i trained a model on a dataset and saved the weight pth file.how do i load it and use the weights to train on a new dataset

You can load the parameters using `model.load_state_dict()`

:

```
# Initialize model
model = MyModel()
# Load state_dict
model.load_state_dict(torch.load('my_weights.pth'))
```

Have a look at the Transfer Learning Tutorial to see how you can fine-tune your model.

Thank you for the help…after training and saving the weights to a pth file how to i predict(probabilities) on my test set form test dataloader

I assume you have already created your `DataLoader`

. If so, you could write a simple loop iterating your `test_loader`

:

```
model.eval()
predictions = []
with torch.no_grad():
for data in test_loader:
output = model(data)
pred = F.softmax(output, 1) # assuming your model outputs logits
predictions.append(pred)
predictions = torch.cat(predictions)
```

`predictions`

will now contain the probabilities for each sample and each class.

i need full code for this retraining model pt with new dataset and save the model with new pt file

In case you would like to retrain a classification model, the ImageNet example might be a good starter.

i solved by loaded pretrained model and loaded paramters iwth model and use train function to retrain the model it is working

i Have input image has tiff tfile uint8 format(0-255) and segmentaed image(target image0 in nrrd file uint16 (0-65535) how can i use this dataset corectly inot my dataloader for segmentaion using unet

please help me for this

You could create a custom `Dataset`

as described here and load the input as well as the target with any library that works for you (e.g. `pynrrd`

for the nrrd file).

Bro I have nrrd file in 16 bit uint which is not supported in pytorch

Why would it not be supported? `float32`

can exactly represent all integers in `[0, 16777216]`

which also includes `uint16`

.

I’m not sure where you are stuck, but why wouldn’t casting to `float32`

work (which is the default dtype in PyTorch)?

how can i correctly convert uint16 to flaot32 image where to use

Assuming your library uses `numpy`

for the `uint16`

images, use `astype`

:

```
arr = np.random.randint(0, 65000, (100,), dtype=np.uint16)
arr = arr.astype(np.float32)
```

then create the tensor via `torch.from_numpy`

.

I’ve already posted the solution: transform the `uint16`

array to `float32`

and then to a tensor.