def tower(towername,
names,
maps,
sizes,
strides,
pads,
act,
bn,
scheme,
typ="bn"):
block = Sequential()
lvlnames = ["%s_%s" % (towername, name) for name in names]
for i, name in enumerate(lvlnames):
if "pool" in name:
block.append(pool2D(sizes[i], strides[i], pads[i], name=names[i]))
else:
act = False if i == len(names) - 1 else act
block.extend(
convBN(maps[i], maps[i + 1], sizes[i], strides[i], pads[i],
lvlnames[i], act, bn, scheme, typ))
return block
def residBlock(inmaps, hmaps, stride, blockname, convShortcut, actInplace,
bnInplace, initscheme):
block = Sequential()
branch = Sequential()
branch.extend(
residMiniBlock(inmaps, hmaps, 1, stride, 0, blockname, "2a", True,
actInplace, bnInplace, initscheme))
branch.extend(
residMiniBlock(hmaps, hmaps, 3, 1, 1, blockname, "2b", True,
actInplace, bnInplace, initscheme))
branch.extend(
residMiniBlock(hmaps, 4 * hmaps, 1, 1, 0, blockname, "2c", False,
actInplace, bnInplace, initscheme))
shortcut = Sequential()
if convShortcut:
shortcut.extend(
residMiniBlock(inmaps, 4 * hmaps, 1, stride, 0, blockname, "1",
False, actInplace, bnInplace, initscheme))
else:
shortcut.append(Identity())
block.append(Replicate(2))
block.append(Parallel().append(branch).append(shortcut))
block.append(Add())
block.append(Activation(relu, inplace=actInplace))
return block
def loadInceptionBN(modelpath,
actInplace=False,
bnInplace=False,
initscheme="none",
name="Inception-BN-0126"):
net = Sequential(name=name)
net.append(
Conv2D(3,
64,
7,
stride=2,
pad=3,
useBias=False,
initscheme=initscheme,
name="conv_1"))
net.append(BatchNorm2D(64, inplace=bnInplace, name="bn_1"))
net.append(Activation(relu, inplace=actInplace, name="relu_1"))
net.append(MaxPool2D(3, 2, pad=1, name="pool_1"))
net.append(
Conv2D(64,
64,
1,
useBias=False,
initscheme=initscheme,
name="conv_2_red"))
net.append(BatchNorm2D(64, inplace=bnInplace, name="bn_2_red"))
net.append(Activation(relu, inplace=actInplace, name="relu_2_red"))
net.append(
Conv2D(64,
192,
3,
pad=1,
useBias=False,
initscheme=initscheme,
name="conv_2"))
net.append(BatchNorm2D(192, inplace=bnInplace, name="bn_2"))
net.append(Activation(relu, inplace=actInplace, name="relu_2"))
net.append(MaxPool2D(3, 2, pad=1, name="pool_2"))
act, bn = actInplace, bnInplace
net.extend(
bnBlock(192, [64], [64, 64], [64, 96, 96], [32],
act=act,
bn=bn,
scheme=initscheme,
name="3a"))
net.extend(
bnBlock(256, [64], [64, 96], [64, 96, 96], [64],
act=act,
bn=bn,
scheme=initscheme,
name="3b"))
net.extend(
bnShrinkBlock(320, [128, 160], [64, 96, 96],
bn=bn,
act=act,
scheme=initscheme,
name="3c"))
net.extend(
bnBlock(576, [224], [64, 96], [96, 128, 128], [128],
act=act,
bn=bn,
scheme=initscheme,
name="4a"))
net.extend(
bnBlock(576, [192], [96, 128], [96, 128, 128], [128],
act=act,
bn=bn,
scheme=initscheme,
name="4b"))
net.extend(
bnBlock(576, [160], [128, 160], [128, 160, 160], [128],
act=act,
bn=bn,
scheme=initscheme,
name="4c"))
net.extend(
bnBlock(608, [96], [128, 192], [160, 192, 192], [128],
act=act,
bn=bn,
scheme=initscheme,
name="4d"))
