SYMBOL INDEX (617 symbols across 70 files)

FILE: classification-NO-activation-function/backbones/pinet_1x1_norelu.py
  class BasicBlock (line 17) | class BasicBlock(BaseModule):
    method __init__ (line 40) | def __init__(self,
    method norm1 (line 107) | def norm1(self):
    method norm2 (line 111) | def norm2(self):
    method norm3 (line 115) | def norm3(self):
    method forward (line 118) | def forward(self, x):
  class Bottleneck (line 152) | class Bottleneck(BaseModule):
    method __init__ (line 175) | def __init__(self,
    method norm1 (line 251) | def norm1(self):
    method norm2 (line 255) | def norm2(self):
    method norm3 (line 259) | def norm3(self):
    method forward (line 262) | def forward(self, x):
  function get_expansion (line 297) | def get_expansion(block, expansion=None):
  class ResLayer (line 332) | class ResLayer(nn.Sequential):
    method __init__ (line 354) | def __init__(self,
  class ResNet (line 418) | class ResNet(BaseBackbone):
    method __init__ (line 478) | def __init__(self,
    method make_res_layer (line 563) | def make_res_layer(self, **kwargs):
    method norm1 (line 567) | def norm1(self):
    method _make_stem_layer (line 570) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 615) | def _freeze_stages(self):
    method init_weights (line 633) | def init_weights(self):
    method forward (line 648) | def forward(self, x):
    method train (line 664) | def train(self, mode=True):
  class ResNetV1c (line 675) | class ResNetV1c(ResNet):
    method __init__ (line 685) | def __init__(self, **kwargs):
  class ResNetV1d (line 691) | class ResNetV1d(ResNet):
    method __init__ (line 702) | def __init__(self, **kwargs):

FILE: classification-NO-activation-function/backbones/pinet_1x1_relu.py
  class BasicBlock (line 17) | class BasicBlock(BaseModule):
    method __init__ (line 40) | def __init__(self,
    method norm1 (line 107) | def norm1(self):
    method norm2 (line 111) | def norm2(self):
    method norm3 (line 115) | def norm3(self):
    method forward (line 118) | def forward(self, x):
  class Bottleneck (line 152) | class Bottleneck(BaseModule):
    method __init__ (line 175) | def __init__(self,
    method norm1 (line 251) | def norm1(self):
    method norm2 (line 255) | def norm2(self):
    method norm3 (line 259) | def norm3(self):
    method forward (line 262) | def forward(self, x):
  function get_expansion (line 297) | def get_expansion(block, expansion=None):
  class ResLayer (line 332) | class ResLayer(nn.Sequential):
    method __init__ (line 354) | def __init__(self,
  class ResNet (line 418) | class ResNet(BaseBackbone):
    method __init__ (line 478) | def __init__(self,
    method make_res_layer (line 563) | def make_res_layer(self, **kwargs):
    method norm1 (line 567) | def norm1(self):
    method _make_stem_layer (line 570) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 615) | def _freeze_stages(self):
    method init_weights (line 633) | def init_weights(self):
    method forward (line 648) | def forward(self, x):
    method train (line 664) | def train(self, mode=True):
  class ResNetV1c (line 675) | class ResNetV1c(ResNet):
    method __init__ (line 685) | def __init__(self, **kwargs):
  class ResNetV1d (line 691) | class ResNetV1d(ResNet):
    method __init__ (line 702) | def __init__(self, **kwargs):

FILE: classification-NO-activation-function/backbones/pinet_norelu.py
  class BasicBlock (line 17) | class BasicBlock(BaseModule):
    method __init__ (line 40) | def __init__(self,
    method norm1 (line 107) | def norm1(self):
    method norm2 (line 111) | def norm2(self):
    method norm3 (line 115) | def norm3(self):
    method forward (line 118) | def forward(self, x):
  class Bottleneck (line 153) | class Bottleneck(BaseModule):
    method __init__ (line 176) | def __init__(self,
    method norm1 (line 252) | def norm1(self):
    method norm2 (line 256) | def norm2(self):
    method norm3 (line 260) | def norm3(self):
    method forward (line 263) | def forward(self, x):
  function get_expansion (line 298) | def get_expansion(block, expansion=None):
  class ResLayer (line 333) | class ResLayer(nn.Sequential):
    method __init__ (line 355) | def __init__(self,
  class ResNet (line 419) | class ResNet(BaseBackbone):
    method __init__ (line 479) | def __init__(self,
    method make_res_layer (line 564) | def make_res_layer(self, **kwargs):
    method norm1 (line 568) | def norm1(self):
    method _make_stem_layer (line 571) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 616) | def _freeze_stages(self):
    method init_weights (line 634) | def init_weights(self):
    method forward (line 649) | def forward(self, x):
    method train (line 665) | def train(self, mode=True):
  class ResNetV1c (line 676) | class ResNetV1c(ResNet):
    method __init__ (line 686) | def __init__(self, **kwargs):
  class ResNetV1d (line 692) | class ResNetV1d(ResNet):
    method __init__ (line 703) | def __init__(self, **kwargs):

FILE: classification-NO-activation-function/backbones/pinet_relu.py
  class BasicBlock (line 17) | class BasicBlock(BaseModule):
    method __init__ (line 40) | def __init__(self,
    method norm1 (line 107) | def norm1(self):
    method norm2 (line 111) | def norm2(self):
    method norm3 (line 115) | def norm3(self):
    method forward (line 118) | def forward(self, x):
  class Bottleneck (line 153) | class Bottleneck(BaseModule):
    method __init__ (line 176) | def __init__(self,
    method norm1 (line 252) | def norm1(self):
    method norm2 (line 256) | def norm2(self):
    method norm3 (line 260) | def norm3(self):
    method forward (line 263) | def forward(self, x):
  function get_expansion (line 298) | def get_expansion(block, expansion=None):
  class ResLayer (line 333) | class ResLayer(nn.Sequential):
    method __init__ (line 355) | def __init__(self,
  class ResNet (line 419) | class ResNet(BaseBackbone):
    method __init__ (line 479) | def __init__(self,
    method make_res_layer (line 564) | def make_res_layer(self, **kwargs):
    method norm1 (line 568) | def norm1(self):
    method _make_stem_layer (line 571) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 616) | def _freeze_stages(self):
    method init_weights (line 634) | def init_weights(self):
    method forward (line 649) | def forward(self, x):
    method train (line 665) | def train(self, mode=True):
  class ResNetV1c (line 676) | class ResNetV1c(ResNet):
    method __init__ (line 686) | def __init__(self, **kwargs):
  class ResNetV1d (line 692) | class ResNetV1d(ResNet):
    method __init__ (line 703) | def __init__(self, **kwargs):

