def test_relu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.array([0.8, 0, 3.3, 0, 0, 0.5]).reshape(3,
2).astype(np.float32)
x = tensor.from_numpy(X)
x.to_device(gpu_dev)
y = autograd.ReLU()(x)[0]
# frontend
model = sonnx.to_onnx([x], [y])
# print('The model is:\n{}'.format(model))
# backend
sg_ir = sonnx.prepare(model, device=gpu_dev)
y_t = sg_ir.run([x])
np.testing.assert_array_almost_equal(tensor.to_numpy(y),
tensor.to_numpy(y_t[0]),
decimal=5)
def __init__(self,
in_filters,
out_filters,
reps,
strides=1,
padding=0,
start_with_relu=True,
grow_first=True):
super(Block, self).__init__()
if out_filters != in_filters or strides != 1:
self.skip = autograd.Conv2d(in_filters,
out_filters,
1,
stride=strides,
padding=padding,
bias=False)
self.skipbn = autograd.BatchNorm2d(out_filters)
else:
self.skip = None
self.layers = []
filters = in_filters
if grow_first:
self.layers.append(autograd.ReLU())
self.layers.append(
autograd.SeparableConv2d(in_filters,
out_filters,
3,
stride=1,
padding=1,
bias=False))
self.layers.append(autograd.BatchNorm2d(out_filters))
filters = out_filters
for i in range(reps - 1):
self.layers.append(autograd.ReLU())
self.layers.append(
autograd.SeparableConv2d(filters,
filters,
3,
stride=1,
padding=1,
bias=False))
self.layers.append(autograd.BatchNorm2d(filters))
if not grow_first:
self.layers.append(autograd.ReLU())
self.layers.append(
autograd.SeparableConv2d(in_filters,
out_filters,
3,
stride=1,
padding=1,
bias=False))
self.layers.append(autograd.BatchNorm2d(out_filters))
if not start_with_relu:
self.layers = self.layers[1:]
else:
self.layers[0] = autograd.ReLU()
if strides != 1:
self.layers.append(autograd.MaxPool2d(3, strides, padding + 1))