SCUNet
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Error in converting to ONNX model
I am getting the following error, while trying to convert the pre-trained model to ONNX model. Can you please look into it and let me know that the pre-trained weights were generated using the current updated model? Conversion code is provided after the error.
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Block Initial Type: W, drop_path_rate:0.000000
Block Initial Type: SW, drop_path_rate:0.000000
Traceback (most recent call last):
File "/home/ayaz_khan/SCUNet/onnx.py", line 2, in
import torch
import torch.onnx
from models.network_scunet import SCUNet # Assuming this is the SCUNet model definition
def convert_to_onnx(model_path, onnx_path, input_shape=(1, 3, 256, 256)):
# Load the pre-trained PyTorch model
model = SCUNet()
model.load_state_dict(torch.load(model_path))
# Set the model to evaluation mode
model.eval()
# Define dummy input data
dummy_input = torch.randn(input_shape)
# Convert the model to ONNX format
torch.onnx.export(model, dummy_input, onnx_path, verbose=True)
print(f"Model converted to ONNX format and saved as {onnx_path}")
# Paths
model_path = "model_zoo/scunet_color_real_psnr.pth"
onnx_path = "./scunet_color_real_gan.onnx"
convert_to_onnx(model_path, onnx_path)
I've converted the models toONNX (dynamic axes for input size). Models are working but take only square input images Divisible by 128
I managed to output the model in onnx form, here is my code:
net = SCUNet()
onnx_path = "onnx_model_name.onnx"
torch.onnx.export(net, torch.randn((2, 3, 64, 64)), onnx_path)
But remember to run the model's classes and methods all through before doing so
I've converted the models toONNX (dynamic axes for input size). Models are working but take only square input images Divisible by 128
Hello, I just checked your homepage but didn't find any content about SCUNet. Can we discuss it?
I am getting the following error, while trying to convert the pre-trained model to ONNX model. Can you please look into it and let me know that the pre-trained weights were generated using the current updated model? Conversion code is provided after the error.
Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Block Initial Type: W, drop_path_rate:0.000000 Block Initial Type: SW, drop_path_rate:0.000000 Traceback (most recent call last): File "/home/ayaz_khan/SCUNet/onnx.py", line 2, in import torch.onnx File "/home/ayaz_khan/.local/lib/python3.9/site-packages/torch/onnx/init.py", line 57, in from ._internal.onnxruntime import ( File "/home/ayaz_khan/.local/lib/python3.9/site-packages/torch/onnx/_internal/onnxruntime.py", line 34, in import onnx File "/home/ayaz_khan/SCUNet/onnx.py", line 25, in convert_to_onnx(model_path, onnx_path) File "/home/ayaz_khan/SCUNet/onnx.py", line 8, in convert_to_onnx model.load_state_dict(torch.load(model_path)) File "/home/ayaz_khan/.local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 2152, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for SCUNet: Missing key(s) in state_dict: "m_down1.2.weight", "m_down2.2.weight", "m_down3.2.weight". Unexpected key(s) in state_dict: "m_down1.3.trans_block.ln1.weight", "m_down1.3.trans_block.ln1.bias", "m_down1.3.trans_block.msa.relative_position_params", "m_down1.3.trans_block.msa.embedding_layer.weight", "m_down1.3.trans_block.msa.embedding_layer.bias", "m_down1.3.trans_block.msa.linear.weight", "m_down1.3.trans_block.msa.linear.bias", "m_down1.3.trans_block.ln2.weight", "m_down1.3.trans_block.ln2.bias", "m_down1.3.trans_block.mlp.0.weight", "m_down1.3.trans_block.mlp.0.bias", "m_down1.3.trans_block.mlp.2.weight", "m_down1.3.trans_block.mlp.2.bias", "m_down1.3.conv1_1.weight", "m_down1.3.conv1_1.bias", "m_down1.3.conv1_2.weight", "m_down1.3.conv1_2.bias", "m_down1.3.conv_block.0.weight", "m_down1.3.conv_block.2.weight", "m_down1.4.weight", "m_down1.2.trans_block.ln1.weight", "m_down1.2.trans_block.ln1.bias", "m_down1.2.trans_block.msa.relative_position_params", "m_down1.2.trans_block.msa.embedding_layer.weight", "m_down1.2.trans_block.msa.embedding_layer.bias", "m_down1.2.trans_block.msa.linear.weight", "m_down1.2.trans_block.msa.linear.bias", "m_down1.2.trans_block.ln2.weight", "m_down1.2.trans_block.ln2.bias", "m_down1.2.trans_block.mlp.0.weight", "m_down1.2.trans_block.mlp.0.bias", "m_down1.2.trans_block.mlp.2.weight", "m_down1.2.trans_block.mlp.2.bias", "m_down1.2.conv1_1.weight", "m_down1.2.conv1_1.bias", "m_down1.2.conv1_2.weight", "m_down1.2.conv1_2.bias", "m_down1.2.conv_block.0.weight", "m_down1.2.conv_block.2.weight", "m_down2.3.trans_block.ln1.weight", "m_down2.3.trans_block.ln1.bias", "m_down2.3.trans_block.msa.relative_position_params", "m_down2.3.trans_block.msa.embedding_layer.weight", "m_down2.3.trans_block.msa.embedding_layer.bias", "m_down2.3.trans_block.msa.linear.weight", "m_down2.3.trans_block.msa.linear.bias", "m_down2.3.trans_block.ln2.weight", "m_down2.3.trans_block.ln2.bias", "m_down2.3.trans_block.mlp.0.weight", "m_down2.3.trans_block.mlp.0.bias", "m_down2.3.trans_block.mlp.2.weight", "m_down2.3.trans_block.mlp.2.bias", "m_down2.3.conv1_1.weight", "m_down2.3.conv1_1.bias", "m_down2.3.conv1_2.weight", "m_down2.3.conv1_2.bias", "m_down2.3.conv_block.0.weight", "m_down2.3.conv_block.2.weight", "m_down2.4.weight", "m_down2.2.trans_block.ln1.weight", "m_down2.2.trans_block.ln1.bias", "m_down2.2.trans_block.msa.relative_position_params", "m_down2.2.trans_block.msa.embedding_layer.weight", "m_down2.2.trans_block.msa.embedding_layer.bias", "m_down2.2.trans_block.msa.linear.weight", "m_down2.2.trans_block.msa.linear.bias", "m_down2.2.trans_block.ln2.weight", "m_down2.2.trans_block.ln2.bias", "m_down2.2.trans_block.mlp.0.weight", "m_down2.2.trans_block.mlp.0.bias", "m_down2.2.trans_block.mlp.2.weight", "m_down2.2.trans_block.mlp.2.bias", "m_down2.2.conv1_1.weight", "m_down2.2.conv1_1.bias", "m_down2.2.conv1_2.weight", "m_down2.2.conv1_2.bias", "m_down2.2.conv_block.0.weight", "m_down2.2.conv_block.2.weight", "m_down3.3.trans_block.ln1.weight", "m_down3.3.trans_block.ln1.bias", "m_down3.3.trans_block.msa.relative_position_params", "m_down3.3.trans_block.msa.embedding_layer.weight", "m_down3.3.trans_block.msa.embedding_layer.bias", "m_down3.3.trans_block.msa.linear.weight", "m_down3.3.trans_block.msa.linear.bias", "m_down3.3.trans_block.ln2.weight", "m_down3.3.trans_block.ln2.bias", "m_down