repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
RocketQA | RocketQA-main/research/DuReader-Retrieval-Baseline/src/utils/init.py | # Copyright (c) 2019 PaddlePaddle Authors. 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 app... | 2,695 | 34.946667 | 108 | py |
RocketQA | RocketQA-main/research/DuReader-Retrieval-Baseline/src/utils/__init__.py | 0 | 0 | 0 | py | |
RocketQA | RocketQA-main/research/DuReader-Retrieval-Baseline/src/model/transformer_encoder.py | # Copyright (c) 2019 PaddlePaddle Authors. 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 app... | 12,649 | 35.666667 | 91 | py |
RocketQA | RocketQA-main/research/DuReader-Retrieval-Baseline/src/model/__init__.py | 0 | 0 | 0 | py | |
RocketQA | RocketQA-main/research/DuReader-Retrieval-Baseline/src/model/ernie.py | # Copyright (c) 2019 PaddlePaddle Authors. 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 app... | 10,858 | 38.631387 | 92 | py |
iglu-2021-builder-baseline-rllib | iglu-2021-builder-baseline-rllib-main/model.py | from typing import Sequence
import gym
import numpy as np
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.modules.noisy_layer import NoisyLayer
from ray.rllib.agents.dqn.dqn_torch_model import DQNTorchModel
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.fra... | 4,341 | 37.087719 | 101 | py |
iglu-2021-builder-baseline-rllib | iglu-2021-builder-baseline-rllib-main/custom_agent.py | import tensorflow as tf
import torch
import gym
from copy import deepcopy as copy
from gym import spaces
import ray
import numpy as np
from torch._C import Value
import yaml
from wrappers import FakeIglu
from train import build_env, register_models
from ray.rllib.agents.registry import get_trainer_class
CONFIG_FIL... | 2,851 | 35.101266 | 92 | py |
iglu-2021-builder-baseline-rllib | iglu-2021-builder-baseline-rllib-main/wrappers.py | from threading import stack_size
import gym
import os
import cv2
import shutil
import datetime
import pickle
import json
import uuid
import logging
from gym.core import ActionWrapper
import numpy as np
from collections import defaultdict
from typing import Generator
from minerl_patched.herobraine.hero import spaces
lo... | 17,626 | 33.767258 | 92 | py |
iglu-2021-builder-baseline-rllib | iglu-2021-builder-baseline-rllib-main/test_submission.py | import os
from collections import defaultdict
import yaml
import gym
import numpy as np
NB_EPISODES = 3
MAX_EPISODE_STEPS = 1000
VISUAL = False
def check_action(action, action_space):
if not isinstance(action, dict):
raise ValueError('action should be a dict')
for k in action:
if k not in acti... | 3,638 | 33.657143 | 91 | py |
iglu-2021-builder-baseline-rllib | iglu-2021-builder-baseline-rllib-main/train.py | import yaml
import ray
import os
import gym
import iglu
import sys
import wandb
import logging
from collections import defaultdict
from filelock import FileLock
from iglu.tasks import RandomTasks, TaskSet
from ray import tune
from ray.rllib.models import ModelCatalog
from ray.tune.logger import DEFAULT_LOGGERS
from ray... | 5,967 | 38.263158 | 96 | py |
honeypot-camera | honeypot-camera-master/camera.py | # Copyright (c) 2014 Alexander Bredo
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or
# without modification, are permitted provided that the
# following conditions are met:
#
# 1. Redistributions of source code must retain the above
# copyright notice, this list of conditions and t... | 3,836 | 33.567568 | 112 | py |
gdelt-doc-api | gdelt-doc-api-main/setup.py | import setuptools
with open("requirements.txt", "r") as f:
requirements = [line.replace("\n", "") for line in f.readlines()]
with open("README.md", "r") as fh:
long_description = fh.read()
with open("gdeltdoc/_version.py", "r") as g:
version = "1.0.0"
for line in g.readlines():
if "version" i... | 1,019 | 30.875 | 92 | py |
gdelt-doc-api | gdelt-doc-api-main/tests/test_filters.py | from gdeltdoc import Filters, near, repeat, multi_repeat, VALID_TIMESPAN_UNITS
import unittest
class FiltersTestCase(unittest.TestCase):
"""
Test that the correct query strings are generated from
various filters.
