code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def experiment_setup(models: Sequence[ModelInfo], c_ref: int, batch_size: int):
"""Return information related to training `models` under compute budget `c_ref`."""
data = []
for m in models:
c_self_per_token = m.self_attn_flops()
d_iso = c_ref / c_self_per_token
s_iso = num_traini... | Return information related to training `models` under compute budget `c_ref`. | experiment_setup | python | krasserm/perceiver-io | examples/scaling/clm/article.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/article.py | Apache-2.0 |
def experiment_ratios(models: Sequence[ModelInfo]):
"""Return compute- and parameter-related ratios, independent of compute budget."""
data = []
for m in models:
c_self_approx_per_token = m.self_attn_flops_approx()
c_self_per_token = m.self_attn_flops()
c_cross_per_token = m.cross_... | Return compute- and parameter-related ratios, independent of compute budget. | experiment_ratios | python | krasserm/perceiver-io | examples/scaling/clm/article.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/article.py | Apache-2.0 |
def self_attn(self, num_channels, num_layers):
"""Self-attention FLOPs per latent token.
Equivalent to a decoder-only transformer.
:param num_channels: model dimension
:param num_layers: number of self attention layers incl hybrid layer
"""
embed = self._input_embed(num... | Self-attention FLOPs per latent token.
Equivalent to a decoder-only transformer.
:param num_channels: model dimension
:param num_layers: number of self attention layers incl hybrid layer
| self_attn | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def cross_attn(self, num_channels, prefix_dropout=0.5):
"""Prefix cross-attention FLOPS per latent token.
Perceiver AR extra compute compared to a decoder-only transformer.
:param num_channels: model dimension
:param prefix_dropout: dropout probability of prefix positions
"""
... | Prefix cross-attention FLOPS per latent token.
Perceiver AR extra compute compared to a decoder-only transformer.
:param num_channels: model dimension
:param prefix_dropout: dropout probability of prefix positions
| cross_attn | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def _self_attn_layer(self, num_channels):
"""Self-attention FLOPs per latent token per layer."""
qkv = 6 * num_channels**2
attn = 2 * num_channels * self.num_latents
out = 2 * num_channels**2
return qkv + attn + out | Self-attention FLOPs per latent token per layer. | _self_attn_layer | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def _cross_attn_layer(self, num_channels):
"""Cross-attention FLOPs per prefix token per layer."""
kv = 4 * num_channels**2
attn = 2 * num_channels * self.num_latents
return kv + attn | Cross-attention FLOPs per prefix token per layer. | _cross_attn_layer | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def __init__(self, num_channels: int, num_layers: int, compute_estimator: ComputeEstimator):
"""...
:param num_channels: model dimension.
:param num_layers: number of self attention layers incl hybrid layer.
"""
self.num_channels = num_channels
self.num_layers = num_laye... | ...
:param num_channels: model dimension.
:param num_layers: number of self attention layers incl hybrid layer.
| __init__ | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def num_self_attn_params(self):
"""Parameter count of self-attention part.
Equivalent to a decoder-only transformer.
"""
return num_self_attn_params(
num_channels=self.num_channels,
num_layers=self.num_layers,
num_latents=self.num_latents,
... | Parameter count of self-attention part.
Equivalent to a decoder-only transformer.
| num_self_attn_params | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def num_cross_attn_params(self):
"""Parameter count of cross-attention part.."""
# parameters for prefix position embedding
return num_cross_attn_params(self.num_channels, self.num_prefix) | Parameter count of cross-attention part.. | num_cross_attn_params | python | krasserm/perceiver-io | examples/scaling/clm/scaling/flops.py | https://github.com/krasserm/perceiver-io/blob/master/examples/scaling/clm/scaling/flops.py | Apache-2.0 |
def encode_midi_files(files: List[Path], num_workers: int) -> List[np.ndarray]:
"""Encode a list of midi files using multiple cpu workers."""
with Pool(processes=num_workers) as pool:
res = list(tqdm(pool.imap(_encode_midi_file, files), total=len(files)))
return [r for r in res if r is not None] | Encode a list of midi files using multiple cpu workers. | encode_midi_files | python | krasserm/perceiver-io | perceiver/data/audio/midi_processor.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/audio/midi_processor.py | Apache-2.0 |
def __init__(
self,
dataset_dir: str,
max_seq_len: int,
min_seq_len: Optional[int] = None,
padding_side: str = "left",
batch_size: int = 16,
num_workers: int = 1,
preproc_workers: Optional[int] = None,
pin_memory: bool = True,
):
"""Bas... | Base class for data preprocessing and loading across different audio data sources using MIDI as the
source data format.
:param dataset_dir: Directory for storing the preprocessed dataset.
:param max_seq_len: Maximum sequence length generated by this data module.
:param min_seq_len: Mini... | __init__ | python | krasserm/perceiver-io | perceiver/data/audio/symbolic.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/audio/symbolic.py | Apache-2.0 |
def mask_words(self, examples):
"""A modified version of whole word masking as described in https://huggingface.co/course/chapter7/3.
The implementation in the linked document replaces words, randomly selected with `wwm_probability`, with mask
tokens (one or more per word). The implementation h... | A modified version of whole word masking as described in https://huggingface.co/course/chapter7/3.
The implementation in the linked document replaces words, randomly selected with `wwm_probability`, with mask
tokens (one or more per word). The implementation here, however, only replaces 80% of selected... | mask_words | python | krasserm/perceiver-io | perceiver/data/text/collator.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/text/collator.py | Apache-2.0 |
def __init__(
self,
dataset_dir: str,
tokenizer: str,
max_seq_len: int,
task: Task = Task.mlm,
mask_prob: float = 0.15,
mask_words: bool = True,
static_masking: bool = False,
add_special_tokens: bool = False,
add_eos_token: bool = False,
... | Base class for consistent data preprocessing and loading across different text data sources.
:param dataset_dir: Directory for storing the preprocessed dataset.
:param tokenizer: Reference to a Hugging Face fast tokenizer (or the `deepmind/language-perceiver` tokenizer).
:param max_seq_len: Max... | __init__ | python | krasserm/perceiver-io | perceiver/data/text/common.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/text/common.py | Apache-2.0 |
def word_ids(self, token_ids):
"""Creates word ids from `token_ids`.
Words boundaries are defined using whitespace boundaries. Whitespaces preceding a word have the same word id as
the actual word following these whitespaces. Special tokens are assigned a `None` word id. Consecutive words do
... | Creates word ids from `token_ids`.