3.3.trans_block.mlp.0.weight", "m_down3.3.trans_block.mlp.0.bias", "m_down3.3.trans_block.mlp.2.weight", "m_down3.3.trans_block.mlp.2.bias", "m_down3.3.conv1_1.weight", "m_down3.3.conv1_1.bias", "m_down3.3.conv1_2.weight", "m_down3.3.conv1_2.bias", "m_down3.3.conv_block.0.weight", "m_down3.3.conv_block.2.weight", "m_down3.4.weight", "m_down3.2.trans_block.ln1.weight", "m_down3.2.trans_block.ln1.bias", "m_down3.2.trans_block.msa.relative_position_params", "m_down3.2.trans_block.msa.embedding_layer.weight", "m_down3.2.trans_block.msa.embedding_layer.bias", "m_down3.2.trans_block.msa.linear.weight", "m_down3.2.trans_block.msa.linear.bias", "m_down3.2.trans_block.ln2.weight", "m_down3.2.trans_block.ln2.bias", "m_down3.2.trans_block.mlp.0.weight", "m_down3.2.trans_block.mlp.0.bias", "m_down3.2.trans_block.mlp.2.weight", "m_down3.2.trans_block.mlp.2.bias", "m_down3.2.conv1_1.weight", "m_down3.2.conv1_1.bias", "m_down3.2.conv1_2.weight", "m_down3.2.conv1_2.bias", "m_down3.2.conv_block.0.weight", "m_down3.2.conv_block.2.weight", "m_body.2.trans_block.ln1.weight", "m_body.2.trans_block.ln1.bias", "m_body.2.trans_block.msa.relative_position_params", "m_body.2.trans_block.msa.embedding_layer.weight", "m_body.2.trans_block.msa.embedding_layer.bias", "m_body.2.trans_block.msa.linear.weight", "m_body.2.trans_block.msa.linear.bias", "m_body.2.trans_block.ln2.weight", "m_body.2.trans_block.ln2.bias", "m_body.2.trans_block.mlp.0.weight", "m_body.2.trans_block.mlp.0.bias", "m_body.2.trans_block.mlp.2.weight", "m_body.2.trans_block.mlp.2.bias", "m_body.2.conv1_1.weight", "m_body.2.conv1_1.bias", "m_body.2.conv1_2.weight", "m_body.2.conv1_2.bias", "m_body.2.conv_block.0.weight", "m_body.2.conv_block.2.weight", "m_body.3.trans_block.ln1.weight", "m_body.3.trans_block.ln1.bias", "m_body.3.trans_block.msa.relative_position_params", "m_body.3.trans_block.msa.embedding_layer.weight", "m_body.3.trans_block.msa.embedding_layer.bias", "m_body.3.trans_block.msa.linear.weight", "m_body.3.trans_block.msa.linear.bias", "m_body.3.trans_block.ln2.weight", "m_body.3.trans_block.ln2.bias", "m_body.3.trans_block.mlp.0.weight", "m_body.3.trans_block.mlp.0.bias", "m_body.3.trans_block.mlp.2.weight", "m_body.3.trans_block.mlp.2.bias", "m_body.3.conv1_1.weight", "m_body.3.conv1_1.bias", "m_body.3.conv1_2.weight", "m_body.3.conv1_2.bias", "m_body.3.conv_block.0.weight", "m_body.3.conv_block.2.weight", "m_up3.3.trans_block.ln1.weight", "m_up3.3.trans_block.ln1.bias", "m_up3.3.trans_block.msa.relative_position_params", "m_up3.3.trans_block.msa.embedding_layer.weight", "m_up3.3.trans_block.msa.embedding_layer.bias", "m_up3.3.trans_block.msa.linear.weight", "m_up3.3.trans_block.msa.linear.bias", "m_up3.3.trans_block.ln2.weight", "m_up3.3.trans_block.ln2.bias", "m_up3.3.trans_block.mlp.0.weight", "m_up3.3.trans_block.mlp.0.bias", "m_up3.3.trans_block.mlp.2.weight", "m_up3.3.trans_block.mlp.2.bias", "m_up3.3.conv1_1.weight", "m_up3.3.conv1_1.bias", "m_up3.3.conv1_2.weight", "m_up3.3.conv1_2.bias", "m_up3.3.conv_block.0.weight", "m_up3.3.conv_block.2.weight", "m_up3.4.trans_block.ln1.weight", "m_up3.4.trans_block.ln1.bias", "m_up3.4.trans_block.msa.relative_position_params", "m_up3.4