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
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neural_graph_evolution | neural_graph_evolution-master/agent/agent.py | # -----------------------------------------------------------------------------
# @brief:
# In this function, we define the base agent.
# The base agent should be responsible for building the policy network,
# fetch the io placeholders / tensors, and set up the variable list
# @author:
# cod... | 22,426 | 41.395085 | 86 | py |
neural_graph_evolution | neural_graph_evolution-master/agent/__init__.py | 0 | 0 | 0 | py | |
neural_graph_evolution | neural_graph_evolution-master/agent/pruning_agent.py | # ------------------------------------------------------------------------------
# @brief:
# The optimization agent is responsible for doing the updates.
# @author:
# modified from the code of kvfran, modified by Tingwu Wang
# -----------------------------------------------------------------------------... | 14,957 | 37.255754 | 86 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/asset_generator.py | # -----------------------------------------------------------------------------
# @brief:
# generate the xml files for each different sub-tasks of one master task
# @author:
# Tingwu Wang, Aug. 30th, 2017
# -----------------------------------------------------------------------------
import argparse
im... | 1,798 | 32.314815 | 79 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/init_path.py | ../tool/init_path.py | 20 | 20 | 20 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/centipede_generator.py | # -----------------------------------------------------------------------------
# @brief:
# generate the centipedes
# @author:
# Tingwu Wang, Sept. 1st, 2017
# -----------------------------------------------------------------------------
import numpy as np
MUJOCO_XML_HEAD = '''
<mujoco model="centiped... | 7,395 | 36.353535 | 163 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/__init__.py | 0 | 0 | 0 | py | |
neural_graph_evolution | neural_graph_evolution-master/environments/reacher_generator.py | # -----------------------------------------------------------------------------
# @brief:
# generate the reacher
# @author:
# Tingwu Wang, Sept. 3rd, 2017
# -----------------------------------------------------------------------------
MUJOCO_XML_HEAD = '''
<mujoco model="modified_reacher">
<compiler... | 4,800 | 40.034188 | 156 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/register.py | # -----------------------------------------------------------------------------
# @brief:
# register the environments here
# @author:
# Tingwu Wang, July 3rd, 2017
# -----------------------------------------------------------------------------
import pdb
from gym.envs.registration import register
impor... | 9,120 | 33.161049 | 79 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/snake_generator.py | # -----------------------------------------------------------------------------
# @brief:
# generate the snakes
# @author:
# Tingwu Wang, Sept. 1st, 2017
# -----------------------------------------------------------------------------
MUJOCO_XML_HEAD = '''
<mujoco model="swimmer">
<compiler angle="de... | 3,940 | 35.155963 | 163 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/snake_env.py | #!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @brief:
# The snake environments.
# @author:
# Tingwu (Wilson) Wang, Aug. 30nd, 2017
# -----------------------------------------------------------------------------
import numpy as np
from gym impor... | 5,418 | 24.804762 | 79 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/fish_env.py | # -----------------------------------------------------------------------------
# @brief:
# The fish environments.
# @author:
# Yuhao Zhou Feb 4th, 2018
# -----------------------------------------------------------------------------
import pdb
import numpy as np
from gim import utils
from gym.envs.mujo... | 384 | 26.5 | 79 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/reacher_env.py | #!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @brief:
# The reacher task
# @author:
# Tingwu (Wilson) Wang, July 22nd, 2017
# -----------------------------------------------------------------------------
import pdb
import numpy as np
from gym i... | 8,795 | 23.433333 | 79 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/antS.py | #!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @brief:
# The slim ant environments.
# @author:
# Tingwu (Wilson) Wang, Aug. 30nd, 2017
# -----------------------------------------------------------------------------
import numpy as np
from gym im... | 2,175 | 34.672131 | 92 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/centipede_env.py | #!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @brief:
# The Centipede environments.
# @author:
# Tingwu (Wilson) Wang, Aug. 30nd, 2017
# -----------------------------------------------------------------------------
import pdb
import numpy as np
... | 7,631 | 27.477612 | 83 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/invpendulum_env.py | #!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @brief:
# The snake environments.
# @author:
# Tingwu (Wilson) Wang, Aug. 30nd, 2017
# -----------------------------------------------------------------------------
import numpy as np
from gym impor... | 3,853 | 26.333333 | 79 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/transfer_env/__init__.py | 0 | 0 | 0 | py | |
neural_graph_evolution | neural_graph_evolution-master/environments/multitask_env/walkers.py | #!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @brief:
# Several Walkers
# @author:
# Tingwu (Wilson) Wang, Nov. 22nd, 2017
# -----------------------------------------------------------------------------
import numpy as np
from gym import utils
... | 15,916 | 30.770459 | 96 | py |
neural_graph_evolution | neural_graph_evolution-master/environments/multitask_env/__init__.py | 0 | 0 | 0 | py | |
BamBirds | BamBirds-master/main.py | #!/usr/bin/env python3
import planner.src.main.python.rebound as rebound
import vision.src.main.python.sciencebirds as vision
import level_selection.src.main.python.prediction as level_selection
import src.main.python.automated_execution as automated_execution
import evaluation
if __name__ == '__main__':
import a... | 1,825 | 35.52 | 129 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/linearModel.py | import pandas as pd
import pickle
import os
import sklearn.linear_model as sklLM
from sklearn import metrics
from sklearn2pmml.pipeline import PMMLPipeline
__all__ = ["linear_model", "train_linear_model", "query_linear_model", "linear_model_to_java_code", "evaluate_linear_model"]
__current_working__dir = os.path.dir... | 4,299 | 38.814815 | 156 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/main.py | import argparse
import copy
import os
from . import simulate
from . import learning
__dir_path__ = os.path.dirname(os.path.abspath(__file__))
bambird_folder = os.path.abspath(os.path.join(__dir_path__, '..','..','..','..', '..'))
testing_data_folder = os.path.abspath(os.path.join(bambird_folder, 'data','testing'))
de... | 7,534 | 52.06338 | 173 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/decisionTreeRg.py | import pickle
import os
import sklearn.tree as sklTree
from sklearn2pmml import sklearn2pmml
from sklearn2pmml.pipeline import PMMLPipeline
__all__ = ["train_regressor"]
__current_working__dir = os.path.dirname(os.path.abspath(__file__))
tr_regressor_output = os.path.join(__current_working__dir,'tr_regressor.pkl')
... | 968 | 32.413793 | 131 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/evaluate.py | import numpy as np
import pandas as pd
import math
from sklearn import metrics
__all__ = ["evaluate_prediction_table", "evaluate_prediction_metrics", "evaluate_prediction_classifier", "calculate_information_values"]
""" Comparison of classifier performance.
:param scores -> classifier predictions
"""
def evaluat... | 5,312 | 42.54918 | 136 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/simulate.py | #!/usr/bin/env python3
import collections
import copy
import csv
import math
import os
import random
import time
from enum import Enum
import numpy as np
import pandas as pd
from sklearn import metrics
from . import query
__current_working__dir = os.path.dirname(os.path.abspath(__file__))
demo_level = []
demo_roun... | 22,393 | 36.261231 | 215 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/decisionTreeCl.py | import pickle
import os
import sklearn.tree as sklTree
from sklearn2pmml import sklearn2pmml
from sklearn2pmml.pipeline import PMMLPipeline
__all__ = ["train_classifier", "tree_to_java_code"]
__current_working__dir = os.path.dirname(os.path.abspath(__file__))
tr_classifier_output = os.path.join(__current_working__di... | 2,683 | 41.603175 | 141 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/learning_dataPrep.py | import math
import pandas as pd
import os
__all__ = ["read_input", "numerical_transform_strategies", "get_weighted_strategies"]
"""" Main function for construction and evaluation of the regression based decision tree.
