jblitzar/github-python-corpus · Datasets at Fast360

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#Global Attention
parser.add_argument('--dk', type=int, default=40, help='Dimensions of the query and key vectors. Note that this will be split amoung each attention head')
parser.add_argument('--dv', type=int, default=4, help='Dimensions of the value vectors. Note that this will be split amoung each attention head')
parser.add_argument('--attention_conv', type=bool, default=False, help='Use attention augmented convolutions')

#Adaptive Attention
parser.add_argument('--R', type=float, default=3.0, help='Variable R in masking function (controls decay of mask to 0)')
parser.add_argument('--z_init', type=float, default=0.1, help='mask variable which controls distance of no mask')
parser.add_argument('--adaptive_span', type=bool, default=False)
parser.add_argument('--span_penalty', type=float, default=0.001,
help='L1 regularizer coefficient for attention span variables')
parser.add_argument('--attention_kernel', type=int, default=3)

# learning rate for adam
parser.add_argument('--decay_factor', type=float, default=0.3, help='factor to decay lr by')
parser.add_argument('--use_adam', type=bool, default=False, help='Whether or not to use Adam optimizer')
parser.add_argument('--adam_lr', type=float, default=0.001)


parser.add_argument('--dataset', type=str, default='CIFAR10', help='CIFAR10, CIFAR100, MNIST, TinyImageNet')
parser.add_argument('--subset', type=float, default=1.0, help='proportion of dataset to use')

parser.add_argument('--test', type=bool, default=False, help='Whether or not on test set')
parser.add_argument('--small_version', type=bool, default=False)
parser.add_argument('--smallest_version', type=bool, default=False)
parser.add_argument('--model-name', type=str, default='ResNet26', help='ResNet26, ResNet38, ResNet50')
parser.add_argument('--batch-size', type=int, default=25)
parser.add_argument('--num-workers', type=int, default=1)
parser.add_argument('--epochs', type=int, default=100)

# scheduler config
parser.add_argument('--no_annealing', type=bool, default=False)
parser.add_argument('--T_max', type=int, default=-1, help='default equals total number of epochs')
parser.add_argument('--eta_min', type=float, default=0.)
parser.add_argument('--warmup_epochs', type=int, default=10)
parser.add_argument('--start_scheduler', type=int, default=0,
help='which epoch to start the scheduler, by default it starts as soon as warmup finishes')
parser.add_argument('--force_cosine_annealing', type=bool, default=False,
help='Force a warmup with cosine annealing learning rate schedule regardless of model type')

# learning rate for SGD
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight-decay', type=float, default=1e-4)

parser.add_argument('--print-interval', type=int, default=100)
parser.add_argument('--cuda', type=bool, default=False)
parser.add_argument('--pretrained-model', type=bool, default=False)
parser.add_argument('--distributed', type=bool, default=False)
parser.add_argument('--gpu-devices', type=int, nargs='+', default=None)
parser.add_argument('--gpu', type=int, default=None)
parser.add_argument('--rank', type=int, default=0, help='current process number')
parser.add_argument('--world-size', type=int, default=1, help='Total number of processes to be used (number of gpus)')
parser.add_argument('--dist-backend', type=str, default='nccl')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:3456', type=str)

parser.add_argument('--xpid', default='example', help='Experiment ID, default = example')

args = parser.parse_args()

# TODO: Remove these comments
logger = None #get_logger('train')
#logger.info(vars(args))

return args, logger

# <FILESEP>
import torch
import torch.nn.functional as F
import numpy as np
from skimage.segmentation import mark_boundaries
import cv2

import sys
sys.path.append('./third_party/cython')
from connectivity import enforce_connectivity

def init_spixel_grid(args, b_train=True):
if b_train:
img_height, img_width = args.train_img_height, args.train_img_width
else:
img_height, img_width = args.input_img_height, args.input_img_width

# get spixel id for the final assignment
n_spixl_h = int(np.floor(img_height/args.downsize))
n_spixl_w = int(np.floor(img_width/args.downsize))

spixel_height = int(img_height / (1. * n_spixl_h))
spixel_width = int(img_width / (1. * n_spixl_w))

spix_values = np.int32(np.arange(0, n_spixl_w * n_spixl_h).reshape((n_spixl_h, n_spixl_w)))
spix_idx_tensor_ = shift9pos(spix_values)

spix_idx_tensor = np.repeat(
np.repeat(spix_idx_tensor_, spixel_height,axis=1), spixel_width, axis=2)

torch_spix_idx_tensor = torch.from_numpy(
np.tile(spix_idx_tensor, (args.batch_size, 1, 1, 1))).type(torch.float).cuda()

