How to use pretrained encoder for customized Unet Question: if you have a standard Unet encoder such as resnet50, then it’s easy to add pertaining to it. for example: ENCODER = ‘resnet50’ ENCODER_WEIGHTS = ‘imagenet’ CLASSES = class_names ACTIVATION = ‘sigmoid’ # could be None for logits or ‘softmax2d’ for multiclass segmentation # create segmentation …
Setting ReLU inplace to 'False' Question: Below I have written code which accepts a pretrained model as argument (vgg, resnet, densenet etc) and returns the model with ReLU state as ‘False’. It is written after testing many different specific architectures. I would like to re-write it in a more compact way as this does not …
Accessing a specific layer in a pretrained model in PyTorch Question: I want to extract the features from certain blocks of the TimeSformer model and also want to remove the last two layers. import torch from timesformer.models.vit import TimeSformer model = TimeSformer(img_size=224, num_classes=400, num_frames=8, attention_type=’divided_space_time’, pretrained_model=’/path/to/pretrained/model.pyth’) The print of the model is as follows: TimeSformer( …
How can I add new layers on pre-trained model with PyTorch? (Keras example given) Question: I am working with Keras and trying to analyze the effects on accuracy that models which are built with some layers with meaningful weights, and some layers with random initializations. Keras: I load VGG19 pre-trained model with include_top = False …
Is there any way I can download the pre-trained models available in PyTorch to a specific path? Question: I am referring to the models that can be found here: https://pytorch.org/docs/stable/torchvision/models.html#torchvision-models Asked By: gopalkrizna || Source Answers: TL;DR: No, it is not possible directly, but you can easily adapt it. I think what you want to …