:param input_folder -> folder with feature input
:param debug -> verbose outputs for debug... | 3,282 | 40.556962 | 161 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/learning.py | import matplotlib.pyplot as plt
import pandas as pd
import pickle
import os
import sklearn.tree as sklTree
import sklearn.model_selection as sklModel
from sklearn.linear_model import LogisticRegression
from . import learning_dataPrep as dataPrep
from . import decisionTreeCl
from . import decisionTreeRg
from . impor... | 16,059 | 56.562724 | 203 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/__init__.py | from . import learning, simulate
from .main import create_argparser, train_models, simulate_level_selection
| 108 | 35.333333 | 74 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/randomForestRg.py | import pickle
import os
from sklearn.ensemble import RandomForestRegressor
from . import randomForestCl
from sklearn2pmml.pipeline import PMMLPipeline
__all__ = ["random_forest", "train_random_forest", "query_random_forest"]
__current_working__dir = os.path.dirname(os.path.abspath(__file__))
rf_regressor_output = o... | 2,012 | 34.946429 | 141 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/query.py | import pickle
import os
from . import learning_dataPrep as dp
__all__ = ["query_predictions", "query_classifier", "query_regressor"]
__dir_path__ = os.path.dirname(os.path.abspath(__file__))
tr_classifier_output = os.path.join(__dir_path__,'tr_classifier.pkl')
rf_classifier_output = os.path.join(__dir_path__,'rf_cla... | 4,320 | 35.310924 | 117 | py |
BamBirds | BamBirds-master/level_selection/src/main/python/prediction/randomForestCl.py | import pickle
import os
from sklearn.ensemble import RandomForestClassifier
import sklearn.tree as sklTree
from sklearn2pmml.pipeline import PMMLPipeline
from sklearn2pmml import sklearn2pmml
__all__ = ["train_rf_classifier", "query_rf_classifier", "forest_to_java_code"]
__current_working__dir = os.path.dirname(os.p... | 4,390 | 43.806122 | 119 | py |
BamBirds | BamBirds-master/planner/src/test/python/rebound/generate_data.py | from . import settings
import os
import planner.src.main.python.rebound as rebound
import numpy as np
import math
import logging
import time
import itertools
import random
import csv
fieldnames = ["file", "num_results", "time_first (s)", "time_last (s)", "time_max (s)", "edge_length", "consistency_check_length", "vari... | 7,077 | 48.84507 | 371 | py |
BamBirds | BamBirds-master/planner/src/test/python/rebound/settings.py | import os
from planner.src.main.python.rebound.settings import ABType
__dir_path__ = os.path.dirname(os.path.abspath(__file__))
__resources_dir__ = os.path.join(__dir_path__, "../../resources")
__scenes_dir__ = os.path.join(__resources_dir__, "scenes")
generator_default = "stop"
scene_files = [
os.path.join(__sc... | 2,920 | 49.362069 | 119 | py |
BamBirds | BamBirds-master/planner/src/test/python/rebound/__init__.py | 0 | 0 | 0 | py | |
BamBirds | BamBirds-master/planner/src/test/python/rebound/test_state.py | import unittest
import os
__dir_path__ = os.path.dirname(os.path.abspath(__file__))
__resources_dir__ = os.path.join(__dir_path__, "../../resources")
__scenes_dir__ = os.path.join(__resources_dir__, "scenes")
scene_files = [
os.path.join(__scenes_dir__, "scene_15.png"),
os.path.join(__scenes_dir__, "scene_16.... | 462 | 21.047619 | 65 | py |
BamBirds | BamBirds-master/planner/src/test/python/rebound/test_running.py | import unittest
import os
import numpy as np
import math
import logging
import time
import itertools
import random
import planner.src.main.python.rebound as rebound
from . import settings
class TestRunning(unittest.TestCase):
@unittest.skip("Outdated")
def test_running_df(self):
print("--- test_runnin... | 4,155 | 35.13913 | 288 | py |
BamBirds | BamBirds-master/planner/src/test/python/rebound/test_utils.py | import unittest
import os
import planner.src.main.python.rebound as rebound
import numpy as np
import math
class TestUtils(unittest.TestCase):
def test_bounce_equal_for_equal_wall_angles(self):
print("--- bounce_equal_for_equal_wall_angles ---")
P = [5,10]
