SYMBOL INDEX (353 symbols across 26 files)

FILE: main.py
  function main (line 14) | def main(args):

FILE: options.py
  class Options (line 8) | class Options():
    method __init__ (line 10) | def __init__(self):
    method init (line 13) | def init(self, parser):

FILE: scripts/datasets/BIH.py
  class BIH (line 27) | class BIH(data.Dataset):
    method __init__ (line 28) | def __init__(self,train,config=None, sample=[],gan_norm=False):
    method __getitem__ (line 81) | def __getitem__(self, index):
    method __len__ (line 98) | def __len__(self):

FILE: scripts/datasets/COCO.py
  class COCO (line 27) | class COCO(data.Dataset):
    method __init__ (line 28) | def __init__(self,train,config=None, sample=[],gan_norm=False):
    method __getitem__ (line 80) | def __getitem__(self, index):
    method __len__ (line 97) | def __len__(self):

FILE: scripts/machines/BasicMachine.py
  class BasicMachine (line 25) | class BasicMachine(object):
    method __init__ (line 26) | def __init__(self, datasets =(None,None), models = None, args = None, ...
    method train (line 82) | def train(self,epoch):
    method test (line 155) | def test(self, ):
    method validate (line 196) | def validate(self, epoch):
    method resume (line 240) | def resume(self,resume_path):
    method save_checkpoint (line 259) | def save_checkpoint(self,filename='checkpoint.pth.tar', snapshot=None):
    method clean (line 284) | def clean(self):
    method record (line 287) | def record(self,k,v,epoch):
    method flush (line 290) | def flush(self):
    method norm (line 294) | def norm(self,x):
    method denorm (line 300) | def denorm(self,x):

FILE: scripts/machines/S2AM.py
  class S2AM (line 25) | class S2AM(object):
    method __init__ (line 26) | def __init__(self, datasets =(None,None), models = None, args = None, ...
    method train (line 82) | def train(self,epoch):
    method test (line 156) | def test(self, ):
    method validate (line 195) | def validate(self, epoch):
    method resume (line 240) | def resume(self,resume_path):
    method save_checkpoint (line 259) | def save_checkpoint(self,filename='checkpoint.pth.tar', snapshot=None):
    method clean (line 284) | def clean(self):
    method record (line 287) | def record(self,k,v,epoch):
    method flush (line 290) | def flush(self):
    method norm (line 294) | def norm(self,x):
    method denorm (line 300) | def denorm(self,x):

FILE: scripts/machines/VX.py
  class Losses (line 23) | class Losses(nn.Module):
    method __init__ (line 24) | def __init__(self, argx, device, norm_func=None, denorm_func=None):
    method forward (line 49) | def forward(self,pred_ims,target,pred_ms,mask,pred_wms,wm):
  class VX (line 87) | class VX(BasicMachine):
    method __init__ (line 88) | def __init__(self,**kwargs):
    method train (line 94) | def train(self,epoch):
    method validate (line 182) | def validate(self, epoch):
    method test (line 254) | def test(self, ):

FILE: scripts/machines/__init__.py
  function basic (line 6) | def basic(**kwargs):
  function s2am (line 9) | def s2am(**kwargs):
  function vx (line 12) | def vx(**kwargs):

FILE: scripts/models/backbone_unet.py
  function vvv4n (line 19) | def vvv4n(**kwargs):
  function vm3 (line 24) | def vm3(**kwargs):
  function urasc (line 29) | def urasc(**kwargs):
  function rascv2 (line 39) | def rascv2(**kwargs):
  function unet (line 45) | def unet(**kwargs):