FILE: classification-NO-activation-function/backbones/resnet_norelu.py
  class BasicBlock (line 17) | class BasicBlock(BaseModule):
    method __init__ (line 40) | def __init__(self,
    method norm1 (line 107) | def norm1(self):
    method norm2 (line 111) | def norm2(self):
    method forward (line 118) | def forward(self, x):
  class Bottleneck (line 152) | class Bottleneck(BaseModule):
    method __init__ (line 175) | def __init__(self,
    method norm1 (line 251) | def norm1(self):
    method norm2 (line 255) | def norm2(self):
    method norm3 (line 259) | def norm3(self):
    method forward (line 262) | def forward(self, x):
  function get_expansion (line 297) | def get_expansion(block, expansion=None):
  class ResLayer (line 332) | class ResLayer(nn.Sequential):
    method __init__ (line 354) | def __init__(self,
  class ResNet (line 418) | class ResNet(BaseBackbone):
    method __init__ (line 478) | def __init__(self,
    method make_res_layer (line 563) | def make_res_layer(self, **kwargs):
    method norm1 (line 567) | def norm1(self):
    method _make_stem_layer (line 570) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 615) | def _freeze_stages(self):
    method init_weights (line 633) | def init_weights(self):
    method forward (line 648) | def forward(self, x):
    method train (line 664) | def train(self, mode=True):
  class ResNetV1c (line 675) | class ResNetV1c(ResNet):
    method __init__ (line 685) | def __init__(self, **kwargs):
  class ResNetV1d (line 691) | class ResNetV1d(ResNet):
    method __init__ (line 702) | def __init__(self, **kwargs):

FILE: face_recognition/config.py
  function generate_config (line 205) | def generate_config(_network, _dataset, _loss):

FILE: face_recognition/data/build_eval_pack.py
  function to_rgb (line 17) | def to_rgb(img):
  function IOU (line 24) | def IOU(Reframe,GTframe):
  function get_norm_crop (line 68) | def get_norm_crop(image_path):

FILE: face_recognition/data/rec2image.py
  function main (line 16) | def main(args):

FILE: face_recognition/eval/lfw.py
  function calculate_roc (line 42) | def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, n...
  function calculate_accuracy (line 91) | def calculate_accuracy(threshold, dist, actual_issame):
  function calculate_val (line 105) | def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, f...
  function calculate_val_far (line 139) | def calculate_val_far(threshold, dist, actual_issame):
  function evaluate (line 149) | def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):
  function get_paths (line 161) | def get_paths(lfw_dir, pairs, file_ext):
  function read_pairs (line 185) | def read_pairs(pairs_filename):
  function load_dataset (line 194) | def load_dataset(lfw_dir, image_size):
  function test (line 218) | def test(lfw_set, mx_model, batch_size):

FILE: face_recognition/eval/verification.py
  class LFold (line 47) | class LFold:
    method __init__ (line 48) | def __init__(self, n_splits = 2, shuffle = False):
    method split (line 53) | def split(self, indices):
  function calculate_roc (line 60) | def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, n...
  function calculate_accuracy (line 110) | def calculate_accuracy(threshold, dist, actual_issame):
  function calculate_val (line 124) | def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, f...
  function calculate_val_far (line 158) | def calculate_val_far(threshold, dist, actual_issame):
  function evaluate (line 170) | def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):
  function load_bin (line 182) | def load_bin(path, image_size):
  function test (line 208) | def test(data_set, mx_model, batch_size, nfolds=10, data_extra = None, l...
  function test_badcase (line 288) | def test_badcase(data_set, mx_model, batch_size, name='', data_extra = N...
  function dumpR (line 464) | def dumpR(data_set, mx_model, batch_size, name='', data_extra = None, la...

FILE: face_recognition/flops_counter.py
  function is_no_bias (line 18) | def is_no_bias(attr):
  function count_fc_flops (line 24) | def count_fc_flops(input_filter, output_filter, attr):
  function count_conv_flops (line 32) | def count_conv_flops(input_shape, output_shape, attr):
  function count_flops (line 48) | def count_flops(sym, **data_shapes):
  function flops_str (line 86) | def flops_str(FLOPs):

FILE: face_recognition/image_iter.py
  class FaceImageIter (line 24) | class FaceImageIter(io.DataIter):
    method __init__ (line 26) | def __init__(self, batch_size, data_shape,
    method reset (line 92) | def reset(self):
    method num_samples (line 101) | def num_samples(self):
    method next_sample (line 104) | def next_sample(self):
    method brightness_aug (line 130) | def brightness_aug(self, src, x):
    method contrast_aug (line 135) | def contrast_aug(self, src, x):
    method saturation_aug (line 144) | def saturation_aug(self, src, x):
    method color_aug (line 154) | def color_aug(self, img, x):
    method mirror_aug (line 164) | def mirror_aug(self, img):
    method compress_aug (line 171) | def compress_aug(self, img):
    method next (line 183) | def next(self):
    method check_data_shape (line 253) | def check_data_shape(self, data_shape):
    method check_valid_image (line 260) | def check_valid_image(self, data):
    method imdecode (line 265) | def imdecode(self, s):
    method read_image (line 271) | def read_image(self, fname):
    method augmentation_transform (line 282) | def augmentation_transform(self, data):
    method postprocess_data (line 288) | def postprocess_data(self, datum):
  class FaceImageIterList (line 292) | class FaceImageIterList(io.DataIter):
    method __init__ (line 293) | def __init__(self, iter_list):
    method reset (line 300) | def reset(self):
    method next (line 303) | def next(self):

