SYMBOL INDEX (210 symbols across 33 files)

FILE: app.py
  function predict_depth (line 43) | def predict_depth(image):
  function on_submit (line 60) | def on_submit(image):

FILE: depth_anything_v2/dinov2.py
  function named_apply (line 26) | def named_apply(fn: Callable, module: nn.Module, name="", depth_first=Tr...
  class BlockChunk (line 37) | class BlockChunk(nn.ModuleList):
    method forward (line 38) | def forward(self, x):
  class DinoVisionTransformer (line 44) | class DinoVisionTransformer(nn.Module):
    method __init__ (line 45) | def __init__(
    method init_weights (line 172) | def init_weights(self):
    method interpolate_pos_encoding (line 179) | def interpolate_pos_encoding(self, x, w, h):
    method prepare_tokens_with_masks (line 212) | def prepare_tokens_with_masks(self, x, masks=None):
    method forward_features_list (line 233) | def forward_features_list(self, x_list, masks_list):
    method forward_features (line 253) | def forward_features(self, x, masks=None):
    method _get_intermediate_layers_not_chunked (line 271) | def _get_intermediate_layers_not_chunked(self, x, n=1):
    method _get_intermediate_layers_chunked (line 283) | def _get_intermediate_layers_chunked(self, x, n=1):
    method get_intermediate_layers (line 297) | def get_intermediate_layers(
    method forward (line 323) | def forward(self, *args, is_training=False, **kwargs):
  function init_weights_vit_timm (line 331) | def init_weights_vit_timm(module: nn.Module, name: str = ""):
  function vit_small (line 339) | def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
  function vit_base (line 353) | def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
  function vit_large (line 367) | def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
  function vit_giant2 (line 381) | def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
  function DINOv2 (line 398) | def DINOv2(model_name):

FILE: depth_anything_v2/dinov2_layers/attention.py
  class Attention (line 29) | class Attention(nn.Module):
    method __init__ (line 30) | def __init__(
    method forward (line 49) | def forward(self, x: Tensor) -> Tensor:
  class MemEffAttention (line 65) | class MemEffAttention(Attention):
    method forward (line 66) | def forward(self, x: Tensor, attn_bias=None) -> Tensor:

FILE: depth_anything_v2/dinov2_layers/block.py
  class Block (line 36) | class Block(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 82) | def forward(self, x: Tensor) -> Tensor:
  function drop_add_residual_stochastic_depth (line 110) | def drop_add_residual_stochastic_depth(
  function get_branges_scales (line 134) | def get_branges_scales(x, sample_drop_ratio=0.0):
  function add_residual (line 142) | def add_residual(x, brange, residual, residual_scale_factor, scaling_vec...
  function get_attn_bias_and_cat (line 157) | def get_attn_bias_and_cat(x_list, branges=None):
  function drop_add_residual_stochastic_depth_list (line 181) | def drop_add_residual_stochastic_depth_list(
  class NestedTensorBlock (line 204) | class NestedTensorBlock(Block):
    method forward_nested (line 205) | def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
    method forward (line 245) | def forward(self, x_or_x_list):

FILE: depth_anything_v2/dinov2_layers/drop_path.py
  function drop_path (line 15) | def drop_path(x, drop_prob: float = 0.0, training: bool = False):
  class DropPath (line 27) | class DropPath(nn.Module):
    method __init__ (line 30) | def __init__(self, drop_prob=None):
    method forward (line 34) | def forward(self, x):

FILE: depth_anything_v2/dinov2_layers/layer_scale.py
  class LayerScale (line 16) | class LayerScale(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 27) | def forward(self, x: Tensor) -> Tensor:

FILE: depth_anything_v2/dinov2_layers/mlp.py
  class Mlp (line 17) | class Mlp(nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 35) | def forward(self, x: Tensor) -> Tensor:

FILE: depth_anything_v2/dinov2_layers/patch_embed.py
  function make_2tuple (line 17) | def make_2tuple(x):
  class PatchEmbed (line 26) | class PatchEmbed(nn.Module):
    method __init__ (line 38) | def __init__(
    method forward (line 69) | def forward(self, x: Tensor) -> Tensor:
    method flops (line 84) | def flops(self) -> float:

FILE: depth_anything_v2/dinov2_layers/swiglu_ffn.py
  class SwiGLUFFN (line 13) | class SwiGLUFFN(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 29) | def forward(self, x: Tensor) -> Tensor:
  class SwiGLUFFNFused (line 45) | class SwiGLUFFNFused(SwiGLU):
    method __init__ (line 46) | def __init__(

FILE: depth_anything_v2/dpt.py
  function _make_fusion_block (line 12) | def _make_fusion_block(features, use_bn, size=None):
  class ConvBlock (line 24) | class ConvBlock(nn.Module):
    method __init__ (line 25) | def __init__(self, in_feature, out_feature):
    method forward (line 34) | def forward(self, x):
  class DPTHead (line 38) | class DPTHead(nn.Module):
    method __init__ (line 39) | def __init__(
    method forward (line 117) | def forward(self, out_features, patch_h, patch_w):
  class DepthAnythingV2 (line 153) | class DepthAnythingV2(nn.Module):
    method __init__ (line 154) | def __init__(
    method forward (line 176) | def forward(self, x):
    method infer_image (line 187) | def infer_image(self, raw_image, input_size=518):
    method image2tensor (line 196) | def image2tensor(self, raw_image, input_size=518):

FILE: depth_anything_v2/util/blocks.py
  function _make_scratch (line 4) | def _make_scratch(in_shape, out_shape, groups=1, expand=False):
  class ResidualConvUnit (line 29) | class ResidualConvUnit(nn.Module):
    method __init__ (line 33) | def __init__(self, features, activation, bn):
    method forward (line 57) | def forward(self, x):
  class FeatureFusionBlock (line 83) | class FeatureFusionBlock(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 123) | def forward(self, *xs, size=None):

FILE: depth_anything_v2/util/transform.py
  class Resize (line 5) | class Resize(object):
    method __init__ (line 9) | def __init__(
    method constrain_to_multiple_of (line 51) | def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
    method get_size (line 62) | def get_size(self, width, height):
    method __call__ (line 109) | def __call__(self, sample):
  class NormalizeImage (line 125) | class NormalizeImage(object):
    method __init__ (line 129) | def __init__(self, mean, std):
    method __call__ (line 133) | def __call__(self, sample):
  class PrepareForNet (line 139) | class PrepareForNet(object):
    method __init__ (line 143) | def __init__(self):
    method __call__ (line 146) | def __call__(self, sample):

FILE: metric_depth/dataset/hypersim.py
  function hypersim_distance_to_depth (line 11) | def hypersim_distance_to_depth(npyDistance):
  class Hypersim (line 26) | class Hypersim(Dataset):
    method __init__ (line 27) | def __init__(self, filelist_path, mode, size=(518, 518)):
    method __getitem__ (line 50) | def __getitem__(self, item):
    method __len__ (line 73) | def __len__(self):

FILE: metric_depth/dataset/kitti.py
  class KITTI (line 9) | class KITTI(Dataset):
    method __init__ (line 10) | def __init__(self, filelist_path, mode, size=(518, 518)):
    method __getitem__ (line 35) | def __getitem__(self, item):
    method __len__ (line 56) | def __len__(self):

FILE: metric_depth/dataset/transform.py
  function apply_min_size (line 8) | def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AR...
  class Resize (line 50) | class Resize(object):
    method __init__ (line 54) | def __init__(
    method constrain_to_multiple_of (line 96) | def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
    method get_size (line 107) | def get_size(self, width, height):
    method __call__ (line 164) | def __call__(self, sample):
  class NormalizeImage (line 207) | class NormalizeImage(object):
    method __init__ (line 211) | def __init__(self, mean, std):
    method __call__ (line 215) | def __call__(self, sample):
  class PrepareForNet (line 221) | class PrepareForNet(object):
    method __init__ (line 225) | def __init__(self):
    method __call__ (line 228) | def __call__(self, sample):
  class Crop (line 247) | class Crop(object):
    method __init__ (line 251) | def __init__(self, size):
    method __call__ (line 257) | def __call__(self, sample):

