SYMBOL INDEX (166 symbols across 15 files)

FILE: efficientnet_pytorch/model.py
  class MBConvBlock (line 36) | class MBConvBlock(nn.Module):
    method __init__ (line 50) | def __init__(self, block_args, global_params, image_size=None):
    method forward (line 91) | def forward(self, inputs, drop_connect_rate=None):
    method set_swish (line 134) | def set_swish(self, memory_efficient=True):
  class EfficientNet (line 143) | class EfficientNet(nn.Module):
    method __init__ (line 163) | def __init__(self, blocks_args=None, global_params=None):
    method set_swish (line 221) | def set_swish(self, memory_efficient=True):
    method extract_endpoints (line 231) | def extract_endpoints(self, inputs):
    method extract_features (line 278) | def extract_features(self, inputs):
    method forward (line 303) | def forward(self, inputs):
    method from_name (line 324) | def from_name(cls, model_name, in_channels=3, **override_params):
    method from_pretrained (line 349) | def from_pretrained(cls, model_name, weights_path=None, advprop=False,
    method get_image_size (line 384) | def get_image_size(cls, model_name):
    method _check_model_name_is_valid (line 398) | def _check_model_name_is_valid(cls, model_name):
    method _change_in_channels (line 410) | def _change_in_channels(self, in_channels):

FILE: efficientnet_pytorch/utils.py
  class Swish (line 58) | class Swish(nn.Module):
    method forward (line 59) | def forward(self, x):
  class SwishImplementation (line 64) | class SwishImplementation(torch.autograd.Function):
    method forward (line 66) | def forward(ctx, i):
    method backward (line 72) | def backward(ctx, grad_output):
  class MemoryEfficientSwish (line 78) | class MemoryEfficientSwish(nn.Module):
    method forward (line 79) | def forward(self, x):
  function round_filters (line 83) | def round_filters(filters, global_params):
  function round_repeats (line 111) | def round_repeats(repeats, global_params):
  function drop_connect (line 129) | def drop_connect(inputs, p, training):
  function get_width_and_height_from_size (line 157) | def get_width_and_height_from_size(x):
  function calculate_output_image_size (line 174) | def calculate_output_image_size(input_image_size, stride):
  function get_same_padding_conv2d (line 199) | def get_same_padding_conv2d(image_size=None):
  class Conv2dDynamicSamePadding (line 215) | class Conv2dDynamicSamePadding(nn.Conv2d):
    method __init__ (line 232) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, d...
    method forward (line 236) | def forward(self, x):
  class Conv2dStaticSamePadding (line 248) | class Conv2dStaticSamePadding(nn.Conv2d):
    method __init__ (line 255) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, i...
    method forward (line 273) | def forward(self, x):
  function get_same_padding_maxPool2d (line 279) | def get_same_padding_maxPool2d(image_size=None):
  class MaxPool2dDynamicSamePadding (line 295) | class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
    method __init__ (line 300) | def __init__(self, kernel_size, stride, padding=0, dilation=1, return_...
    method forward (line 306) | def forward(self, x):
  class MaxPool2dStaticSamePadding (line 319) | class MaxPool2dStaticSamePadding(nn.MaxPool2d):
    method __init__ (line 324) | def __init__(self, kernel_size, stride, image_size=None, **kwargs):
    method forward (line 343) | def forward(self, x):
  class BlockDecoder (line 361) | class BlockDecoder(object):
    method _decode_block_string (line 367) | def _decode_block_string(block_string):
    method _encode_block_string (line 402) | def _encode_block_string(block):
    method decode (line 426) | def decode(string_list):
    method encode (line 442) | def encode(blocks_args):
  function efficientnet_params (line 457) | def efficientnet_params(model_name):
  function efficientnet (line 482) | def efficientnet(width_coefficient=None, depth_coefficient=None, image_s...
  function get_model_params (line 531) | def get_model_params(model_name, override_params):
  function load_pretrained_weights (line 584) | def load_pretrained_weights(model, model_name, weights_path=None, load_f...