rg_start = [40,50]
v_star... | 13,175 | 44.434483 | 120 | py |
BamBirds | BamBirds-master/planner/src/main/python/rebound/main.py | import logging
from .state import State
from . import search, settings, utils
from PIL import Image
import os
import numpy as np
log = logging.getLogger("rebound")
generator_default = "stop"
def run(img_file: str, start_point: tuple, entry_edge: int, angle_interval: list, velocity: float, target: tuple, search_metho... | 9,663 | 41.951111 | 433 | py |
BamBirds | BamBirds-master/planner/src/main/python/rebound/settings.py | from typing import Final
from enum import Enum
import math
FLY: Final[int] = 0
BOUNCE: Final[int] = 1
SLIDE: Final[int] = 2
GOAL: Final[int] = 3
class ABType(Enum):
"""Enum for the object types in Angry Birds
"""
Background = 0
Ground = 1
Hill = 2
Sling = 3
RedBird = 4
YellowBird = 5 ... | 1,402 | 16.987179 | 75 | py |
BamBirds | BamBirds-master/planner/src/main/python/rebound/state.py | import logging
import math
import random
import copy
from matplotlib import pyplot as plt
import numpy as np
from . import settings, utils
log = logging.getLogger("rebound.State")
class State(object):
"""Defines a state in the simulation
Arguments:
turn {State} -- Last State where the direction chang... | 28,215 | 39.136558 | 191 | py |
BamBirds | BamBirds-master/planner/src/main/python/rebound/utils.py | import logging
import math
import copy
import numpy as np
from . import settings
import PIL.Image as Image
log = logging.getLogger("rebound.utils")
def cw(i):
"""The index of the next edge clockwise
Arguments:
i {int} -- edge
Returns:
int -- new edge
"""
return (i-1) % 4
def c... | 44,913 | 32.025 | 168 | py |
BamBirds | BamBirds-master/planner/src/main/python/rebound/__init__.py | from .state import State
from .utils import *
from .main import run, main
from .search import *
import logging
import argparse
import ast
import os
logging.basicConfig(level=logging.DEBUG, filename="logs/rebound.log", filemode="a", format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
def create_argparser(pa... | 4,976 | 80.590164 | 351 | py |
BamBirds | BamBirds-master/planner/src/main/python/rebound/search.py | import logging
from math import dist
from queue import Queue, PriorityQueue
from . import settings, utils
from .state import State
from dataclasses import dataclass, field
from typing import Iterator
from PIL import Image
import numpy as np
from operator import itemgetter
log = logging.getLogger("rebound.search")
def... | 14,918 | 37.550388 | 290 | py |
BamBirds | BamBirds-master/src/main/python/automated_execution.py | #!/usr/bin/env python3
import os
import subprocess
import time
import numpy as np
import copy
import argparse
import platform
# Modify to your own satisfaction/ needs
learning_starting_levels = 8
learning_last_levels = 12 # current max: 12
learning_rounds = 5
learning_folder = os.path.join('data', 'learning')
testi... | 5,420 | 44.554622 | 193 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/__init__.py | from .vision import *
| 22 | 10.5 | 21 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/vision.py | from . import watershed as w
from .polygon_operations import A_Star as a
from .polygon_operations import Polygon as p
import cv2 as cv
import numpy as np
import argparse
import random as rng
import matplotlib.pyplot as plt
import math
from .watershed import DiscreteCurve_2 as dc
def calc_dist(vertex1, vertex2):
... | 2,755 | 33.024691 | 116 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/erosion/testscript.py | from PIL import Image
import os
import numpy as np
import ntpath
ground_truth_file = "/home/nomis/Documents/AIBIRDS/Doc/analysis/pixel_diff/ground_truth_130721_standard_framerate.csv"
result_file = "/home/nomis/Documents/AIBIRDS/Doc/analysis/pixel_diff/ground_truth_130721_standard_framerate_dependence_result.csv"
comp... | 8,069 | 45.37931 | 306 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/erosion/dilate.py | import os
import cv2 as cv
import ntpath
import numpy as np
from PIL import Image
import testscript
dilate_folder = "/home/nomis/Documents/AIBIRDS/Doc/dil_results/"
dilate_path1 = ""
