KeremTurgutlu

# a sample down block
def make_conv_bn_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1):
    return [
        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,  stride=stride, padding=padding, bias=False),
        nn.BatchNorm2d(out_channels),
        nn.ReLU(inplace=True)
    ]
self.down1 = nn.Sequential(
    *make_conv_bn_relu(in_channels, 64, kernel_size=3, stride=1, padding=1 ),
    *make_conv_bn_relu(64, 64, kernel_size=3, stride=1, padding=1 ),

KeremTurgutlu

# a sample up block
def make_conv_bn_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1):
    return [
        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,  stride=stride, padding=padding, bias=False),
        nn.BatchNorm2d(out_channels),
        nn.ReLU(inplace=True)
    ]
self.up4 = nn.Sequential(
    *make_conv_bn_relu(128,64, kernel_size=3, stride=1, padding=1 ),
    *make_conv_bn_relu(64,64, kernel_size=3, stride=1, padding=1 )

KeremTurgutlu

from collections import Counter

def nn_interpolate(A, new_size):
    """
    Nearest Neighbor Interpolation, Step by Step
    """
    # get sizes
    old_size = A.shape
    
    # calculate row and column ratios

KeremTurgutlu

def construct(A,B,C):
    """
    Given Matrices A, B, C construct 3D Tensor 
        A : i, r
        B: j, r
        C : k, r
    """
    X_tilde = 0
    r = A.shape[1]
    for i in range(r):

KeremTurgutlu

from fastai.vision import *
import math

__all__ = ['MeshNet', 'VolumetricUnet', 'conv_relu_bn_drop', 'res3dmodel', 'get_total_params',
           'VolumetricResidualUnet', 'model_dict', 'experiment_model_dict', 'one_by_one_conv',
           'model_split_dict']


####################
   ## GET MODELS ##

KeremTurgutlu

from fastai.vision.all import *
from torch.distributions import Beta
from copy import deepcopy

__all__ = ["ELR", "ELR_plusA", "ELR_plusB"]


class ELR(Callback):
    '''
    The selected values are β = 0.7 and λ = 3 for symmetric noise, β = 0.9 and λ = 1 for

KeremTurgutlu

# CLIP contrastive loss is calculated all the negative batch samples from all the GPUs
# How to implement that?
# For more info: https://github.com/openai/CLIP/issues/29

import os
import sys
import tempfile
import torch
import torch.distributed as dist
import torch.nn as nn

KeremTurgutlu

import os
import torch
import torch.distributed as dist
from torch.multiprocessing import Process
from torchvision import datasets, transforms
import numpy as np

import  torch.nn as nn
import torch.nn.functional as F
import random

KeremTurgutlu

@call_parse
def main(
    size:      Param("Image resolution", int)=224,    
    bs:        Param("Batch Size", int)=128,
    epochs:    Param("Number of epochs for training", int)=1,    
    lr:        Param("Learning rate for training", float)=5e-5):
    
    WANDB = True
        
    # start wandb

KeremTurgutlu

import wandb
from fastai.callback.wandb import WandbCallback
from fastai.distributed import *
torch.backends.cudnn.benchmark = True

from zero_optimizer import ZeroRedundancyOptimizer

@patch
def after_batch(self: WandbCallback):