.trans_block.msa.embedding_layer.weight", "m_up3.4.trans_block.msa.embedding_layer.bias", "m_up3.4.trans_block.msa.linear.weight", "m_up3.4.trans_block.msa.linear.bias", "m_up3.4.trans_block.ln2.weight", "m_up3.4.trans_block.ln2.bias", "m_up3.4.trans_block.mlp.0.weight", "m_up3.4.trans_block.mlp.0.bias", "m_up3.4.trans_block.mlp.2.weight", "m_up3.4.trans_block.mlp.2.bias", "m_up3.4.conv1_1.weight", "m_up3.4.conv1_1.bias", "m_up3.4.conv1_2.weight", "m_up3.4.conv1_2.bias", "m_up3.4.conv_block.0.weight", "m_up3.4.conv_block.2.weight", "m_up2.3.trans_block.ln1.weight", "m_up2.3.trans_block.ln1.bias", "m_up2.3.trans_block.msa.relative_position_params", "m_up2.3.trans_block.msa.embedding_layer.weight", "m_up2.3.trans_block.msa.embedding_layer.bias", "m_up2.3.trans_block.msa.linear.weight", "m_up2.3.trans_block.msa.linear.bias", "m_up2.3.trans_block.ln2.weight", "m_up2.3.trans_block.ln2.bias", "m_up2.3.trans_block.mlp.0.weight", "m_up2.3.trans_block.mlp.0.bias", "m_up2.3.trans_block.mlp.2.weight", "m_up2.3.trans_block.mlp.2.bias", "m_up2.3.conv1_1.weight", "m_up2.3.conv1_1.bias", "m_up2.3.conv1_2.weight", "m_up2.3.conv1_2.bias", "m_up2.3.conv_block.0.weight", "m_up2.3.conv_block.2.weight", "m_up2.4.trans_block.ln1.weight", "m_up2.4.trans_block.ln1.bias", "m_up2.4.trans_block.msa.relative_position_params", "m_up2.4.trans_block.msa.embedding_layer.weight", "m_up2.4.trans_block.msa.embedding_layer.bias", "m_up2.4.trans_block.msa.linear.weight", "m_up2.4.trans_block.msa.linear.bias", "m_up2.4.trans_block.ln2.weight", "m_up2.4.trans_block.ln2.bias", "m_up2.4.trans_block.mlp.0.weight", "m_up2.4.trans_block.mlp.0.bias", "m_up2.4.trans_block.mlp.2.weight", "m_up2.4.trans_block.mlp.2.bias", "m_up2.4.conv1_1.weight", "m_up2.4.conv1_1.bias", "m_up2.4.conv1_2.weight", "m_up2.4.conv1_2.bias", "m_up2.4.conv_block.0.weight", "m_up2.4.conv_block.2.weight", "m_up1.3.trans_block.ln1.weight", "m_up1.3.trans_block.ln1.bias", "m_up1.3.trans_block.msa.relative_position_params", "m_up1.3.trans_block.msa.embedding_layer.weight", "m_up1.3.trans_block.msa.embedding_layer.bias", "m_up1.3.trans_block.msa.linear.weight", "m_up1.3.trans_block.msa.linear.bias", "m_up1.3.trans_block.ln2.weight", "m_up1.3.trans_block.ln2.bias", "m_up1.3.trans_block.mlp.0.weight", "m_up1.3.trans_block.mlp.0.bias", "m_up1.3.trans_block.mlp.2.weight", "m_up1.3.trans_block.mlp.2.bias", "m_up1.3.conv1_1.weight", "m_up1.3.conv1_1.bias", "m_up1.3.conv1_2.weight", "m_up1.3.conv1_2.bias", "m_up1.3.conv_block.0.weight", "m_up1.3.conv_block.2.weight", "m_up1.4.trans_block.ln1.weight", "m_up1.4.trans_block.ln1.bias", "m_up1.4.trans_block.msa.relative_position_params", "m_up1.4.trans_block.msa.embedding_layer.weight", "m_up1.4.trans_block.msa.embedding_layer.bias", "m_up1.4.trans_block.msa.linear.weight", "m_up1.4.trans_block.msa.linear.bias", "m_up1.4.trans_block.ln2.weight", "m_up1.4.trans_block.ln2.bias", "m_up1.4.trans_block.mlp.0.weight", "m_up1.4.trans_block.mlp.0.bias", "m_up1.4.trans_block.mlp.2.weight", "m_up1.4.trans_block.mlp.2.bias", "m_up1.4.conv1_1.weight", "m_up1.4.conv1_1.bias", "m_up1.4.conv1_2.weight", "m_up1.4.conv1_2.bias", "m_up1.4.conv_block.0.weight", "m_up1.4.conv_block.2.weight".