FILE: face_recognition/metric.py
  class AccMetric (line 4) | class AccMetric(mx.metric.EvalMetric):
    method __init__ (line 5) | def __init__(self):
    method update (line 13) | def update(self, labels, preds):
  class LossValueMetric (line 29) | class LossValueMetric(mx.metric.EvalMetric):
    method __init__ (line 30) | def __init__(self):
    method update (line 37) | def update(self, labels, preds):

FILE: face_recognition/parall_module_local_v1.py
  class ParallModule (line 23) | class ParallModule(BaseModule):
    method __init__ (line 24) | def __init__(self, symbol, data_names, label_names,
    method _reset_bind (line 71) | def _reset_bind(self):
    method data_names (line 76) | def data_names(self):
    method output_names (line 80) | def output_names(self):
    method data_shapes (line 84) | def data_shapes(self):
    method label_shapes (line 89) | def label_shapes(self):
    method output_shapes (line 94) | def output_shapes(self):
    method get_export_params (line 98) | def get_export_params(self):
    method get_params (line 105) | def get_params(self):
    method set_params (line 118) | def set_params(self, arg_params, aux_params, allow_missing=False, forc...
    method init_params (line 139) | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_...
    method bind (line 157) | def bind(self, data_shapes, label_shapes=None, for_training=True,
    method init_optimizer (line 186) | def init_optimizer(self, kvstore='local', optimizer='sgd',
    method kv_push (line 200) | def kv_push(self, key, value):
    method forward (line 209) | def forward(self, data_batch, is_train=None):
    method get_ndarray (line 231) | def get_ndarray(self, context, name, shape):
    method get_ndarray2 (line 241) | def get_ndarray2(self, context, name, arr):
    method backward (line 252) | def backward(self, out_grads=None):
    method update (line 321) | def update(self):
    method get_outputs (line 329) | def get_outputs(self, merge_multi_context=True):
    method get_input_grads (line 334) | def get_input_grads(self, merge_multi_context=True):
    method update_metric (line 338) | def update_metric(self, eval_metric, labels):
    method install_monitor (line 345) | def install_monitor(self, mon):
    method forward_backward (line 350) | def forward_backward(self, data_batch):
    method fit (line 355) | def fit(self, train_data, eval_data=None, eval_metric='acc',

FILE: face_recognition/sample_config.py
  function generate_config (line 203) | def generate_config(_network, _dataset, _loss):

FILE: face_recognition/symbol/fdensenet.py
  function Act (line 33) | def Act():
  function _make_dense_block (line 39) | def _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_i...
  function _make_dense_layer (line 46) | def _make_dense_layer(growth_rate, bn_size, dropout):
  function _make_transition (line 65) | def _make_transition(num_output_features):
  class DenseNet (line 75) | class DenseNet(nn.HybridBlock):
    method __init__ (line 95) | def __init__(self, num_init_features, growth_rate, block_config,
    method hybrid_forward (line 121) | def hybrid_forward(self, F, x):
  function get_symbol (line 135) | def get_symbol():

FILE: face_recognition/symbol/fmnasnet.py
  function Act (line 12) | def Act():
  function ConvBlock (line 18) | def ConvBlock(channels, kernel_size, strides, **kwargs):
  function Conv1x1 (line 29) | def Conv1x1(channels, is_linear=False, **kwargs):
  function DWise (line 41) | def DWise(channels, strides, kernel_size=3, **kwargs):
  class SepCONV (line 52) | class SepCONV(nn.HybridBlock):
    method __init__ (line 53) | def __init__(self, inp, output, kernel_size, depth_multiplier=1, with_...
    method hybrid_forward (line 80) | def hybrid_forward(self, F ,x):
  class ExpandedConv (line 88) | class ExpandedConv(nn.HybridBlock):
    method __init__ (line 89) | def __init__(self, inp, oup, t, strides, kernel=3, same_shape=True, **...
    method hybrid_forward (line 101) | def hybrid_forward(self, F, x):
  function ExpandedConvSequence (line 107) | def ExpandedConvSequence(t, k, inp, oup, repeats, first_strides, **kwargs):
  class MNasNet (line 117) | class MNasNet(nn.HybridBlock):
    method __init__ (line 118) | def __init__(self, m=1.0, **kwargs):
    method hybrid_forward (line 149) | def hybrid_forward(self, F, x):
    method num_output_channel (line 154) | def num_output_channel(self):
  function get_symbol (line 157) | def get_symbol():

FILE: face_recognition/symbol/fmobilefacenet.py
  function Act (line 10) | def Act(data, act_type, name):
  function Conv (line 18) | def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), n...
  function Linear (line 24) | def Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0),...
  function ConvOnly (line 29) | def ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0...
  function DResidual (line 34) | def DResidual(data, num_out=1, kernel=(3, 3), stride=(2, 2), pad=(1, 1),...
  function Residual (line 40) | def Residual(data, num_block=1, num_out=1, kernel=(3, 3), stride=(1, 1),...
  function get_symbol (line 49) | def get_symbol():

FILE: face_recognition/symbol/fmobilenet.py
  function Act (line 25) | def Act(data, act_type, name):
  function Conv (line 33) | def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), n...
  function ConvOnly (line 39) | def ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0...
  function get_symbol (line 43) | def get_symbol():

FILE: face_recognition/symbol/fresnet.py
  function Conv (line 39) | def Conv(**kwargs):
  function Act (line 48) | def Act(data, act_type, name):
  function residual_unit_v1 (line 55) | def residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_n...
  function residual_unit_v1_L (line 144) | def residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle...
  function residual_unit_v2 (line 233) | def residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_n...
  function residual_unit_v3 (line 318) | def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_n...
  function residual_unit_v3_x (line 416) | def residual_unit_v3_x(data, num_filter, stride, dim_match, name, bottle...
  function residual_unit (line 480) | def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck...
  function resnet (line 496) | def resnet(units, num_stages, filter_list, num_classes, bottle_neck):
  function get_symbol (line 584) | def get_symbol():

