import torch.nn as nn
import torchvision.transforms as transforms
import torch


# for slurm script compatibility
def boolean_string(s):
    if s not in {'False', 'True'}:
        raise ValueError('Not a valid boolean string')
    return s == 'True'


# custom weights initialization called on netG and netD
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.0002)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.0002)
        nn.init.constant_(m.bias.data, 0)


# count number of trainable parameters for a model
def parameters_count(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


# model name maker
def name_maker(args):
    model_name = 'E{}'.format(args.E_lr) + \
        '_advG{}'.format(args.advG_lr) + \
        '_defG{}'.format(args.defG_lr) + \
        '_targeted' + \
        '_low' + \
        '_{}'.format(args.seeds) + '/'

    return model_name


# image normalizer
def normalized_eval(x, target_model, batch_size=128):
    x_copy = x.clone()
    batch_size = x_copy.size()[0]
    x_copy = torch.stack([transforms.functional.normalize(x_copy[i], mean=[mu/255.0 for mu in [125.3, 123.0, 113.9]],
                                                          std=[sig/255.0 for sig in [63.0, 62.1, 66.7]]) for i in range(batch_size)])

    return target_model(x_copy)

000cd6f331da343c8e50cdc08d35023c46864f69
<mask token>


def boolean_string(s):
    if s not in {'False', 'True'}:
        raise ValueError('Not a valid boolean string')
    return s == 'True'


<mask token>


def parameters_count(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


def name_maker(args):
    model_name = 'E{}'.format(args.E_lr) + '_advG{}'.format(args.advG_lr
        ) + '_defG{}'.format(args.defG_lr
        ) + '_targeted' + '_low' + '_{}'.format(args.seeds) + '/'
    return model_name


<mask token>

<mask token>


def boolean_string(s):
    if s not in {'False', 'True'}:
        raise ValueError('Not a valid boolean string')
    return s == 'True'


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.0002)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.0002)
        nn.init.constant_(m.bias.data, 0)


def parameters_count(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


def name_maker(args):
    model_name = 'E{}'.format(args.E_lr) + '_advG{}'.format(args.advG_lr
        ) + '_defG{}'.format(args.defG_lr
        ) + '_targeted' + '_low' + '_{}'.format(args.seeds) + '/'
    return model_name


<mask token>

<mask token>


def boolean_string(s):
    if s not in {'False', 'True'}:
        raise ValueError('Not a valid boolean string')
    return s == 'True'


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.0002)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.0002)
        nn.init.constant_(m.bias.data, 0)


def parameters_count(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


def name_maker(args):
    model_name = 'E{}'.format(args.E_lr) + '_advG{}'.format(args.advG_lr
        ) + '_defG{}'.format(args.defG_lr
        ) + '_targeted' + '_low' + '_{}'.format(args.seeds) + '/'
    return model_name


def normalized_eval(x, target_model, batch_size=128):
    x_copy = x.clone()
    batch_size = x_copy.size()[0]
    x_copy = torch.stack([transforms.functional.normalize(x_copy[i], mean=[
        (mu / 255.0) for mu in [125.3, 123.0, 113.9]], std=[(sig / 255.0) for
        sig in [63.0, 62.1, 66.7]]) for i in range(batch_size)])
    return target_model(x_copy)

import torch.nn as nn
import torchvision.transforms as transforms
import torch


def boolean_string(s):
    if s not in {'False', 'True'}:
        raise ValueError('Not a valid boolean string')
    return s == 'True'


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.0002)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.0002)
        nn.init.constant_(m.bias.data, 0)


def parameters_count(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


def name_maker(args):
    model_name = 'E{}'.format(args.E_lr) + '_advG{}'.format(args.advG_lr
        ) + '_defG{}'.format(args.defG_lr
        ) + '_targeted' + '_low' + '_{}'.format(args.seeds) + '/'
    return model_name


def normalized_eval(x, target_model, batch_size=128):
    x_copy = x.clone()
    batch_size = x_copy.size()[0]
    x_copy = torch.stack([transforms.functional.normalize(x_copy[i], mean=[
        (mu / 255.0) for mu in [125.3, 123.0, 113.9]], std=[(sig / 255.0) for
        sig in [63.0, 62.1, 66.7]]) for i in range(batch_size)])
    return target_model(x_copy)

import torch.nn as nn
import torchvision.transforms as transforms
import torch


# for slurm script compatibility
def boolean_string(s):
    if s not in {'False', 'True'}:
        raise ValueError('Not a valid boolean string')
    return s == 'True'


# custom weights initialization called on netG and netD
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.0002)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.0002)
        nn.init.constant_(m.bias.data, 0)


# count number of trainable parameters for a model
def parameters_count(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


# model name maker
def name_maker(args):
    model_name = 'E{}'.format(args.E_lr) + \
        '_advG{}'.format(args.advG_lr) + \
        '_defG{}'.format(args.defG_lr) + \
        '_targeted' + \
        '_low' + \
        '_{}'.format(args.seeds) + '/'

    return model_name


# image normalizer
def normalized_eval(x, target_model, batch_size=128):
    x_copy = x.clone()
    batch_size = x_copy.size()[0]
    x_copy = torch.stack([transforms.functional.normalize(x_copy[i], mean=[mu/255.0 for mu in [125.3, 123.0, 113.9]],
                                                          std=[sig/255.0 for sig in [63.0, 62.1, 66.7]]) for i in range(batch_size)])

    return target_model(x_copy)