FILE: scripts/models/blocks.py
  class BasicLearningBlock (line 15) | class BasicLearningBlock(nn.Module):
    method __init__ (line 17) | def __init__(self,channel):
    method forward (line 24) | def forward(self,feature):
  class GaussianSmoothing (line 30) | class GaussianSmoothing(nn.Module):
    method __init__ (line 43) | def __init__(self, channels, kernel_size, sigma, dim=2):
    method forward (line 85) | def forward(self, input):
  class ChannelPool (line 95) | class ChannelPool(nn.Module):
    method __init__ (line 96) | def __init__(self,types):
    method forward (line 105) | def forward(self, input):
  class SEBlock (line 114) | class SEBlock(nn.Module):
    method __init__ (line 116) | def __init__(self, channel,reducation=16):
    method forward (line 125) | def forward(self,x):
  class GlobalAttentionModule (line 133) | class GlobalAttentionModule(nn.Module):
    method __init__ (line 135) | def __init__(self, channel,reducation=16):
    method forward (line 145) | def forward(self,x):
  class SpatialAttentionModule (line 152) | class SpatialAttentionModule(nn.Module):
    method __init__ (line 154) | def __init__(self, channel,reducation=16):
    method forward (line 164) | def forward(self,x):
  class GlobalAttentionModuleJustSigmoid (line 174) | class GlobalAttentionModuleJustSigmoid(nn.Module):
    method __init__ (line 176) | def __init__(self, channel,reducation=16):
    method forward (line 186) | def forward(self,x):
  class BasicBlock (line 195) | class BasicBlock(nn.Module):
    method __init__ (line 196) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi...
    method forward (line 203) | def forward(self, x):
  class Flatten (line 211) | class Flatten(nn.Module):
    method forward (line 212) | def forward(self, x):
  class ChannelGate (line 215) | class ChannelGate(nn.Module):
    method __init__ (line 216) | def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg...
    method forward (line 226) | def forward(self, x):
  function logsumexp_2d (line 251) | def logsumexp_2d(tensor):
  class ChannelPoolX (line 257) | class ChannelPoolX(nn.Module):
    method forward (line 258) | def forward(self, x):
  class SpatialGate (line 261) | class SpatialGate(nn.Module):
    method __init__ (line 262) | def __init__(self):
    method forward (line 267) | def forward(self, x):
  class CBAM (line 273) | class CBAM(nn.Module):
    method __init__ (line 274) | def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg...
    method forward (line 280) | def forward(self, x):

FILE: scripts/models/discriminator.py
  class SNCoXvWithActivation (line 17) | class SNCoXvWithActivation(torch.nn.Module):
    method __init__ (line 21) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, p...
    method forward (line 29) | def forward(self, input):
  function l2normalize (line 36) | def l2normalize(v, eps=1e-12):
  class SpectralNorm (line 40) | class SpectralNorm(nn.Module):
    method __init__ (line 41) | def __init__(self, module, name='weight', power_iterations=1):
    method _update_u_v (line 49) | def _update_u_v(self):
    method _made_params (line 63) | def _made_params(self):
    method _make_params (line 73) | def _make_params(self):
    method forward (line 92) | def forward(self, *args):
  function get_pad (line 97) | def get_pad(in_,  ksize, stride, atrous=1):
  class SNDiscriminator (line 101) | class SNDiscriminator(nn.Module):
    method __init__ (line 102) | def __init__(self,channel=6):
    method forward (line 116) | def forward(self, img_A, img_B):
  class Discriminator (line 124) | class Discriminator(nn.Module):
    method __init__ (line 125) | def __init__(self, in_channels=3):
    method forward (line 145) | def forward(self, img_A, img_B):
  function patchgan (line 151) | def patchgan():
  function sngan (line 156) | def sngan():
  function maskedsngan (line 161) | def maskedsngan():

FILE: scripts/models/rasc.py
  class CAWapper (line 15) | class CAWapper(nn.Module):
    method __init__ (line 18) | def __init__(self, channel, type_of_connection=BasicLearningBlock):
    method forward (line 22) | def forward(self, feature, mask):
  class NLWapper (line 34) | class NLWapper(nn.Module):
    method __init__ (line 37) | def __init__(self, channel, type_of_connection=BasicLearningBlock):
    method forward (line 41) | def forward(self, feature, mask):
  class SENet (line 52) | class SENet(nn.Module):
    method __init__ (line 54) | def __init__(self,channel,type_of_connection=BasicLearningBlock):
    method forward (line 58) | def forward(self,feature,mask):
  class CBAMConnect (line 69) | class CBAMConnect(nn.Module):
    method __init__ (line 70) | def __init__(self,channel):
    method forward (line 74) | def forward(self,feature,mask):
  class RASC (line 80) | class RASC(nn.Module):
    method __init__ (line 81) | def __init__(self,channel,type_of_connection=BasicLearningBlock):
    method forward (line 89) | def forward(self,feature,mask):
  class UNO (line 110) | class UNO(nn.Module):
    method __init__ (line 111) | def __init__(self,channel):
    method forward (line 114) | def forward(self,feature,_m):
  class URASC (line 118) | class URASC(nn.Module):
    method __init__ (line 119) | def __init__(self,channel,type_of_connection=BasicLearningBlock):
    method forward (line 127) | def forward(self,feature, m=None):
  class MaskedURASC (line 139) | class MaskedURASC(nn.Module):
    method __init__ (line 140) | def __init__(self,channel,type_of_connection=BasicLearningBlock):
    method forward (line 148) | def forward(self,feature):

