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)
