InnovArul

from torch.utils.data import DataLoader, Dataset
import torch, torch.nn as nn
import numpy as np

class DS(Dataset):
    # Constructor
    def __init__(self):
        super().__init__()      
        X = list(np.arange(15000))
        self.x = X

InnovArul

import torch, torch.nn as nn, torch.nn.functional as F

def perform_non_parallelconv(input, convs):
    outs = []
    for i in range(len(convs)):
        o = convs[i](input[:, i])
        outs.append(o)

    outs = torch.cat(outs, dim=1)
    return outs

InnovArul

import torch
with torch.autograd.set_detect_anomaly(True):
    x = torch.randn(5,6, requires_grad=True)
    z = x.sum(-1)
    z += z * z
    z.sum().backward()

InnovArul

import torch
import torch.nn.functional as F

is_cuda = True

input = torch.randn(1, 19, 32, 64, requires_grad=True)
target = torch.randint(22, size=(1,32,64,))
print(input.dim())

print("number of out-of-bound targets", (target > 18).sum())

InnovArul

mv Day.. day_  # move Day folder to day_ folder

# move MTS files to root dir 
mv ./01/2019/* .

# to concate MTS files into MP4
ffmpeg -i "concat:$(echo *.MTS | tr ' ' '|')" -strict -2 concat_out.mp4   

InnovArul

TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python setup.py build
python setup.py install

D:\cmder_mini\Cmder.exe "%ActivDir%"

InnovArul

import torchvision, copy
import torch, torch.nn as nn

def reset_all_weights(model: nn.Module) -> None:
    """
    refs:
        - https://discuss.pytorch.org/t/how-to-re-set-alll-parameters-in-a-network/20819/6
        - https://stackoverflow.com/questions/63627997/reset-parameters-of-a-neural-network-in-pytorch
        - https://pytorch.org/docs/stable/generated/torch.nn.Module.html
    """

InnovArul

import torch, torch.nn as nn

class LowlevelModule(nn.Module):
    def __init__(self, custom_val):
        super().__init__()
        self.custom_val = custom_val

    def print_custom_val(self):
        print(self.custom_val.item())

InnovArul

import torch, torch.nn as nn                                                         
import torch.optim as optim, torch.nn.functional as F                                
                                                                                     
class CustomLinearNoWeightDecay(nn.Module):                                                       
    def __init__(self, mask):                                                        
        super().__init__()                                                           
        self.register_buffer("mask", mask)                                           
        out_channels, in_channels = mask.shape                                       
        self.weight = nn.Parameter(torch.randn(out_channels, in_channels))       
        

InnovArul

command:
--------
sudo useradd -m -d /home/<user> -s /bin/bash -c "<rollnumber>" -U <user>

password:
---------
sudo passwd <user>

Add user to sudo
-----------------