FILE: face_recognition/symbol/memonger.py
  function prod (line 4) | def prod(shape):
  function is_param (line 13) | def is_param(name):
  function make_mirror_plan (line 29) | def make_mirror_plan(sym, threshold, plan_info=None, **kwargs):
  function get_cost (line 109) | def get_cost(sym, type_dict=None, **kwargs):
  function search_plan (line 122) | def search_plan(sym, ntrial=6, type_dict=None, **kwargs):

FILE: face_recognition/symbol/symbol_utils.py
  function Conv (line 7) | def Conv(**kwargs):
  function Act (line 15) | def Act(data, act_type, name):
  function Linear (line 24) | def Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0),...
  function get_fc1 (line 29) | def get_fc1(last_conv, num_classes, fc_type, input_channel=512):
  function residual_unit_v3 (line 114) | def residual_unit_v3(data, num_filter, stride, dim_match, name, **kwargs):
  function residual_unit_v1l (line 157) | def residual_unit_v1l(data, num_filter, stride, dim_match, name, bottle_...
  function get_head (line 246) | def get_head(data, version_input, num_filter):

FILE: face_recognition/symbol/vargfacenet.py
  function Act (line 39) | def Act(data, act_type, name):
  function get_setting_params (line 47) | def get_setting_params(**kwargs):
  function se_block (line 74) | def se_block(data, num_filter, setting_params, name):
  function separable_conv2d (line 103) | def separable_conv2d(data,
  function vargnet_block (line 173) | def vargnet_block(data,
  function vargnet_branch_merge_block (line 248) | def vargnet_branch_merge_block(data,
  function add_vargnet_conv_block (line 330) | def add_vargnet_conv_block(data,
  function add_head_block (line 374) | def add_head_block(data,
  function add_emb_block (line 433) | def add_emb_block(data,
  function get_symbol (line 504) | def get_symbol():

FILE: face_recognition/train.py
  function parse_args (line 40) | def parse_args():
  function get_symbol (line 64) | def get_symbol(args):
  function train_net (line 149) | def train_net(args):
  function main (line 372) | def main():

FILE: face_recognition/train_parall.py
  function parse_args (line 45) | def parse_args():
  function get_symbol_embedding (line 71) | def get_symbol_embedding():
  function get_symbol_arcface (line 80) | def get_symbol_arcface(args):
  function train_net (line 123) | def train_net(args):
  function main (line 347) | def main():

FILE: face_recognition/triplet_image_iter.py
  class FaceImageIter (line 29) | class FaceImageIter(io.DataIter):
    method __init__ (line 31) | def __init__(self, batch_size, data_shape,
    method pairwise_dists (line 112) | def pairwise_dists(self, embeddings):
    method pick_triplets (line 133) | def pick_triplets(self, embeddings, nrof_images_per_class):
    method triplet_reset (line 173) | def triplet_reset(self):
    method time_reset (line 191) | def time_reset(self):
    method time_elapsed (line 194) | def time_elapsed(self):
    method select_triplets (line 200) | def select_triplets(self):
    method hard_mining_reset (line 294) | def hard_mining_reset(self):
    method reset (line 384) | def reset(self):
    method num_samples (line 422) | def num_samples(self):
    method next_sample (line 425) | def next_sample(self):
    method brightness_aug (line 438) | def brightness_aug(self, src, x):
    method contrast_aug (line 443) | def contrast_aug(self, src, x):
    method saturation_aug (line 452) | def saturation_aug(self, src, x):
    method color_aug (line 462) | def color_aug(self, img, x):
    method mirror_aug (line 471) | def mirror_aug(self, img):
    method next (line 479) | def next(self):
    method check_data_shape (line 549) | def check_data_shape(self, data_shape):
    method check_valid_image (line 556) | def check_valid_image(self, data):
    method imdecode (line 561) | def imdecode(self, s):
    method read_image (line 567) | def read_image(self, fname):
    method augmentation_transform (line 578) | def augmentation_transform(self, data):
    method postprocess_data (line 584) | def postprocess_data(self, datum):
  class FaceImageIterList (line 588) | class FaceImageIterList(io.DataIter):
    method __init__ (line 589) | def __init__(self, iter_list):
    method reset (line 596) | def reset(self):
    method next (line 599) | def next(self):

FILE: image_generation_chainer/datasets/mnist.py
  class MnistDb (line 6) | class MnistDb(chainer.dataset.DatasetMixin):
    method __init__ (line 7) | def __init__(self, path, ims_suff='train_ims.npy', labels_suff=None, i...
    method __len__ (line 40) | def __len__(self):
    method get_example (line 43) | def get_example(self, i):

FILE: image_generation_chainer/dis_models/resblocks_dis.py
  function _downsample (line 8) | def _downsample(x):
  class Block (line 13) | class Block(chainer.Chain):
    method __init__ (line 14) | def __init__(self, in_channels, out_channels, hidden_channels=None, ks...
    method residual (line 38) | def residual(self, x):
    method shortcut (line 48) | def shortcut(self, x):
    method __call__ (line 58) | def __call__(self, x):
  class OptimizedBlock (line 68) | class OptimizedBlock(chainer.Chain):
    method __init__ (line 69) | def __init__(self, in_channels, out_channels, ksize=3, pad=1,
    method residual (line 87) | def residual(self, x):
    method shortcut (line 96) | def shortcut(self, x):
    method __call__ (line 100) | def __call__(self, x):

FILE: image_generation_chainer/dis_models/snresnet_32.py
  class SNResNetProjectionDiscriminator (line 8) | class SNResNetProjectionDiscriminator(chainer.Chain):
    method __init__ (line 9) | def __init__(self, ch=128, n_classes=0, activation=F.relu, sn=False, c...
    method __call__ (line 22) | def __call__(self, x, y=None, return_feature=False):

