repo_full_name stringlengths 6 93 | repo_url stringlengths 25 112 | repo_api_url stringclasses 28
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values | stars int64 617 98.8k | forks int64 31 355 ⌀ | watchers int64 990 999 ⌀ | license stringclasses 2
values | default_branch stringclasses 2
values | repo_created_at timestamp[s]date 2012-07-24 23:12:50 2025-06-16 08:07:28 ⌀ | repo_updated_at timestamp[s]date 2026-02-23 15:23:15 2026-05-03 18:52:12 ⌀ | repo_topics listlengths 0 13 ⌀ | repo_languages unknown | is_fork bool 1
class | open_issues int64 3 104 ⌀ | file_path stringlengths 3 208 | file_name stringclasses 509
values | file_extension stringclasses 1
value | file_size_bytes int64 101 84k ⌀ | file_url stringclasses 627
values | file_raw_url stringclasses 627
values | file_sha stringclasses 624
values | language stringclasses 8
values | parsed_at stringdate 2026-05-04 01:12:36 2026-05-04 19:41:55 | text stringlengths 100 102k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/charts/rolling_sharp_ratio.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:53.559860 | import pandas as pd
import plotly.graph_objects as go
def get_rolling_sharpe_ratio_chart(rolling_sharpe_ratio_series):
"""
Generates a Plotly figure showing the rolling Sharpe ratio series.
Args:
rolling_sharpe_ratio_series: List of tuples with rolling Sharpe
ratio data. Each tuple sh... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/generate.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:53.618502 | import os
import logging
import pandas as pd
from jinja2 import Environment, FileSystemLoader
from .tables import create_html_time_metrics_table, \
create_html_trade_metrics_table, create_html_key_metrics_table, \
create_html_trades_table
from .charts import get_equity_curve_with_drawdown_chart, \
get_roll... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/tables/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:53.653757 | from .trades_table import create_html_trades_table
from .key_metrics_table import create_html_key_metrics_table
from .trade_metrics_table import create_html_trade_metrics_table
from .time_metrics_table import create_html_time_metrics_table
__all__ = [
"create_html_trades_table",
"create_html_key_metrics_table"... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/tables/key_metrics_table.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:53.728202 | import pandas as pd
from .utils import safe_format, safe_format_percentage
def highlight_sharpe_and_sortino(row):
"""
| -------------- | ------------------------------------------- |
| **< 0** | Bad: Underperforms risk-free asset |
| **0.0 – 1.0** | Suboptimal: Returns do not justify... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/charts/yearly_returns_barchart.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:53.850612 | import pandas as pd
import plotly.express as px
def get_yearly_returns_bar_chart(yearly_returns_series):
"""
Create a bar chart showing yearly returns.
This chart visualizes the yearly returns of the backtest report.
Args:
yearly_returns_series: The yearly returns data as a series.
Retur... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/tables/trades_table.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:53.995323 | import pandas as pd
def highlight_net_gain(row):
"""
Apply conditional formatting to the 'Net Gain' column based on numeric value.
"""
try:
# Extract numeric value before the first space (assumes format like "123.45 USDT (10.23%)")
value_str = row['Net Gain'].split()[2]
value_s... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/tables/utils.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.151895 | import pandas as pd
def safe_format(value, format_str, default_value='N/A'):
if value is None:
return default_value
if isinstance(value, (int, float)):
return format_str.format(value)
return value
def safe_format_percentage(value, format_str, default_value='N/A'):
if value is None:
... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/stateless/action_handlers/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.276372 | from enum import Enum
from investing_algorithm_framework.app.stateless.action_handlers \
.check_online_handler import CheckOnlineHandler
from investing_algorithm_framework.app.stateless.action_handlers \
.run_strategy_handler import RunStrategyHandler
from investing_algorithm_framework.domain.exceptions import... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/stateless/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.313315 | from investing_algorithm_framework.app.stateless.action_handlers \
import ActionHandler
from investing_algorithm_framework.app.stateless.action_handlers import \
StatelessAction
from investing_algorithm_framework.app.stateless.exception_handler import \
handle_exception
from investing_algorithm_framework.do... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/stateless/action_handlers/action_handler_strategy.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.321582 | from abc import ABC, abstractmethod
class ActionHandlerStrategy(ABC):
@abstractmethod
def handle_event(self, payload, context, strategy_orchestrator_service):
pass
|
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/stateless/action_handlers/check_online_handler.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.480031 | import json
from investing_algorithm_framework.app.stateless.action_handlers \
.action_handler_strategy import ActionHandlerStrategy
class CheckOnlineHandler(ActionHandlerStrategy):
MESSAGE = {"message": "online"}
def handle_event(self, payload, context, strategy_orchestrator_service):
return {
... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/stateless/action_handlers/run_strategy_handler.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.561581 | import json
from investing_algorithm_framework.app.stateless.action_handlers \
.action_handler_strategy import ActionHandlerStrategy
class RunStrategyHandler(ActionHandlerStrategy):
"""
RunStrategyHandler is an action handler that runs a strategy and its tasks
synchronously.
If the run was succe... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/stateless/exception_handler.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.758483 | import json
import logging
from typing import Dict, List
from investing_algorithm_framework.domain import OperationalException
logger = logging.getLogger("investing_algorithm_framework")
def create_error_response(error_message, status_code: int = 400):
response = json.dumps({"error_message": error_message})
... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/strategy.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.864623 | import logging
from datetime import datetime
from typing import List, Dict, Any, Union
import pandas as pd
from investing_algorithm_framework.domain import OperationalException, \
Position, PositionSize, TimeUnit, StrategyProfile, Trade, \
DataSource, DataType, OrderSide, StopLossRule, TakeProfitRule, Order, ... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/task.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:54.911810 | from investing_algorithm_framework.domain import \
TimeUnit
class Task:
time_unit: str = None
interval: int = None
worker_id: str = None
decorated = None
def __init__(
self,
time_unit=None,
interval=None,
worker_id=None,
decorated=None
):
if... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/backtest_report.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:56.380104 | import os
import csv
import base64
import tempfile
import webbrowser
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Union
from datetime import datetime, timedelta
from jinja2 import Environment, FileSystemLoader
from investing_algorithm_framework.domain impor... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/tables/time_metrics_table.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:57.416223 | import pandas as pd
from .utils import safe_format_percentage, safe_format_date
def create_html_time_metrics_table(results, report):
copy_results = results.to_dict().copy()
start_date = report.backtest_start_date
end_date = report.backtest_end_date
string_format = "{:.2f}"
# Format dates
copy... |
coding-kitties/investing-algorithm-framework | https://github.com/coding-kitties/investing-algorithm-framework | null | null | null | null | 965 | null | null | apache-2.0 | null | null | null | null | null | null | null | investing_algorithm_framework/app/reporting/tables/trade_metrics_table.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:57.458538 | import pandas as pd
from investing_algorithm_framework.domain import DEFAULT_DATETIME_FORMAT
from .utils import safe_format, safe_format_date, safe_format_percentage
def highlight_win_rate(row):
"""
| **Winning Percentage** | **Interpretation** |
|-------... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/gec_model.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.833901 | """Wrapper of AllenNLP model. Fixes errors based on model predictions"""
import logging
import os
import sys
from time import time
import torch
from allennlp.data.dataset import Batch
from allennlp.data.fields import TextField
from allennlp.data.instance import Instance
from allennlp.data.tokenizers import Token
from ... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | predict.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.834951 | import argparse
from utils.helpers import read_lines, normalize
from gector.gec_model import GecBERTModel
def predict_for_file(input_file, output_file, model, batch_size=32, to_normalize=False):
test_data = read_lines(input_file)
predictions = []
cnt_corrections = 0
batch = []
for sent in test_da... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/datareader.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.841457 | """Tweaked AllenNLP dataset reader."""
import logging
import re
from random import random
from typing import Dict, List
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import TextField, SequenceLabelField, MetadataField... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | train.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.842989 | import argparse
import os
from random import seed
import torch
from allennlp.data.iterators import BucketIterator
from allennlp.data.vocabulary import DEFAULT_OOV_TOKEN, DEFAULT_PADDING_TOKEN
from allennlp.data.vocabulary import Vocabulary
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from ... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | utils/filter_brackets.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.846008 | import argparse
import re
from helpers import write_lines
def filter_line(line):
if "-LRB-" in line and "-RRB-" in line:
rep = re.sub(r'\-.*?LRB.*?\-.*?\-.*?RRB.*?\-', '', line)
line_cleaned = rep
elif ("-LRB-" in line and "-RRB-" not in line) or (
"-LRB-" not in line and "-RRB-" ... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/seq2labels_model.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.846780 | """Basic model. Predicts tags for every token"""
from typing import Dict, Optional, List, Any
import numpy
import torch
import torch.nn.functional as F
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import TimeDistributed, TextFieldEmbedder
from allennlp.nn import In... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/bert_token_embedder.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.944745 | """Tweaked version of corresponding AllenNLP file"""
import logging
from copy import deepcopy
from typing import Dict
import torch
import torch.nn.functional as F
from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
from allennlp.nn import util
from transformers import AutoModel, PreTrainedModel
... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/tokenizer_indexer.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.947761 | """Tweaked version of corresponding AllenNLP file"""
import logging
from collections import defaultdict
from typing import Dict, List, Callable
from allennlp.common.util import pad_sequence_to_length
from allennlp.data.token_indexers.token_indexer import TokenIndexer
from allennlp.data.tokenizers.token import Token
fr... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/tokenization.py | null | null | null | null | null | null | Python | 2026-05-04T02:37:59.963574 | import os
from time import time
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
def get_bpe_groups(token_offsets, bpe_offsets, input_ids, max_bpe_pieces=5):
bpe_groups = []
last_used_bpe = 0
# find the size of offsets
if (0, 0) in bpe_offsets:
bpe_size = bpe_offsets.index((0, 0))
else:
... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | gector/trainer.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:00.029857 | """Tweaked version of corresponding AllenNLP file"""
import datetime
import logging
import math
import os
import time
import traceback
from typing import Dict, Optional, List, Tuple, Union, Iterable, Any
import torch
import torch.optim.lr_scheduler
from allennlp.common import Params
from allennlp.common.checks import ... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | utils/prepare_clc_fce_data.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:00.403381 | #!/usr/bin/env python
"""
Convert CLC-FCE dataset (The Cambridge Learner Corpus) to the parallel sentences format.
"""
import argparse
import glob
import os
import re
from xml.etree import cElementTree
from nltk.tokenize import sent_tokenize, word_tokenize
from tqdm import tqdm
def annotate_fce_doc(xml):
"""Tak... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | utils/preprocess_data.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:00.403981 | import argparse
import os
from difflib import SequenceMatcher
import Levenshtein
import numpy as np
from tqdm import tqdm
from helpers import write_lines, read_parallel_lines, encode_verb_form, \
apply_reverse_transformation, SEQ_DELIMETERS, START_TOKEN
def perfect_align(t, T, insertions_allowed=0,
... |
grammarly/gector | https://github.com/grammarly/gector | null | null | null | null | 964 | null | null | apache-2.0 | null | null | null | null | null | null | null | utils/helpers.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:00.435252 | import os
from pathlib import Path
VOCAB_DIR = Path(__file__).resolve().parent.parent / "data"
PAD = "@@PADDING@@"
UNK = "@@UNKNOWN@@"
START_TOKEN = "$START"
SEQ_DELIMETERS = {"tokens": " ",
"labels": "SEPL|||SEPR",
"operations": "SEPL__SEPR"}
REPLACEMENTS = {
"''": '"',
'-... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/flops.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:03.325567 | '''
This opcounter is adapted from https://github.com/sovrasov/flops-counter.pytorch and https://github.com/Lyken17/pytorch-OpCounter
Copyright (C) 2021 Sovrasov V. - All Rights Reserved
* You may use, distribute and modify this code under the
* terms of the MIT license.
* You should have received a copy of the MIT... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sd/pipeline_utils.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:03.375540 | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.a... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sdxl/pipeline_stable_diffusion_xl_img2img.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:04.466785 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sd/pipeline_stable_diffusion.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.306042 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sd/pipeline_text_to_video_zero.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.382665 | import copy
from dataclasses import dataclass
from typing import Callable, List, Optional, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from torch.nn.functional import grid_sample
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.models ... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/extension/deepcache.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.491275 | class DeepCacheSDHelper(object):
def __init__(self, pipe=None):
if pipe is not None: self.pipe = pipe
def enable(self, pipe=None):
assert self.pipe is not None
self.reset_states()
self.wrap_modules()
def disable(self):
self.unwrap_modules()
self.reset_states... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sd/unet_2d_condition.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.492949 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/svd/unet_3d_blocks.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.761668 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sdxl/unet_2d_condition.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.783436 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/svd/unet_spatio_temporal_condition.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:05.889371 | from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import UNet2DConditionLoadersMixin
from diffusers.utils import BaseOutput, logging
from diffusers.models... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/clip_score.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.088486 | import os
import sys
from PIL import Image
import torch
from tqdm import tqdm
from torchmetrics.functional.multimodal.clip_score import _get_clip_model_and_processor, _clip_score_update
from torchvision.transforms.functional import to_pil_image
import open_clip
path = sys.argv[1]
if os.path.isdir(path):
files = o... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddim.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.156709 | import argparse
import traceback
import shutil
import logging
import yaml
import random
import sys
import os
import torch
import numpy as np
from ddpm.utils.logging import Logger, EmptyLogger
from ddpm.utils.tools import set_random_seed
from accelerate import Accelerator, DistributedDataParallelKwargs
torch.set_print... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/datasets/celeba.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.386741 | import torch
import os
import PIL
from .vision import VisionDataset
from .utils import download_file_from_google_drive, check_integrity
class CelebA(VisionDataset):
"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.
Args:
root (string)... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/datasets/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.388120 | import os
import torch
import numbers
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
from torchvision.datasets import CIFAR10
from .celeba import CelebA
from .ffhq import FFHQ
from .lsun import LSUN
from torch.utils.data import Subset
import numpy as np
class Crop(object):
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/datasets/ffhq.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.492698 | from io import BytesIO
import lmdb
from PIL import Image
from torch.utils.data import Dataset
class FFHQ(Dataset):
def __init__(self, path, transform, resolution=8):
self.env = lmdb.open(
path,
max_readers=32,
readonly=True,
lock=False,
readahea... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/datasets/utils.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.658976 | import os
import os.path
import hashlib
import errno
from torch.utils.model_zoo import tqdm
def gen_bar_updater():
pbar = tqdm(total=None)
def bar_update(count, block_size, total_size):
if pbar.total is None and total_size:
pbar.total = total_size
progress_bytes = count * block_si... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/datasets/lsun.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.764858 | from .vision import VisionDataset
from PIL import Image
import os
import os.path
import io
from collections.abc import Iterable
import pickle
from torchvision.datasets.utils import verify_str_arg, iterable_to_str
class LSUNClass(VisionDataset):
def __init__(self, root, transform=None, target_transform=None):
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/datasets/vision.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.830906 | import os
import torch
import torch.utils.data as data
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, transform=None, target_transform=None):
if isinstance(root, torch._six.string_classes):
root = os.path.expanduser(root)
self.root = ... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/functions/ckpt_util.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:06.957178 | import os, hashlib
import requests
from tqdm import tqdm
URL_MAP = {
"cifar10": "https://heibox.uni-heidelberg.de/f/869980b53bf5416c8a28/?dl=1",
"ema_cifar10": "https://heibox.uni-heidelberg.de/f/2e4f01e2d9ee49bab1d5/?dl=1",
"lsun_bedroom": "https://heibox.uni-heidelberg.de/f/f179d4f21ebc4d43bbfe/?dl=1",
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/functions/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.036990 | import torch.optim as optim
def get_optimizer(config, parameters):
if config.optim.optimizer == 'Adam':
return optim.Adam(parameters, lr=config.optim.lr, weight_decay=config.optim.weight_decay,
betas=(config.optim.beta1, 0.999), amsgrad=config.optim.amsgrad,
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/functions/deepcache_denoising.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.081632 | import torch
from scipy.stats import shapiro
import numpy as np
def sample_gaussian_centered(n=1000, sample_size=100, std_dev=100, shift=0):
samples = []
while len(samples) < sample_size:
# Sample from a Gaussian centered at n/2
sample = int(np.random.normal(loc=n/2+shift, scale=std_dev))... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/functions/denoising.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.206422 | import torch
def compute_alpha(beta, t):
beta = torch.cat([torch.zeros(1).to(beta.device), beta], dim=0)
a = (1 - beta).cumprod(dim=0).index_select(0, t + 1).view(-1, 1, 1, 1)
return a
def generalized_steps(x, seq, model, b, **kwargs):
with torch.no_grad():
n = x.size(0)
seq_next = [... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/functions/losses.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.310069 | import torch
def noise_estimation_loss(model,
x0: torch.Tensor,
t: torch.LongTensor,
e: torch.Tensor,
b: torch.Tensor, keepdim=False):
a = (1-b).cumprod(dim=0).index_select(0, t).view(-1, 1, 1, 1)
x = x0 * ... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/models/deepcache_diffusion.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.432849 | import copy
import math
import torch
import torch.nn as nn
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/models/diffusion.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.575017 | import math
import torch
import torch.nn as nn
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/models/ema.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.640616 | import torch.nn as nn
import torch
import copy
class EMAHelper(object):
def __init__(self, mu=0.999):
self.mu = mu
self.shadow = {}
def register(self, module):
if isinstance(module, nn.DataParallel):
module = module.module
for name, param in module.named_parameters(... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/runners/deepcache.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.807620 | import os
import logging
import time
import glob
import numpy as np
import tqdm
import torch
import torch.utils.data as data
from torch.nn.functional import adaptive_avg_pool2d
from ..models.ema import EMAHelper
from ..functions import get_optimizer
from ..functions.losses import loss_registry
from ..datasets import ... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/runners/diffusion.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:07.906257 | import os
import logging
import time
import glob
import numpy as np
import tqdm
import torch
import torch.utils.data as data
from ..models.ema import EMAHelper
from ..functions import get_optimizer
from ..functions.losses import loss_registry
from ..datasets import get_dataset, data_transform, inverse_data_transform
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/utils/logging.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.030126 | import os
import sys
import time
import codecs
import logging
class Logger():
def __init__(self, config, root_dir = 'runtime_log',
sub_name = None, overwrite = False, append=False):
self.log_dir = root_dir
self.overwrite = overwrite
self.format = logging.Formatter("%(a... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/ddpm/utils/tools.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.148306 | import torch
import random
import numpy as np
def unwrap_module(state_dict):
unwrap_state_dict = {}
for key, value in state_dict.items():
if key.startswith("module."):
unwrap_state_dict[key[7:]] = value
else:
unwrap_state_dict[key] = value
return unwrap_state_dict
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ddpm/fid.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.255207 | """Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run as a stand-alone progr... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/generate.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.306208 |
import time
import argparse
import numpy as np
import random
import os
from tqdm import tqdm
import torch
from datasets import load_dataset
def set_random_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def main(args):
if args.dataset... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ldm/ldm/data/base.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.484846 | from abc import abstractmethod
from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
class Txt2ImgIterableBaseDataset(IterableDataset):
'''
Define an interface to make the IterableDatasets for text2img data chainable
'''
def __init__(self, num_records=0, valid_ids=None, si... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ldm/ldm/data/imagenet.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.655685 | import os, yaml, pickle, shutil, tarfile, glob
import cv2
import albumentations
import PIL
import numpy as np
import torchvision.transforms.functional as TF
from omegaconf import OmegaConf
from functools import partial
from PIL import Image
from tqdm import tqdm
from torch.utils.data import Dataset, Subset
import tami... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ldm/ldm/data/lsun.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.744520 | import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
class LSUNBase(Dataset):
def __init__(self,
txt_file,
data_root,
size=None,
interpolation="bicubic",
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ldm/ldm/lr_scheduler.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.845793 | import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ldm/ldm/models/autoencoder.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.919845 | import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGa... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sdxl/pipeline_stable_diffusion_xl.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:08.951210 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | experiments/ldm/ldm/models/diffusion/classifier.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:09.026715 | import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modu... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/svd/pipeline_stable_video_diffusion.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:10.137672 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
horseee/DeepCache | https://github.com/horseee/DeepCache | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | DeepCache/sdxl/pipeline_utils.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:10.263659 | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.a... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nus-3d.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.912977 | # If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-50, -50, -5, 50, 50, 3]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 't... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nuim_instance.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.942821 | dataset_type = 'CocoDataset'
data_root = 'data/nuimages/'
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | .dev_scripts/gather_models.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.943936 | """Script to gather benchmarked models and prepare them for upload.
Usage:
python gather_models.py ${root_path} ${out_dir}
"""
import argparse
import glob
import json
import mmcv
import shutil
import subprocess
import torch
from os import path as osp
# build schedule look-up table to automatically find the final mod... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_cam_cp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.964945 | # If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', '... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/coco_instance.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.980893 | dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800),... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nus_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.981901 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
voxel_size = [0.075, 0.075, 0.2]
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = ... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_cam_pp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.990756 | point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_LC.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:12.992065 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_lidar=True,
use_... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_cam_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.021542 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
evaluation = dict(interval=20)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = di... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_cp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.047822 | # If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', '... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_fov60_cp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.668369 | # If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', '... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_fov60_pp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.748900 | point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_fov90_pp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.833725 | point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_halfbox_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.847404 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = di... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_halfbox_cp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.848953 | # If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', '... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_halfbox_pp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.854405 | point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_fov60_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:13.920505 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = di... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_pp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.056424 | point_cloud_range = [-50, -50, -5, 50, 50, 3]
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
evaluation = dict(interval=24)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.289146 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = di... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_tf_aug.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.293108 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = di... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/waymo-3d-3class_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.394579 | # dataset settings
# D5 in the config name means the whole dataset is divided into 5 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See http... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/default_runtime.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.424280 | checkpoint_config = dict(interval=1)
# yapf:disable push
# By default we use textlogger hook and tensorboard
# For more loggers see
# https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardL... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/waymoD5-3d-3class.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.445476 | # dataset settings
# D5 in the config name means the whole dataset is divided into 5 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See http... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/models/3dssd.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.476045 | model = dict(
type='SSD3DNet',
backbone=dict(
type='PointNet2SAMSG',
in_channels=4,
num_points=(4096, 512, (256, 256)),
radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
num_samples=((32, 32, 64), (32, 32, 64), (32, 32, 32)),
sa_channels=(((16, 16, 32), (... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_fov90_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.500747 | point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
evaluation = dict(interval=36)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = di... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/waymoD5-3d-3class_cam.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.514957 | # dataset settings
# D5 in the config name means the whole dataset is divided into 5 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See http... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/models/cascade_mask_rcnn_r50_fpn.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.653792 | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... |
ADLab-AutoDrive/BEVFusion | https://github.com/ADLab-AutoDrive/BEVFusion | null | null | null | null | 963 | null | null | apache-2.0 | null | null | null | null | null | null | null | configs/_base_/datasets/nusc_fov90_cp.py | null | null | null | null | null | null | Python | 2026-05-04T02:38:14.682511 | # If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', '... |
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