import torch import torch.onnx from models.network_scunet import SCUNet # Assuming this is the SCUNet model definition def convert_to_onnx(model_path, onnx_path, input_shape=(1, 3, 256, 256)): # Load the pre-trained PyTorch model model = SCUNet() model.load_state_dict(torch.load(model_path)) # Set the model to evaluation mode model.eval() # Define dummy input data dummy_input = torch.randn(input_shape) # Convert the model to ONNX format torch.onnx.export(model, dummy_input, onnx_path, verbose=True) print(f"Model converted to ONNX format and saved as {onnx_path}") # Paths model_path = "model_zoo/scunet_color_real_psnr.pth" onnx_path = "./scunet_color_real_gan.onnx" convert_to_onnx(model_path, onnx_path)
hello，Dr. Ayaz H. Khan ,do u have solved this question?
I've converted the models toONNX (dynamic axes for input size). Models are working but take only square input images Divisible by 128
Hello, I just checked your homepage but didn't find any content about SCUNet. Can we discuss it?
I only converted it for local use. I can search for my conversion script and post it here. If I find it
Thank you so much. Your reply has given me hope. Thank you.
I've converted the models toONNX (dynamic axes for input size). Models are working but take only square input images Divisible by 128
Hello, I just checked your homepage but didn't find any content about SCUNet. Can we discuss it?
I only converted it for local use. I can search for my conversion script and post it here. If I find it
Thank you so much. Your reply has given me hope. Thank you.
My conversion code, edit to convert model full or model half: (input has to be divisible by 128, (128x128), (256x256) ......
Takes some time, be patient... Also some warnings, but onnx model working
import argparse
import numpy as np
import torch
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='models/scunet_color_real_psnr.pth', help='scunet_color_real_psnrpth, scunet_color_real_gan.pth')
args = parser.parse_args()
n_channels = 3 # color model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#device = 'cpu'
from models.network_scunet import SCUNet as net
model = net(in_nc=n_channels,config=[4,4,4,4,4,4,4],dim=64)
model.load_state_dict(torch.load(args.model), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
#model = model.half()
model = model.to(device)
onnx_path = 'scunet_color_real_psnr.onnx'
dummy_input = torch.randn(1, 3, 256, 256)
#model half:
#dummy_input = torch.randn(1, 3, 256, 256, dtype=torch.float16)
#or
#model:
dummy_input = torch.randn(1, 3, 256, 256, dtype=torch.float32)
#only if cuda available
dummy_input = dummy_input.to('cuda')
with torch.no_grad():
torch.onnx.export(
model,
dummy_input,
onnx_path,
do_constant_folding=False,
input_names=['input'],
output_names=['output'],
opset_version=12,
dynamic_axes={'input': {2: 'height', 3: 'width'}, 'output': {2: 'height', 3: 'width'}}
)
input("Conversion done")
#
My conversion code, edit to convert model full or model half: (input has to be divisible by 128, (128x128), (256x256) ......