FILE: image_generation_chainer/evaluations/extensions.py
  function gen_images (line 36) | def gen_images(gen, n=50000, batchsize=100, seed=None, z=None):
  function gen_images_with_condition (line 56) | def gen_images_with_condition(gen, c=0, n=500, batchsize=100, z=None):
  function sample_generate_light (line 77) | def sample_generate_light(gen, dst, rows=5, cols=5, batchsize=5, seed=0):
  function sample_generate (line 98) | def sample_generate(gen, dst, rows=10, cols=10, batchsize=10, seed=0):
  function sample_generate_conditional (line 122) | def sample_generate_conditional(gen, dst, rows=10, n_classes=1000, seed=...
  function divergence_gen (line 154) | def divergence_gen(gen, gt_db, batch=1000, metric='kl', normalize=False,
  function divergence_trainer (line 207) | def divergence_trainer(gen, db, metric='kl', export_best=True, **kwargs):
  function load_inception_model (line 235) | def load_inception_model(path=None, gpu=0):
  function calc_inception (line 244) | def calc_inception(gen, batchsize=10, dst=None, path=None, n_ims=50000, ...
  function get_mean_cov (line 269) | def get_mean_cov(model, ims, batch_size=100, verbose=False):
  function monitor_largest_singular_values (line 297) | def monitor_largest_singular_values(dis, dst):
  function FID (line 329) | def FID(m0, c0, m1, c1):
  function calc_FID (line 336) | def calc_FID(gen, batchsize=100, stat_file="%s/cifar-10-fid.npz" % os.pa...
  function get_batch (line 366) | def get_batch(iterator, xp):
  function validation_loss_and_acc (line 386) | def validation_loss_and_acc(cls, iterator, n=50000, dis=None):

FILE: image_generation_chainer/evaluations/ndb.py
  class NDB (line 8) | class NDB:
    method __init__ (line 9) | def __init__(self, training_data=None, number_of_bins=100, significanc...
    method construct_bins (line 53) | def construct_bins(self, training_samples, bins_file):
    method evaluate (line 95) | def evaluate(self, query_samples, model_label=None):
    method print_results (line 129) | def print_results(self):
    method plot_results (line 135) | def plot_results(self, models_to_plot=None):
    method __calculate_bin_proportions (line 174) | def __calculate_bin_proportions(self, samples):
    method __read_from_bins_file (line 197) | def __read_from_bins_file(self, bins_file):
    method __write_to_bins_file (line 210) | def __write_to_bins_file(self, bins_file):
    method two_proportions_z_test (line 222) | def two_proportions_z_test(p1, n1, p2, n2, significance_level, z_thres...
    method jensen_shannon_divergence (line 235) | def jensen_shannon_divergence(p, q):
    method kl_divergence (line 243) | def kl_divergence(p, q):

FILE: image_generation_chainer/gen_models/cnn_gen_custom_prodpoly.py
  function return_norm (line 18) | def return_norm(norm):
  function _upsample (line 29) | def _upsample(x):
  class ProdPolyConvGenerator (line 34) | class ProdPolyConvGenerator(chainer.Chain):
    method __init__ (line 35) | def __init__(self, layer_d, use_bn=False, sn=False, out_ch=2, mult_lat...
    method return_injected (line 252) | def return_injected(self, h, z, n_layer, mult_until_exec=None):
    method prod_poly_FC (line 278) | def prod_poly_FC(self, z, mult_until_exec, batchsize=None, y=None):
    method __call__ (line 349) | def __call__(self, batchsize=None, y=None, z=None, mult_until_exec=Non...
    method __str__ (line 402) | def __str__(self, **kwargs):

FILE: image_generation_chainer/jobs/pinet/train_mn_mnist.py
  function make_optimizer (line 24) | def make_optimizer(model, comm, alpha=0.001, beta1=0.9, beta2=0.999, chm...
  function main (line 37) | def main():

FILE: image_generation_chainer/source/functions/cond_bn_wrapper.py
  function cond_bn_wrapper (line 13) | def cond_bn_wrapper(x, c, bn, gamma, beta):

FILE: image_generation_chainer/source/functions/losses.py
  function _tensor_to_matrix (line 7) | def _tensor_to_matrix(tensor, axis=0):
  function cosine_loss (line 19) | def cosine_loss(tens1, tens2, absol=True):
  function decov_loss (line 43) | def decov_loss(tensor, xp=None, axis=1):
  function decov_loss_matrix (line 72) | def decov_loss_matrix(tensor, xp=None, axis=1):
  function loss_revKL_dis (line 106) | def loss_revKL_dis(dis_fake, dis_real):
  function loss_revKL_gen (line 113) | def loss_revKL_gen(dis_fake):
  function loss_dcgan_dis (line 119) | def loss_dcgan_dis(dis_fake, dis_real):
  function loss_dcgan_gen (line 126) | def loss_dcgan_gen(dis_fake):
  function loss_hinge_dis (line 132) | def loss_hinge_dis(dis_fake, dis_real):
  function loss_hinge_gen (line 138) | def loss_hinge_gen(dis_fake):
  function loss_wgan_dis (line 144) | def loss_wgan_dis(dis_fake, dis_real):
  function loss_wgan_gen (line 149) | def loss_wgan_gen(dis_fake):
  function gradient_penalty (line 154) | def gradient_penalty(dis_output, x):
  function gradient_penalty_wgangp (line 160) | def gradient_penalty_wgangp(dis_output, x, lipnorm):

FILE: image_generation_chainer/source/functions/max_sv.py
  function _l2normalize_gpu (line 9) | def _l2normalize_gpu(v, eps=1e-12):
  function _l2normalize_cpu (line 20) | def _l2normalize_cpu(v, eps=1e-12):
  function max_singular_value (line 24) | def max_singular_value(W, u=None, Ip=1):

