Replace input channel and kernel size in pre-trained model

vision
1

I tried using pre-trained resnet-50 with image size of 400X400 and modified Conv1 input channel and kernel size. I didn’t get any error. How will be the weights initialized in this case.

import torch
import torchvision
import torch.nn as nn
from torchsummary import summary
from torchvision import datasets, models, transforms

model_ft = models.resnet50(pretrained=False)
model_ft.conv1=nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(3, 3), bias=False)

When I tried to print the summary I got

print(summary(model_ft.cuda(),input_size=(1,400,400)))
------------------------------------------------------------------------
        Layer (type)               Output Shape         Param #
==========================================
            Conv2d-1         [-1, 64, 202, 202]             576
       BatchNorm2d-2         [-1, 64, 202, 202]             128
              ReLU-3         [-1, 64, 202, 202]               0
         MaxPool2d-4         [-1, 64, 101, 101]               0
            Conv2d-5         [-1, 64, 101, 101]           4,096

Without modifying anything in pre-trained model, I got summary as

model_ft = models.resnet50(pretrained=True)
print(summary(model_ft.cuda(),input_size=(3,400,400)))
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
=========================================
            Conv2d-1         [-1, 64, 200, 200]           9,408
       BatchNorm2d-2         [-1, 64, 200, 200]             128
              ReLU-3         [-1, 64, 200, 200]               0
         MaxPool2d-4         [-1, 64, 100, 100]               0
            Conv2d-5         [-1, 64, 100, 100]           4,096

If I try to remove/change layers in pre-trained model, Pytorch doesn’t throw any error. How will be the weights are initilized after changing the pre-trained model?