dilate_path2 = ""
def check_for_existing_results_file(res_file_path):
if os.path.isfile(res_file_path):
with open(res_file_... | 6,022 | 45.689922 | 218 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/feature_extraction/BOW.py | import pickle
from sklearn.cluster import KMeans
import cv2 as cv
import vision_watershed as w
import DiscreteCurve_2 as dc
import SIFT as s
import numpy as np
import matplotlib.pylab as plab
import matplotlib.pyplot as plt
image = cv.imread('/home/rocketqueen/sciencebirdsframework/screenshots/8.png')
gray_image = cv... | 3,173 | 28.943396 | 121 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/feature_extraction/SIFT.py | import cv2 as cv
import matplotlib.pyplot as plt
import vision_watershed as w
import DiscreteCurve_2 as dc
def get_sift(object):
# object is the cropped object in grayscale
# sift
sift = cv.SIFT_create(contrastThreshold=0.04, sigma=0.8)
keypoints, descriptors = sift.detectAndCompute(object, None)
... | 1,928 | 27.791045 | 108 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/feature_extraction/NaiveBayes.py | from sklearn import preprocessing
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
import BOW as b
class NaiveBayes:
def __init__(self, _features, _labels):
... | 3,185 | 39.846154 | 116 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/feature_extraction/__init__.py | 0 | 0 | 0 | py | |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/watershed/watershed_dist_transform.py |
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
import matplotlib.pyplot as plt
rng.seed(12345)
if __name__ == "__main__":
image = cv.imread('/home/rocketqueen/sciencebirdsframework/screenshots/8.png')
# image = cv.imread('/home/rocketqueen/Bil... | 3,567 | 28.983193 | 86 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/watershed/Representation.py | import cv2 as cv2
import numpy as np
def checkRep(b, g, r):
if (b == g).all() and (b == r).all():
print("Gray")
elif np.all(b == 0) or np.all(r == 0) or np.all(g == 0):
print('One Color')
else:
print("Colored")
if __name__ == '__main__':
img = cv2.imread('/home/rocketqueen/sc... | 732 | 22.645161 | 82 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/watershed/DiscreteCurve_2.py | from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
import matplotlib.pyplot as plt
import math
from ..polygon_operations import A_Star as a
import vision as v
from ..polygon_operations import Polygon as p
from . import vision_watershed as w
rng.seed(12345)
#... | 5,232 | 29.074713 | 117 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/watershed/watershed.py | from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
import matplotlib.pyplot as plt
rng.seed(12345)
if __name__ == "__main__":
image = cv.imread('tmp/sb_image_701632605.png')
# image = cv.imread('/home/rocketqueen/Bilder/water_coins.jpg')
crop_... | 3,327 | 28.714286 | 86 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/watershed/vision_watershed.py | from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
class Watershed:
def __init__(self, image):
self.crop_img = image[120:394, 70:700]
# scale_percent = 125
# calculate the 50 percent of original dimensions
... | 2,939 | 30.956522 | 90 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/watershed/__init__.py | from .vision_watershed import Watershed | 39 | 39 | 39 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/polygon_operations/Polygon.py | import math
from . import Intersect as inter
import numpy as np
# Here you can set all parameters
# function refine()
REFINE_ANGLE_MARGIN = 50
# function is_rect()
RECT_DIAGONAL_MARGIN = 0.9
# function is_circle_2()
CIRCLE_DST_MARGIN = 0.8
def latex2poly(latex):
poly = latex.replace('--', ',')
poly = pol... | 14,462 | 35.98977 | 1,344 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/polygon_operations/A_Star.py | from . import Polygon as p
from . import SplitPolygon as sp
import math
def is_goal(polygons_list: list) -> bool:
for polygon in polygons_list:
if not polygon.is_primitive():
return False
return True
def calc_heuristic(polygons_list: list) -> int:
heuristic = 0