FILE: scripts/models/sa_resunet.py
  function weight_init (line 9) | def weight_init(m):
  function reset_params (line 14) | def reset_params(model):
  function conv3x3 (line 19) | def conv3x3(in_channels, out_channels, stride=1,
  function up_conv2x2 (line 31) | def up_conv2x2(in_channels, out_channels, transpose=True):
  function conv1x1 (line 44) | def conv1x1(in_channels, out_channels, groups=1):
  class UpCoXvD (line 53) | class UpCoXvD(nn.Module):
    method __init__ (line 55) | def __init__(self, in_channels, out_channels, blocks, residual=True,no...
    method forward (line 88) | def forward(self, from_up, from_down, mask=None,se=None):
  class DownCoXvD (line 118) | class DownCoXvD(nn.Module):
    method __init__ (line 120) | def __init__(self, in_channels, out_channels, blocks, pooling=True, no...
    method __call__ (line 143) | def __call__(self, x):
    method forward (line 146) | def forward(self, x):
  class UnetDecoderD (line 162) | class UnetDecoderD(nn.Module):
    method __init__ (line 163) | def __init__(self, in_channels=512, out_channels=3, norm=nn.BatchNorm2...
    method __call__ (line 200) | def __call__(self, x, encoder_outs=None):
    method forward (line 203) | def forward(self, x, encoder_outs=None):
  class UnetDecoderDatt (line 217) | class UnetDecoderDatt(nn.Module):
    method __init__ (line 218) | def __init__(self, in_channels=512, out_channels=3, depth=5, blocks=1,...
    method forward (line 258) | def forward(self, input, encoder_outs=None):
  class UnetEncoderD (line 286) | class UnetEncoderD(nn.Module):
    method __init__ (line 288) | def __init__(self, in_channels=3, depth=5, blocks=1, start_filters=32,...
    method __call__ (line 303) | def __call__(self, x):
    method forward (line 306) | def forward(self, x):
  class ResDown (line 313) | class ResDown(nn.Module):
    method __init__ (line 314) | def __init__(self, in_size, out_size, pooling=True, use_att=False):
    method forward (line 318) | def forward(self, x):
  class ResUp (line 321) | class ResUp(nn.Module):
    method __init__ (line 322) | def __init__(self, in_size, out_size, use_att=False):
    method forward (line 326) | def forward(self, x, skip_input, mask=None):
  class ResDownNew (line 329) | class ResDownNew(nn.Module):
    method __init__ (line 330) | def __init__(self, in_size, out_size, pooling=True, use_att=False):
    method forward (line 334) | def forward(self, x):
  class ResUpNew (line 337) | class ResUpNew(nn.Module):
    method __init__ (line 338) | def __init__(self, in_size, out_size, use_att=False):
    method forward (line 342) | def forward(self, x, skip_input, mask=None):
  class VMSingle (line 347) | class VMSingle(nn.Module):
    method __init__ (line 348) | def __init__(self, in_channels=3, out_channels=3, down=ResDown, up=Res...
    method forward (line 366) | def forward(self, input):
  class VMSingleS2AM (line 385) | class VMSingleS2AM(nn.Module):
    method __init__ (line 386) | def __init__(self, in_channels=3, out_channels=3, down=ResDown, up=Res...
    method forward (line 408) | def forward(self, input):
  class UnetVMS2AMv4 (line 430) | class UnetVMS2AMv4(nn.Module):
    method __init__ (line 432) | def __init__(self, in_channels=3, depth=5, shared_depth=0, use_vm_deco...
    method set_optimizers (line 480) | def set_optimizers(self):
    method zero_grad_all (line 491) | def zero_grad_all(self):
    method step_all (line 501) | def step_all(self):
    method step_optimizer_image (line 511) | def step_optimizer_image(self):
    method __call__ (line 514) | def __call__(self, synthesized):
    method forward (line 517) | def forward(self, synthesized):
    method unshared_forward (line 520) | def unshared_forward(self, synthesized):
    method shared_forward (line 531) | def shared_forward(self, synthesized):