net.extend(
bnShrinkBlock(608, [128, 192], [192, 256, 256],
act=act,
bn=bn,
scheme=initscheme,
name="4e"))
net.extend(
bnBlock(1056, [352], [192, 320], [160, 224, 224], [128],
act=act,
bn=bn,
scheme=initscheme,
name="5a"))
net.extend(
bnBlock(1024, [352], [192, 320], [192, 224, 224], [128],
act=act,
bn=bn,
scheme=initscheme,
name="5b"))
net.append(AvgPool2D(7, 1, name="global_pool"))
net.append(Flatten(name="flatten"))
net.append(Linear(1024, 1000, initscheme=initscheme, name="fc1"))
net.append(SoftMax(name="softmax"))
if modelpath is not None:
net.load(modelpath, assumeUniqueNames=True)
return net
def loadInceptionV3(modelpath,
actInplace=False,
bnInplace=False,
initscheme="none",
name="Inception-7-0001"):
net = Sequential(name=name)
net.append(
Conv2D(3,
32,
3,
stride=2,
useBias=False,
initscheme=initscheme,
name="conv_conv2d"))
net.append(BatchNorm2D(32, name="conv_batchnorm"))
net.append(Activation(relu, inplace=actInplace, name="conv_relu"))
net.append(
Conv2D(32,
32,
3,
useBias=False,
initscheme=initscheme,
name="conv_1_conv2d"))
net.append(BatchNorm2D(32, name="conv_1_batchnorm"))
net.append(Activation(relu, inplace=actInplace, name="conv_1_relu"))
net.append(
Conv2D(32,
64,
3,
pad=1,
useBias=False,
initscheme=initscheme,
name="conv_2_conv2d"))
net.append(BatchNorm2D(64, name="conv_2_batchnorm"))
net.append(Activation(relu, inplace=actInplace, name="conv_2_relu"))
net.append(MaxPool2D(3, 2, name="pool"))
net.append(
Conv2D(64,
80,
1,
useBias=False,
initscheme=initscheme,
name="conv_3_conv2d"))
net.append(BatchNorm2D(80, name="conv_3_batchnorm"))
net.append(Activation(relu, inplace=actInplace, name="conv_3_relu"))
net.append(
Conv2D(80,
192,
3,
useBias=False,
initscheme=initscheme,
name="conv_4_conv2d"))
net.append(BatchNorm2D(192, name="conv_4_batchnorm"))
net.append(Activation(relu, inplace=actInplace, name="conv_4_relu"))
net.append(MaxPool2D(3, 2, name="pool1"))
act, bn = actInplace, bnInplace
net.extend(
bnBlock(192, [64], [48, 64], [64, 96, 96], [32], "mixed", act, bn,
initscheme, 5, 2, "v3"))
net.extend(
bnBlock(256, [64], [48, 64], [64, 96, 96], [64], "mixed_1", act, bn,
initscheme, 5, 2, "v3"))
net.extend(
bnBlock(288, [64], [48, 64], [64, 96, 96], [64], "mixed_2", act, bn,
initscheme, 5, 2, "v3"))
net.extend(
bnShrinkBlock(288, [384], [64, 96, 96], "mixed_3", act, bn, initscheme,
False, 0, "v3"))
net.extend(
factorBlock(768, [192], [128, 128, 192], [128, 128, 128, 128, 192],
[192], "mixed_4", act, bn, initscheme))
net.extend(
factorBlock(768, [192], [160, 160, 192], [160, 160, 160, 160, 192],
[192], "mixed_5", act, bn, initscheme))
net.extend(
factorBlock(768, [192], [160, 160, 192], [160, 160, 160, 160, 192],
[192], "mixed_6", act, bn, initscheme))
net.extend(
factorBlock(768, [192], [192, 192, 192], [192, 192, 192, 192, 192],
[192], "mixed_7", act, bn, initscheme))
net.extend(
v3ShrinkBlock(768, [192, 320], [192, 192, 192, 192], "mixed_8", act,
bn, initscheme))
net.extend(
expandBlock(1280, [320], [384, 384, 384], [448, 384, 384, 384], [192],