for polygon in pol... | 3,044 | 35.686747 | 118 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/polygon_operations/SplitPolygon.py | from . import Polygon as p
import math
# Parameters
# for function cut_polygon()
COST_STRAIGHT = 1
COST_STEEP = 1
SLOPE_MARGIN = 0.5
# for function split_polygon()
CLOSEST_NUM = 3
def calc_heuristic(polygon, primitive):
# number of lines in polygon
N = len(polygon.vertices)
n = len(primitive.vertices)
... | 5,397 | 32.116564 | 300 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/polygon_operations/Intersect.py | # This code is implemented using this tutorial:
# https://www.geeksforgeeks.org/check-if-two-given-line-segments-intersect/
class Point:
def __init__(self, a, b):
self.a = a
self.b = b
def on_segment(x, y, z):
if ((y.a <= max(x.a, z.a)) and (y.a >= min(x.a, z.a)) and
(y.b <= max(x... | 1,080 | 19.788462 | 77 | py |
BamBirds | BamBirds-master/vision/src/main/python/sciencebirds/polygon_operations/__init__.py | 0 | 0 | 0 | py | |
TREMBA | TREMBA-master/DataLoader.py | import torch
import torch.nn as nn
import torchvision.datasets as dset
import torch.utils.data
import torchvision.transforms as transforms
import os
import json
import numpy as np
from torch.utils.data import Dataset, DataLoader
def imagenet(state):
if 'defense' in state and state['defense']:
mean = n... | 1,142 | 29.891892 | 67 | py |
TREMBA | TREMBA-master/attack.py | import argparse
import torchvision.models as models
import os
import json
import DataLoader
from utils import *
from FCN import *
from Normalize import Normalize, Permute
from imagenet_model.Resnet import resnet152_denoise, resnet101_denoise
def EmbedBA(function, encoder, decoder, image, label, config, latent=None):
... | 7,438 | 36.570707 | 185 | py |
TREMBA | TREMBA-master/utils.py | import torch.nn as nn
import torch
import numpy as np
class MarginLoss(nn.Module):
def __init__(self, margin=1.0, target=False):
super(MarginLoss, self).__init__()
self.margin = margin
self.target = target
def forward(self, logits, label):
if not self.target:
one... | 3,786 | 35.413462 | 117 | py |
TREMBA | TREMBA-master/Normalize.py | import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, mean, std):
super(Normalize, self).__init__()
self.mean = mean
self.std = std
def forward(self, input):
size = input.size()
x = input.clone()
for i in range(size[1]):
... | 606 | 20.678571 | 56 | py |
TREMBA | TREMBA-master/FCN.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
class Imagenet_Encoder(nn.Module):
def __init__(self):
super().__init__()
self.conv1_1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
... | 4,477 | 32.924242 | 107 | py |
TREMBA | TREMBA-master/train_generator.py | import argparse
import os
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from Normalize import Normalize, Permute
import DataLoader
import numpy as np
from FCN import *
from utils import *
import torchvision.models as models
import copy
from imagenet_model.Resnet import *
if __name__ ==... | 5,548 | 36.748299 | 136 | py |
TREMBA | TREMBA-master/imagenet_model/Resnet.py | import torch.nn as nn
import torch
import math
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def ... | 10,508 | 35.113402 | 108 | py |
DeepSatData | DeepSatData-main/dataset/__init__.py | 0 | 0 | 0 | py | |
DeepSatData | DeepSatData-main/dataset/labelled_dense/extract_images_for_labels.py | """
Given a set of S2 tiles and a labelled_dense lable map, extract crops of images matching the location of labels
"""
import argparse
import pandas as pd
import rasterio
import numpy as np
import os
from glob import glob
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
fro... | 8,590 | 40.105263 | 169 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/find_parcel_dimensions.py | import argparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from shapely import geometry
import os
from glob import glob
from multiprocessing import Pool
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path import dirname as dir
path.insert(0, dir(d... | 5,985 | 39.445946 | 121 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/extract_images_for_parcel_labels.py | """