Words boundaries are defined using whitespace boundaries. Whitespaces preceding a word have the same word id as
the actual word following these whitespaces. Special tokens are assigned a `None` word id. Consecutive words do
not necessarily have consecutive wor... | word_ids | python | krasserm/perceiver-io | perceiver/data/text/utils.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/text/utils.py | Apache-2.0 |
def _extract_image_patches(self, x: torch.Tensor, kernel: int, stride: int = 1, dilation: int = 1):
"""Equivalent to the implementation of https://www.tensorflow.org/api_docs/python/tf/image/extract_patches
using "SAME" padding.
From: https://discuss.pytorch.org/t/tf-extract-image-patches-in-py... | Equivalent to the implementation of https://www.tensorflow.org/api_docs/python/tf/image/extract_patches
using "SAME" padding.
From: https://discuss.pytorch.org/t/tf-extract-image-patches-in-pytorch/43837/9
| _extract_image_patches | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def _pad(x: torch.Tensor, kernel: int, stride: int = 1, dilation: int = 1) -> torch.Tensor:
"""Applies a pad to the input using "SAME" strategy."""
*_, h, w = x.shape
h2 = math.ceil(h / stride)
w2 = math.ceil(w / stride)
pad_row = (h2 - 1) * stride + (kernel - 1) * dilation + 1 -... | Applies a pad to the input using "SAME" strategy. | _pad | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def _compute_patch_grid_indices(self, img_shape: Tuple[int, ...]) -> List[Tuple[int, int]]:
"""From https://github.com/deepmind/deepmind-research/blob/master/perceiver/colabs/optical_flow.ipynb."""
ys = list(range(0, img_shape[0], self.patch_size[0] - self.patch_min_overlap))
xs = list(range(0, ... | From https://github.com/deepmind/deepmind-research/blob/master/perceiver/colabs/optical_flow.ipynb. | _compute_patch_grid_indices | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def preprocess(
self, image_pair: Union[Tuple[np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor]]
) -> torch.Tensor:
"""Creates the input features for the model for a pair of images.
The input images are stacked and split into image patches of size `patch_size`. For each pixel of ea... | Creates the input features for the model for a pair of images.
The input images are stacked and split into image patches of size `patch_size`. For each pixel of each
individual patch, 3x3 patches are extracted and stacked in the channel dimension.
Output shape: torch.Size(nr_patches, 2, 27, pa... | preprocess | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def preprocess_batch(
self,
image_pairs: Union[List[Tuple[np.ndarray, np.ndarray]], List[Tuple[torch.Tensor, torch.Tensor]]],
) -> torch.Tensor:
"""Creates the input features for the model for a batch of image pairs.
For each image pair the images are stacked and split into image pa... | Creates the input features for the model for a batch of image pairs.
For each image pair the images are stacked and split into image patches of size `patch_size`. For each pixel
of each individual patch, 3x3 patches are extracted and stacked in the channel dimension.
Output shape: torch.Size(b... | preprocess_batch | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def postprocess(self, predictions: torch.Tensor, img_shape: Tuple[int, ...]) -> torch.Tensor:
"""Combines optical flow predictions for individual image patches into a single prediction per image pair.
Predictions can be supplied for a single image pair or a batch of image pairs, hence the supported inp... | Combines optical flow predictions for individual image patches into a single prediction per image pair.
Predictions can be supplied for a single image pair or a batch of image pairs, hence the supported input shapes
are:
* (nr_patches, patch_size[0], patch_size[1], 2) and
* (batch_size,... | postprocess | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def process(
self,
model,
image_pairs: Union[List[Tuple[np.ndarray, np.ndarray]], List[Tuple[torch.Tensor, torch.Tensor]]],
batch_size: int,
) -> torch.Tensor:
"""Combines preprocessing, inference and postprocessing steps for the optical flow.
The input features for ... | Combines preprocessing, inference and postprocessing steps for the optical flow.
The input features for model are created by stacking each image pair in the channel dimension and splitting the
result into image patches of size `patch_size`. For each pixel in each individual patch, 3x3 patches
a... | process | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def render_optical_flow(flow: np.ndarray) -> np.ndarray:
"""Renders optical flow predictions produced by an optical flow model."""
hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8)
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang / np.pi / 2 * 180
hsv[..., 1... | Renders optical flow predictions produced by an optical flow model. | render_optical_flow | python | krasserm/perceiver-io | perceiver/data/vision/optical_flow.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/data/vision/optical_flow.py | Apache-2.0 |
def __init__(self, num_input_channels: int, *args, **kwargs):
"""Transforms and position-encodes task-specific input to generic encoder input.
:param num_input_channels: Number of channels of the generic encoder input produced by this adapter.
"""
super().__init__()
self._num_in... | Transforms and position-encodes task-specific input to generic encoder input.
:param num_input_channels: Number of channels of the generic encoder input produced by this adapter.
| __init__ | python | krasserm/perceiver-io | perceiver/model/core/adapter.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/adapter.py | Apache-2.0 |
def __init__(self, rotated_channels_per_head: int, *args, **kwargs):
"""An input adapter mixin that additionally generates a frequency position encoding for input sequence
`x`."""
super().__init__(*args, **kwargs)
self.frq_pos_encoding = FrequencyPositionEncoding(dim=rotated_channels_per... | An input adapter mixin that additionally generates a frequency position encoding for input sequence
`x`. | __init__ | python | krasserm/perceiver-io | perceiver/model/core/adapter.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/adapter.py | Apache-2.0 |
def generate(
self,
inputs: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
num_latents: int = 1,
**kwargs,
):
"""Augments `GenerationMixin.generate` to support a `num_latents` argument.
This argument determines the initial number of ... | Augments `GenerationMixin.generate` to support a `num_latents` argument.
This argument determines the initial number of latents positions assigned to the end of a prompt. During
generation, first, the number of latent positions grows until `self.backend_model.max_latents` is reached, then
the p... | generate | python | krasserm/perceiver-io | perceiver/model/core/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/huggingface.py | Apache-2.0 |
def __init__(
self,
num_heads: int,
num_q_input_channels: int,
num_kv_input_channels: int,
num_qk_channels: Optional[int] = None,
num_v_channels: Optional[int] = None,
num_output_channels: Optional[int] = None,
max_heads_parallel: Optional[int] = None,
... | Multi-head attention as specified in https://arxiv.org/abs/2107.14795 Appendix E plus support for rotary
position embeddings (https://arxiv.org/abs/2104.09864) and causal attention. Causal attention requires
queries and keys to be right-aligned, if they have different length.
:param num_heads: ... | __init__ | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def forward(
self,
x_q: torch.Tensor,
x_kv: torch.Tensor,
pad_mask: Optional[torch.Tensor] = None,
rot_pos_emb_q: Optional[RotaryPositionEmbedding] = None,
rot_pos_emb_k: Optional[RotaryPositionEmbedding] = None,
kv_cache: Optional[KVCache] = None,
):
... | ...
:param x_q: Query input of shape (B, N, D) where B is the batch size, N the query sequence length and D the
number of query input channels (= `num_q_input_channels`)
:param x_kv: Key/value input of shape (B, L, C) where B is the batch size, L the key/value sequence length and C
... | forward | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def __init__(
self,
num_heads: int,
num_q_input_channels: int,
num_kv_input_channels: int,
num_qk_channels: Optional[int] = None,
num_v_channels: Optional[int] = None,
max_heads_parallel: Optional[int] = None,
causal_attention: bool = False,
dropou... | Pre-layer-norm cross-attention (see `MultiHeadAttention` for attention details). | __init__ | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def forward(
self,
x_q: torch.Tensor,
x_kv: Optional[torch.Tensor] = None,
x_kv_prefix: Optional[torch.Tensor] = None,
pad_mask: Optional[torch.Tensor] = None,
rot_pos_emb_q: Optional[RotaryPositionEmbedding] = None,
rot_pos_emb_k: Optional[RotaryPositionEmbedding... | Pre-layer-norm cross-attention of query input `x_q` to key/value input (`x_kv` or `x_kv_prefix`).
If `x_kv_prefix` is defined, the entire key/value input is a concatenation of `x_kv_prefix` and `x_q` along
the sequence dimension. In this case, the query attends to itself at the end of the key/value seq... | forward | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def __init__(
self,
num_heads: int,
num_channels: int,
num_qk_channels: Optional[int] = None,
num_v_channels: Optional[int] = None,
max_heads_parallel: Optional[int] = None,
causal_attention: bool = False,
dropout: float = 0.0,
qkv_bias: bool = Tru... | Pre-layer norm self-attention (see `MultiHeadAttention` and for attention details). | __init__ | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
pad_mask: Optional[torch.Tensor] = None,
rot_pos_emb: Optional[RotaryPositionEmbedding] = None,
kv_cache: Optional[KVCache] = None,
):
"""Pre-layer-norm self-attention of input `x`."""
x = self.norm(x)
return self.at... | Pre-layer-norm self-attention of input `x`. | forward | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def __init__(
self,
input_adapter: InputAdapter,
num_latents: int,
num_latent_channels: int,
num_cross_attention_heads: int = 4,
num_cross_attention_qk_channels: Optional[int] = None,
num_cross_attention_v_channels: Optional[int] = None,
num_cross_attentio... | Generic Perceiver IO encoder.
:param input_adapter: Transforms and position-encodes task-specific input to generic encoder input of shape (B,
M, C) where B is the batch size, M the input sequence length and C the number of key/value input
channels. C is determined by the `num_in... | __init__ | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def __init__(
self,
output_adapter: OutputAdapter,
output_query_provider: QueryProvider,
num_latent_channels: int,
num_cross_attention_heads: int = 4,
num_cross_attention_qk_channels: Optional[int] = None,
num_cross_attention_v_channels: Optional[int] = None,
... | Generic Perceiver IO decoder.
:param output_adapter: Transforms generic decoder cross-attention output of shape (B, O, F) to task-specific
output. B is the batch size, O the output sequence length and F the number of cross-attention output
channels.
:param output_query_p... | __init__ | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def __init__(
self,
input_adapter: RotarySupport,
num_heads: int = 8,
max_heads_parallel: Optional[int] = None,
num_self_attention_layers: int = 6,
num_self_attention_rotary_layers: int = 1,
self_attention_widening_factor: int = 4,
cross_attention_widening... | Implementation of Perceiver AR (https://arxiv.org/abs/2202.07765).
:param input_adapter: Transforms an input sequence to generic Perceiver AR input. An input adapter may choose to
add (absolute) position information to transformed inputs while `PerceiverAR` additionally computes a
... | __init__ | python | krasserm/perceiver-io | perceiver/model/core/modules.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/modules.py | Apache-2.0 |
def _positions(self, v_min=-1.0, v_max=1.0):
"""Create evenly spaced position coordinates for self.spatial_shape with values in [v_min, v_max].
:param v_min: minimum coordinate value per dimension.
:param v_max: maximum coordinate value per dimension.
:return: position coordinates tenso... | Create evenly spaced position coordinates for self.spatial_shape with values in [v_min, v_max].
:param v_min: minimum coordinate value per dimension.
:param v_max: maximum coordinate value per dimension.
:return: position coordinates tensor of shape (*shape, len(shape)).
| _positions | python | krasserm/perceiver-io | perceiver/model/core/position.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/position.py | Apache-2.0 |
def _position_encodings(
self, p: torch.Tensor, max_frequencies: Optional[Tuple[int, ...]] = None, include_positions: bool = True
) -> torch.Tensor:
"""Fourier-encode positions p using self.num_bands frequency bands.
:param p: positions of shape (*d, c) where c = len(d).
:param max_... | Fourier-encode positions p using self.num_bands frequency bands.
:param p: positions of shape (*d, c) where c = len(d).
:param max_frequencies: maximum frequency for each dimension (1-tuple for sequences, 2-tuple for images, ...).
If `None` values are derived from shape of p.
:p... | _position_encodings | python | krasserm/perceiver-io | perceiver/model/core/position.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/core/position.py | Apache-2.0 |
def convert_checkpoint(save_dir, ckpt_url, tokenizer_name, id2label=None, label2id=None, **kwargs):
"""Convert a `LitTextClassifier` checkpoint to a persistent `PerceiverTextClassifier`."""
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, verbose=False)
tokenizer.save_pretrained(save_dir, **kwargs... | Convert a `LitTextClassifier` checkpoint to a persistent `PerceiverTextClassifier`. | convert_checkpoint | python | krasserm/perceiver-io | perceiver/model/text/classifier/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/text/classifier/huggingface.py | Apache-2.0 |
def convert_checkpoint(save_dir, ckpt_url, tokenizer_name, **kwargs):
"""Convert a `LitCausalLanguageModel` checkpoint to a persistent `PerceiverCausalLanguageModel`."""
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, padding_side="left", verbose=False)
tokenizer.save_pretrained(save_dir, **kwarg... | Convert a `LitCausalLanguageModel` checkpoint to a persistent `PerceiverCausalLanguageModel`. | convert_checkpoint | python | krasserm/perceiver-io | perceiver/model/text/clm/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/text/clm/huggingface.py | Apache-2.0 |
def convert_checkpoint(save_dir, ckpt_url, tokenizer_name, **kwargs):
"""Convert a `LitMaskedLanguageModel` checkpoint to a persistent `PerceiverMaskedLanguageModel`."""
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, verbose=False)
tokenizer.save_pretrained(save_dir, **kwargs)
model = Perce... | Convert a `LitMaskedLanguageModel` checkpoint to a persistent `PerceiverMaskedLanguageModel`. | convert_checkpoint | python | krasserm/perceiver-io | perceiver/model/text/mlm/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/text/mlm/huggingface.py | Apache-2.0 |
def convert_model(save_dir, source_repo_id="deepmind/language-perceiver", **kwargs):
"""Convert a Hugging Face `PerceiverForMaskedLM` to a persistent `PerceiverMaskedLanguageModel`."""
src_model = transformers.PerceiverForMaskedLM.from_pretrained(source_repo_id)
tgt_config = PerceiverMaskedLanguageModelCon... | Convert a Hugging Face `PerceiverForMaskedLM` to a persistent `PerceiverMaskedLanguageModel`. | convert_model | python | krasserm/perceiver-io | perceiver/model/text/mlm/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/text/mlm/huggingface.py | Apache-2.0 |
def convert_config(config: transformers.PerceiverConfig) -> MaskedLanguageModelConfig:
"""Convert a Hugging Face `PerceiverConfig` to a `PerceiverMaskedLanguageModelConfig`."""
assert config.hidden_act == "gelu"
assert config.tie_word_embeddings
encoder_config = TextEncoderConfig(
vocab_size=c... | Convert a Hugging Face `PerceiverConfig` to a `PerceiverMaskedLanguageModelConfig`. | convert_config | python | krasserm/perceiver-io | perceiver/model/text/mlm/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/text/mlm/huggingface.py | Apache-2.0 |
def convert_checkpoint(save_dir, ckpt_url, image_processor, id2label=None, label2id=None, **kwargs):
"""Convert a `LitImageClassifier` checkpoint to a persistent `PerceiverImageClassifier`."""
image_processor.save_pretrained(save_dir, **kwargs)
model = PerceiverImageClassifier.from_checkpoint(ckpt_url)
... | Convert a `LitImageClassifier` checkpoint to a persistent `PerceiverImageClassifier`. | convert_checkpoint | python | krasserm/perceiver-io | perceiver/model/vision/image_classifier/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/vision/image_classifier/huggingface.py | Apache-2.0 |
def convert_config(config: transformers.PerceiverConfig) -> ImageClassifierConfig:
"""Convert a Hugging Face `PerceiverConfig` to a `PerceiverImageClassifierConfig`."""
assert config.hidden_act == "gelu"
encoder_config = ImageEncoderConfig(
image_shape=(224, 224, 3),
num_frequency_bands=64... | Convert a Hugging Face `PerceiverConfig` to a `PerceiverImageClassifierConfig`. | convert_config | python | krasserm/perceiver-io | perceiver/model/vision/image_classifier/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/vision/image_classifier/huggingface.py | Apache-2.0 |
def convert_model(save_dir, source_repo_id="deepmind/vision-perceiver-fourier", **kwargs):
"""Convert a Hugging Face `PerceiverForImageClassificationFourier` to a persistent
`PerceiverImageClassifier`."""
src_model = transformers.PerceiverForImageClassificationFourier.from_pretrained(source_repo_id)
tg... | Convert a Hugging Face `PerceiverForImageClassificationFourier` to a persistent
`PerceiverImageClassifier`. | convert_model | python | krasserm/perceiver-io | perceiver/model/vision/image_classifier/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/vision/image_classifier/huggingface.py | Apache-2.0 |
def convert_model(save_dir, source_repo_id="deepmind/optical-flow-perceiver", **kwargs):
"""Convert a Hugging Face `PerceiverForOpticalFlow` to a persistent `OpticalFlowPerceiver`."""
src_model = transformers.PerceiverForOpticalFlow.from_pretrained(source_repo_id)
tgt_config = OpticalFlowPerceiverConfig(co... | Convert a Hugging Face `PerceiverForOpticalFlow` to a persistent `OpticalFlowPerceiver`. | convert_model | python | krasserm/perceiver-io | perceiver/model/vision/optical_flow/huggingface.py | https://github.com/krasserm/perceiver-io/blob/master/perceiver/model/vision/optical_flow/huggingface.py | Apache-2.0 |
def is_datetime_naive(dt):
"""
This method returns true if the datetime is naive else returns false
"""
if dt.tzinfo is None:
return True
else:
return False |
This method returns true if the datetime is naive else returns false
| is_datetime_naive | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def _move_datetime(dt, direction, delta):
"""
Move datetime given delta by given direction
"""
if direction == 'next':
dt = dt + delta
elif direction == 'last':
dt = dt - delta
else:
pass
# raise some delorean error here
return dt |
Move datetime given delta by given direction
| _move_datetime | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def move_datetime_month(dt, direction, num_shifts):
"""
Move datetime 1 month in the chosen direction.
unit is a no-op, to keep the API the same as the day case
"""
delta = relativedelta(months=+num_shifts)
return _move_datetime(dt, direction, delta) |
Move datetime 1 month in the chosen direction.
unit is a no-op, to keep the API the same as the day case
| move_datetime_month | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def move_datetime_week(dt, direction, num_shifts):
"""
Move datetime 1 week in the chosen direction.
unit is a no-op, to keep the API the same as the day case
"""
delta = relativedelta(weeks=+num_shifts)
return _move_datetime(dt, direction, delta) |
Move datetime 1 week in the chosen direction.
unit is a no-op, to keep the API the same as the day case
| move_datetime_week | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def move_datetime_year(dt, direction, num_shifts):
"""
Move datetime 1 year in the chosen direction.
unit is a no-op, to keep the API the same as the day case
"""
delta = relativedelta(years=+num_shifts)
return _move_datetime(dt, direction, delta) |
Move datetime 1 year in the chosen direction.
unit is a no-op, to keep the API the same as the day case
| move_datetime_year | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def datetime_timezone(tz):
"""
This method given a timezone returns a localized datetime object.
"""
utc_datetime_naive = datetime.utcnow()
# return a localized datetime to UTC
utc_localized_datetime = localize(utc_datetime_naive, 'UTC')
# normalize the datetime to given timezone
normali... |
This method given a timezone returns a localized datetime object.
| datetime_timezone | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def localize(dt, tz):
"""
Given a naive datetime object this method will return a localized
datetime object
"""
if not isinstance(tz, tzinfo):
tz = pytz.timezone(tz)
return tz.localize(dt) |
Given a naive datetime object this method will return a localized
datetime object
| localize | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def normalize(dt, tz):
"""
Given a object with a timezone return a datetime object
normalized to the proper timezone.
This means take the give localized datetime and returns the
datetime normalized to match the specified timezone.
"""
if not isinstance(tz, tzinfo):
tz = pytz.timezon... |
Given a object with a timezone return a datetime object
normalized to the proper timezone.
This means take the give localized datetime and returns the
datetime normalized to match the specified timezone.
| normalize | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def __getattr__(self, name):
"""
Implement __getattr__ to call `shift_date` function when function
called does not exist
"""
func_parts = name.split('_')
# is the func we are trying to call the right length?
if len(func_parts) != 2:
raise AttributeErro... |
Implement __getattr__ to call `shift_date` function when function
called does not exist
| __getattr__ | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def _shift_date(self, direction, unit, *args):
"""
Shift datetime in `direction` in _VALID_SHIFT_DIRECTIONS and by some
unit in _VALID_SHIFTS and shift that amount by some multiple,
defined by by args[0] if it exists
"""
this_module = sys.modules[__name__]
num_sh... |
Shift datetime in `direction` in _VALID_SHIFT_DIRECTIONS and by some
unit in _VALID_SHIFTS and shift that amount by some multiple,
defined by by args[0] if it exists
| _shift_date | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def truncate(self, s):
"""
Truncate the delorian object to the nearest s
(second, minute, hour, day, month, year)
This is a destructive method, modifies the internal datetime
object associated with the Delorean object.
.. testsetup::
from datetime import da... |
Truncate the delorian object to the nearest s
(second, minute, hour, day, month, year)
This is a destructive method, modifies the internal datetime
object associated with the Delorean object.
.. testsetup::
from datetime import datetime
from delorean i... | truncate | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def shift(self, timezone):
"""
Shifts the timezone from the current timezone to the specified timezone associated with the Delorean object,
modifying the Delorean object and returning the modified object.
.. testsetup::
from datetime import datetime
from delorea... |
Shifts the timezone from the current timezone to the specified timezone associated with the Delorean object,
modifying the Delorean object and returning the modified object.
.. testsetup::
from datetime import datetime
from delorean import Delorean
.. doctest:... | shift | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def epoch(self):
"""
Returns the total seconds since epoch associated with
the Delorean object.
.. testsetup::
from datetime import datetime
from delorean import Delorean
.. doctest::
>>> d = Delorean(datetime(2015, 1, 1), timezone='US/Paci... |
Returns the total seconds since epoch associated with
the Delorean object.
.. testsetup::
from datetime import datetime
from delorean import Delorean
.. doctest::
>>> d = Delorean(datetime(2015, 1, 1), timezone='US/Pacific')
>>> d.epoc... | epoch | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def humanize(self):
"""
Humanize relative to now:
.. testsetup::
from datetime import timedelta
from delorean import Delorean
.. doctest::
>>> past = Delorean.utcnow() - timedelta(hours=1)
>>> past.humanize()
'an hour ago'
... |
Humanize relative to now:
.. testsetup::
from datetime import timedelta
from delorean import Delorean
.. doctest::
>>> past = Delorean.utcnow() - timedelta(hours=1)
>>> past.humanize()
'an hour ago'
| humanize | python | myusuf3/delorean | delorean/dates.py | https://github.com/myusuf3/delorean/blob/master/delorean/dates.py | MIT |
def parse(datetime_str, timezone=None, isofirst=True, dayfirst=True, yearfirst=True):
"""
Parses a datetime string and returns a `Delorean` object.
:param datetime_str: The string to be interpreted into a `Delorean` object.
:param timezone: Pass this parameter and the returned Delorean object will be n... |
Parses a datetime string and returns a `Delorean` object.
:param datetime_str: The string to be interpreted into a `Delorean` object.
:param timezone: Pass this parameter and the returned Delorean object will be normalized to this timezone. Any
offsets passed as part of datetime_str will be ignore... | parse | python | myusuf3/delorean | delorean/interface.py | https://github.com/myusuf3/delorean/blob/master/delorean/interface.py | MIT |
def stops(freq, interval=1, count=None, wkst=None, bysetpos=None,
bymonth=None, bymonthday=None, byyearday=None, byeaster=None,
byweekno=None, byweekday=None, byhour=None, byminute=None,
bysecond=None, timezone='UTC', start=None, stop=None):
"""
This will create a list of delorean ... |
This will create a list of delorean objects the apply to
setting possed in.
| stops | python | myusuf3/delorean | delorean/interface.py | https://github.com/myusuf3/delorean/blob/master/delorean/interface.py | MIT |
def test_timezone_delorean_to_datetime_to_delorean_non_utc(self):
"""Test if when you create Delorean object from Delorean's datetime
it still behaves the same
"""
d1 = delorean.Delorean(timezone='America/Chicago')
d2 = delorean.Delorean(d1.datetime)
# these deloreans sh... | Test if when you create Delorean object from Delorean's datetime
it still behaves the same
| test_timezone_delorean_to_datetime_to_delorean_non_utc | python | myusuf3/delorean | tests/delorean_tests.py | https://github.com/myusuf3/delorean/blob/master/tests/delorean_tests.py | MIT |
def test_stops_bymonth(self):
"""Test if create stops, checks bymonth, bymonthday, count
and start parameters work properly
"""
days = list(delorean.interface.stops(
delorean.MONTHLY,
bymonth=(1, 4, 7, 10),
bymonthday=15,
count=4,
... | Test if create stops, checks bymonth, bymonthday, count
and start parameters work properly
| test_stops_bymonth | python | myusuf3/delorean | tests/delorean_tests.py | https://github.com/myusuf3/delorean/blob/master/tests/delorean_tests.py | MIT |
def create_table():
"""
Creates the 'images' table in the SQLite database if it doesn't exist.
"""
with connection:
connection.execute('''
CREATE TABLE IF NOT EXISTS images (
id INTEGER PRIMARY KEY,
filename TEXT NOT NULL,
file_path TEX... |
Creates the 'images' table in the SQLite database if it doesn't exist.
| create_table | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def file_generator(directory):
"""
Generates file paths for all files in the specified directory and its subdirectories.
:param directory: The directory path to search for files.
:return: A generator yielding file paths.
"""
logger.debug(f"Generating file paths for directory: {directory}")
... |
Generates file paths for all files in the specified directory and its subdirectories.
:param directory: The directory path to search for files.
:return: A generator yielding file paths.
| file_generator | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def hydrate_cache(directory, cache_file_path):
"""
Loads or generates a cache of file paths for the specified directory.
:param directory: The directory path to search for files.
:param cache_file_path: The path to the cache file.
:return: A list of cached file paths.
"""
logger.info(f"Hydr... |
Loads or generates a cache of file paths for the specified directory.
:param directory: The directory path to search for files.
:param cache_file_path: The path to the cache file.
:return: A list of cached file paths.
| hydrate_cache | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def update_db(image):
"""
Updates the database with the image embeddings.
:param image: A dictionary containing image information.
"""
try:
embeddings_blob = sqlite3.Binary(msgpack.dumps(image.get('embeddings', [])))
with sqlite3.connect(SQLITE_DB_FILEPATH) as conn:
conn... |
Updates the database with the image embeddings.
:param image: A dictionary containing image information.
| update_db | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def process_image(file_path):
"""
Processes an image file by extracting metadata and inserting it into the database.
:param file_path: The path to the image file.
"""
file = os.path.basename(file_path)
file_date = time.ctime(os.path.getmtime(file_path))
with open(file_path, 'rb') as f:
... |
Processes an image file by extracting metadata and inserting it into the database.
:param file_path: The path to the image file.
| process_image | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def process_embeddings(photo):
"""
Processes image embeddings by uploading them to the embedding server and updating the database.
:param photo: A dictionary containing photo information.
"""
logger.debug(f"Processing photo: {photo['filename']}")
if photo['embeddings']:
logger.debug(f"P... |
Processes image embeddings by uploading them to the embedding server and updating the database.
:param photo: A dictionary containing photo information.
| process_embeddings | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def main():
"""
Main function to process images and embeddings.
"""
cache_start_time = time.time()
cached_files = hydrate_cache(SOURCE_IMAGE_DIRECTORY, FILELIST_CACHE_FILEPATH)
cache_end_time = time.time()
logger.info(f"Cache operation took {cache_end_time - cache_start_time:.2f} seconds")
... |
Main function to process images and embeddings.
| main | python | harperreed/photo-similarity-search | generate_embeddings.py | https://github.com/harperreed/photo-similarity-search/blob/master/generate_embeddings.py | MIT |
def serve_image(filename):
"""
Serve a resized image directly from the filesystem outside of the static directory.
"""
# Construct the full file path. Be careful with security implications.
# Ensure that you validate `filename` to prevent directory traversal attacks.
filepath = os.path.join(SO... |
Serve a resized image directly from the filesystem outside of the static directory.
| serve_image | python | harperreed/photo-similarity-search | start_web.py | https://github.com/harperreed/photo-similarity-search/blob/master/start_web.py | MIT |
def preprocess_rpn_training_data(self):
"""
Discard samples which don't have current classes, which will not be used for training.
Valid sample_id is stored in self.sample_id_list
"""
self.logger.info('Loading %s samples from %s ...' % (self.mode, self.label_dir))
for idx... |
Discard samples which don't have current classes, which will not be used for training.
Valid sample_id is stored in self.sample_id_list
| preprocess_rpn_training_data | python | sshaoshuai/PointRCNN | lib/datasets/kitti_rcnn_dataset.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/datasets/kitti_rcnn_dataset.py | MIT |
def filtrate_objects(self, obj_list):
"""
Discard objects which are not in self.classes (or its similar classes)
:param obj_list: list
:return: list
"""
type_whitelist = self.classes
if self.mode == 'TRAIN' and cfg.INCLUDE_SIMILAR_TYPE:
type_whitelist ... |
Discard objects which are not in self.classes (or its similar classes)
:param obj_list: list
:return: list
| filtrate_objects | python | sshaoshuai/PointRCNN | lib/datasets/kitti_rcnn_dataset.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/datasets/kitti_rcnn_dataset.py | MIT |
def get_valid_flag(pts_rect, pts_img, pts_rect_depth, img_shape):
"""
Valid point should be in the image (and in the PC_AREA_SCOPE)
:param pts_rect:
:param pts_img:
:param pts_rect_depth:
:param img_shape:
:return:
"""
val_flag_1 = np.logical_and(p... |
Valid point should be in the image (and in the PC_AREA_SCOPE)
:param pts_rect:
:param pts_img:
:param pts_rect_depth:
:param img_shape:
:return:
| get_valid_flag | python | sshaoshuai/PointRCNN | lib/datasets/kitti_rcnn_dataset.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/datasets/kitti_rcnn_dataset.py | MIT |
def aug_roi_by_noise(self, roi_info):
"""
add noise to original roi to get aug_box3d
:param roi_info:
:return:
"""
roi_box3d, gt_box3d = roi_info['roi_box3d'], roi_info['gt_box3d']
original_iou = roi_info['iou3d']
temp_iou = cnt = 0
pos_thresh = mi... |
add noise to original roi to get aug_box3d
:param roi_info:
:return:
| aug_roi_by_noise | python | sshaoshuai/PointRCNN | lib/datasets/kitti_rcnn_dataset.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/datasets/kitti_rcnn_dataset.py | MIT |
def distance_based_proposal(self, scores, proposals, order):
"""
propose rois in two area based on the distance
:param scores: (N)
:param proposals: (N, 7)
:param order: (N)
"""
nms_range_list = [0, 40.0, 80.0]
pre_tot_top_n = cfg[self.mode].RPN_PRE_NMS_T... |
propose rois in two area based on the distance
:param scores: (N)
:param proposals: (N, 7)
:param order: (N)
| distance_based_proposal | python | sshaoshuai/PointRCNN | lib/rpn/proposal_layer.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/rpn/proposal_layer.py | MIT |
def data_augmentation(self, pts, rois, gt_of_rois):
"""
:param pts: (B, M, 512, 3)
:param rois: (B, M. 7)
:param gt_of_rois: (B, M, 7)
:return:
"""
batch_size, boxes_num = pts.shape[0], pts.shape[1]
# rotation augmentation
angles = (torch.rand((ba... |
:param pts: (B, M, 512, 3)
:param rois: (B, M. 7)
:param gt_of_rois: (B, M, 7)
:return:
| data_augmentation | python | sshaoshuai/PointRCNN | lib/rpn/proposal_target_layer.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/rpn/proposal_target_layer.py | MIT |
def decode_bbox_target(roi_box3d, pred_reg, loc_scope, loc_bin_size, num_head_bin, anchor_size,
get_xz_fine=True, get_y_by_bin=False, loc_y_scope=0.5, loc_y_bin_size=0.25, get_ry_fine=False):
"""
:param roi_box3d: (N, 7)
:param pred_reg: (N, C)
:param loc_scope:
:param loc_bin... |
:param roi_box3d: (N, 7)
:param pred_reg: (N, C)
:param loc_scope:
:param loc_bin_size:
:param num_head_bin:
:param anchor_size:
:param get_xz_fine:
:param get_y_by_bin:
:param loc_y_scope:
:param loc_y_bin_size:
:param get_ry_fine:
:return:
| decode_bbox_target | python | sshaoshuai/PointRCNN | lib/utils/bbox_transform.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/bbox_transform.py | MIT |
def camera_dis_to_rect(self, u, v, d):
"""
Can only process valid u, v, d, which means u, v can not beyond the image shape, reprojection error 0.02
:param u: (N)
:param v: (N)
:param d: (N), the distance between camera and 3d points, d^2 = x^2 + y^2 + z^2
:return:
... |
Can only process valid u, v, d, which means u, v can not beyond the image shape, reprojection error 0.02
:param u: (N)
:param v: (N)
:param d: (N), the distance between camera and 3d points, d^2 = x^2 + y^2 + z^2
:return:
| camera_dis_to_rect | python | sshaoshuai/PointRCNN | lib/utils/calibration.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/calibration.py | MIT |
def dist_to_plane(plane, points):
"""
Calculates the signed distance from a 3D plane to each point in a list of points
:param plane: (a, b, c, d)
:param points: (N, 3)
:return: (N), signed distance of each point to the plane
"""
a, b, c, d = plane
points = np.array(points)
x = point... |
Calculates the signed distance from a 3D plane to each point in a list of points
:param plane: (a, b, c, d)
:param points: (N, 3)
:return: (N), signed distance of each point to the plane
| dist_to_plane | python | sshaoshuai/PointRCNN | lib/utils/kitti_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/kitti_utils.py | MIT |
def rotate_pc_along_y(pc, rot_angle):
"""
params pc: (N, 3+C), (N, 3) is in the rectified camera coordinate
params rot_angle: rad scalar
Output pc: updated pc with XYZ rotated
"""
cosval = np.cos(rot_angle)
sinval = np.sin(rot_angle)
rotmat = np.array([[cosval, -sinval], [sinval, cosval]... |
params pc: (N, 3+C), (N, 3) is in the rectified camera coordinate
params rot_angle: rad scalar
Output pc: updated pc with XYZ rotated
| rotate_pc_along_y | python | sshaoshuai/PointRCNN | lib/utils/kitti_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/kitti_utils.py | MIT |
def rotate_pc_along_y_torch(pc, rot_angle):
"""
:param pc: (N, 512, 3 + C)
:param rot_angle: (N)
:return:
TODO: merge with rotate_pc_along_y_torch in bbox_transform.py
"""
cosa = torch.cos(rot_angle).view(-1, 1) # (N, 1)
sina = torch.sin(rot_angle).view(-1, 1) # (N, 1)
raw_1 = tor... |
:param pc: (N, 512, 3 + C)
:param rot_angle: (N)
:return:
TODO: merge with rotate_pc_along_y_torch in bbox_transform.py
| rotate_pc_along_y_torch | python | sshaoshuai/PointRCNN | lib/utils/kitti_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/kitti_utils.py | MIT |
def get_iou3d(corners3d, query_corners3d, need_bev=False):
"""
:param corners3d: (N, 8, 3) in rect coords
:param query_corners3d: (M, 8, 3)
:return:
"""
from shapely.geometry import Polygon
A, B = corners3d, query_corners3d
N, M = A.shape[0], B.shape[0]
iou3d = np.zeros((N, M), d... |
:param corners3d: (N, 8, 3) in rect coords
:param query_corners3d: (M, 8, 3)
:return:
| get_iou3d | python | sshaoshuai/PointRCNN | lib/utils/kitti_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/kitti_utils.py | MIT |
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
:return:
"""
input = torch.sigmoid(input.view(-1))
target = target.float().view(-1)
mask = (target != self.ignore_target).float()
return 1.0 - (torch.min(inpu... |
:param input: (N), logit
:param target: (N), {0, 1}
:return:
| forward | python | sshaoshuai/PointRCNN | lib/utils/loss_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/loss_utils.py | MIT |
def __init__(self, gamma=2.0, alpha=0.25):
"""Constructor.
Args:
gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
alpha: optional alpha weighting factor to balance positives vs negatives.
all_zero_negative: bool. if True, will treat all zero as background.
... | Constructor.
Args:
gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
alpha: optional alpha weighting factor to balance positives vs negatives.
all_zero_negative: bool. if True, will treat all zero as background.
else, will treat first label as background... | __init__ | python | sshaoshuai/PointRCNN | lib/utils/loss_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/loss_utils.py | MIT |
def forward(self,
prediction_tensor,
target_tensor,
weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
... | Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing one-hot ... | forward | python | sshaoshuai/PointRCNN | lib/utils/loss_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/loss_utils.py | MIT |
def get_reg_loss(pred_reg, reg_label, loc_scope, loc_bin_size, num_head_bin, anchor_size,
get_xz_fine=True, get_y_by_bin=False, loc_y_scope=0.5, loc_y_bin_size=0.25, get_ry_fine=False):
"""
Bin-based 3D bounding boxes regression loss. See https://arxiv.org/abs/1812.04244 for more details.
... |
Bin-based 3D bounding boxes regression loss. See https://arxiv.org/abs/1812.04244 for more details.
:param pred_reg: (N, C)
:param reg_label: (N, 7) [dx, dy, dz, h, w, l, ry]
:param loc_scope: constant
:param loc_bin_size: constant
:param num_head_bin: constant
:param anchor_size: (N, ... | get_reg_loss | python | sshaoshuai/PointRCNN | lib/utils/loss_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/loss_utils.py | MIT |
def to_bev_box2d(self, oblique=True, voxel_size=0.1):
"""
:param bev_shape: (2) for bev shape (h, w), => (y_max, x_max) in image
:param voxel_size: float, 0.1m
:param oblique:
:return: box2d (4, 2)/ (4) in image coordinate
"""
if oblique:
corners3d = s... |
:param bev_shape: (2) for bev shape (h, w), => (y_max, x_max) in image
:param voxel_size: float, 0.1m
:param oblique:
:return: box2d (4, 2)/ (4) in image coordinate
| to_bev_box2d | python | sshaoshuai/PointRCNN | lib/utils/object3d.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/object3d.py | MIT |
def nms_gpu(boxes, scores, thresh):
"""
:param boxes: (N, 5) [x1, y1, x2, y2, ry]
:param scores: (N)
:param thresh:
:return:
"""
# areas = (x2 - x1) * (y2 - y1)
order = scores.sort(0, descending=True)[1]
boxes = boxes[order].contiguous()
keep = torch.LongTensor(boxes.size(0))
... |
:param boxes: (N, 5) [x1, y1, x2, y2, ry]
:param scores: (N)
:param thresh:
:return:
| nms_gpu | python | sshaoshuai/PointRCNN | lib/utils/iou3d/iou3d_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/iou3d/iou3d_utils.py | MIT |
def roipool3d_gpu(pts, pts_feature, boxes3d, pool_extra_width, sampled_pt_num=512):
"""
:param pts: (B, N, 3)
:param pts_feature: (B, N, C)
:param boxes3d: (B, M, 7)
:param pool_extra_width: float
:param sampled_pt_num: int
:return:
pooled_features: (B, M, 512, 3 + C)
pooled_... |
:param pts: (B, N, 3)
:param pts_feature: (B, N, C)
:param boxes3d: (B, M, 7)
:param pool_extra_width: float
:param sampled_pt_num: int
:return:
pooled_features: (B, M, 512, 3 + C)
pooled_empty_flag: (B, M)
| roipool3d_gpu | python | sshaoshuai/PointRCNN | lib/utils/roipool3d/roipool3d_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/roipool3d/roipool3d_utils.py | MIT |
def pts_in_boxes3d_cpu(pts, boxes3d):
"""
:param pts: (N, 3) in rect-camera coords
:param boxes3d: (M, 7)
:return: boxes_pts_mask_list: (M), list with [(N), (N), ..]
"""
if not pts.is_cuda:
pts = pts.float().contiguous()
boxes3d = boxes3d.float().contiguous()
pts_flag = t... |
:param pts: (N, 3) in rect-camera coords
:param boxes3d: (M, 7)
:return: boxes_pts_mask_list: (M), list with [(N), (N), ..]
| pts_in_boxes3d_cpu | python | sshaoshuai/PointRCNN | lib/utils/roipool3d/roipool3d_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/roipool3d/roipool3d_utils.py | MIT |
def roipool_pc_cpu(pts, pts_feature, boxes3d, sampled_pt_num):
"""
:param pts: (N, 3)
:param pts_feature: (N, C)
:param boxes3d: (M, 7)
:param sampled_pt_num: int
:return:
"""
pts = pts.cpu().float().contiguous()
pts_feature = pts_feature.cpu().float().contiguous()
boxes3d = boxe... |
:param pts: (N, 3)
:param pts_feature: (N, C)
:param boxes3d: (M, 7)
:param sampled_pt_num: int
:return:
| roipool_pc_cpu | python | sshaoshuai/PointRCNN | lib/utils/roipool3d/roipool3d_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/roipool3d/roipool3d_utils.py | MIT |
def roipool3d_cpu(boxes3d, pts, pts_feature, pts_extra_input, pool_extra_width, sampled_pt_num=512,
canonical_transform=True):
"""
:param boxes3d: (N, 7)
:param pts: (N, 3)
:param pts_feature: (N, C)
:param pts_extra_input: (N, C2)
:param pool_extra_width: constant
:param s... |
:param boxes3d: (N, 7)
:param pts: (N, 3)
:param pts_feature: (N, C)
:param pts_extra_input: (N, C2)
:param pool_extra_width: constant
:param sampled_pt_num: constant
:return:
| roipool3d_cpu | python | sshaoshuai/PointRCNN | lib/utils/roipool3d/roipool3d_utils.py | https://github.com/sshaoshuai/PointRCNN/blob/master/lib/utils/roipool3d/roipool3d_utils.py | MIT |
def extend_body_states(
self,
extend_body_pos: torch.Tensor,
extend_body_parent_ids: list[int],
):
"""
This function is for appending the link states to the robot state. For example, the H1 robot doesn't have hands
and a head in its robot state. However, we are still ... |
This function is for appending the link states to the robot state. For example, the H1 robot doesn't have hands
and a head in its robot state. However, we are still interested in computing its error and considering these as
important key points. Thus, we will use this function to add the head a... | extend_body_states | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/body_state.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/body_state.py | Apache-2.0 |
def get_observations(self) -> torch.Tensor:
"""Gets policy observations for each environment based on the mode."""
if self._mode.is_distill_mode():
return self.get_student_observations()
return self.get_teacher_observations() | Gets policy observations for each environment based on the mode. | get_observations | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/environment_wrapper.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/environment_wrapper.py | Apache-2.0 |
def _init_empty_frames(self, frame: Frame):
"""Initialize empty frame buffers to store trajectory data for all environments.
Creates zero-filled tensors/arrays sized to hold the maximum possible number of frames
and environments, matching the data types and shapes of the input frame.
""... | Initialize empty frame buffers to store trajectory data for all environments.
Creates zero-filled tensors/arrays sized to hold the maximum possible number of frames
and environments, matching the data types and shapes of the input frame.
| _init_empty_frames | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/evaluator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/evaluator.py | Apache-2.0 |
def add_frame(self, frame: Frame):
"""Add a frame to each trajectory in the episode.
Args:
frame (Frame): Frame containing trajectory data for all environments at this timestep
"""
# Initialize frame buffers if this is the first frame being added
if len(self._frames)... | Add a frame to each trajectory in the episode.
Args:
frame (Frame): Frame containing trajectory data for all environments at this timestep
| add_frame | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/evaluator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/evaluator.py | Apache-2.0 |
def complete(self):
"""Aggregate frames into episode data more efficiently.
Instead of splitting data environment by environment, we can use tensor operations
to split all environments at once, significantly reducing loop overhead.
"""
num_envs = self.max_frames_per_env.shape[0]... | Aggregate frames into episode data more efficiently.
Instead of splitting data environment by environment, we can use tensor operations
to split all environments at once, significantly reducing loop overhead.
| complete | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/evaluator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/evaluator.py | Apache-2.0 |
def filter(self, ids: list[int]) -> Episode:
"""Filter episode data to only include specified environment indices."""
# Create new empty episode to store filtered data
filtered = Episode(self.max_frames_per_env)
# Iterate through all attributes of this episode
for attr, data in ... | Filter episode data to only include specified environment indices. | filter | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/evaluator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/evaluator.py | Apache-2.0 |
def trim(self, terminated_frame: torch.Tensor, end_id: int):
"""Helper method to cut data based on terminated frame.
This function creates a new Episode object with truncated data. For each environment,
it keeps only the frames up to the termination point specified in terminated_frame.
... | Helper method to cut data based on terminated frame.
This function creates a new Episode object with truncated data. For each environment,
it keeps only the frames up to the termination point specified in terminated_frame.
It then further filters to keep only environments up to end_id.
... | trim | python | NVlabs/HOVER | neural_wbc/core/neural_wbc/core/evaluator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/core/neural_wbc/core/evaluator.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.