def AlexNet(b):
# 4D input image tensor with dimensions bx3x227x227
img = nn_ops.InputTensor((b, 3, 227, 227))
# Conv1 + relu + maxpool
# Conv input parameters: image, filter dimensions, stride, padding,
# no. of pointwise operations that follow the convolution.
# Pooling input parameters: image, filter dimensions, stride, padding
conv1 = nn_ops.Conv(img, (96, 3, 11, 11), stride=4, pw_op_cnt=1)
pool1 = nn_ops.Pooling(conv1.GetOutTensor(0), (3, 3), stride=2)
# Conv2 + relu + maxpool
conv2 = nn_ops.Conv(pool1.GetOutTensor(0), (256, 96, 5, 5),
pad=2,
pw_op_cnt=1)
pool2 = nn_ops.Pooling(conv2.GetOutTensor(0), (3, 3), stride=2)
# Conv3 + relu
conv3 = nn_ops.Conv(pool2.GetOutTensor(0), (384, 256, 3, 3),
stride=1,
pad=1,
pw_op_cnt=1)
# Conv4 + relu
conv4 = nn_ops.Conv(conv3.GetOutTensor(0), (384, 384, 3, 3),
stride=1,
pad=1,
pw_op_cnt=1)
# Conv5 + relu + maxpool
conv5 = nn_ops.Conv(conv4.GetOutTensor(0), (256, 384, 3, 3),
stride=1,
pad=1,
pw_op_cnt=1)
pool5 = nn_ops.Pooling(conv5.GetOutTensor(0), (3, 3), stride=2)
# Reshape
reshape = nn_ops.Reshape(pool5.GetOutTensor(0), (b, 256 * 6 * 6))
# FC6 + relu
fc6 = nn_ops.FC(reshape.GetOutTensor(0), 4096, pw_op_cnt=1)
# FC7 + relu
fc7 = nn_ops.FC(fc6.GetOutTensor(0), 4096, pw_op_cnt=1)
# FC8
fc8 = nn_ops.FC(fc7.GetOutTensor(0), 1024)
# Softmax + cross-entropy loss
loss = nn_ops.SoftmaxCrossEntropy(fc8.GetOutTensor(0))
return nn_ops.Ops.G
def Bottleneck(img, inplanes, planes, stride=1, downsample=None):
identity = img
conv1 = nn_ops.Conv(img, (planes, inplanes, 1, 1))
bn1 = nn_ops.BatchNorm(conv1.GetOutTensor(0))
conv2 = nn_ops.Conv(bn1.GetOutTensor(0), (planes, planes, 3, 3),
stride=stride,
pad=1)
bn2 = nn_ops.BatchNorm(conv2.GetOutTensor(0))
conv3 = nn_ops.Conv(bn2.GetOutTensor(0),
(planes * expansion, planes, 1, 1))
bn3 = nn_ops.BatchNorm(conv3.GetOutTensor(0))
if downsample is not None:
identity = downsample(img)
out = nn_ops.Elementwise(bn3.GetOutTensor(0), identity, pw_op_cnt=1)
return out
def MakeLayer(img, planes, blocks, stride=1):
downsample = None
nonlocal inplanes
if stride != 1 or inplanes != planes * expansion:
downsample = lambda x: nn_ops.BatchNorm(
nn_ops.Conv(x, (planes * expansion, inplanes, 1, 1),
stride=stride).GetOutTensor(0)).GetOutTensor(0)
layers = Bottleneck(img, inplanes, planes, stride, downsample)
inplanes = planes * expansion
for _ in range(1, blocks):
layers = Bottleneck(layers.GetOutTensor(0), inplanes, planes)
return layers
def ResNet101(b):
img = nn_ops.InputTensor((b, 3, 227, 227))
layers = (3, 4, 23, 3)
num_classes = 1000
expansion = 4
inplanes = 64
# Bottleneck connections architecture
def Bottleneck(img, inplanes, planes, stride=1, downsample=None):
identity = img
conv1 = nn_ops.Conv(img, (planes, inplanes, 1, 1))
bn1 = nn_ops.BatchNorm(conv1.GetOutTensor(0))
conv2 = nn_ops.Conv(bn1.GetOutTensor(0), (planes, planes, 3, 3),
stride=stride,
pad=1)
bn2 = nn_ops.BatchNorm(conv2.GetOutTensor(0))
conv3 = nn_ops.Conv(bn2.GetOutTensor(0),
(planes * expansion, planes, 1, 1))
bn3 = nn_ops.BatchNorm(conv3.GetOutTensor(0))
if downsample is not None:
identity = downsample(img)
out = nn_ops.Elementwise(bn3.GetOutTensor(0), identity, pw_op_cnt=1)
return out
def MakeLayer(img, planes, blocks, stride=1):
downsample = None
nonlocal inplanes
if stride != 1 or inplanes != planes * expansion:
downsample = lambda x: nn_ops.BatchNorm(
nn_ops.Conv(x, (planes * expansion, inplanes, 1, 1),
stride=stride).GetOutTensor(0)).GetOutTensor(0)
layers = Bottleneck(img, inplanes, planes, stride, downsample)
inplanes = planes * expansion
for _ in range(1, blocks):
layers = Bottleneck(layers.GetOutTensor(0), inplanes, planes)
return layers
# Conv1 + bn + relu + maxpool
conv1 = nn_ops.Conv(img, (64, 3, 7, 7), stride=2, pad=3)
bn1 = nn_ops.BatchNorm(conv1.GetOutTensor(0))
pool1 = nn_ops.Pooling(bn1.GetOutTensor(0), (3, 3), stride=2, pad=1)
# Layers
layer1 = MakeLayer(pool1.GetOutTensor(0), 64, layers[0])
layer2 = MakeLayer(layer1.GetOutTensor(0), 128, layers[1], stride=2)
layer3 = MakeLayer(layer2.GetOutTensor(0), 256, layers[2], stride=2)
layer4 = MakeLayer(layer3.GetOutTensor(0), 512, layers[3], stride=2)
# Avg pooling + FC
mean = nn_ops.ReduceMean(layer4.GetOutTensor(0), axis=[2, 3])
fc = nn_ops.FC(mean.GetOutTensor(0), num_classes)
# Softmax + cross-entropy loss
loss = nn_ops.SoftmaxCrossEntropy(fc.GetOutTensor(0))
return nn_ops.Ops.G
def AddBasicConv(img, fltr, stride=1, padding=0):
conv = nn_ops.Conv(img, fltr, stride, padding)
bn = nn_ops.BatchNorm(conv.GetOutTensor(0))
return bn