Given a set of S2 tiles and a labelled_dense lable map, extract crops of images matching the location of labels
"""
import argparse
import pandas as pd
import rasterio
import numpy as np
import os
from glob import glob
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
fro... | 5,689 | 34.786164 | 119 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/extract_labels_raster.py | import argparse
import pandas as pd
import numpy as np
from shapely import geometry
import os
from glob import glob
from multiprocessing import Pool
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path import dirname as dir
path.insert(0, dir(dir(path[0])))
__package__ = ... | 12,439 | 42.957597 | 136 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/__init__.py | 0 | 0 | 0 | py | |
DeepSatData | DeepSatData-main/dataset/labelled_dense/make_image_timeseries_for_parcel_labels.py | """
For a set of extracted image crops and a labelled_dense label map, make a timeseries of all positions matched with labels
"""
import argparse
import pandas as pd
import numpy as np
import os
import shutil
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path impo... | 6,805 | 35.202128 | 129 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/extract_parcel_ground_truths.py | import argparse
import pandas as pd
import numpy as np
from shapely import geometry
import os
from glob import glob
from multiprocessing import Pool
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path import dirname as dir
path.insert(0, dir(dir(path[0])))
__package__ = ... | 13,449 | 43.389439 | 136 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/make_image_timeseries_for_labels.py | """
For a set of extracted image crops and a labelled_dense label map, make a timeseries of all positions matched with labels
"""
import argparse
import pandas as pd
import numpy as np
import os
import shutil
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path impo... | 6,723 | 35.150538 | 169 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/SS/extract_images_for_parcel_labels.py | """
Given a set of S2 tiles and a labelled_dense lable map, extract crops of images matching the location of labels
"""
import argparse
import pandas as pd
import rasterio
import numpy as np
import os
from glob import glob
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
fro... | 8,998 | 37.95671 | 128 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/SS/extract_parcel_labels_raster.py | import argparse
import pandas as pd
import numpy as np
from shapely import geometry
import os
from glob import glob
from multiprocessing import Pool
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path import dirname as dir
path.insert(0, dir(dir(path[0])))
__package__ = ... | 18,582 | 38.122105 | 135 | py |
DeepSatData | DeepSatData-main/dataset/labelled_dense/SS/__init__.py | 0 | 0 | 0 | py | |
DeepSatData | DeepSatData-main/dataset/labelled_dense/SS/make_image_timeseries_for_parcel_labels.py | """
For a set of extracted image crops and a labelled_dense label map, make a timeseries of all positions matched with labels
"""
import argparse
import pandas as pd
import numpy as np
import os
import shutil
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path impo... | 8,698 | 37.321586 | 129 | py |
DeepSatData | DeepSatData-main/dataset/France_RPG/RPG2DF.py | import argparse
import shapefile
from shapely import geometry
import pandas as pd
import os
def main():
args = parser.parse_args()
rpg_file = os.path.join(args.rpg_dir, 'PARCELLES_GRAPHIQUES')
sf = shapefile.Reader(rpg_file)
year = args.rpg_dir.split("-")[-1]
# print(year)
data = []
for... | 1,807 | 30.719298 | 103 | py |
DeepSatData | DeepSatData-main/dataset/France_RPG/exploreRPG_labels.py | 0 | 0 | 0 | py | |
DeepSatData | DeepSatData-main/dataset/unlabelled/extract_images.py | """
Given a directory of Sentinel tiles extract crops of images
"""
import argparse
import pandas as pd
import rasterio
import numpy as np
import os
from glob import glob
import pickle
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path import dirname as dir
path.insert(0, d... | 6,313 | 35.49711 | 119 | py |
