python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
aristo-leaderboard-master | eqasc/code/allennlp_reasoning_explainqa/common/__init__.py | |
#!/usr/bin/env python3
# % cat testfiles-5/predictions.tsv | sort | python3 explainer.py
# In paragraph 4, sentence 2, the participant "plants" is moved from an unknown location to sediment
# In paragraph 4, sentence 3, the participant "bacteria" is moved from an unknown location to sediment
# In paragraph 4, sentence... | aristo-leaderboard-master | propara/evaluator/explainer.py |
aristo-leaderboard-master | propara/evaluator/__init__.py | |
#!/usr/bin/env python3
import argparse
import json
from typing import Dict
from evaluation import Evaluation
from process import sentences_from_sentences_file, ActionFile
from scoring import QuestionScores
from errors import corrupted_action_file, corrupted_sentences_file
def main(answers_file: str, predictions_fil... | aristo-leaderboard-master | propara/evaluator/evaluator.py |
from typing import List, NamedTuple, Callable, TypeVar, Optional
from evaluation.metric import Metric
from text import terms
from process import ProcessSummary, Conversion, Move, Input, Output
# Question types used in functions here
QType = TypeVar("QType", Input, Output, Conversion, Move)
class QuestionScores(Name... | aristo-leaderboard-master | propara/evaluator/scoring/question.py |
from scoring.question import QuestionScores
| aristo-leaderboard-master | propara/evaluator/scoring/__init__.py |
import unittest
from process import ProcessSummary, Conversion, Move, Input, Output
from scoring import question, QuestionScores
class TestScoring(unittest.TestCase):
def test_compare_locations(self):
self.assertEquals(question._compare_locations('', ''), 1.0)
self.assertEquals(question._compare... | aristo-leaderboard-master | propara/evaluator/scoring/test_scoring.py |
from typing import Dict, NamedTuple, Iterable
from evaluation.metric import Metric
class EvaluationAverages(NamedTuple):
inputs: float
outputs: float
conversions: float
moves: float
overall: float
class Evaluation:
def __init__(self, scores: Dict[int, "QuestionScores"]) -> None: # type: ig... | aristo-leaderboard-master | propara/evaluator/evaluation/evaluation.py |
from evaluation.metric import Metric
from evaluation.evaluation import Evaluation
| aristo-leaderboard-master | propara/evaluator/evaluation/__init__.py |
from typing import Dict, NamedTuple
class Metric(NamedTuple):
precision: float
recall: float
def F1(self):
if self.precision + self.recall == 0:
return 0.0
return 2 * self.precision * self.recall / (self.precision + self.recall)
def diagnostics(self) -> Dict[str, float]:... | aristo-leaderboard-master | propara/evaluator/evaluation/metric.py |
from typing import List, Set
from text.stemmer import PorterStemmer
# Extract term sets from a phrase containing " AND " and " OR " tokens. A phrase like "foo OR bar AND fnord OR gnarf"
# is turned into a list of term sets like [{"foo", "bar"}, {"fnord", "gnarf"}] to match to another phrase's term sets.
def extract_... | aristo-leaderboard-master | propara/evaluator/text/terms.py |
aristo-leaderboard-master | propara/evaluator/text/__init__.py | |
import unittest
from text import terms
class TestTerms(unittest.TestCase):
def test_extract_termsets(self):
# one term
self.assertEqual(terms.extract_termsets("dew"), [{'dew'}])
# one term with a word that should not be stemmed
self.assertEqual(terms.extract_termsets("raining"),... | aristo-leaderboard-master | propara/evaluator/text/test_terms.py |
"""
This was copied from the NLTK source:
https://github.com/nltk/nltk/blob/7e06fcb2be41a7dbc23bf0b4f666aef7b915d402/nltk/stem/porter.py
It was modified slightly to run outside NLTK.
