def test_ssd300_infer():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
model = Model(ssd300_infer())
model.compile()
loc, score = model.predict(ts.ones((1, 3, 300, 300)))
print(loc.asnumpy(), score.asnumpy())
def test_mobilenetv2_infer():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
model = Model(mobilenetv2_infer())
model.compile()
z = model.predict(ts.ones((1, 3, 224, 224)))
print(z.asnumpy())
def test_densenetBC_100():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
model = Model(densenetBC_100())
model.compile()
z = model.predict(ts.ones((1, 3, 32, 32)))
print(z.asnumpy())
def test_sequential():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
net = layers.SequentialLayer([
layers.Conv2d(1, 6, 5, pad_mode='valid', weight_init="ones"),
layers.ReLU(),
layers.MaxPool2d(kernel_size=2, stride=2)
])
model = Model(net)
model.compile()
z = model.predict(ts.ones((1, 1, 32, 32)))
print(z.asnumpy())
def _initialize_weights(self):
self.init_parameters_data()
for _, m in self.cells_and_names():
if isinstance(m, layers.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.set_data(
Tensor(
np.random.normal(0, np.sqrt(
2. / n), m.weight.data.shape).astype("float32")))
if m.bias is not None:
m.bias.set_data(ts.zeros(m.bias.data.shape))
elif isinstance(m, layers.BatchNorm2d):
m.gamma.set_data(ts.ones(m.gamma.data.shape))
m.beta.set_data(ts.zeros(m.beta.data.shape))