FILE: scripts/models/unet.py
  class MinimalUnetV2 (line 9) | class MinimalUnetV2(nn.Module):
    method __init__ (line 11) | def __init__(self, down=None,up=None,submodule=None,attention=None,wit...
    method forward (line 22) | def forward(self,x,mask=None):
  class MinimalUnet (line 39) | class MinimalUnet(nn.Module):
    method __init__ (line 41) | def __init__(self, down=None,up=None,submodule=None,attention=None,wit...
    method forward (line 52) | def forward(self,x,mask=None):
  class UnetSkipConnectionBlock (line 72) | class UnetSkipConnectionBlock(nn.Module):
    method __init__ (line 73) | def __init__(self, outer_nc, inner_nc, input_nc=None,
    method forward (line 129) | def forward(self, x,mask=None):
  class UnetGenerator (line 133) | class UnetGenerator(nn.Module):
    method __init__ (line 134) | def __init__(self, input_nc, output_nc, num_downs=8, ngf=64,norm_layer...
    method forward (line 153) | def forward(self, input):

FILE: scripts/models/vgg.py
  class Vgg16 (line 7) | class Vgg16(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, requires_grad=False):
    method forward (line 31) | def forward(self, X):
  class Vgg19 (line 47) | class Vgg19(torch.nn.Module):
    method __init__ (line 48) | def __init__(self, requires_grad=False):
    method forward (line 71) | def forward(self, X, indices=None):

FILE: scripts/models/vmu.py
  function weight_init (line 9) | def weight_init(m):
  function reset_params (line 14) | def reset_params(model):
  function conv3x3 (line 19) | def conv3x3(in_channels, out_channels, stride=1,
  function up_conv2x2 (line 31) | def up_conv2x2(in_channels, out_channels, transpose=True):
  function conv1x1 (line 44) | def conv1x1(in_channels, out_channels, groups=1):
  class UpCoXvD (line 55) | class UpCoXvD(nn.Module):
    method __init__ (line 57) | def __init__(self, in_channels, out_channels, blocks, residual=True, b...
    method forward (line 85) | def forward(self, from_up, from_down, mask=None):
  class DownCoXvD (line 111) | class DownCoXvD(nn.Module):
    method __init__ (line 113) | def __init__(self, in_channels, out_channels, blocks, pooling=True, re...
    method __call__ (line 133) | def __call__(self, x):
    method forward (line 136) | def forward(self, x):
  class UnetDecoderD (line 152) | class UnetDecoderD(nn.Module):
    method __init__ (line 153) | def __init__(self, in_channels=512, out_channels=3, depth=5, blocks=1,...
    method __call__ (line 178) | def __call__(self, x, encoder_outs=None):
    method forward (line 181) | def forward(self, x, encoder_outs=None):
  class UnetEncoderD (line 192) | class UnetEncoderD(nn.Module):
    method __init__ (line 194) | def __init__(self, in_channels=3, depth=5, blocks=1, start_filters=32,...
    method __call__ (line 209) | def __call__(self, x):
    method forward (line 212) | def forward(self, x):
  class UnetVM (line 221) | class UnetVM(nn.Module):
    method __init__ (line 223) | def __init__(self, in_channels=3, depth=5, shared_depth=0, use_vm_deco...
    method set_optimizers (line 262) | def set_optimizers(self):
    method zero_grad_all (line 271) | def zero_grad_all(self):
    method step_all (line 280) | def step_all(self):
    method step_optimizer_image (line 289) | def step_optimizer_image(self):
    method __call__ (line 292) | def __call__(self, synthesized):
    method forward (line 295) | def forward(self, synthesized):
    method unshared_forward (line 298) | def unshared_forward(self, synthesized):
    method shared_forward (line 309) | def shared_forward(self, synthesized):