"""
"""
Porter Stemmer
This is the Porter stemming algorithm. It follows the algorithm
presented in
Porter, M. "An algorithm for... | aristo-leaderboard-master | propara/evaluator/text/stemmer.py |
from errors.errors import corrupted_action_file, corrupted_sentences_file
| aristo-leaderboard-master | propara/evaluator/errors/__init__.py |
import sys
def corrupted_action_file(filename: str, details: str, line_num: int = None):
if line_num is None:
print(f"Corrupted or empty action file {filename} ({details})")
else:
print(f"Corrupted action file {filename} on line {line_num} ({details})")
sys.exit(2)
def corrupted_sentence... | aristo-leaderboard-master | propara/evaluator/errors/errors.py |
# Locations
NO_LOCATION = 'null' # This location is used of a participant that doesn't exist (was destroyed, or not yet created)
LOCATION_UNKNOWN = 'unk'
# Actions
NO_ACTION = 'NONE'
MOVE = 'MOVE'
CREATE = 'CREATE'
DESTROY = 'DESTROY'
| aristo-leaderboard-master | propara/evaluator/process/constants.py |
from process.process import Process, Conversion, Move, Input, Output
from process.summary import ProcessSummary
from process.action_file import ActionFile
from process.sentence_file import sentences_from_sentences_file
| aristo-leaderboard-master | propara/evaluator/process/__init__.py |
from collections import defaultdict
from typing import Dict, List, Tuple
def sentences_from_sentences_file(sentences_filename: str) -> Dict[int, List[str]]:
all_sentences = dict() # type: Dict[Tuple[int, int], str]
with open(sentences_filename) as f:
for line in f:
process_id_str, sentenc... | aristo-leaderboard-master | propara/evaluator/process/sentence_file.py |
from typing import Dict, List, NamedTuple
from process.process import Conversion, Move, Input, Output
class ProcessSummary(NamedTuple):
process_id: int
inputs: List[Input]
outputs: List[Output]
conversions: List[Conversion]
moves: List[Move]
def __repr__(self):
return f"Process {self... | aristo-leaderboard-master | propara/evaluator/process/summary.py |
import unittest
from collections import OrderedDict
from process import process, Process, Conversion, Move, Input, Output
from process.constants import NO_ACTION as NO_ACT, NO_LOCATION as NO_LOC, CREATE, DESTROY, MOVE
class TestProcess(unittest.TestCase):
def test_qa(self):
p = Process(
proc... | aristo-leaderboard-master | propara/evaluator/process/test_process.py |
from collections import OrderedDict, defaultdict
from typing import NamedTuple, Dict, List
from errors import corrupted_action_file
from process.constants import LOCATION_UNKNOWN, NO_LOCATION, NO_ACTION, CREATE, MOVE, DESTROY
from process import ProcessSummary, Process
def _accumulate_action(locations, actions, num_... | aristo-leaderboard-master | propara/evaluator/process/action_file.py |
from typing import List, NamedTuple, Dict
from process.constants import NO_LOCATION, CREATE, DESTROY, MOVE
class Input(NamedTuple):
participants: str
class Output(NamedTuple):
participants: str
class Conversion(NamedTuple):
created: str
destroyed: str
locations: str
step_id: str
class M... | aristo-leaderboard-master | propara/evaluator/process/process.py |
import unittest
from collections import OrderedDict
from process.action_file import ActionFile
from process.constants import NO_ACTION as NO_ACT
from process.constants import NO_LOCATION as NO_LOC, CREATE, DESTROY, MOVE
class TestSummarize(unittest.TestCase):
def test_load(self):
# Spot-check values load... | aristo-leaderboard-master | propara/evaluator/process/test_action_file.py |
import os
import evaluator
import unittest
import tempfile
import typing
class TestAccuracy(unittest.TestCase):
def test_EverythingCorrect(self):
qa = {"Q1": "A", "Q2": "A", "Q3": "A"}
p = {"Q1": ["A"], "Q2": ["A"], "Q3": ["A"]}
self.assertEqual(3.0 / 3.0, evaluator.calculate_accuracy(qa... | aristo-leaderboard-master | openbookqa/evaluator/test_evaluator.py |
#!/usr/bin/env python3
import csv
from typing import *
import logging
import sys
import json
EXIT_STATUS_ANSWERS_MALFORMED = 1
EXIT_STATUS_PREDICTIONS_MALFORMED = 2
EXIT_STATUS_PREDICTIONS_EXTRA = 3
EXIT_STATUS_PREDICTION_MISSING = 4
def calculate_accuracy(question_answers: Dict[str, str], predictions: Dict[str, Li... | aristo-leaderboard-master | openbookqa/evaluator/evaluator.py |