FILE: examples/imagenet/main.py
  function main (line 85) | def main():
  function main_worker (line 120) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 285) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function validate (line 329) | def validate(val_loader, model, criterion, args):
  function save_checkpoint (line 371) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 377) | class AverageMeter(object):
    method __init__ (line 379) | def __init__(self, name, fmt=':f'):
    method reset (line 384) | def reset(self):
    method update (line 390) | def update(self, val, n=1):
    method __str__ (line 396) | def __str__(self):
  class ProgressMeter (line 401) | class ProgressMeter(object):
    method __init__ (line 402) | def __init__(self, num_batches, *meters, prefix=""):
    method print (line 407) | def print(self, batch):
    method _get_batch_fmtstr (line 412) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 418) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 425) | def accuracy(output, target, topk=(1,)):

FILE: hubconf.py
  function _create_model_fn (line 6) | def _create_model_fn(model_name):

FILE: setup.py
  class UploadCommand (line 58) | class UploadCommand(Command):
    method status (line 65) | def status(s):
    method initialize_options (line 69) | def initialize_options(self):
    method finalize_options (line 72) | def finalize_options(self):
    method run (line 75) | def run(self):

FILE: sotabench.py
  function get_img_id (line 54) | def get_img_id(image_name):

FILE: tests/test_model.py
  function model (line 13) | def model(request):
  function pretrained (line 18) | def pretrained(request):
  function net (line 23) | def net(model, pretrained):
  function test_forward (line 30) | def test_forward(net, img_size):
  function test_dropout_training (line 37) | def test_dropout_training(net):
  function test_dropout_eval (line 43) | def test_dropout_eval(net):
  function test_dropout_update (line 49) | def test_dropout_update(net):
  function test_modify_dropout (line 62) | def test_modify_dropout(net, img_size):
  function test_modify_pool (line 83) | def test_modify_pool(net, img_size):
  function test_extract_endpoints (line 111) | def test_extract_endpoints(net, img_size):

FILE: tf_to_pytorch/convert_tf_to_pt/load_tf_weights.py
  function load_param (line 7) | def load_param(checkpoint_file, conversion_table, model_name):
  function load_efficientnet (line 29) | def load_efficientnet(model, checkpoint_file, model_name):
  function load_and_save_temporary_tensorflow_model (line 128) | def load_and_save_temporary_tensorflow_model(model_name, model_ckpt, exa...

FILE: tf_to_pytorch/convert_tf_to_pt/load_tf_weights_tf1.py
  function load_param (line 5) | def load_param(checkpoint_file, conversion_table, model_name):
  function load_efficientnet (line 27) | def load_efficientnet(model, checkpoint_file, model_name):
  function load_and_save_temporary_tensorflow_model (line 126) | def load_and_save_temporary_tensorflow_model(model_name, model_ckpt, exa...

FILE: tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_builder.py
  function efficientnet_params (line 35) | def efficientnet_params(model_name):
  class BlockDecoder (line 53) | class BlockDecoder(object):
    method _decode_block_string (line 56) | def _decode_block_string(self, block_string):
    method _encode_block_string (line 88) | def _encode_block_string(self, block):
    method decode (line 109) | def decode(self, string_list):
    method encode (line 124) | def encode(self, blocks_args):
  function swish (line 138) | def swish(features, use_native=True, use_hard=False):
  function efficientnet (line 178) | def efficientnet(width_coefficient=None,
  function get_model_params (line 204) | def get_model_params(model_name, override_params):
  function build_model (line 226) | def build_model(images,
  function build_model_base (line 299) | def build_model_base(images, model_name, training, override_params=None):

FILE: tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_model.py
  function conv_kernel_initializer (line 58) | def conv_kernel_initializer(shape, dtype=None, partition_info=None):
  function dense_kernel_initializer (line 82) | def dense_kernel_initializer(shape, dtype=None, partition_info=None):
  function superpixel_kernel_initializer (line 103) | def superpixel_kernel_initializer(shape, dtype='float32', partition_info...
  function round_filters (line 138) | def round_filters(filters, global_params):
  function round_repeats (line 157) | def round_repeats(repeats, global_params):
  class MBConvBlock (line 165) | class MBConvBlock(tf.keras.layers.Layer):
    method __init__ (line 172) | def __init__(self, block_args, global_params):
    method block_args (line 214) | def block_args(self):
    method _build (line 217) | def _build(self):
    method _call_se (line 318) | def _call_se(self, input_tensor):
    method call (line 333) | def call(self, inputs, training=True, survival_prob=None):
  class MBConvBlockWithoutDepthwise (line 414) | class MBConvBlockWithoutDepthwise(MBConvBlock):
    method _build (line 417) | def _build(self):
    method call (line 448) | def call(self, inputs, training=True, survival_prob=None):
  class Model (line 487) | class Model(tf.keras.Model):
    method __init__ (line 493) | def __init__(self, blocks_args=None, global_params=None):
    method _get_conv_block (line 515) | def _get_conv_block(self, conv_type):
    method _build (line 519) | def _build(self):
    method call (line 621) | def call(self,