"""
def test_single_keyword_filter(self):
f = Filters(keyword="airline", st... | 4,541 | 40.669725 | 123 | py |
gdelt-doc-api | gdelt-doc-api-main/tests/test_client.py | import pandas as pd
import unittest
from gdeltdoc import GdeltDoc, Filters
from datetime import datetime, timedelta
class ArticleSearchTestCast(unittest.TestCase):
"""
Test that the API client behaves correctly when doing an article search query
"""
def setUp(self):
self.start_date = (dateti... | 3,475 | 31.185185 | 102 | py |
gdelt-doc-api | gdelt-doc-api-main/tests/__init__.py | 0 | 0 | 0 | py | |
gdelt-doc-api | gdelt-doc-api-main/gdeltdoc/errors.py | 0 | 0 | 0 | py | |
gdelt-doc-api | gdelt-doc-api-main/gdeltdoc/_version.py | version = "1.5.0" | 17 | 17 | 17 | py |
gdelt-doc-api | gdelt-doc-api-main/gdeltdoc/__init__.py | from gdeltdoc.api_client import GdeltDoc
from gdeltdoc.filters import Filters, near, repeat, multi_repeat, VALID_TIMESPAN_UNITS
from gdeltdoc._version import version
__version__ = version
| 189 | 30.666667 | 86 | py |
gdelt-doc-api | gdelt-doc-api-main/gdeltdoc/filters.py | from typing import Optional, List, Union, Tuple
from string import ascii_lowercase, digits
Filter = Union[List[str], str]
VALID_TIMESPAN_UNITS = ["min", "h", "hours", "d", "days", "w", "weeks", "m", "months"]
def near(n: int, *args) -> str:
"""
Build the filter to find articles containing words that occur wi... | 9,253 | 33.401487 | 144 | py |
gdelt-doc-api | gdelt-doc-api-main/gdeltdoc/helpers.py | import json
def load_json(json_message, max_recursion_depth: int = 100, recursion_depth: int = 0):
"""
tries to load a json formatted string and removes offending characters if present
https://stackoverflow.com/questions/37805751/simplejson-scanner-jsondecodeerror-invalid-x-escape-sequence-us-line-1-colu
... | 1,166 | 37.9 | 124 | py |
gdelt-doc-api | gdelt-doc-api-main/gdeltdoc/api_client.py | import requests
import pandas as pd
from gdeltdoc.filters import Filters
from typing import Dict
from gdeltdoc.helpers import load_json
from gdeltdoc._version import version
class GdeltDoc:
"""
API client for the GDELT 2.0 Doc API
```
from gdeltdoc import GdeltDoc, Filters
f = Filters(
... | 6,042 | 34.757396 | 116 | py |
SPIGA | SPIGA-main/spiga/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/eval/results_gen.py | import pkg_resources
import json
import copy
import torch
import spiga.data.loaders.dl_config as dl_cfg
import spiga.data.loaders.dataloader as dl
import spiga.inference.pretreatment as pretreat
from spiga.inference.framework import SPIGAFramework
from spiga.inference.config import ModelConfig
def main():
import... | 3,346 | 37.034091 | 123 | py |
SPIGA | SPIGA-main/spiga/eval/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/eval/benchmark/evaluator.py | import json
import pkg_resources
from collections import OrderedDict
# Paths
data_path = pkg_resources.resource_filename('spiga', 'data/annotations')
def main():
import argparse
pars = argparse.ArgumentParser(description='Benchmark alignments evaluator')
pars.add_argument('pred_file', nargs='+', type=str... | 3,631 | 32.321101 | 118 | py |
SPIGA | SPIGA-main/spiga/eval/benchmark/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/eval/benchmark/metrics/landmarks.py | import os
import numpy as np
import json
from collections import OrderedDict
from scipy.integrate import simps
from spiga.data.loaders.dl_config import db_anns_path
from spiga.eval.benchmark.metrics.metrics import Metrics
class MetricsLandmarks(Metrics):
def __init__(self, name='landmarks'):
super().__i... | 9,779 | 40.265823 | 104 | py |
SPIGA | SPIGA-main/spiga/eval/benchmark/metrics/pose.py | import numpy as np
from sklearn.metrics import confusion_matrix
