Could not find an implementation for LeakyRelu(6) in onnx runtime #512

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claunia · 2026-01-29T21:48:25Z

claunia commented

2026-01-29 21:48:25 +00:00

Originally created by @SamedHrmn on GitHub (Oct 21, 2024).

Hi, I was converting GFPGANv1.3.pth to onnx format. But I got a error when I try to inference.

onnx: 1.17.0
onnxruntime: 1.19.2
torch: 2.4.1+cu121
onnxsim: 0.4.36

import torch
import onnx
import onnxruntime as ort
from onnxsim import simplify

from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean


def convert_static_GFPGANv1Clean_1_3_onnx():
    onnx_path = "./pretrained/GFPGANv1.3.onnx"
    sim_onnx_path = "./pretrained/GFPGANv1.3_sim.onnx"
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model_path = 'experiments/pretrained_models/GFPGANv1.3.pth'

    inference_model = GFPGANv1Clean(
        out_size=512,
        num_style_feat=512,
        channel_multiplier=2,
        decoder_load_path=None,
        fix_decoder=False,
        num_mlp=8,
        input_is_latent=True,
        different_w=True,
        narrow=1,
        sft_half=True).to(device)
    
    loadnet = torch.load(model_path)
    if 'params_ema' in loadnet:
        keyname = 'params_ema'
    else:
        keyname = 'params'
    #inference_model.load_state_dict(loadnet[keyname], strict=True)
    inference_model.load_state_dict(loadnet[keyname], strict=False)
    
    
    inference_model = inference_model.eval()
    
    img_path = input_image_path
    input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
    img = cv2.resize(input_img, (512, 512))
    cropped_face_t = img2tensor(img / 255., bgr2rgb=True, float32=True)
    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
    cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
    
    mat1 = torch.randn(3, 512, 512).cpu()  # moving the tensor to cpu
    mat1 = mat1.unsqueeze(0).to(device)
    return_rgb=False
    
    torch.onnx.export(inference_model,  # model being run
                      (cropped_face_t, return_rgb),  # model input (or a tuple for multiple inputs)
                      onnx_path,  # where to save the model (can be a file or file-like object)
                      export_params=True,  # store the trained parameter weights inside the model file
                      opset_version=11,  # the ONNX version to export the model to
                      do_constant_folding=True,  # whether to execute constant folding for optimization
                      verbose=True,
                      input_names=['input'],  # the model's input names
                      output_names=['out_ab']  # the model's output names
                      )

    print("export GFPGANv1.3 onnx done.")
    onnx_model = onnx.load(onnx_path)
    onnx.checker.check_model(onnx_model)

    model_simp, check = simplify(onnx_model, check_n=3)
    onnx.save(model_simp, sim_onnx_path)
    print("export GFPGANv1.3 onnx sim done.")


convert_static_GFPGANv1Clean_1_3_onnx()

// Inference
import onnxruntime as ort

session = ort.InferenceSession("pretrained/GFPGANv1.3.onnx")

# Prepare input
input_name = session.get_inputs()[0].name
input_data = prepare_input_data(input_image_path)

# Run inference
outputs = session.run(None, {input_name: input_data})

# Process the output
output = post_process_output(outputs)
output_im = Image.fromarray(output)

Error:

[/usr/local/lib/python3.10/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py](https://localhost:8080/#) in _create_inference_session(self, providers, provider_options, disabled_optimizers)
    489 
    490         # initialize the C++ InferenceSession
--> 491         sess.initialize_session(providers, provider_options, disabled_optimizers)
    492 
    493         self._sess = sess

NotImplemented: [ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for LeakyRelu(6) node with name '/stylegan_decoder/style_conv1/activate/LeakyRelu'
`` `

Originally created by @SamedHrmn on GitHub (Oct 21, 2024). Hi, I was converting GFPGANv1.3.pth to onnx format. But I got a error when I try to inference. onnx: 1.17.0 onnxruntime: 1.19.2 torch: 2.4.1+cu121 onnxsim: 0.4.36 ``` import torch import onnx import onnxruntime as ort from onnxsim import simplify from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean def convert_static_GFPGANv1Clean_1_3_onnx(): onnx_path = "./pretrained/GFPGANv1.3.onnx" sim_onnx_path = "./pretrained/GFPGANv1.3_sim.onnx" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model_path = 'experiments/pretrained_models/GFPGANv1.3.pth' inference_model = GFPGANv1Clean( out_size=512, num_style_feat=512, channel_multiplier=2, decoder_load_path=None, fix_decoder=False, num_mlp=8, input_is_latent=True, different_w=True, narrow=1, sft_half=True).to(device) loadnet = torch.load(model_path) if 'params_ema' in loadnet: keyname = 'params_ema' else: keyname = 'params' #inference_model.load_state_dict(loadnet[keyname], strict=True) inference_model.load_state_dict(loadnet[keyname], strict=False) inference_model = inference_model.eval() img_path = input_image_path input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) img = cv2.resize(input_img, (512, 512)) cropped_face_t = img2tensor(img / 255., bgr2rgb=True, float32=True) normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) cropped_face_t = cropped_face_t.unsqueeze(0).to(device) mat1 = torch.randn(3, 512, 512).cpu() # moving the tensor to cpu mat1 = mat1.unsqueeze(0).to(device) return_rgb=False torch.onnx.export(inference_model, # model being run (cropped_face_t, return_rgb), # model input (or a tuple for multiple inputs) onnx_path, # where to save the model (can be a file or file-like object) export_params=True, # store the trained parameter weights inside the model file opset_version=11, # the ONNX version to export the model to do_constant_folding=True, # whether to execute constant folding for optimization verbose=True, input_names=['input'], # the model's input names output_names=['out_ab'] # the model's output names ) print("export GFPGANv1.3 onnx done.") onnx_model = onnx.load(onnx_path) onnx.checker.check_model(onnx_model) model_simp, check = simplify(onnx_model, check_n=3) onnx.save(model_simp, sim_onnx_path) print("export GFPGANv1.3 onnx sim done.") convert_static_GFPGANv1Clean_1_3_onnx() // Inference import onnxruntime as ort session = ort.InferenceSession("pretrained/GFPGANv1.3.onnx") # Prepare input input_name = session.get_inputs()[0].name input_data = prepare_input_data(input_image_path) # Run inference outputs = session.run(None, {input_name: input_data}) # Process the output output = post_process_output(outputs) output_im = Image.fromarray(output) ``` Error: ``` [/usr/local/lib/python3.10/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py](https://localhost:8080/#) in _create_inference_session(self, providers, provider_options, disabled_optimizers) 489 490 # initialize the C++ InferenceSession --> 491 sess.initialize_session(providers, provider_options, disabled_optimizers) 492 493 self._sess = sess NotImplemented: [ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for LeakyRelu(6) node with name '/stylegan_decoder/style_conv1/activate/LeakyRelu' `` `

claunia commented

2026-01-29 21:48:25 +00:00

@JulisCheng2020 commented on GitHub (Nov 12, 2024):

does anybody solved it?
I has same problem

@JulisCheng2020 commented on GitHub (Nov 12, 2024): does anybody solved it? I has same problem

claunia commented

2026-01-29 21:48:26 +00:00

@hanhaonwu commented on GitHub (Mar 19, 2025):

does anybody solved it? I has same problem

have you solved it?

@hanhaonwu commented on GitHub (Mar 19, 2025): > does anybody solved it? I has same problem have you solved it?

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Reference: TencentARC/GFPGAN#512