FILE: metric_depth/dataset/vkitti2.py
  class VKITTI2 (line 9) | class VKITTI2(Dataset):
    method __init__ (line 10) | def __init__(self, filelist_path, mode, size=(518, 518)):
    method __getitem__ (line 33) | def __getitem__(self, item):
    method __len__ (line 53) | def __len__(self):

FILE: metric_depth/depth_anything_v2/dinov2.py
  function named_apply (line 26) | def named_apply(fn: Callable, module: nn.Module, name="", depth_first=Tr...
  class BlockChunk (line 37) | class BlockChunk(nn.ModuleList):
    method forward (line 38) | def forward(self, x):
  class DinoVisionTransformer (line 44) | class DinoVisionTransformer(nn.Module):
    method __init__ (line 45) | def __init__(
    method init_weights (line 172) | def init_weights(self):
    method interpolate_pos_encoding (line 179) | def interpolate_pos_encoding(self, x, w, h):
    method prepare_tokens_with_masks (line 212) | def prepare_tokens_with_masks(self, x, masks=None):
    method forward_features_list (line 233) | def forward_features_list(self, x_list, masks_list):
    method forward_features (line 253) | def forward_features(self, x, masks=None):
    method _get_intermediate_layers_not_chunked (line 271) | def _get_intermediate_layers_not_chunked(self, x, n=1):
    method _get_intermediate_layers_chunked (line 283) | def _get_intermediate_layers_chunked(self, x, n=1):
    method get_intermediate_layers (line 297) | def get_intermediate_layers(
    method forward (line 323) | def forward(self, *args, is_training=False, **kwargs):
  function init_weights_vit_timm (line 331) | def init_weights_vit_timm(module: nn.Module, name: str = ""):
  function vit_small (line 339) | def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
  function vit_base (line 353) | def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
  function vit_large (line 367) | def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
  function vit_giant2 (line 381) | def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
  function DINOv2 (line 398) | def DINOv2(model_name):

FILE: metric_depth/depth_anything_v2/dinov2_layers/attention.py
  class Attention (line 29) | class Attention(nn.Module):
    method __init__ (line 30) | def __init__(
    method forward (line 49) | def forward(self, x: Tensor) -> Tensor:
  class MemEffAttention (line 65) | class MemEffAttention(Attention):
    method forward (line 66) | def forward(self, x: Tensor, attn_bias=None) -> Tensor:

FILE: metric_depth/depth_anything_v2/dinov2_layers/block.py
  class Block (line 36) | class Block(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 82) | def forward(self, x: Tensor) -> Tensor:
  function drop_add_residual_stochastic_depth (line 110) | def drop_add_residual_stochastic_depth(
  function get_branges_scales (line 134) | def get_branges_scales(x, sample_drop_ratio=0.0):
  function add_residual (line 142) | def add_residual(x, brange, residual, residual_scale_factor, scaling_vec...
  function get_attn_bias_and_cat (line 157) | def get_attn_bias_and_cat(x_list, branges=None):
  function drop_add_residual_stochastic_depth_list (line 181) | def drop_add_residual_stochastic_depth_list(
  class NestedTensorBlock (line 204) | class NestedTensorBlock(Block):
    method forward_nested (line 205) | def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
    method forward (line 245) | def forward(self, x_or_x_list):

FILE: metric_depth/depth_anything_v2/dinov2_layers/drop_path.py
  function drop_path (line 15) | def drop_path(x, drop_prob: float = 0.0, training: bool = False):
  class DropPath (line 27) | class DropPath(nn.Module):
    method __init__ (line 30) | def __init__(self, drop_prob=None):
    method forward (line 34) | def forward(self, x):