"mixed_9",
act,
bn,
initscheme,
pool="avg"))
net.extend(
expandBlock(2048, [320], [384, 384, 384], [448, 384, 384, 384], [192],
"mixed_10",
act,
bn,
initscheme,
pool="max"))
net.append(AvgPool2D(8, 1, name="global_pool"))
net.append(Flatten(name="flatten"))
net.append(Linear(2048, 1008, name="fc1"))
net.append(SoftMax(name="softmax"))
if modelpath is not None:
net.load(modelpath, assumeUniqueNames=True)
return net
def loadMiniYolo(modelpath, numOutput, actInplace=False, initscheme="none"):
net = Sequential(name="YOLONet")
block0 = block(idx=["1"],
inmaps=[3],
outmaps=[64],
sizeconv=[7],
strideconv=[2],
initscheme=initscheme,
actInPlace=actInplace)
net.extend(block0)
block1 = block(idx=["2"],
inmaps=[64],
outmaps=[192],
sizeconv=[3],
strideconv=[1],
initscheme=initscheme,
actInPlace=actInplace)
net.extend(block1)
block2 = block(idx=["3", "4", "5", "6"],
inmaps=[192, 128, 256, 256],
outmaps=[128, 256, 256, 512],
sizeconv=[1, 3, 1, 3],
strideconv=[1, 1, 1, 1],
initscheme=initscheme,
actInPlace=actInplace)
net.extend(block2)
block3 = block(
idx=["7", "8", "9", "10", "11", "12", "13", "14", "15", "16"],
inmaps=[512, 256, 512, 256, 512, 256, 512, 256, 512, 512],
outmaps=[256, 512, 256, 512, 256, 512, 256, 512, 512, 1024],
sizeconv=[1, 3, 1, 3, 1, 3, 1, 3, 1, 3],
strideconv=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
initscheme=initscheme,
actInPlace=actInplace)
net.extend(block3)
block4 = block(idx=["17", "18", "19", "20", "21", "22", "23", "24"],
inmaps=[1024, 512, 1024, 512, 1024, 1024, 1024, 1024],
outmaps=[512, 1024, 512, 1024, 1024, 1024, 1024, 1024],
sizeconv=[1, 3, 1, 3, 3, 3, 3, 3],
strideconv=[1, 1, 1, 1, 1, 2, 1, 1],
initscheme=initscheme,
actInPlace=actInplace,
addMaxpool=False)
net.extend(block4)
net.append(Flatten())
insize = int(np.prod(net.dataShapeFrom((1, 3, 448, 448))))
net.append(Linear(insize, 512, initscheme=initscheme, name="fc25"))
net.append(Activation(relu, inplace=actInplace, name="fc_relu24"))
net.append(Linear(512, 4096, initscheme=initscheme, name="fc26"))
net.append(Activation(relu, inplace=actInplace, name="fc_relu25"))
net.append(Linear(4096, numOutput, initscheme=initscheme, name="fc27"))
net.append(SoftMax())
if modelpath is not None:
net.load(modelpath)
return net
def loadResNet(modelpath,
layers,
actInplace=False,
bnInplace=False,
initscheme="none",
name=None):
if layers == "50":
if name is None:
name = "ResNet-50"
level3names = ["3%s" % alpha for alpha in string.ascii_lowercase[1:4]]
level4names = ["4%s" % alpha for alpha in string.ascii_lowercase[1:6]]
elif layers == "101":
if name is None:
name = "ResNet-101"
level3names = ["3b%s" % num for num in range(1, 4)]
level4names = ["4b%s" % num for num in range(1, 23)]
elif layers == "152":
if name is None:
name = "ResNet-152"
level3names = ["3b%s" % num for num in range(1, 8)]
level4names = ["4b%s" % num for num in range(1, 36)]
else:
raise ValueError("Unsupported ResNet layers mode")
net = Sequential(name=name)
net.append(
Conv2D(3,
64,
7,
stride=2,
pad=3,
name="conv1",
initscheme=initscheme,