Takes some time, be patient... Also some warnings, but onnx model working
import argparse import numpy as np import torch parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default='models/scunet_color_real_psnr.pth', help='scunet_color_real_psnrpth, scunet_color_real_gan.pth') args = parser.parse_args() n_channels = 3 # color model device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') #device = 'cpu' from models.network_scunet import SCUNet as net model = net(in_nc=n_channels,config=[4,4,4,4,4,4,4],dim=64) model.load_state_dict(torch.load(args.model), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False #model = model.half() model = model.to(device) onnx_path = 'scunet_color_real_psnr.onnx' dummy_input = torch.randn(1, 3, 256, 256) #model half: #dummy_input = torch.randn(1, 3, 256, 256, dtype=torch.float16) #or #model: dummy_input = torch.randn(1, 3, 256, 256, dtype=torch.float32) #only if cuda available dummy_input = dummy_input.to('cuda') with torch.no_grad(): torch.onnx.export( model, dummy_input, onnx_path, do_constant_folding=False, input_names=['input'], output_names=['output'], opset_version=12, dynamic_axes={'input': {2: 'height', 3: 'width'}, 'output': {2: 'height', 3: 'width'}} ) input("Conversion done") #
Thank you very much, let me give it a try. Happy Chinese New Year, and I wish you a prosperous and successful year ahead
This is how I proceeded, thank you very much. But my resulting image (originally 128x128) is then divided into 9 parts, which contain the channel information separately. How do I get a normal image again? I use onnx-runtime within my C++ code.
ONNX Model input/output shapes should be input (1,3,h,w) and output (1,3,h,w) or (batch, channels, height, width)
It's a very long time ago when I coded in C++ So this is pre- and post-processing of your image using python
def denoise(self, img):
img = img.astype(np.float32)
img = img.transpose((2, 0, 1))
img = img /255
img = np.expand_dims(img, axis=0).astype(np.float32)
res = self.session.run(None,{'input':img})[0]
res = (res.squeeze().transpose((1,2,0)) * 255).clip(0, 255).astype(np.uint8)
return res
Maybe this is the way to do in C++:
#include <opencv2/opencv.hpp>
#include <onnxruntime/core/session/onnxruntime_cxx_api.h>
cv::Mat denoise(Ort::Session& session, const cv::Mat& img) {
cv::Mat floatImg;
img.convertTo(floatImg, CV_32F, 1.0 / 255);
// Convert HWC to CHW format
std::vector<cv::Mat> channels(3);
cv::split(floatImg, channels);
for (int i = 0; i < 3; i++) {
channels[i] = channels[i].reshape(1, 1);
}
cv::Mat chwImg;
cv::hconcat(channels, chwImg);
// Expand dimensions (1, 3, H, W)
std::vector<int64_t> inputShape = {1, 3, img.rows, img.cols};
std::vector<float> inputTensorValues(chwImg.begin<float>(), chwImg.end<float>());
// Create ONNX Runtime input tensor
Ort::MemoryInfo memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
Ort::Value inputTensor = Ort::Value::CreateTensor<float>(memoryInfo, inputTensorValues.data(), inputTensorValues.size(), inputShape.data(), inputShape.size());
// Run inference
auto outputTensors = session.Run(Ort::RunOptions{nullptr}, {"input"}, &inputTensor, 1, session.GetOutputNames().data(), session.GetOutputNames().size());
// Get output tensor
float* outputData = outputTensors.front().GetTensorMutableData<float>();
cv::Mat outputMat(3, img.size(), CV_32F, outputData);
// Convert CHW back to HWC
std::vector<cv::Mat> outputChannels = {
cv::Mat(img.rows, img.cols, CV_32F, outputMat.ptr(0)),
cv::Mat(img.rows, img.cols, CV_32F, outputMat.ptr(1)),
cv::Mat(img.rows, img.cols, CV_32F, outputMat.ptr(2))
};
cv::Mat finalImg;
cv::merge(outputChannels, finalImg);
// Convert to uint8
finalImg = (finalImg * 255.0f).clip(0, 255);
finalImg.convertTo(finalImg, CV_8U);
return finalImg;
}
Hello, thank you very much! That really seemed to be my problem. To make that tensors from an cv::Mat in the right way. Now it works as expected. I only have some problems with the pixelation at the edges, but that has nothing to do with the model.