from spiga.eval.benchmark.metrics.metrics import Metrics
class MetricsHeadpose(Metrics):
def __init__(self, name='headpose'):
super().__init__(name)
# Angles
self.angles = ['yaw', 'pitch', 'roll']
# Confusion matrix... | 6,373 | 38.8375 | 113 | py |
SPIGA | SPIGA-main/spiga/eval/benchmark/metrics/metrics.py | from collections import OrderedDict
class Metrics:
def __init__(self, name='metrics'):
# Data dicts
self.error = OrderedDict()
self.metrics_log = OrderedDict()
self.name = name
self.database = None
self.data_type = None
def compute_error(self, data_anns, data... | 1,458 | 32.930233 | 86 | py |
SPIGA | SPIGA-main/spiga/eval/benchmark/metrics/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/models/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/models/spiga.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import spiga.models.gnn.pose_proj as pproj
from spiga.models.cnn.cnn_multitask import MultitaskCNN
from spiga.models.gnn.step_regressor import StepRegressor, RelativePositionEncoder
class SPIGA(nn.Module):
def __init__(self, num_landmarks=98, num... | 6,704 | 37.982558 | 125 | py |
SPIGA | SPIGA-main/spiga/models/cnn/layers.py | from torch import nn
class Conv(nn.Module):
def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True):
super(Conv, self).__init__()
self.inp_dim = inp_dim
self.conv = nn.Conv2d(inp_dim, out_dim, kernel_size, stride, padding=(kernel_size - 1) // 2, bias=False)
... | 2,619 | 31.75 | 112 | py |
SPIGA | SPIGA-main/spiga/models/cnn/coord_conv.py | import torch
import torch.nn as nn
class AddCoordsTh(nn.Module):
def __init__(self, x_dim=64, y_dim=64, with_r=False):
super(AddCoordsTh, self).__init__()
self.x_dim = x_dim
self.y_dim = y_dim
self.with_r = with_r
xx_channel, yy_channel = self._prepare_coords()
sel... | 2,053 | 33.813559 | 92 | py |
SPIGA | SPIGA-main/spiga/models/cnn/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/models/cnn/hourglass.py | import torch.nn as nn
from spiga.models.cnn.layers import Conv, Deconv, Residual
class Hourglass(nn.Module):
def __init__(self, n, f, bn=None, increase=0):
super(Hourglass, self).__init__()
nf = f + increase
self.up1 = Residual(f, f)
# Lower branch
self.pool1 = Conv(f, f, ... | 1,575 | 28.185185 | 75 | py |
SPIGA | SPIGA-main/spiga/models/cnn/transform_e2p.py | import torch
from torch import nn
class E2Ptransform(nn.Module):
"""Edge to Points trasnformation"""
def __init__(self, points, edges, out_dim=64):
super(E2Ptransform, self).__init__()
self.ones = nn.parameter.Parameter(torch.ones((1, out_dim, out_dim)), requires_grad=False)
edge_matri... | 16,877 | 64.418605 | 107 | py |
SPIGA | SPIGA-main/spiga/models/cnn/cnn_multitask.py | from torch import nn
from spiga.models.cnn.layers import Conv, Residual
from spiga.models.cnn.hourglass import HourglassCore
from spiga.models.cnn.coord_conv import AddCoordsTh
from spiga.models.cnn.transform_e2p import E2Ptransform
class MultitaskCNN(nn.Module):
def __init__(self, nstack=4, num_landmarks=98, nu... | 4,196 | 43.178947 | 139 | py |
SPIGA | SPIGA-main/spiga/models/gnn/step_regressor.py | import torch.nn as nn
from spiga.models.gnn.layers import MLP
from spiga.models.gnn.gat import GAT
class StepRegressor(nn.Module):
def __init__(self, input_dim: int, feature_dim: int, nstack=4, decoding=[256, 128, 64, 32]):
super(StepRegressor, self).__init__()
assert nstack > 0
self.nst... | 1,423 | 31.363636 | 96 | py |
SPIGA | SPIGA-main/spiga/models/gnn/layers.py | from torch import nn
def MLP(channels: list):
n = len(channels)
layers = []
for i in range(1, n):
layers.append(nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True))
if i < (n-1):
layers.append(nn.BatchNorm1d(channels[i]))
layers.append(nn.ReLU())
r... | 349 | 25.923077 | 88 | py |