FILE: scripts/utils/evaluation.py
  function get_preds (line 13) | def get_preds(scores):
  function calc_dists (line 32) | def calc_dists(preds, target, normalize):
  function dist_acc (line 44) | def dist_acc(dists, thr=0.5):
  function accuracy (line 53) | def accuracy(output, target, thr=0.5):
  function final_preds (line 77) | def final_preds(output, center, scale, res):
  class AverageMeter (line 102) | class AverageMeter(object):
    method __init__ (line 104) | def __init__(self):
    method reset (line 107) | def reset(self):
    method update (line 113) | def update(self, val, n=1):

FILE: scripts/utils/imutils.py
  function im_to_numpy (line 10) | def im_to_numpy(img):
  function im_to_torch (line 15) | def im_to_torch(img):
  function load_image (line 22) | def load_image(img_path):
  function imread_all (line 26) | def imread_all(img_path):
  function load_image_gray (line 29) | def load_image_gray(img_path):
  function resize (line 35) | def resize(img, owidth, oheight):
  function gaussian (line 54) | def gaussian(shape=(7,7),sigma=1):
  function draw_labelmap (line 65) | def draw_labelmap(img, pt, sigma, type='Gaussian'):
  function gauss (line 104) | def gauss(x, a, b, c, d=0):
  function color_heatmap (line 107) | def color_heatmap(x):
  function imshow (line 117) | def imshow(img):
  function show_joints (line 122) | def show_joints(img, pts):
  function show_sample (line 130) | def show_sample(inputs, target):
  function sample_with_heatmap (line 146) | def sample_with_heatmap(inp, out, num_rows=2, parts_to_show=None):
  function batch_with_heatmap (line 181) | def batch_with_heatmap(inputs, outputs, mean=torch.Tensor([0.5, 0.5, 0.5...
  function normalize_batch (line 191) | def normalize_batch(batch):
  function show_image_tensor (line 198) | def show_image_tensor(tensor):
  function get_jet (line 211) | def get_jet():
  function clamp (line 221) | def clamp(num, min_value, max_value):
  function gray2color (line 224) | def gray2color(gray_array, color_map):
  class objectview (line 236) | class objectview(object):
    method __init__ (line 237) | def __init__(self, *args, **kwargs):

FILE: scripts/utils/logger.py
  function savefig (line 12) | def savefig(fname, dpi=None):
  function plot_overlap (line 16) | def plot_overlap(logger, names=None):
  class Logger (line 24) | class Logger(object):
    method __init__ (line 26) | def __init__(self, fpath, title=None, resume=False):
    method set_names (line 48) | def set_names(self, names):
    method append (line 62) | def append(self, numbers):
    method plot (line 71) | def plot(self, names=None):
    method close (line 80) | def close(self):
  class LoggerMonitor (line 84) | class LoggerMonitor(object):
    method __init__ (line 86) | def __init__ (self, paths):
    method plot (line 93) | def plot(self, names=None):

FILE: scripts/utils/losses.py
  class WeightedBCE (line 10) | class WeightedBCE(nn.Module):
    method __init__ (line 11) | def __init__(self):
    method forward (line 14) | def forward(self, pred, gt):
  function l1_relative (line 28) | def l1_relative(reconstructed, real, mask):
  function is_dic (line 40) | def is_dic(x):
  class Losses (line 43) | class Losses(nn.Module):
    method __init__ (line 44) | def __init__(self, argx, device):
    method forward (line 70) | def forward(self,imgx,target,attx,mask,wmx,wm):
  function gram_matrix (line 116) | def gram_matrix(feat):
  class MeanShift (line 124) | class MeanShift(nn.Conv2d):
    method __init__ (line 125) | def __init__(self, data_mean, data_std, data_range=1, norm=True):
  function VGGLoss (line 142) | def VGGLoss(losstype):
  class VGGLossA (line 156) | class VGGLossA(nn.Module):
    method __init__ (line 157) | def __init__(self, vgg=None, weights=None, indices=None, normalize=True):
    method forward (line 171) | def forward(self, x, y):
  class VGG16FeatureExtractor (line 182) | class VGG16FeatureExtractor(nn.Module):
    method __init__ (line 183) | def __init__(self):
    method forward (line 195) | def forward(self, image):
  class VGGLossX (line 202) | class VGGLossX(nn.Module):
    method __init__ (line 203) | def __init__(self, normalize=True, mask=False, relative=False):
    method forward (line 216) | def forward(self, x, y, Xmask=None):
  class GANLosses (line 239) | class GANLosses(object):
    method __init__ (line 241) | def __init__(self, gantype):
    method g_loss (line 247) | def g_loss(self,dis_fake):
    method d_loss (line 253) | def d_loss(self,dis_fake,dis_real):
  class gen_gan (line 260) | class gen_gan(nn.Module):
    method __init__ (line 261) | def __init__(self,gantype):
    method forward (line 270) | def forward(self,dis_fake):
  class dis_gan (line 273) | class dis_gan(nn.Module):
    method __init__ (line 274) | def __init__(self,gantype):
    method forward (line 283) | def forward(self,dis_fake,dis_real):
  function gen_hinge (line 307) | def gen_hinge(dis_fake, dis_real=None):
  function dis_hinge (line 310) | def dis_hinge(dis_fake, dis_real):

