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

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

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, 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

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
import torch.nn.functional as F
import os, sys
import copy
import torch.optim as optim

class ConvReLU(nn.Module):
    def __init__(self, indim, outdim):
        super().__init__()
        self.conv = nn.Conv2d(indim, outdim, kernel_size=1)

InnovArul

def get_dataloader_from_pth(path, batch_size=4):
    contents = torch.load(path)
    dataset = torch.utils.data.TensorDataset(contents['x'], contents['y'])
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)
    return dataloader
    
#----------------------------------------------------------------------

import os.path as osp

InnovArul

InnovArul

import imgaug as ia 
import imgaug.augmenters  as iaa 
print("loaded") 
prob = 0.5 
sometimes = lambda aug: iaa.Sometimes(prob, aug) 
output_shape=(473,473) 
seq = iaa.Sequential([ 
    # apply the following augmenters to most images 
    iaa.Fliplr(0.5),
    sometimes(iaa.Affine(

InnovArul

from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt