloatTensor) # self.std_tensor = torch.from_numpy(np.array([0.229, 0.224, 0.225])).reshape(1, 3, 1, 1).cuda().type(torch.cuda.FloatTensor) # self.mean_tensor = torch.from_numpy(np.array([0.406, 0.456, 0.485])).reshape(1, 3, 1, 1).cuda().type( # torch.cuda.FloatTensor) # self.std_tensor = torch.from_numpy(np.array([0.225, 0.224, 0.229])).reshape(1, 3, 1, 1).cuda().type( # torch.cuda.FloatTensor) # self.mean_tensor = torch.from_numpy(np.array([0.5, 0.5, 0.5])).reshape(1, 3, 1, 1).cuda().type( # torch.cuda.FloatTensor) # self.std_tensor = torch.from_numpy(np.array([0.5, 0.5, 0.5])).reshape(1, 3, 1, 1).cuda().type( # torch.cuda.FloatTensor) if deps is None: if num_blocks == [3, 4, 6, 3]: deps = RESNET50_ORIGIN_DEPS_FLATTENED elif num_blocks == [3, 4, 23, 3]: deps = resnet_bottleneck_origin_deps_flattened(101) else: raise ValueError('???') self.conv1 = builder.Conv2dBNReLU(3, deps[0], kernel_size=7, stride=2, padding=3) self.maxpool = builder.Maxpool2d(kernel_size=3, stride=2, padding=1) # every stage has num_block * 3 + 1 nls = [n * 3 + 1 for n in num_blocks] # num layers in each stage self.stage1 = ResNetBottleneckStage(builder=builder, in_planes=deps[0], stage_deps=deps[1:nls[0] + 1]) self.stage2 = ResNetBottleneckStage(builder=builder, in_planes=deps[nls[0]], stage_deps=deps[nls[0] + 1:nls[0] + 1 + nls[1]], stride=2) self.stage3 = ResNetBottleneckStage( builder=builder, in_planes=deps[nls[0] + nls[1]], stage_deps=deps[nls[0] + nls[1] + 1:nls[0] + 1 + nls[1] + nls[2]], stride=2) self.stage4 = ResNetBottleneckStage( builder=builder, in_planes=deps[nls[0] + nls[1] + nls[2]], stage_deps=deps[nls[0] + nls[1] + nls[2] + 1:nls[0] + 1 + nls[1] + nls[2] + nls[3]], stride=2) self.gap = builder.GAP(kernel_size=7) self.fc = builder.Linear(deps[-1], num_classes)
def __init__(self, builder:ConvBuilder, num_classes): super(MobileV1CifarNet, self).__init__() self.conv1 = builder.Conv2dBNReLU(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1) blocks = [] in_planes = cifar_cfg[0] for x in cifar_cfg: out_planes = x if isinstance(x, int) else x[0] stride = 1 if isinstance(x, int) else x[1] blocks.append(MobileV1Block(builder=builder, in_planes=in_planes, out_planes=out_planes, stride=stride)) in_planes = out_planes self.stem = builder.Sequential(*blocks) self.gap = builder.GAP(kernel_size=8) self.linear = builder.Linear(cifar_cfg[-1], num_classes)
def __init__(self, builder:ConvBuilder, num_classes, deps=None): super(MobileV1ImagenetNet, self).__init__() if deps is None: deps = MI1_ORIGIN_DEPS assert len(deps) == 27 self.conv1 = builder.Conv2dBNReLU(in_channels=3, out_channels=deps[0], kernel_size=3, stride=2, padding=1) blocks = [] for block_idx in range(13): depthwise_channels = int(deps[block_idx * 2 + 1]) pointwise_channels = int(deps[block_idx * 2 + 2]) stride = 2 if block_idx in [1, 3, 5, 11] else 1 blocks.append(MobileV1Block(builder=builder, in_planes=depthwise_channels, out_planes=pointwise_channels, stride=stride)) self.stem = builder.Sequential(*blocks) self.gap = builder.GAP(kernel_size=7) self.linear = builder.Linear(imagenet_cfg[-1], num_classes)
def __init__(self, builder: ConvBuilder, num_blocks, num_classes=1000, deps=None): super(SBottleneckResNet, self).__init__() if deps is None: if num_blocks == [3, 4, 6, 3]: deps = RESNET50_ORIGIN_DEPS_FLATTENED elif num_blocks == [3, 4, 23, 3]: deps = resnet_bottleneck_origin_deps_flattened(101) else: raise ValueError('???') self.conv1 = builder.Conv2dBNReLU(3, deps[0], kernel_size=7, stride=2, padding=3) self.maxpool = builder.Maxpool2d(kernel_size=3, stride=2, padding=1) # every stage has num_block * 3 + 1 nls = [n * 3 + 1 for n in num_blocks] # num layers in each stage self.stage1 = ResNetBottleneckStage(builder=builder, in_planes=deps[0], stage_deps=deps[1:nls[0] + 1]) self.stage2 = ResNetBottleneckStage(builder=builder, in_planes=deps[nls[0]], stage_deps=deps[nls[0] + 1:nls[0] + 1 + nls[1]], stride=2) self.stage3 = ResNetBottleneckStage( builder=builder, in_planes=deps[nls[0] + nls[1]], stage_deps=deps[nls[0] + nls[1] + 1:nls[0] + 1 + nls[1] + nls[2]], stride=2) self.stage4 = ResNetBottleneckStage( builder=builder, in_planes=deps[nls[0] + nls[1] + nls[2]], stage_deps=deps[nls[0] + nls[1] + nls[2] + 1:nls[0] + 1 + nls[1] + nls[2] + nls[3]], stride=2) self.gap = builder.GAP(kernel_size=7) self.fc = builder.Linear(deps[-1], num_classes)
def __init__(self, builder: ConvBuilder, block, num_blocks, num_classes=10, width_multiplier=None): super(ResNet, self).__init__() print('width multiplier: ', width_multiplier) if width_multiplier is None: width_multiplier = 1 else: width_multiplier = width_multiplier[0] self.bd = builder self.in_planes = int(64 * width_multiplier) self.conv1 = builder.Conv2dBNReLU(3, int(64 * width_multiplier), kernel_size=7, stride=2, padding=3) self.stage1 = self._make_stage(block, int(64 * width_multiplier), num_blocks[0], stride=1) self.stage2 = self._make_stage(block, int(128 * width_multiplier), num_blocks[1], stride=2) self.stage3 = self._make_stage(block, int(256 * width_multiplier), num_blocks[2], stride=2) self.stage4 = self._make_stage(block, int(512 * width_multiplier), num_blocks[3], stride=2) self.gap = builder.GAP(kernel_size=7) self.linear = self.bd.Linear( int(512 * block.expansion * width_multiplier), num_classes)