DeepSatData | DeepSatData-main/dataset/unlabelled/make_image_timeseries.py | """
For a set of extracted image crops, make a timeseries for all locations
"""
import argparse
import pandas as pd
import numpy as np
import os
import shutil
import pickle
from multiprocessing import Pool
if __name__ == "__main__" and __package__ is None:
from sys import path
from os.path import dirname as dir... | 6,021 | 36.17284 | 120 | py |
DeepSatData | DeepSatData-main/dataset/unlabelled/__init__.py | 0 | 0 | 0 | py | |
DeepSatData | DeepSatData-main/utils/geospatial_data_utils.py | import matplotlib.pyplot as plt
import numpy as np
from shapely import geometry
from shapely.geometry import Polygon
from pyproj import Proj, transform
import re
from simplification.cutil import simplify_coords
from sentinelsat import geojson_to_wkt
class GeoTransform:
def __init__(self, intr, outtr, loc2loc=Fal... | 8,344 | 28.487633 | 124 | py |
DeepSatData | DeepSatData-main/utils/multiprocessing_utils.py | from multiprocessing import Pool
def flatten_list(l):
return [item for sublist in l for item in sublist]
def run_pool(x, f, num_cores, split=False):
if not split:
x = split_num_segments(x, num_cores)
print(len(x))
# x = [[x_, i] for i, x_ in enumerate(x)]
pool = Pool(num_cores)
res =... | 1,176 | 25.155556 | 80 | py |
DeepSatData | DeepSatData-main/utils/data_utils.py | import re
import os
import zipfile
def unzip_all(dir_name, extension=".zip"):
for item in os.listdir(dir_name):
if item.endswith(extension):
file_name = os.path.join(dir_name, item)
zip_ref = zipfile.ZipFile(file_name)
zip_ref.extractall(dir_name)
zip_ref.cl... | 494 | 22.571429 | 52 | py |
DeepSatData | DeepSatData-main/utils/sentinel_products_utils.py | import os
from glob import glob
import pandas as pd
import rasterio
def get_S2prod_info(imdirs):
data = []
for imdir in imdirs:
imname = "%s_%s" % (imdir.split("/")[-2].split("_")[-3], imdir.split("/")[-4].split("_")[-5])
f = rasterio.open("%s/%s_B02.jp2" % (imdir, imname))
tile_transf... | 1,920 | 40.76087 | 123 | py |
DeepSatData | DeepSatData-main/utils/__init__.py | 0 | 0 | 0 | py | |
DeepSatData | DeepSatData-main/utils/date_utils.py | import datetime
import os
from glob import glob
import pandas as pd
def get_doy(date):
Y = date[:4]
m = date[4:6]
d = date[6:]
date = "%s.%s.%s" % (Y, m, d)
dt = datetime.datetime.strptime(date, '%Y.%m.%d')
return dt.timetuple().tm_yday
def get_date(day):
"""
:param day: day of the y... | 2,077 | 22.348315 | 71 | py |
DeepSatData | DeepSatData-main/download/sentinelsat_download_tileid.py | # spatial data processing pipelines
import argparse
import pandas as pd
from sentinelsat import SentinelAPI # , read_geojson, geojson_to_wkt
import os
from glob import glob
from collections import OrderedDict
# USER INPUT -----------------------------------------------------------------------------------------------... | 2,015 | 38.529412 | 123 | py |
DeepSatData | DeepSatData-main/download/__init__.py | 0 | 0 | 0 | py | |
deepnl | deepnl-master/setup.py |
try:
from setuptools import setup, Extension
except ImportError:
from distutils.core import setup, Extension
from Cython.Build import cythonize
import numpy as np
import glob
def readme():
with open('README.rst') as f:
text = f.read()
return text
extensions = [
Extension('deepnl/words',
... | 2,309 | 27.875 | 70 | py |
deepnl | deepnl-master/bin/toIOB.py | #!/usr/bin/python
"""
Upgrade to new IOB convention: Inside, Outside, Begin.
"""
# O I -> O B
# I B -> I B
# I I -> I I
# I O -> I O
from __future__ import print_function
import sys
import getopt
def usage():
print('usage:', sys.argv[0], '[-hr] < inFile ')
print(' -r revert to old convention.')
sys.ex... | 1,253 | 20.62069 | 75 | py |
deepnl | deepnl-master/bin/knn.py | #! /usr/bin/env python
"""
Show the knn words in the embeddings to a given word.
Usage:
./knn.py embeddings vocabulary
Options:
-h, --help : display this help and exit
"""
## Required
#
# sudo apt-get install build-essential python-dev python-numpy python-setuptools python-scipy libatlas-dev libatl... | 8,288 | 30.044944 | 141 | py |
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