#Global Attention

parser.add_argument('--dk', type=int, default=40, help='Dimensions of the query and key vectors. Note that this will be split amoung each attention head')

parser.add_argument('--dv', type=int, default=4, help='Dimensions of the value vectors. Note that this will be split amoung each attention head')

parser.add_argument('--attention_conv', type=bool, default=False, help='Use attention augmented convolutions')

#Adaptive Attention

parser.add_argument('--R', type=float, default=3.0, help='Variable R in masking function (controls decay of mask to 0)')

parser.add_argument('--z_init', type=float, default=0.1, help='mask variable which controls distance of no mask')

parser.add_argument('--adaptive_span', type=bool, default=False)

parser.add_argument('--span_penalty', type=float, default=0.001,

help='L1 regularizer coefficient for attention span variables')

parser.add_argument('--attention_kernel', type=int, default=3)

# learning rate for adam

parser.add_argument('--decay_factor', type=float, default=0.3, help='factor to decay lr by')

parser.add_argument('--use_adam', type=bool, default=False, help='Whether or not to use Adam optimizer')

parser.add_argument('--adam_lr', type=float, default=0.001)

parser.add_argument('--dataset', type=str, default='CIFAR10', help='CIFAR10, CIFAR100, MNIST, TinyImageNet')

parser.add_argument('--subset', type=float, default=1.0, help='proportion of dataset to use')

parser.add_argument('--test', type=bool, default=False, help='Whether or not on test set')

parser.add_argument('--small_version', type=bool, default=False)

parser.add_argument('--smallest_version', type=bool, default=False)

parser.add_argument('--model-name', type=str, default='ResNet26', help='ResNet26, ResNet38, ResNet50')

parser.add_argument('--batch-size', type=int, default=25)

parser.add_argument('--num-workers', type=int, default=1)

parser.add_argument('--epochs', type=int, default=100)

# scheduler config

parser.add_argument('--no_annealing', type=bool, default=False)

parser.add_argument('--T_max', type=int, default=-1, help='default equals total number of epochs')

parser.add_argument('--eta_min', type=float, default=0.)

parser.add_argument('--warmup_epochs', type=int, default=10)

parser.add_argument('--start_scheduler', type=int, default=0,

help='which epoch to start the scheduler, by default it starts as soon as warmup finishes')

parser.add_argument('--force_cosine_annealing', type=bool, default=False,

help='Force a warmup with cosine annealing learning rate schedule regardless of model type')

# learning rate for SGD

parser.add_argument('--lr', type=float, default=0.1)

parser.add_argument('--momentum', type=float, default=0.9)

parser.add_argument('--weight-decay', type=float, default=1e-4)

parser.add_argument('--print-interval', type=int, default=100)

parser.add_argument('--cuda', type=bool, default=False)

parser.add_argument('--pretrained-model', type=bool, default=False)

parser.add_argument('--distributed', type=bool, default=False)

parser.add_argument('--gpu-devices', type=int, nargs='+', default=None)

parser.add_argument('--gpu', type=int, default=None)

parser.add_argument('--rank', type=int, default=0, help='current process number')

parser.add_argument('--world-size', type=int, default=1, help='Total number of processes to be used (number of gpus)')

parser.add_argument('--dist-backend', type=str, default='nccl')

parser.add_argument('--dist-url', default='tcp://127.0.0.1:3456', type=str)

parser.add_argument('--xpid', default='example', help='Experiment ID, default = example')

args = parser.parse_args()

# TODO: Remove these comments

logger = None #get_logger('train')

#logger.info(vars(args))

return args, logger

# <FILESEP>

import torch

import torch.nn.functional as F

import numpy as np

from skimage.segmentation import mark_boundaries

import cv2

import sys

sys.path.append('./third_party/cython')

from connectivity import enforce_connectivity

def init_spixel_grid(args, b_train=True):

if b_train:

img_height, img_width = args.train_img_height, args.train_img_width

else:

img_height, img_width = args.input_img_height, args.input_img_width

# get spixel id for the final assignment

n_spixl_h = int(np.floor(img_height/args.downsize))

n_spixl_w = int(np.floor(img_width/args.downsize))

spixel_height = int(img_height / (1. * n_spixl_h))

spixel_width = int(img_width / (1. * n_spixl_w))

spix_values = np.int32(np.arange(0, n_spixl_w * n_spixl_h).reshape((n_spixl_h, n_spixl_w)))

spix_idx_tensor_ = shift9pos(spix_values)

spix_idx_tensor = np.repeat(

np.repeat(spix_idx_tensor_, spixel_height,axis=1), spixel_width, axis=2)

torch_spix_idx_tensor = torch.from_numpy(

np.tile(spix_idx_tensor, (args.batch_size, 1, 1, 1))).type(torch.float).cuda()