def WideResnet(n_blocks=3, widen_factor=1, n_output_classes=10, mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
n_blocks: int, number of blocks in a group. total layers = 6n + 4.
widen_factor: int, widening factor of each group. k=1 is vanilla resnet.
n_output_classes: int, number of distinct output classes.
mode: Whether we are training or evaluating or doing inference.
Returns:
The list of layers comprising a WideResnet model with the given parameters.
"""
return tl.Model(
tl.ToFloat(),
tl.Conv(16, (3, 3), padding='SAME'),
WideResnetGroup(n_blocks, 16 * widen_factor, mode=mode),
WideResnetGroup(n_blocks, 32 * widen_factor, (2, 2), mode=mode),
WideResnetGroup(n_blocks, 64 * widen_factor, (2, 2), mode=mode),
tl.BatchNorm(mode=mode),
tl.Relu(),
tl.AvgPool(pool_size=(8, 8)),
tl.Flatten(),
tl.Dense(n_output_classes),
tl.LogSoftmax(),
)
def WideResnet(n_blocks=3, d_hidden=64, n_output_classes=10, mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
n_blocks: int, number of blocks in a group.
d_hidden: Dimensionality of the first hidden layer (multiplied later).
n_output_classes: int, number of distinct output classes.
mode: Whether we are training or evaluating or doing inference.
Returns:
The list of layers comprising a WideResnet model with the given parameters.
"""
del mode
return tl.Model(
tl.ToFloat(),
tl.Conv(d_hidden, (3, 3), padding='SAME'),
WideResnetGroup(n_blocks, d_hidden),
WideResnetGroup(n_blocks, d_hidden * 2, (2, 2)),
WideResnetGroup(n_blocks, d_hidden * 4, (2, 2)),
tl.BatchNorm(),
tl.Relu(),
tl.AvgPool(pool_size=(8, 8)),
tl.Flatten(),
tl.Dense(n_output_classes),
tl.LogSoftmax(),
)
def WideResnet(num_blocks=3, hidden_size=64, num_output_classes=10,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, number of classes to distinguish.
mode: is it training or eval.
Returns:
The WideResnet model with given layer and output sizes.
"""
del mode
return tl.Serial(
tl.Conv(hidden_size, (3, 3), padding='SAME'),
WideResnetGroup(num_blocks, hidden_size),
WideResnetGroup(num_blocks, hidden_size * 2, (2, 2)),
WideResnetGroup(num_blocks, hidden_size * 4, (2, 2)),
tl.BatchNorm(),
tl.Relu(),
tl.AvgPool(pool_size=(8, 8)),
tl.Flatten(),
tl.Dense(num_output_classes),
tl.LogSoftmax()
)
def Resnet50(d_hidden=64, n_output_classes=1001, mode='train'):
"""ResNet.
Args:
d_hidden: Dimensionality of the first hidden layer (multiplied later).
n_output_classes: Number of distinct output classes.
mode: Whether we are training or evaluating or doing inference.
Returns:
The list of layers comprising a ResNet model with the given parameters.
"""
return tl.Model(
tl.ToFloat(),
tl.Conv(d_hidden, (7, 7), (2, 2), 'SAME'),
tl.BatchNorm(mode=mode),
tl.Relu(),
tl.MaxPool(pool_size=(3, 3), strides=(2, 2)),
ConvBlock(3, [d_hidden, d_hidden, 4 * d_hidden], (1, 1), mode=mode),
IdentityBlock(3, [d_hidden, d_hidden, 4 * d_hidden], mode=mode),
IdentityBlock(3, [d_hidden, d_hidden, 4 * d_hidden], mode=mode),
ConvBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden], (2, 2),
mode=mode),
IdentityBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden],
mode=mode),
IdentityBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden],
mode=mode),
IdentityBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden],
mode=mode),
ConvBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden], (2, 2),
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
ConvBlock(3, [8 * d_hidden, 8 * d_hidden, 32 * d_hidden], (2, 2),
mode=mode),
IdentityBlock(3, [8 * d_hidden, 8 * d_hidden, 32 * d_hidden],
mode=mode),
IdentityBlock(3, [8 * d_hidden, 8 * d_hidden, 32 * d_hidden],
mode=mode),
tl.AvgPool(pool_size=(7, 7)),
tl.Flatten(),
tl.Dense(n_output_classes),
tl.LogSoftmax(),
)
def Resnet50(hidden_size=64, num_output_classes=1001, mode='train'):
"""ResNet.
Args:
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: how many classes to distinguish.
mode: whether we are training or evaluating or doing inference.
Returns:
The ResNet model with the given layer and output sizes.
"""
del mode
return tl.Serial(
tl.Conv(hidden_size, (7, 7), (2, 2),
'SAME'), tl.BatchNorm(), tl.Relu(),
tl.MaxPool(pool_size=(3, 3), strides=(2, 2)),
ConvBlock(3, [hidden_size, hidden_size, 4 * hidden_size], (1, 1)),
IdentityBlock(3, [hidden_size, hidden_size, 4 * hidden_size]),
IdentityBlock(3, [hidden_size, hidden_size, 4 * hidden_size]),
ConvBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size],
(2, 2)),
IdentityBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size]),
IdentityBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size]),
IdentityBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size]),
ConvBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size],
(2, 2)),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
ConvBlock(3, [8 * hidden_size, 8 * hidden_size, 32 * hidden_size],
(2, 2)),
IdentityBlock(3, [8 * hidden_size, 8 * hidden_size, 32 * hidden_size]),
IdentityBlock(3, [8 * hidden_size, 8 * hidden_size, 32 * hidden_size]),
tl.AvgPool(pool_size=(7, 7)), tl.Flatten(),
tl.Dense(num_output_classes), tl.LogSoftmax())