if __name__ == '__main__':
max_error = 0
for change_ordering in [False, True]:
for kernel_size in [1, 3, 5, 7]:
for padding in [0, 1, 3]:
for stride in [1, 2, 3, 4]:
# RuntimeError: invalid argument 2: pad should be smaller than half of kernel size, but got padW = 1, padH = 1, kW = 1,
if padding > kernel_size / 2:
continue
model = LayerTest(kernel_size=kernel_size, padding=padding, stride=stride)
model.eval()
input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
input_var = Variable(torch.FloatTensor(input_np))
torch.onnx.export(model, input_var, "_tmpnet.onnx", verbose=True, input_names=['test_in'],
output_names=['test_out'])
onnx_model = onnx.load('_tmpnet.onnx')
k_model = onnx_to_keras(onnx_model, ['test_in'], change_ordering=change_ordering)
error = check_torch_keras_error(model, k_model, input_np, change_ordering=change_ordering)
print('Error:', error)
if max_error < error:
max_error = error
print('Max error: {0}'.format(max_error))
import torch
from torch.autograd import Variable
from onnx2keras_custom import onnx_to_keras, check_torch_keras_error
import onnx
from torchvision.models.densenet import densenet121
if __name__ == '__main__':
model = densenet121()
model.eval()
input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
input_var = Variable(torch.FloatTensor(input_np), requires_grad=False)
output = model(input_var)
torch.onnx.export(model, (input_var),
"_tmpnet.onnx",
verbose=True,
input_names=['test_in1'],
output_names=['test_out'])
onnx_model = onnx.load('_tmpnet.onnx')
k_model = onnx_to_keras(onnx_model, ['test_in1', 'test_in2'],
change_ordering=True)
error = check_torch_keras_error(model,
k_model,
input_np,
change_ordering=True)
print('Max error: {0}'.format(error))