FILE: image_generation_chainer/source/inception/download.py
  function parse_args (line 27) | def parse_args():
  function copy_conv (line 33) | def copy_conv(sess, tftensor, layer):
  function copy_bn (line 45) | def copy_bn(sess, tftensor, layer):
  function copy_inception (line 68) | def copy_inception(sess, model):
  function download_tf_params (line 180) | def download_tf_params():
  function set_tf_params (line 203) | def set_tf_params(model, write_graph=False):
  function main (line 224) | def main(args):

FILE: image_generation_chainer/source/inception/example.py
  function parse_args (line 11) | def parse_args():
  function main (line 19) | def main(args):

FILE: image_generation_chainer/source/inception/inception_score.py
  function inception_forward (line 10) | def inception_forward(model, ims, batch_size):
  function inception_score (line 43) | def inception_score(model, ims, batch_size=100, splits=10, verbose=True):
  function inception_accuracy (line 78) | def inception_accuracy(model, ims, labels, batch_size=100, splits=10, la...
  class Mixed (line 115) | class Mixed(Chain):
    method __init__ (line 116) | def __init__(self, trunk):
    method __call__ (line 122) | def __call__(self, x):
  class Tower (line 130) | class Tower(Chain):
    method __init__ (line 131) | def __init__(self, trunk):
    method __call__ (line 138) | def __call__(self, x):
  function _average_pooling_2d (line 151) | def _average_pooling_2d(h):
  function _max_pooling_2d (line 155) | def _max_pooling_2d(h):
  function _max_pooling_2d_320 (line 159) | def _max_pooling_2d_320(h):
  class Inception (line 163) | class Inception(Chain):
    method __init__ (line 164) | def __init__(self):
    method __call__ (line 587) | def __call__(self, x, get_feature=False):

FILE: image_generation_chainer/source/inception/inception_score_tf.py
  function inception_forward (line 26) | def inception_forward(images, layer):
  function get_mean_and_cov (line 49) | def get_mean_and_cov(images):
  function get_fid (line 56) | def get_fid(images, ref_stats=None, images_ref=None, splits=10):
  function get_inception_score (line 75) | def get_inception_score(images, splits=10):
  function get_inception_accuracy (line 88) | def get_inception_accuracy(images, labels, lab_range=None):
  function _init_inception (line 115) | def _init_inception():

FILE: image_generation_chainer/source/links/categorical_conditional_batch_normalization.py
  class CategoricalConditionalBatchNormalization (line 16) | class CategoricalConditionalBatchNormalization(ConditionalBatchNormaliza...
    method __init__ (line 46) | def __init__(self, size, n_cat, decay=0.9, eps=2e-5, dtype=numpy.float32,
    method __call__ (line 63) | def __call__(self, x, c, **kwargs):
  function start_finetuning (line 101) | def start_finetuning(self):

FILE: image_generation_chainer/source/links/conditional_batch_normalization.py
  class ConditionalBatchNormalization (line 16) | class ConditionalBatchNormalization(chainer.Chain):
    method __init__ (line 46) | def __init__(self, size, n_cat, decay=0.9, eps=2e-5, dtype=numpy.float...
    method __call__ (line 62) | def __call__(self, x, gamma, beta, **kwargs):
  function start_finetuning (line 119) | def start_finetuning(self):

FILE: image_generation_chainer/source/links/resnet_layer.py
  class ResNetLayers (line 36) | class ResNetLayers(link.Chain):
    method __init__ (line 75) | def __init__(self, pretrained_model, n_layers):
    method functions (line 112) | def functions(self):
    method available_layers (line 126) | def available_layers(self):
    method convert_caffemodel_to_npz (line 130) | def convert_caffemodel_to_npz(cls, path_caffemodel, path_npz, n_layers...
    method __call__ (line 153) | def __call__(self, x, layers=['prob'], **kwargs):
    method extract (line 187) | def extract(self, images, layers=['pool5'], size=(224, 224), **kwargs):
    method predict (line 226) | def predict(self, images, oversample=True):
  class ResNet50Layers (line 255) | class ResNet50Layers(ResNetLayers):
    method __init__ (line 298) | def __init__(self, pretrained_model='auto'):
  class ResNet101Layers (line 304) | class ResNet101Layers(ResNetLayers):
    method __init__ (line 343) | def __init__(self, pretrained_model='auto'):
  class ResNet152Layers (line 349) | class ResNet152Layers(ResNetLayers):
    method __init__ (line 387) | def __init__(self, pretrained_model='auto'):
  function prepare (line 393) | def prepare(image, size=(224, 224)):
  class BuildingBlock (line 437) | class BuildingBlock(link.Chain):
    method __init__ (line 449) | def __init__(self, n_layer, in_channels, mid_channels,
    method __call__ (line 462) | def __call__(self, x):
    method forward (line 469) | def forward(self):
  class BottleneckA (line 473) | class BottleneckA(link.Chain):
    method __init__ (line 484) | def __init__(self, in_channels, mid_channels, out_channels,
    method __call__ (line 505) | def __call__(self, x):
  class BottleneckB (line 513) | class BottleneckB(link.Chain):
    method __init__ (line 522) | def __init__(self, in_channels, mid_channels, initialW=None):
    method __call__ (line 538) | def __call__(self, x):
  function _global_average_pooling_2d (line 545) | def _global_average_pooling_2d(x):
  function _transfer_components (line 552) | def _transfer_components(src, dst_conv, dst_bn, bname, cname):
  function _transfer_bottleneckA (line 563) | def _transfer_bottleneckA(src, dst, name):
  function _transfer_bottleneckB (line 570) | def _transfer_bottleneckB(src, dst, name):
  function _transfer_block (line 576) | def _transfer_block(src, dst, names):
  function _transfer_resnet50 (line 583) | def _transfer_resnet50(src, dst):
  function _transfer_resnet101 (line 600) | def _transfer_resnet101(src, dst):
  function _transfer_resnet152 (line 617) | def _transfer_resnet152(src, dst):
  function _make_npz (line 635) | def _make_npz(path_npz, path_caffemodel, model, n_layers):
  function _retrieve (line 655) | def _retrieve(n_layers, name_npz, name_caffemodel, model):

