def get_classifier(input_data, num_classes):
"""
Get VGG classifier layers as fc layers.
"""
flatten = sym.flatten(data=input_data, name="flatten")
fc1 = sym.dense(data=flatten, units=32, name="fc1")
relu1 = sym.relu(data=fc1, name="relu1")
drop1 = sym.dropout(data=relu1, rate=0.5, name="drop1")
fc2 = sym.dense(data=drop1, units=32, name="fc2")
relu2 = sym.relu(data=fc2, name="relu2")
drop2 = sym.dropout(data=relu2, rate=0.5, name="drop2")
fc3 = sym.dense(data=drop2, units=num_classes, name="fc3")
return fc3
def check(dim, axis, nstep):
eps = 0.01
x = sym.Variable("x") + 1
beta = sym.Variable("beta")
gamma = sym.Variable("gamma")
moving_var = sym.Variable("moving_var")
moving_mean = sym.Variable("moving_mean")
y1, y2 = x, sym.Variable("xx") + 1
ishape = {"x": tuple(10 for i in range(dim))}
for i in range(nstep):
y1 = sym.batch_norm(y1 + 1,
gamma,
beta,
moving_mean,
moving_var,
epsilon=eps,
axis=axis)
y1 = sym.dropout(y1)
y2 = simple_bn(y2 + 1,
gamma,
beta,
moving_mean,
moving_var,
epsilon=eps,
axis=axis,
shape=ishape["x"])
g = nnvm.graph.create(y1)
g2 = nnvm.graph.create(y2)
graph_attr.set_shape_inputs(g, ishape)
g1 = g.apply("InferShape").apply("SimplifyInference")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def get_symbol(num_classes, version, **kwargs):
"""Get symbol of SqueezeNet
Parameters
----------
num_classes: int
The number of classification results
version : str, optional
"1.0" or "1.1" of SqueezeNet
"""
assert version == '1.1', ("Unsupported SqueezeNet version {version}:"
"1.1 expected".format(version=version))
net = sym.Variable("data")
net = sym.conv2d(net, channels=64, kernel_size=(3, 3), strides=(2, 2))
net = sym.relu(net)
net = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
net = _make_fire(net, 16, 64, 64)
net = _make_fire(net, 16, 64, 64)
net = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
net = _make_fire(net, 32, 128, 128)
net = _make_fire(net, 32, 128, 128)
net = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
net = _make_fire(net, 48, 192, 192)
net = _make_fire(net, 48, 192, 192)
net = _make_fire(net, 64, 256, 256)
net = _make_fire(net, 64, 256, 256)
net = sym.dropout(net, rate=0.5)
net = sym.conv2d(net, channels=num_classes, kernel_size=(1, 1))
net = sym.relu(net)
net = sym.global_avg_pool2d(net)
return sym.softmax(net, axis=1)
def get_symbol(num_classes, version, **kwargs):
"""Get symbol of SqueezeNet
Parameters
----------
num_classes: int
The number of classification results
version : str, optional
"1.0" or "1.1" of SqueezeNet
"""
assert version == '1.1', ("Unsupported SqueezeNet version {version}:"
"1.1 expected".format(version=version))
net = sym.Variable("data")
net = sym.conv2d(net, channels=64, kernel_size=(3, 3), strides=(2, 2))
net = sym.relu(net)
net = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
net = _make_fire(net, 16, 64, 64)
net = _make_fire(net, 16, 64, 64)
net = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
net = _make_fire(net, 32, 128, 128)
net = _make_fire(net, 32, 128, 128)
net = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
net = _make_fire(net, 48, 192, 192)
net = _make_fire(net, 48, 192, 192)
net = _make_fire(net, 64, 256, 256)
net = _make_fire(net, 64, 256, 256)
net = sym.dropout(net, rate=0.5)
net = sym.conv2d(net, channels=num_classes, kernel_size=(1, 1))
net = sym.relu(net)
net = sym.global_avg_pool2d(net)
return sym.softmax(net, axis=1)
def check(dim, axis, nstep):
eps = 0.01
x = sym.Variable("x") + 1
beta = sym.Variable("beta")
gamma = sym.Variable("gamma")
moving_var = sym.Variable("moving_var")
moving_mean = sym.Variable("moving_mean")
y1, y2 = x, sym.Variable("xx") + 1
ishape = {"x": tuple(10 for i in range(dim))}
for i in range(nstep):
y1 = sym.batch_norm(
y1 + 1, gamma, beta, moving_mean, moving_var, epsilon=eps, axis=axis)
y1 = sym.dropout(y1)
y2 = simple_bn(y2 + 1, gamma, beta, moving_mean, moving_var,
epsilon=eps, axis=axis, shape=ishape["x"])
g = nnvm.graph.create(y1)
g2 = nnvm.graph.create(y2)
graph_attr.set_shape_inputs(g, ishape)
g1 = g.apply("InferShape").apply("SimplifyInference")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def forward(self, inputs):
return sym.dropout(data=inputs, rate=self._rate)