AttributeError: 'list' object has no attribute 'val'. When set flexible input shapes #2037
Description
🐞Describing the bug
Traceback (most recent call last):
File "/media/anlab/data-2tb/ANLAB_THUY/ImageSearcher/ConvertSolar2Coreml.py", line 119, in <module>
mlprogram = ct.convert(
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/_converters_entry.py", line 551, in convert
mlmodel = mil_convert(
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 188, in mil_convert
return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 212, in _mil_convert
proto, mil_program = mil_convert_to_proto(
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
prog = frontend_converter(model, **kwargs)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 108, in __call__
return load(*args, **kwargs)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 75, in load
return _perform_torch_convert(converter, debug)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 114, in _perform_torch_convert
prog = converter.convert()
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 484, in convert
convert_nodes(self.context, self.graph)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 93, in convert_nodes
add_op(context, node)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 3923, in avg_pool2d
_avg_pool(context, node, inputs)
File "/home/anlab/anaconda3/envs/convertmodel1/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 3876, in _avg_pool
strides = mb.const(val=kernel_sizes.val, name=strides.name)
AttributeError: 'list' object has no attribute 'val'
To Reproduce
- I try convert model SOLAR- (https://github.com/tonyngjichun/SOLAR/tree/master)
Init model pytorch:
class Network(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model.cpu()
self.mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
self.std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
def forward(self,x):
out1 = self.model(x)
reshaped_tensor1 = out1.view(1, 2048)
return reshaped_tensor1
state = torch.load(os.path.join(get_data_root(), 'networks/model_best.pth.tar'),map_location=torch.device('cpu'))
net_params = {}
net_params['architecture'] = state['meta']['architecture']
net_params['pooling'] = state['meta']['pooling']
net_params['local_whitening'] = state['meta'].get('local_whitening', False)
net_params['regional'] = state['meta'].get('regional', False)
net_params['whitening'] = state['meta'].get('whitening', True)
net_params['mean'] = state['meta']['mean']
net_params['std'] = state['meta']['std']
net_params['pretrained'] = False
net = load_network('model_best.pth.tar')
net.load_state_dict(state['state_dict'])
net.eval()
test_model = Network(net)
Convert pytorch to coreml
scale = 1/(0.226*255.0)
bias = [- 0.485/(0.229) , - 0.456/(0.224), - 0.406/(0.225)]
input_shape = ct.Shape(shape=(1, 3, ct.RangeDim(lower_bound=100, upper_bound=800),
ct.RangeDim(lower_bound=100, upper_bound=800)))
dummy_input = torch.rand(1,3,300,300)
input_tensor = ct.ImageType(name="my_input", shape=input_shape,scale=scale, bias=bias)
traced_model = torch.jit.trace(test_model.eval(), dummy_input)
traced_model.eval()
mlprogram = ct.convert(
traced_model,
minimum_deployment_target=ct.target.iOS13,
inputs=[input_tensor],
outputs=[ct.TensorType(name="embeddings")],
convert_to="neuralnetwork",
compute_units=ct.ComputeUnit.CPU_ONLY,
)
saved_model = 'ModelConvert/TestModel/Solar300_image_CPU_FlexibleInput.mlmodel'
outputmodel.save(saved_model)
System environment
- coremltools version: 7.0
- OS (e.g. MacOS version or Linux type): Linux
- Any other relevant version information (e.g. PyTorch or TensorFlow version): Torch 1.9.1