FILE: metric_depth/depth_anything_v2/dinov2_layers/layer_scale.py
  class LayerScale (line 16) | class LayerScale(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 27) | def forward(self, x: Tensor) -> Tensor:

FILE: metric_depth/depth_anything_v2/dinov2_layers/mlp.py
  class Mlp (line 17) | class Mlp(nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 35) | def forward(self, x: Tensor) -> Tensor:

FILE: metric_depth/depth_anything_v2/dinov2_layers/patch_embed.py
  function make_2tuple (line 17) | def make_2tuple(x):
  class PatchEmbed (line 26) | class PatchEmbed(nn.Module):
    method __init__ (line 38) | def __init__(
    method forward (line 69) | def forward(self, x: Tensor) -> Tensor:
    method flops (line 84) | def flops(self) -> float:

FILE: metric_depth/depth_anything_v2/dinov2_layers/swiglu_ffn.py
  class SwiGLUFFN (line 13) | class SwiGLUFFN(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 29) | def forward(self, x: Tensor) -> Tensor:
  class SwiGLUFFNFused (line 45) | class SwiGLUFFNFused(SwiGLU):
    method __init__ (line 46) | def __init__(

FILE: metric_depth/depth_anything_v2/dpt.py
  function _make_fusion_block (line 12) | def _make_fusion_block(features, use_bn, size=None):
  class ConvBlock (line 24) | class ConvBlock(nn.Module):
    method __init__ (line 25) | def __init__(self, in_feature, out_feature):
    method forward (line 34) | def forward(self, x):
  class DPTHead (line 38) | class DPTHead(nn.Module):
    method __init__ (line 39) | def __init__(
    method forward (line 116) | def forward(self, out_features, patch_h, patch_w):
  class DepthAnythingV2 (line 152) | class DepthAnythingV2(nn.Module):
    method __init__ (line 153) | def __init__(
    method forward (line 178) | def forward(self, x):
    method infer_image (line 188) | def infer_image(self, raw_image, input_size=518):
    method image2tensor (line 197) | def image2tensor(self, raw_image, input_size=518):

FILE: metric_depth/depth_anything_v2/util/blocks.py
  function _make_scratch (line 4) | def _make_scratch(in_shape, out_shape, groups=1, expand=False):
  class ResidualConvUnit (line 29) | class ResidualConvUnit(nn.Module):
    method __init__ (line 33) | def __init__(self, features, activation, bn):
    method forward (line 57) | def forward(self, x):
  class FeatureFusionBlock (line 83) | class FeatureFusionBlock(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 123) | def forward(self, *xs, size=None):

FILE: metric_depth/depth_anything_v2/util/transform.py
  class Resize (line 5) | class Resize(object):
    method __init__ (line 9) | def __init__(
    method constrain_to_multiple_of (line 51) | def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
    method get_size (line 62) | def get_size(self, width, height):
    method __call__ (line 109) | def __call__(self, sample):
  class NormalizeImage (line 125) | class NormalizeImage(object):
    method __init__ (line 129) | def __init__(self, mean, std):
    method __call__ (line 133) | def __call__(self, sample):
  class PrepareForNet (line 139) | class PrepareForNet(object):
    method __init__ (line 143) | def __init__(self):
    method __call__ (line 146) | def __call__(self, sample):

FILE: metric_depth/depth_to_pointcloud.py
  function main (line 34) | def main():

FILE: metric_depth/train.py
  function main (line 43) | def main():

FILE: metric_depth/util/dist_helper.py
  function setup_distributed (line 8) | def setup_distributed(backend="nccl", port=None):

FILE: metric_depth/util/loss.py
  class SiLogLoss (line 5) | class SiLogLoss(nn.Module):
    method __init__ (line 6) | def __init__(self, lambd=0.5):
    method forward (line 10) | def forward(self, pred, target, valid_mask):

FILE: metric_depth/util/metric.py
  function eval_depth (line 4) | def eval_depth(pred, target):

FILE: metric_depth/util/utils.py
  function init_log (line 9) | def init_log(name, level=logging.INFO):