self.batchnorm15(self.conv15(out)), negative_slope=0.1)
out = F.leaky_relu(
self.batchnorm16(self.conv16(out)), negative_slope=0.1)
out = F.leaky_relu(
self.batchnorm17(self.conv17(out)), negative_slope=0.1)
out = F.leaky_relu(
self.batchnorm18(self.conv18(out)), negative_slope=0.1)
out = F.leaky_relu(
self.batchnorm19(self.conv19(out)), negative_slope=0.1)
out = F.leaky_relu(
self.batchnorm20(self.conv20(out)), negative_slope=0.1)
out = torch.cat([passthrough, out], 1)
out = F.leaky_relu(
self.batchnorm21(self.conv21(out)), negative_slope=0.1)
out = self.conv22(out)
return out
model = Yolov2()
model.eval()
xb = torch.rand((1, 3, 224, 224))
yp = model(xb)
export_onnx_with_validation(
model, (xb, ),
'sample_yolov2', ['image'], ['pred'],
verbose=True,
training=False)
self.down3 = down(256, 512)
self.down4 = down(512, 512)
self.up1 = up(1024, 256)
self.up2 = up(512, 128)
self.up3 = up(256, 64)
self.up4 = up(128, 64)
self.outc = outconv(64, n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
x = self.outc(x)
return F.sigmoid(x)
model = UNet(3, 80)
model.eval()
xb = torch.rand((1, 3, 512, 512))
yp = model(xb)
export_onnx_with_validation(model, [xb],
'sample_unet', ['image'], ['pred'],
verbose=True,
training=False)
def forward(self, x):
y = x
y = self.fc(y)
return y
model = Model()
model.eval()
xb = torch.rand((2, 3))
yp = model(xb)
idx += 1
print('index: ', idx)
export_onnx_with_validation(
model, (xb, ),
prefix + str(idx), ['x'], ['y'],
verbose=True,
training=False)
######## example: compare ########
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x0, x1):
x0 = x0.clamp(-1, 1)
a = torch.max(x0, x1) == x1
b = x0 < x1
c = x0 > x1
h, c = self.lstm(h, (h2, c2))
return h, c
model = Model()
model.eval()
xb = torch.rand((7, 6))
h1 = torch.zeros((7, 5))
h2 = torch.zeros((7, 4))
c2 = torch.zeros((7, 4))
yp = model(xb, h1, h2, c2)
idx += 1
print('index: ', idx)
export_onnx_with_validation(model, [xb, h1, h2, c2],
prefix + str(idx), ['x', 'h1', 'h2', 'c2'],
['h', 'c'],
verbose=True,
training=False)
######## example: RNN ########
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.gru = nn.GRU(6, 5, 3)
self.lstm = nn.LSTM(5, 4, 2)
def forward(self, x, h1, h2, c2):
y, h1 = self.gru(x, h1)
y, (h2, c2) = self.lstm(y, (h2, c2))
