Model conversion failed

[myuzhao]

Model conversion failed, and the error " for i, op_out in enumerate(op.outputs): AttributeError: 'NoneType' object has no attribute 'outputs'" was reported. The model and conversion script are in the attachment. Please help me take a look.

test.zip

[myuzhao]

完整的日志 test.log

[myuzhao]

更新测试脚本，内部增加了pytorch 流式处理的代码，运行正确。
import os
import sys
import time
import torch
CURRENT_PATH = os.path.abspath(os.path.dirname(file))

sys.path.insert(1, os.path.join(CURRENT_PATH, '../../'))

from examples.models.gtcrn.gtcrn_stream import DEFAULT_STATE_DICT, StreamGTCRN
from tinynn.converter import TFLiteConverter
from tinynn.util.converter_util import export_converter_files

def main_worker():
device = torch.device("cpu")
model_path = "test_model.pt"
model = torch.load(model_path, map_location=torch.device('cpu'))
model.to(device)
model.eval()

#pytorch stream infer test
pytorch_stream_test = True
if pytorch_stream_test:
    from tqdm import tqdm
    import soundfile as sf
    import numpy as np

    frame_size = 64
    fft_size = 128
    x = torch.from_numpy(sf.read('mix.wav', dtype='float32')[0])
    x = torch.stft(x, fft_size, frame_size, fft_size, torch.hann_window(fft_size).pow(0.5), return_complex=False)[None]

    ### online (streaming) inference
    conv_cache = torch.zeros(2, 1, 8, 16, 17).to(device)
    tra_cache = torch.zeros(2, 3, 1, 1, 8).to(device)
    inter_cache = torch.zeros(2, 1, 17, 8).to(device)
    ys = []
    for i in tqdm(range(x.shape[2])):
        xi = x[:,:,i:i+1]
        with torch.no_grad():
            yi, mask, conv_cache, tra_cache, inter_cache = model(xi, conv_cache, tra_cache, inter_cache)
        ys.append(yi)
    ys = torch.cat(ys, dim=2)

    ys = ys.contiguous()
    ys = torch.view_as_complex(ys)
    ys = torch.istft(ys, fft_size, frame_size, fft_size, torch.hann_window(fft_size).pow(0.5)).detach().cpu().numpy()
    sf.write('enh_stream.wav', ys.squeeze(), 16000)


# model convert 
x = torch.rand((1, 65, 1, 2)).to(device)
conv_cache = torch.zeros(2, 1, 8, 16, 17).to(device)
tra_cache = torch.zeros(2, 3, 1, 1, 8).to(device)
inter_cache = torch.zeros(2, 1, 17, 8).to(device)

dummy_input = [x, conv_cache, tra_cache, inter_cache]

output_path = os.path.join(CURRENT_PATH, 'out', 'small.tflite')

# When converting quantized models, please ensure the quantization backend is set.
#torch.backends.quantized.engine = 'qnnpack'

# The code section below is used to convert the model to the TFLite format
# If you want perform dynamic quantization on the float models,
# you may refer to `dynamic.py`, which is in the same folder.
# As for static quantization (e.g. quantization-aware training and post-training quantization),
# please refer to the code examples in the `examples/quantization` folder.

export_converter_files(model, dummy_input, "test", "test_model")

converter = TFLiteConverter(model, dummy_input, output_path)
converter.convert()

if name == 'main':
main_worker()

test.zip