FILE: image_generation_chainer/source/links/sn_convolution_2d.py
  class SNConvolution2D (line 10) | class SNConvolution2D(Convolution2D):
    method __init__ (line 58) | def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
    method W_bar (line 70) | def W_bar(self):
    method _initialize_params (line 87) | def _initialize_params(self, in_size):
    method __call__ (line 95) | def __call__(self, x):

FILE: image_generation_chainer/source/links/sn_convolution_nd.py
  class SNConvolutionND (line 12) | class SNConvolutionND(link.Link):
    method __init__ (line 58) | def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, p...
    method W_bar (line 90) | def W_bar(self):
    method __call__ (line 106) | def __call__(self, x):

FILE: image_generation_chainer/source/links/sn_dilated_convolution_2d.py
  class SNDilatedConvolution2D (line 16) | class SNDilatedConvolution2D(DilatedConvolution2D):
    method __init__ (line 22) | def __init__(self, in_channels, out_channels, ksize=None, stride=1, pa...
    method W_bar (line 48) | def W_bar(self):
    method _initialize_params (line 63) | def _initialize_params(self, in_channels):
    method __call__ (line 71) | def __call__(self, x):
  function _pair (line 84) | def _pair(x):

FILE: image_generation_chainer/source/links/sn_embed_id.py
  class SNEmbedID (line 11) | class SNEmbedID(link.Link):
    method __init__ (line 39) | def __init__(self, in_size, out_size, initialW=None, ignore_label=None...
    method W_bar (line 52) | def W_bar(self):
    method __call__ (line 65) | def __call__(self, x):

FILE: image_generation_chainer/source/links/sn_linear.py
  class SNLinear (line 9) | class SNLinear(Linear):
    method __init__ (line 46) | def __init__(self, in_size, out_size, use_gamma=False, nobias=False,
    method W_bar (line 58) | def W_bar(self):
    method _initialize_params (line 74) | def _initialize_params(self, in_size):
    method __call__ (line 81) | def __call__(self, x):

FILE: image_generation_chainer/source/links/sn_symmetric_linear.py
  class SNSymmetricLinear (line 9) | class SNSymmetricLinear(Linear):
    method __init__ (line 43) | def __init__(self, in_size, out_size, use_gamma=False, nobias=False,
    method W_sym (line 53) | def W_sym(self):
    method W_bar (line 57) | def W_bar(self):
    method _initialize_params (line 69) | def _initialize_params(self, in_size):
    method __call__ (line 76) | def __call__(self, x):

FILE: image_generation_chainer/source/misc_train_utils.py
  function printtime (line 18) | def printtime(msg, time_format='%a %d/%m %H:%M:%S'):
  function get_by_nested_path (line 26) | def get_by_nested_path(root, items):
  function get_config_value (line 31) | def get_config_value(config, path, default=None):
  function create_result_dir (line 44) | def create_result_dir(result_dir, config_path, config):
  function load_models (line 67) | def load_models(config):
  function load_models_cgan (line 75) | def load_models_cgan(config, rocgan=False):
  function ensure_config_paths (line 93) | def ensure_config_paths(config, pb=None, verbose=True):
  function moving_average (line 135) | def moving_average(values, n=3):
  function plot_losses_log (line 142) | def plot_losses_log(pout, savefig=True, title_len=30, name_out='losses.p...

FILE: image_generation_chainer/source/miscs/model_moiving_average.py
  function copypersistents (line 5) | def copypersistents(src, dst):
  function namedpersistents (line 19) | def namedpersistents(src):
  function apply_exponential_averaging (line 27) | def apply_exponential_averaging(src_model, dst_model, alpha):
  class ModelMovingAverage (line 46) | class ModelMovingAverage(object):
    method __init__ (line 49) | def __init__(self, alpha, model=None):
    method update (line 53) | def update(self, src_model):
    method copy_avg_model_to (line 58) | def copy_avg_model_to(self, model):
    method set_avg_model (line 63) | def set_avg_model(self, model):

FILE: image_generation_chainer/source/miscs/random_samples.py
  function sample_continuous (line 5) | def sample_continuous(dim, batchsize, distribution='normal', xp=np):
  function sample_categorical (line 22) | def sample_categorical(n_cat, batchsize, distribution='uniform', xp=np, ...
  function sample_from_categorical_distribution (line 31) | def sample_from_categorical_distribution(batch_probs):

FILE: image_generation_chainer/source/yaml_utils.py
  class Config (line 15) | class Config(object):
    method __init__ (line 16) | def __init__(self, config_dict):
    method __getattr__ (line 19) | def __getattr__(self, key):
    method __getitem__ (line 25) | def __getitem__(self, key):
    method __repr__ (line 28) | def __repr__(self):
  function load_dataset (line 32) | def load_dataset(config, validation=False, valid_path=None):
  function load_module (line 51) | def load_module(fn, name):
  function load_model (line 58) | def load_model(model_fn, model_name, args=None, GPU=0):
  function load_updater_class (line 66) | def load_updater_class(config):

FILE: image_generation_chainer/updaters/gans/updater.py
  class Updater (line 10) | class Updater(chainer.training.StandardUpdater):
    method __init__ (line 11) | def __init__(self, *args, iter_incndis=None, iter_ndis=1, start_ndison...
    method _generate_samples (line 39) | def _generate_samples(self, n_gen_samples=None):
    method get_batch (line 50) | def get_batch(self, xp):
    method update_core (line 61) | def update_core(self):

FILE: image_generation_pytorch/FID/fid_score.py
  function tqdm (line 49) | def tqdm(x): return x
  function get_activations (line 67) | def get_activations(files, model, batch_size=50, dims=2048,
  function calculate_frechet_distance (line 136) | def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
  function calculate_activation_statistics (line 193) | def calculate_activation_statistics(files, model, batch_size=50,
  function _compute_statistics_of_path (line 218) | def _compute_statistics_of_path(path, model, batch_size, dims, cuda):
  function calculate_fid_given_paths (line 230) | def calculate_fid_given_paths(paths, batch_size, cuda, dims):

