Hi.
When I exporting a model that final layer is an “interpolate layer”.
That model doesn’t have specific output shape.
I tested flowing simple model that has only interpolate layer.
When I print output shape of ort_session its show ['batch_size', 'Resizeoutput_dim_1', 'Resizeoutput_dim_2', 'Resizeoutput_dim_3'].
import onnxruntime
import onnx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleTest(nn.Module):
def __init__(self):
super(SimpleTest, self).__init__()
def forward(self, x):
y = F.interpolate(x, size=(x.shape[2] * 2, x.shape[2] * 2))
return y
if __name__ == "__main__":
model = SimpleTest()
model.cuda()
model.eval()
dummy_input = torch.rand(1, 1, 4, 4).cuda()
out = model(dummy_input)
torch.onnx.export(model, dummy_input,"./results/simple_test.onnx",export_params=True,
opset_version=11, do_constant_folding=True,
input_names=['input'], output_names=['output'],
dynamic_axes={'input':{0: 'batch_size'},
'output':{0: 'batch_size'}})
onnx_model = onnx.load("./results/simple_test.onnx")
onnx.checker.check_model(onnx_model)
dummy_input = np.array([[[[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 2.0], [3.0, 3.0, 3.0, 3.0], [4.0, 4.0, 4.0, 4.0]]]], dtype=np.float32)
ort_session = onnxruntime.InferenceSession("./results/simple_test.onnx")
ort_inputs = {ort_session.get_inputs()[0].name: dummy_input}
print("Input_Shape: ", ort_session.get_inputs()[0].shape)
print("Output_Shape: ", ort_session.get_outputs()[0].shape)
ort_outs = ort_session.run(None, ort_inputs)
print(ort_outs[0])
"""
>>> Input_Shape: ['batch_size', 1, 4, 4]
>>> Output_Shape: ['batch_size', 'Resizeoutput_dim_1', 'Resizeoutput_dim_2', 'Resizeoutput_dim_3']
>>>[[[[1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1.]
[2. 2. 2. 2. 2. 2. 2. 2.]
[2. 2. 2. 2. 2. 2. 2. 2.]
[3. 3. 3. 3. 3. 3. 3. 3.]
[3. 3. 3. 3. 3. 3. 3. 3.]
[4. 4. 4. 4. 4. 4. 4. 4.]
[4. 4. 4. 4. 4. 4. 4. 4.]]]]
"""
How to specify the interpolate layers output shape when export to onnx?