SPIGA | SPIGA-main/spiga/models/gnn/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/models/gnn/gat.py | from copy import deepcopy
import torch
from torch import nn
import torch.nn.functional as F
from spiga.models.gnn.layers import MLP
class GAT(nn.Module):
def __init__(self, input_dim: int, output_dim: int, num_heads=4):
super().__init__()
num_heads_in = num_heads
self.reshape = None
... | 2,284 | 35.269841 | 92 | py |
SPIGA | SPIGA-main/spiga/models/gnn/pose_proj.py | import torch
import math
def euler_to_rotation_matrix(euler):
# http://euclideanspace.com/maths/geometry/rotations/conversions/eulerToMatrix/index.htm
# Change coordinates system
euler[:, 0] = -(euler[:, 0]-90)
euler[:, 1] = -euler[:, 1]
euler[:, 2] = -(euler[:, 2]+90)
# Convert to radians
... | 2,046 | 25.24359 | 92 | py |
SPIGA | SPIGA-main/spiga/demo/app.py | import os
import cv2
import pkg_resources
# My libs
import spiga.demo.analyze.track.get_tracker as tr
import spiga.demo.analyze.extract.spiga_processor as pr_spiga
from spiga.demo.analyze.analyzer import VideoAnalyzer
from spiga.demo.visualize.viewer import Viewer
# Paths
video_out_path_dft = pkg_resources.resource_f... | 4,280 | 38.638889 | 119 | py |
SPIGA | SPIGA-main/spiga/demo/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/visualize/viewer.py | import os
import cv2
import copy
import time
import numpy as np
# Demo libs
import spiga.demo.visualize.plotter as plt
class Viewer:
def __init__(self, window_title, width=None, height=None, fps=30):
"""
Initialization of the viewer canvas using width and height in pixels
:param window_t... | 5,277 | 31.182927 | 117 | py |
SPIGA | SPIGA-main/spiga/demo/visualize/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/visualize/plotter.py | # Demo libs
import spiga.demo.visualize.layouts.plot_basics as pl_basic
import spiga.demo.visualize.layouts.plot_bbox as pl_bbox
import spiga.demo.visualize.layouts.plot_landmarks as pl_lnd
import spiga.demo.visualize.layouts.plot_headpose as pl_hpose
class Plotter:
def __init__(self):
self.basic = pl_ba... | 475 | 30.733333 | 61 | py |
SPIGA | SPIGA-main/spiga/demo/visualize/layouts/plot_bbox.py | import cv2
# Demo libs
from spiga.demo.visualize.layouts.plot_basics import BasicLayout
class BboxLayout(BasicLayout):
BasicLayout.thickness_dft['bbox'] = 2
def __init__(self):
super().__init__()
def draw_bbox(self, canvas, bbox, score_thr=0, show_score=True, thick=None, color=BasicLayout.colo... | 1,993 | 35.925926 | 119 | py |
SPIGA | SPIGA-main/spiga/demo/visualize/layouts/plot_basics.py | import numpy as np
import cv2
class BasicLayout:
# Variables
colors = {'green': (0, 255, 0),
'red': (0, 0, 255),
'blue': (255, 0, 0),
'purple': (128, 0, 128),
'white': (255, 255, 255),
'black': (0, 0, 0)}
thickness_dft = {'circle': 2}... | 1,086 | 25.512195 | 80 | py |
SPIGA | SPIGA-main/spiga/demo/visualize/layouts/plot_landmarks.py | import numpy as np
# Demo libs
from spiga.demo.visualize.layouts.plot_basics import BasicLayout
class LandmarkLayout(BasicLayout):
BasicLayout.thickness_dft['lnd'] = 3
def __init__(self):
super().__init__()
def draw_landmarks(self, image, landmarks, visible=None, mask=None,
... | 1,746 | 31.351852 | 100 | py |
SPIGA | SPIGA-main/spiga/demo/visualize/layouts/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/visualize/layouts/plot_headpose.py | import numpy as np
import cv2
# Demo libs
from spiga.demo.visualize.layouts.plot_basics import BasicLayout
class HeadposeLayout(BasicLayout):
BasicLayout.thickness_dft['hpose'] = 2
def __init__(self):
super().__init__()
self.hpose_axe_length = 2
self.focal_ratio = 1
def draw_he... | 2,800 | 39.014286 | 135 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/analyzer.py | import copy
# Demo libs
import spiga.demo.analyze.extract.processor as pr
class VideoAnalyzer:
def __init__(self, tracker, processor=pr.EmptyProcessor()):
self.tracker = tracker
self.processor = processor
self.tracked_obj = []
def process_frame(self, image):
image = copy.copy... | 1,697 | 30.444444 | 84 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/analyze/features/face.py | import numpy as np
# Demo libs
from spiga.demo.analyze.features.basic import ObjectAnalyzed
class Face(ObjectAnalyzed):
def __init__(self):
super().__init__()
self.bbox = np.zeros(5)
self.key_landmarks = - np.ones((5, 2))
self.landmarks = None
self.face_id = -1
se... | 377 | 17 | 60 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/features/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/analyze/features/basic.py |
class ObjectAnalyzed:
def __init__(self):
# Processor addons
self.attributes = []
self.drawers = []
def has_processor(self):
if len(self.attributes) > 0:
return True
else:
return False
def plot_features(self, image, plotter, show_attributes... | 1,053 | 25.35 | 72 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/track/tracker.py |
class Tracker:
"""
Object detection and tracking interface in a video stream
"""
def __init__(self):
self.attributes = []
def process_frame(self, image, tracked_obj):
"""
Detect and track objects in the input image.
:param image: OpenCV image.
:param tracke... | 831 | 31 | 83 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/track/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/analyze/track/get_tracker.py | # Demo libs
import spiga.demo.analyze.track.retinasort.zoo as zoo_rs
zoos = [zoo_rs]
def get_tracker(model_name):
for zoo in zoos:
model = zoo.get_tracker(model_name)
if model is not None:
return model
raise NotImplementedError('Tracker name not available')
| 298 | 20.357143 | 59 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/track/retinasort/face_tracker.py | import numpy as np
# Third party algorithms. Implementation maintained by SPIGA authors.
import sort_tracker
import retinaface
# My libs
import spiga.demo.analyze.track.retinasort.config as cfg
import spiga.demo.analyze.track.tracker as tracker
import spiga.demo.analyze.features.face as ft_face
class RetinaSortTrac... | 3,202 | 37.590361 | 131 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/track/retinasort/zoo.py | # My libs
import spiga.demo.analyze.track.retinasort.face_tracker as tr
import spiga.demo.analyze.track.retinasort.config as cfg_tr
def get_tracker(model_name):
# MobileNet Backbone
if model_name == 'RetinaSort':
return tr.RetinaSortTracker()
# ResNet50 Backbone
if model_name == 'RetinaSort_R... | 744 | 30.041667 | 64 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/track/retinasort/config.py |
cfg_retinasort = {
'retina': {
'model_name': 'mobile0.25',
'extra_features': ['landmarks'],
'postreat': {
'resize': 1.,
'score_thr': 0.75,
'top_k': 5000,
'nms_thr': 0.4,
'keep_top_k': 50}
},
'sort': {
'max_ag... | 1,562 | 18.060976 | 40 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/track/retinasort/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/analyze/extract/spiga_processor.py | # SPIGA library
import spiga.inference.config as model_cfg
from spiga.inference.framework import SPIGAFramework
# Demo modules
import spiga.demo.analyze.extract.processor as pr
class SPIGAProcessor(pr.Processor):
def __init__(self,
dataset='wflw',
features=('lnd', 'pose'),
... | 2,243 | 35.786885 | 105 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/extract/processor.py |
class Processor:
def __init__(self):
self.attributes = []
def process_frame(self, frame, tracked_obj):
"""
Process tracked objects to extract interesting features.
:param frame: OpenCV image.
:param tracked_obj: List with the objects to be processed.
"""
... | 1,726 | 28.271186 | 83 | py |
SPIGA | SPIGA-main/spiga/demo/analyze/extract/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/demo/utils/frames2video.py | import os
import cv2
def main():
import argparse
pars = argparse.ArgumentParser(description='Frames to video converter')
pars.add_argument('frames', type=str, help='Frames directory')
pars.add_argument('vidname', type=str, help='Output video name')
pars.add_argument('-o', '--outpath', type=str, de... | 2,184 | 33.68254 | 108 | py |
SPIGA | SPIGA-main/spiga/demo/utils/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/data/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/data/loaders/alignments.py | import os
import json
import cv2
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from spiga.data.loaders.transforms import get_transformers
class AlignmentsDataset(Dataset):
'''Loads datasets of images with landmarks and bounding boxes.
'''
... | 5,540 | 33.849057 | 118 | py |
SPIGA | SPIGA-main/spiga/data/loaders/dataloader.py | from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import spiga.data.loaders.alignments as zoo_alignments
zoos = [zoo_alignments]
def get_dataset(data_config, pretreat=None, debug=False):
for zoo in zoos:
dataset = zoo.get_dataset(data_config, pretreat=p... | 1,360 | 31.404762 | 105 | py |
SPIGA | SPIGA-main/spiga/data/loaders/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/data/loaders/dl_config.py | import os
import json
import pkg_resources
from collections import OrderedDict
# Default data paths
db_img_path = pkg_resources.resource_filename('spiga', 'data/databases')
db_anns_path = pkg_resources.resource_filename('spiga', 'data/annotations') + "/{database}/{file_name}.json"
class AlignConfig:
def __init__... | 5,944 | 33.766082 | 117 | py |
SPIGA | SPIGA-main/spiga/data/loaders/transforms.py | import cv2
import numpy as np
import torch
from spiga.data.loaders.augmentors.modern_posit import PositPose
from spiga.data.loaders.augmentors.heatmaps import Heatmaps
from spiga.data.loaders.augmentors.boundary import AddBoundary
from spiga.data.loaders.augmentors.landmarks import HorizontalFlipAug, RSTAug, Occlusion... | 3,558 | 40.870588 | 109 | py |
SPIGA | SPIGA-main/spiga/data/loaders/augmentors/modern_posit.py | import os
import pkg_resources
import numpy as np
import cv2
# My libs
from spiga.data.loaders.augmentors.utils import rotation_matrix_to_euler
# Model file nomenclature
model_file_dft = pkg_resources.resource_filename('spiga', 'data/models3D') + '/mean_face_3D_{num_ldm}.txt'
class PositPose:
def __init__(self... | 7,711 | 37.949495 | 127 | py |
SPIGA | SPIGA-main/spiga/data/loaders/augmentors/landmarks.py | import random
import cv2
import numpy as np
from PIL import Image
from torchvision import transforms
# My libs
import spiga.data.loaders.augmentors.utils as dlu
class HorizontalFlipAug:
def __init__(self, ldm_flip_order, prob=0.5):
self.prob = prob
self.ldm_flip_order = ldm_flip_order
def __... | 11,374 | 35.931818 | 114 | py |
SPIGA | SPIGA-main/spiga/data/loaders/augmentors/utils.py | import numpy as np
def affine2homogeneous(points):
'''Returns the points completed with a new last coordinate
equal to 1
Arguments
---------
points: np.array of shape (num_points, dim)
Returns
-------
hpoints: np.array of shape (num_points, dim + 1),
of the points completed wit... | 4,716 | 30.871622 | 102 | py |
SPIGA | SPIGA-main/spiga/data/loaders/augmentors/boundary.py | import numpy as np
from scipy import interpolate
import cv2
class AddBoundary(object):
def __init__(self, num_landmarks=68, map_size=64, sigma=1, min_dpi=64):
self.num_landmarks = num_landmarks
self.sigma = sigma
if isinstance(map_size, (tuple, list)):
self.width = map_size[0]... | 6,156 | 49.056911 | 106 | py |
SPIGA | SPIGA-main/spiga/data/loaders/augmentors/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/data/loaders/augmentors/heatmaps.py | import numpy as np
class Heatmaps:
def __init__(self, num_maps, map_size, sigma, stride=1, norm=True):
self.num_maps = num_maps
self.sigma = sigma
self.double_sigma_pw2 = 2*sigma*sigma
self.doublepi_sigma_pw2 = self.double_sigma_pw2 * np.pi
self.stride = stride
sel... | 1,463 | 35.6 | 119 | py |
SPIGA | SPIGA-main/spiga/data/visualize/plotting.py | import matplotlib.pyplot as plt
import numpy as np
import cv2
import spiga.data.loaders.augmentors.utils as dlu
BLUE = (255, 0, 0)
GREEN = (0, 255, 0)
RED = (0, 0, 255)
PURPLE = (128, 0, 128)
def draw_landmarks(image, landmarks, visible=None, mask=None, thick_scale=1, colors=(GREEN, RED)):
# Fix variable
th... | 3,051 | 30.142857 | 103 | py |
SPIGA | SPIGA-main/spiga/data/visualize/inspect_heatmaps.py | import cv2
import numpy as np
from spiga.data.visualize.inspect_dataset import DatasetInspector, inspect_parser
class HeatmapInspector(DatasetInspector):
def __init__(self, database, anns_type, data_aug=True, image_shape=(256,256)):
super().__init__(database, anns_type, data_aug=data_aug, pose=False, i... | 2,871 | 29.88172 | 101 | py |
SPIGA | SPIGA-main/spiga/data/visualize/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/data/visualize/inspect_dataset.py | import cv2
import random
import numpy as np
import spiga.data.loaders.dl_config as dl_cfg
import spiga.data.loaders.dataloader as dl
import spiga.data.visualize.plotting as plot
def inspect_parser():
import argparse
pars = argparse.ArgumentParser(description='Data augmentation and dataset visualization. '
... | 6,554 | 35.016484 | 122 | py |
SPIGA | SPIGA-main/spiga/data/models3D/visualization.py | import argparse
import numpy as np
import matplotlib.pyplot as plt
def main():
# Input arguments control
pars = argparse.ArgumentParser(description='3D model visualization')
pars.add_argument('file', type=str, help='File txt path')
args = pars.parse_args()
visualize_3Dmodel(args.file)
def visual... | 875 | 22.052632 | 72 | py |
SPIGA | SPIGA-main/spiga/data/models3D/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/spiga/inference/pretreatment.py | from torchvision import transforms
import numpy as np
from PIL import Image
import cv2
from spiga.data.loaders.transforms import TargetCrop, ToOpencv, AddModel3D
def get_transformers(data_config):
transformer_seq = [
Opencv2Pil(),
TargetCrop(data_config.image_size, data_config.target_dist),
... | 825 | 24.8125 | 74 | py |
SPIGA | SPIGA-main/spiga/inference/framework.py | import os
import pkg_resources
import copy
import torch
import numpy as np
# Paths
weights_path_dft = pkg_resources.resource_filename('spiga', 'models/weights')
import spiga.inference.pretreatment as pretreat
from spiga.models.spiga import SPIGA
from spiga.inference.config import ModelConfig
class SPIGAFramework:
... | 5,368 | 37.905797 | 120 | py |
SPIGA | SPIGA-main/spiga/inference/config.py | from collections import OrderedDict
from spiga.data.loaders.dl_config import DatabaseStruct
MODELS_URL = {'wflw': 'https://drive.google.com/uc?export=download&confirm=yes&id=1h0qA5ysKorpeDNRXe9oYkVcVe8UYyzP7',