FILE: scripts/utils/misc.py
  function to_numpy (line 12) | def to_numpy(tensor):
  function resize_to_match (line 20) | def resize_to_match(fm,to):
  function to_torch (line 25) | def to_torch(ndarray):
  function save_checkpoint (line 34) | def save_checkpoint(machine,filename='checkpoint.pth.tar', snapshot=None):
  function save_pred (line 61) | def save_pred(preds, checkpoint='checkpoint', filename='preds_valid.mat'):
  function adjust_learning_rate (line 67) | def adjust_learning_rate(datasets,optimizer, epoch, lr,args):

FILE: scripts/utils/model_init.py
  function weights_init_normal (line 6) | def weights_init_normal(m):
  function weights_init_xavier (line 18) | def weights_init_xavier(m):
  function weights_init_kaiming (line 30) | def weights_init_kaiming(m):
  function weights_init_orthogonal (line 42) | def weights_init_orthogonal(m):

FILE: scripts/utils/osutils.py
  function mkdir_p (line 6) | def mkdir_p(dir_path):
  function isfile (line 13) | def isfile(fname):
  function isdir (line 16) | def isdir(dirname):
  function join (line 19) | def join(path, *paths):

FILE: scripts/utils/parallel.py
  function allreduce (line 27) | def allreduce(*inputs):
  class AllReduce (line 33) | class AllReduce(Function):
    method forward (line 35) | def forward(ctx, num_inputs, *inputs):
    method backward (line 47) | def backward(ctx, *inputs):
  class Reduce (line 56) | class Reduce(Function):
    method forward (line 58) | def forward(ctx, *inputs):
    method backward (line 64) | def backward(ctx, gradOutput):
  class DistributedDataParallelModel (line 67) | class DistributedDataParallelModel(DistributedDataParallel):
    method gather (line 91) | def gather(self, outputs, output_device):
  class DataParallelModel (line 94) | class DataParallelModel(DataParallel):
    method gather (line 124) | def gather(self, outputs, output_device):
    method replicate (line 127) | def replicate(self, module, device_ids):
  class DataParallelCriterion (line 133) | class DataParallelCriterion(DataParallel):
    method forward (line 151) | def forward(self, inputs, *targets, **kwargs):
  function _criterion_parallel_apply (line 166) | def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None,...
  class CallbackContext (line 229) | class CallbackContext(object):
  function execute_replication_callbacks (line 233) | def execute_replication_callbacks(modules):
  function patch_replication_callback (line 257) | def patch_replication_callback(data_parallel):

FILE: scripts/utils/transforms.py
  function color_normalize (line 14) | def color_normalize(x, mean, std):
  function flip_back (line 23) | def flip_back(flip_output, dataset='mpii'):
  function shufflelr (line 47) | def shufflelr(x, width, dataset='mpii'):
  function fliplr (line 71) | def fliplr(x):
  function get_transform (line 80) | def get_transform(center, scale, res, rot=0):
  function transform (line 110) | def transform(pt, center, scale, res, invert=0, rot=0):
  function transform_preds (line 120) | def transform_preds(coords, center, scale, res):
  function crop (line 129) | def crop(img, center, scale, res, rot=0):
  function get_right (line 165) | def get_right(img,gray=False):
  class NormalizeInverse (line 177) | class NormalizeInverse(torchvision.transforms.Normalize):
    method __init__ (line 182) | def __init__(self, mean, std):
    method __call__ (line 189) | def __call__(self, tensor):

FILE: test.py
  function main (line 14) | def main(args):