FILE: image_generation_pytorch/FID/inception.py
  class InceptionV3 (line 6) | class InceptionV3(nn.Module):
    method __init__ (line 21) | def __init__(self,
    method forward (line 107) | def forward(self, inp):

FILE: image_generation_pytorch/IS/inception_score.py
  function inception_score (line 12) | def inception_score(imgs, cuda=True, batch_size=1, resize=True, splits=1):

FILE: image_generation_pytorch/data_loader.py
  function get_loader (line 6) | def get_loader(data_path, batch_size, mode, num_workers=4):

FILE: image_generation_pytorch/logger.py
  class Logger (line 11) | class Logger(object):
    method __init__ (line 13) | def __init__(self, log_dir):
    method scalar_summary (line 17) | def scalar_summary(self, tag, value, step):
    method image_summary (line 22) | def image_summary(self, tag, images, step):
    method histo_summary (line 45) | def histo_summary(self, tag, values, step, bins=1000):

FILE: image_generation_pytorch/main.py
  function save_config (line 9) | def save_config(config, shared_path):
  function str2bool (line 16) | def str2bool(v):
  function get_time (line 25) | def get_time():
  function main (line 29) | def main(config):

FILE: image_generation_pytorch/model.py
  class Generator (line 9) | class Generator(nn.Module):
    method __init__ (line 10) | def __init__(self, g_layers=[], activation_fn=True, inject_z=True, tra...
    method forward (line 108) | def forward(self, x):
  class Discriminator (line 127) | class Discriminator(nn.Module):
    method __init__ (line 128) | def __init__(self, d_layers=[], activation_fn=True, spectral_norm=False):
    method forward (line 176) | def forward(self, x):

FILE: image_generation_pytorch/solver.py
  function save_is (line 19) | def save_is(base_path, score):
  function denorm (line 28) | def denorm(x):
  function to_cuda (line 34) | def to_cuda(x):
  function to_numpy (line 40) | def to_numpy(x):
  class Solver (line 46) | class Solver(object):
    method __init__ (line 47) | def __init__(self, config, data_loader):
    method build_model (line 91) | def build_model(self):
    method reset_grad (line 114) | def reset_grad(self):
    method weights_init (line 119) | def weights_init(self, m):
    method gradient_penalty (line 127) | def gradient_penalty(self, real_data, generated_data):
    method train (line 157) | def train(self):
    method sample (line 258) | def sample(self, n_samples):

FILE: mesh_pi_nets/autoencoder_dataset.py
  class autoencoder_dataset (line 7) | class autoencoder_dataset(Dataset):
    method __init__ (line 9) | def __init__(self, root_dir, points_dataset, shapedata, normalization=...
    method __len__ (line 18) | def __len__(self):
    method __getitem__ (line 21) | def __getitem__(self, idx):

FILE: mesh_pi_nets/mesh_sampling.py
  function compute_downsampling (line 22) | def compute_downsampling(downsample_directory, downsample_method, shaped...
  function vertex_quadrics (line 55) | def vertex_quadrics(mesh):
  function setup_deformation_transfer (line 83) | def setup_deformation_transfer(source, target, use_normals=False):
  function qslim_decimator_transformer (line 134) | def qslim_decimator_transformer(mesh, factor=None, n_verts_desired=None):
  function _get_sparse_transform (line 251) | def _get_sparse_transform(faces, num_original_verts):
  function generate_transform_matrices (line 267) | def generate_transform_matrices(mesh, factors):
  function generate_transform_matrices_given_downsamples (line 305) | def generate_transform_matrices_given_downsamples(mesh, downsample_direc...

FILE: mesh_pi_nets/models.py
  class SpiralPoly (line 8) | class SpiralPoly(nn.Module):
    method __init__ (line 9) | def __init__(self, in_c,
    method forward (line 79) | def forward(self, x, spiral_adj):
  class SpiralPolyAE (line 176) | class SpiralPolyAE(nn.Module):
    method __init__ (line 177) | def __init__(self,
    method encode (line 251) | def encode(self, x):
    method decode (line 268) | def decode(self, z):
    method forward (line 287) | def forward(self, x):

FILE: mesh_pi_nets/shape_data.py
  class ShapeData (line 17) | class ShapeData(object):
    method __init__ (line 18) | def __init__(self, nVal,
    method load (line 54) | def load(self):
    method normalize (line 66) | def normalize(self):
    method save_meshes (line 90) | def save_meshes(self, filename, meshes, mesh_indices):

FILE: mesh_pi_nets/spiral_pi_nets.py
  function str2bool (line 39) | def str2bool(v):
  function str2list2int (line 48) | def str2list2int(v):
  function str2ListOfLists2int (line 52) | def str2ListOfLists2int(v):
  function str2list2float (line 56) | def str2list2float(v):
  function str2list2bool (line 60) | def str2list2bool(v):
  function str2ListOfLists2bool (line 64) | def str2ListOfLists2bool(v):
  function loss_l1 (line 68) | def loss_l1(outputs, targets):
  function main (line 73) | def main(args):

FILE: mesh_pi_nets/spiral_utils.py
  function get_adj_trigs (line 9) | def get_adj_trigs(A, F, reference_mesh, meshpackage = 'mpi-mesh'):
  function generate_spirals (line 45) | def generate_spirals(step_sizes, M, Adj, Trigs, reference_points, dilati...
  function distance (line 101) | def distance(v,w):
  function single_source_shortest_path (line 104) | def single_source_shortest_path(V,E,source,dist=None,prev=None):
  function get_spirals (line 130) | def get_spirals(mesh, adj, trig, reference_points, n_steps=1, padding='z...

FILE: mesh_pi_nets/test_funcs.py
  function test_autoencoder_dataloader (line 8) | def test_autoencoder_dataloader(device, model, dataloader_test, shapedat...

FILE: mesh_pi_nets/train_funcs.py
  function isnotebook (line 1) | def isnotebook():
  function train_autoencoder_dataloader (line 21) | def train_autoencoder_dataloader(dataloader_train,