useBias=False))
net.append(BatchNorm2D(64, name="bn_conv1", inplace=bnInplace))
net.append(Activation(relu, inplace=actInplace, name="conv1_relu"))
net.append(MaxPool2D(3, 2, name="pool1"))
net.extend(
residBlock(64, 64, 1, "2a", True, actInplace, bnInplace, initscheme))
net.extend(
residBlock(256, 64, 1, "2b", False, actInplace, bnInplace, initscheme))
net.extend(
residBlock(256, 64, 1, "2c", False, actInplace, bnInplace, initscheme))
net.extend(
residBlock(256, 128, 2, "3a", True, actInplace, bnInplace, initscheme))
for name in level3names:
net.extend(
residBlock(512, 128, 1, name, False, actInplace, bnInplace,
initscheme))
net.extend(
residBlock(512, 256, 2, "4a", True, actInplace, bnInplace, initscheme))
for name in level4names:
net.extend(
residBlock(1024, 256, 1, name, False, actInplace, bnInplace,
initscheme))
net.extend(
residBlock(1024, 512, 2, "5a", True, actInplace, bnInplace,
initscheme))
net.extend(
residBlock(2048, 512, 1, "5b", False, actInplace, bnInplace,
initscheme))
net.extend(
residBlock(2048, 512, 1, "5c", False, actInplace, bnInplace,
initscheme))
net.append(AvgPool2D(7, 1))
net.append(Flatten())
net.append(Linear(2048, 1000, initscheme=initscheme, name="fc1000"))
net.append(SoftMax())
if modelpath is not None:
net.load(modelpath, assumeUniqueNames=True)
return net
def loadW2L(modelpath, inmaps, nlabels, initscheme=None, name="w2l"):
net = Sequential(name=name)
net.extend(
convBlock(inmaps,
256,
size=11,
stride=2,
pad=5,
dropout=0.2,
initscheme=initscheme,
name="conv1d_0"))
net.extend(
convBlock(256,
256,
size=11,
stride=1,
pad=5,
dropout=0.2,
initscheme=initscheme,
name="conv1d_1"))
net.extend(
convBlock(256,
256,
size=11,
stride=1,
pad=5,
dropout=0.2,
initscheme=initscheme,
name="conv1d_2"))
net.extend(
convBlock(256,
256,
size=11,
stride=1,
pad=5,
dropout=0.2,
initscheme=initscheme,
name="conv1d_3"))
net.extend(
convBlock(256,
384,
size=13,
stride=1,
pad=6,
dropout=0.2,
initscheme=initscheme,
name="conv1d_4"))
net.extend(
convBlock(384,
384,
size=13,
stride=1,
pad=6,
dropout=0.2,
initscheme=initscheme,
name="conv1d_5"))
net.extend(
convBlock(384,
384,
size=13,
stride=1,
pad=6,
dropout=0.2,
initscheme=initscheme,
name="conv1d_6"))
net.extend(
convBlock(384,
512,
size=17,
stride=1,
pad=8,
dropout=0.2,
initscheme=initscheme,
name="conv1d_7"))
net.extend(
convBlock(512,
512,
size=17,
stride=1,
pad=8,
dropout=0.2,
initscheme=initscheme,
name="conv1d_8"))
net.extend(
convBlock(512,
512,
size=17,
stride=1,
pad=8,
dropout=0.2,
initscheme=initscheme,
name="conv1d_9"))
net.extend(
convBlock(512,
640,
size=21,
stride=1,
pad=10,
dropout=0.3,
initscheme=initscheme,
name="conv1d_10"))
net.extend(
convBlock(640,
640,
size=21,
stride=1,
pad=10,
dropout=0.3,
initscheme=initscheme,
name="conv1d_11"))
net.extend(
convBlock(640,
640,
size=21,
stride=1,
pad=10,
dropout=0.3,
initscheme=initscheme,
name="conv1d_12"))
net.extend(
convBlock(640,
768,
size=25,
stride=1,
pad=12,
dropout=0.3,
initscheme=initscheme,
name="conv1d_13"))
net.extend(
convBlock(768,