FILE: tf_to_pytorch/convert_tf_to_pt/original_tf/eval_ckpt_main.py
  class EvalCkptDriver (line 61) | class EvalCkptDriver(object):
    method __init__ (line 71) | def __init__(self, model_name='efficientnet-b0', batch_size=1):
    method restore_model (line 80) | def restore_model(self, sess, ckpt_dir):
    method build_model (line 93) | def build_model(self, features, is_training):
    method build_dataset (line 103) | def build_dataset(self, filenames, labels, is_training):
    method run_inference (line 125) | def run_inference(self, ckpt_dir, image_files, labels):
  function eval_example_images (line 146) | def eval_example_images(model_name, ckpt_dir, image_files, labels_map_fi...
  function eval_imagenet (line 171) | def eval_imagenet(model_name,
  function main (line 210) | def main(unused_argv):

FILE: tf_to_pytorch/convert_tf_to_pt/original_tf/eval_ckpt_main_tf1.py
  class EvalCkptDriver (line 59) | class EvalCkptDriver(object):
    method __init__ (line 69) | def __init__(self, model_name='efficientnet-b0', batch_size=1):
    method restore_model (line 78) | def restore_model(self, sess, ckpt_dir):
    method build_model (line 91) | def build_model(self, features, is_training):
    method build_dataset (line 101) | def build_dataset(self, filenames, labels, is_training):
    method run_inference (line 121) | def run_inference(self, ckpt_dir, image_files, labels):
  function eval_example_images (line 142) | def eval_example_images(model_name, ckpt_dir, image_files, labels_map_fi...
  function eval_imagenet (line 167) | def eval_imagenet(model_name,
  function main (line 206) | def main(unused_argv):

FILE: tf_to_pytorch/convert_tf_to_pt/original_tf/preprocessing.py
  function distorted_bounding_box_crop (line 29) | def distorted_bounding_box_crop(image_bytes,
  function _at_least_x_are_equal (line 81) | def _at_least_x_are_equal(a, b, x):
  function _decode_and_random_crop (line 88) | def _decode_and_random_crop(image_bytes, image_size):
  function _decode_and_center_crop (line 111) | def _decode_and_center_crop(image_bytes, image_size):
  function _flip (line 131) | def _flip(image):
  function preprocess_for_train (line 137) | def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE,
  function preprocess_for_eval (line 191) | def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
  function preprocess_image (line 209) | def preprocess_image(image_bytes,

FILE: tf_to_pytorch/convert_tf_to_pt/original_tf/utils.py
  function build_learning_rate (line 32) | def build_learning_rate(initial_lr,
  function build_optimizer (line 66) | def build_optimizer(learning_rate,
  class TpuBatchNormalization (line 89) | class TpuBatchNormalization(tf.layers.BatchNormalization):
    method __init__ (line 93) | def __init__(self, fused=False, **kwargs):
    method _cross_replica_average (line 98) | def _cross_replica_average(self, t, num_shards_per_group):
    method _moments (line 113) | def _moments(self, inputs, reduction_axes, keep_dims):
  class BatchNormalization (line 139) | class BatchNormalization(tf.layers.BatchNormalization):
    method __init__ (line 142) | def __init__(self, name='tpu_batch_normalization', **kwargs):
  function drop_connect (line 146) | def drop_connect(inputs, is_training, survival_prob):
  function archive_ckpt (line 164) | def archive_ckpt(ckpt_eval, ckpt_objective, ckpt_path):
  function get_ema_vars (line 209) | def get_ema_vars():
  class DepthwiseConv2D (line 219) | class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, tf.layers.Layer):
  class EvalCkptDriver (line 225) | class EvalCkptDriver(object):
    method __init__ (line 236) | def __init__(self,
    method restore_model (line 249) | def restore_model(self, sess, ckpt_dir, enable_ema=True, export_ckpt=N...
    method build_model (line 273) | def build_model(self, features, is_training):
    method get_preprocess_fn (line 278) | def get_preprocess_fn(self):
    method build_dataset (line 281) | def build_dataset(self, filenames, labels, is_training):
    method run_inference (line 314) | def run_inference(self,
    method eval_example_images (line 341) | def eval_example_images(self,
    method eval_imagenet (line 370) | def eval_imagenet(self, ckpt_dir, imagenet_eval_glob,