This code snippet shows how we can change a layer in a pretrained model. In the following code, we change all the ReLU activation functions with SELU in a resnet18 model.
import torch
from torchvision import model
resnet18 = model.resnet18(pretrained=True)
def funct(list_mods):
| layer filters size input output | |
| 0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 | |
| 1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 | |
| 2 conv 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 | |
| 3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 | |
| 4 Shortcut Layer: 1 | |
| 5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 | |
| 6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 | |
| 7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 | |
| 8 Shortcut Layer: 5 |
| import cv2 # opencv | |
| import numpy as np | |
| font_scale = 1.5 | |
| font = cv2.FONT_HERSHEY_PLAIN | |
| # set the rectangle background to white | |
| rectangle_bgr = (255, 255, 255) | |
| # make a black image | |
| img = np.zeros((500, 500)) |