'300wpublic': 'https://drive.google.com/uc?export=download&confirm=yes&id=1YrbScfMzrAAWMJQYgxd... | 2,338 | 38.644068 | 124 | py |
SPIGA | SPIGA-main/spiga/inference/__init__.py | 0 | 0 | 0 | py | |
SPIGA | SPIGA-main/colab_tutorials/video_tools/record.py | from IPython.display import display, Javascript, HTML
from google.colab.output import eval_js
from base64 import b64decode, b64encode
def record_video(filename):
js = Javascript("""
async function recordVideo() {
const options = { mimeType: "video/webm; codecs=vp9" };
const div = document.createEl... | 2,563 | 31.871795 | 94 | py |
SPIGA | SPIGA-main/colab_tutorials/video_tools/utils.py | import numpy as np
import PIL
import io
import cv2
from base64 import b64decode, b64encode
def js_to_image(js_reply):
"""
Convert the JavaScript object into an OpenCV image.
@param js_reply: JavaScript object containing image from webcam
@return img: OpenCV BGR image
"""
# decode base64 image... | 1,589 | 29.576923 | 104 | py |
ReconVAT | ReconVAT-master/train_baseline_Thickstun.py | import os
from datetime import datetime
import pickle
import numpy as np
from sacred import Experiment
from sacred.commands import print_config, save_config
from sacred.observers import FileStorageObserver
from torch.optim.lr_scheduler import StepLR, CyclicLR
from torch.utils.data import DataLoader, ConcatDataset
fro... | 5,952 | 36.677215 | 142 | py |
ReconVAT | ReconVAT-master/evaluate.py | import numpy as np
from sacred import Experiment
from sacred.commands import print_config, save_config
from sacred.observers import FileStorageObserver
from torch.nn.utils import clip_grad_norm_
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader
from torch.utils.tensorboard import Summa... | 3,416 | 34.226804 | 117 | py |
ReconVAT | ReconVAT-master/train_UNet_Onset_VAT.py | import os
from datetime import datetime
import pickle
import numpy as np
from sacred import Experiment
from sacred.commands import print_config, save_config
from sacred.observers import FileStorageObserver
from torch.optim.lr_scheduler import StepLR, CyclicLR
from torch.utils.data import DataLoader
from tqdm import t... | 7,486 | 41.782857 | 219 | py |
ReconVAT | ReconVAT-master/transcribe_files.py | import pickle
import os
import numpy as np
from model import *
from sacred import Experiment
from sacred.commands import print_config, save_config
from sacred.observers import FileStorageObserver
ex = Experiment('transcription')
def transcribe2midi(data, model, model_type, onset_threshold=0.5, frame_threshold=0.5, s... | 2,933 | 36.615385 | 182 | py |
ReconVAT | ReconVAT-master/train_baseline_Multi_Inst.py | import os
from datetime import datetime
import pickle
import numpy as np
from sacred import Experiment
from sacred.commands import print_config, save_config
from sacred.observers import FileStorageObserver
from torch.optim.lr_scheduler import StepLR, CyclicLR
from torch.utils.data import DataLoader, ConcatDataset
fro... | 7,722 | 41.202186 | 230 | py |
ReconVAT | ReconVAT-master/train_baseline_Prestack.py | import os
from datetime import datetime
import pickle
import numpy as np
from sacred import Experiment
from sacred.commands import print_config, save_config
from sacred.observers import FileStorageObserver
from torch.optim.lr_scheduler import StepLR, CyclicLR
from torch.utils.data import DataLoader, ConcatDataset
fro... | 6,274 | 37.030303 | 142 | py |
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