768,
size=25,
stride=1,
pad=12,
dropout=0.3,
initscheme=initscheme,
name="conv1d_14"))
net.extend(
convBlock(768,
768,
size=25,
stride=1,
pad=12,
dropout=0.3,
initscheme=initscheme,
name="conv1d_15"))
net.extend(
convBlock(768,
896,
size=29,
stride=1,
pad=28,
dropout=0.4,
initscheme=initscheme,
dilation=2,
name="conv1d_16"))
net.extend(
convBlock(896,
1024,
size=1,
stride=1,
pad=0,
dropout=0.4,
initscheme=initscheme,
name="conv1d_17"))
net.extend(
convBlock(1024,
nlabels,
size=1,
stride=1,
pad=0,
dropout=0.0,
initscheme=initscheme,
bnAct=False,
name="conv1d_18"))
if modelpath is not None:
net.load(modelpath)
return net
def loadNet(name="", inplace=True, modelpath=None):
net = Sequential(name)
net.append(Conv2D(3, 64, 3, pad=1, initscheme="none", name="conv1_1"))
net.append(Activation(relu, name="relu1_1", inplace=inplace))
net.append(Conv2D(64, 64, 3, pad=1, initscheme="none", name="conv1_2"))
net.append(Activation(relu, name="relu1_2", inplace=inplace))
net.append(MaxPool2D(name="pool1_stage1"))
net.append(Conv2D(64, 128, 3, pad=1, initscheme="none", name="conv2_1"))
net.append(Activation(relu, name="relu2_1", inplace=inplace))
net.append(Conv2D(128, 128, 3, pad=1, initscheme="none", name="conv2_2"))
net.append(Activation(relu, name="relu2_2", inplace=inplace))
net.append(MaxPool2D(name="pool2_stage1"))
net.append(Conv2D(128, 256, 3, pad=1, initscheme="none", name="conv3_1"))
net.append(Activation(relu, name="relu3_1", inplace=inplace))
net.append(Conv2D(256, 256, 3, pad=1, initscheme="none", name="conv3_2"))
net.append(Activation(relu, name="relu3_2", inplace=inplace))
net.append(Conv2D(256, 256, 3, pad=1, initscheme="none", name="conv3_3"))
net.append(Activation(relu, name="relu3_3", inplace=inplace))
net.append(Conv2D(256, 256, 3, pad=1, initscheme="none", name="conv3_4"))
net.append(Activation(relu, name="relu3_4", inplace=inplace))
net.append(MaxPool2D(name="pool3_stage1"))
net.append(Conv2D(256, 512, 3, pad=1, initscheme="none", name="conv4_1"))
net.append(Activation(relu, name="relu4_1", inplace=inplace))
net.append(Conv2D(512, 512, 3, pad=1, initscheme="none", name="conv4_2"))
net.append(Activation(relu, name="relu4_2", inplace=inplace))
net.append(
Conv2D(512, 256, 3, pad=1, initscheme="none", name="conv4_3_CPM"))
net.append(Activation(relu, name="relu4_3_CPM"))
net.append(
Conv2D(256, 128, 3, pad=1, initscheme="none", name="conv4_4_CPM"))
net.append(Activation(relu, name="relu4_4_CPM"))
block2 = buildSmallBlock(inplace=inplace)
block3 = buildBigBlock(stage=2, prenet=block2, inplace=inplace)
block4 = buildBigBlock(stage=3, prenet=block3, inplace=inplace)
block5 = buildBigBlock(stage=4, prenet=block4, inplace=inplace)
block6 = buildBigBlock(stage=5, prenet=block5, inplace=inplace)
net.extend(block6)
net.append(Replicate(2))
net.append(Parallel().append(buildBranch(stage=6, num=2,
inplace=inplace)).append(
buildBranch(stage=6,
num=1,
inplace=inplace)))
net.append(Concat(axis=1))
if modelpath is not None:
net.load(modelpath, assumeUniqueNames=True)
return net