import os
import evaluator
import unittest
import tempfile
import typing
class TestAccuracy(unittest.TestCase):
def test_EverythingCorrect(self):
qa = {"Q1": "A", "Q2": "A", "Q3": "A"}
p = {"Q1": ["A"], "Q2": ["A"], "Q3": ["A"]}
self.assertEqual(3.0 / 3.0, evaluator.calculate_accuracy(qa... | aristo-leaderboard-master | qasc/evaluator/test_evaluator.py |
#!/usr/bin/env python3
import csv
from typing import *
import logging
import sys
import json
EXIT_STATUS_ANSWERS_MALFORMED = 1
EXIT_STATUS_PREDICTIONS_MALFORMED = 2
EXIT_STATUS_PREDICTIONS_EXTRA = 3
EXIT_STATUS_PREDICTION_MISSING = 4
def calculate_accuracy(question_answers: Dict[str, str], predictions: Dict[str, Li... | aristo-leaderboard-master | qasc/evaluator/evaluator.py |
import os
import evaluator
import unittest
import tempfile
import typing
class TestAccuracy(unittest.TestCase):
def test_EverythingCorrect(self):
qa = {"Q1": "A", "Q2": "A", "Q3": "A"}
p = {"Q1": ["A"], "Q2": ["A"], "Q3": ["A"]}
self.assertEqual(3.0 / 3.0, evaluator.calculate_accuracy(qa... | aristo-leaderboard-master | arc/evaluator/test_evaluator.py |
#!/usr/bin/env python3
import csv
from typing import *
import logging
import sys
import json
EXIT_STATUS_ANSWERS_MALFORMED = 1
EXIT_STATUS_PREDICTIONS_MALFORMED = 2
EXIT_STATUS_PREDICTIONS_EXTRA = 3
EXIT_STATUS_PREDICTION_MISSING = 4
def calculate_accuracy(question_answers: Dict[str, str], predictions: Dict[str, Li... | aristo-leaderboard-master | arc/evaluator/evaluator.py |
import ast
import hashlib
import json
import os
from collections import defaultdict
from typing import Tuple, Sequence, Dict, Optional, Union, Any, Set
import compress_pickle
import matplotlib.pyplot as plt
import numpy as np
import pandas
import pandas as pd
from filelock import FileLock
from allenact.utils.misc_uti... | advisor-main | summarization_utils.py |
"""Defining the PPO loss for actor critic type models."""
import abc
import math
from typing import Dict, Union, Optional, Tuple, cast, Callable
import numpy as np
import torch
import torch.nn.functional as F
from stable_baselines3.common.running_mean_std import RunningMeanStd
from allenact.algorithms.offpolicy_sync.... | advisor-main | advisor_losses.py |
import os
from pathlib import Path
MINIGRID_EXPERT_TRAJECTORIES_DIR = os.path.abspath(
os.path.join(os.path.dirname(Path(__file__)), "minigrid_data", "minigrid_demos")
)
MINIGRID_ENV_NAMES_SUPPORTED = (
"CrossingS25N10", # LavaCrossing (S25, N10)
"WallCrossingS25N10", # WallCrossing (S25, N10)
"AskFo... | advisor-main | minigrid_constants.py |
import os
from pathlib import Path
import matplotlib.pyplot as plt
from allenact.utils.misc_utils import TABLEAU10_RGB
ADVISOR_TOP_LEVEL_DIR = os.path.abspath(os.path.dirname(Path(__file__)))
NICE_COLORS12_RGB = TABLEAU10_RGB + (
(31, 119, 180),
(255, 127, 14),
(44, 160, 44),
(214, 39, 40),
(148... | advisor-main | advisor_constants.py |
from typing import Callable, Dict, Optional, Any, cast
import gym
import numpy as np
import torch
from gym.spaces.dict import Dict as SpaceDict
from torch import nn, autograd
from allenact.algorithms.onpolicy_sync.policy import ActorCriticModel
from allenact.base_abstractions.distributions import CategoricalDistr
fro... | advisor-main | gail_models.py |
import glob
import json
import math
import os
import shutil
import time
from typing import Optional
import torch
import torch.multiprocessing as mp
from allenact.algorithms.onpolicy_sync.runner import OnPolicyRunner
from allenact.main import get_args, init_logging, load_config
from allenact_plugins.lighthouse_plugin.... | advisor-main | lighthouse_scripts/save_pairwise_imitation_data.py |
import copy
import glob
import os
import sys
from collections import defaultdict
from typing import Dict, Optional, Tuple, Union, Sequence
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from statsmodels.stats.proportion import proportion_confint
from advisor_constants import ADVISOR_TOP_LEVEL_... | advisor-main | lighthouse_scripts/summarize_pairwise_imitation_data.py |
advisor-main | lighthouse_scripts/__init__.py | |
import glob
import json
import os
import traceback
import warnings
from collections import defaultdict
from typing import Dict, Tuple, List
import matplotlib.pyplot as plt
import pandas as pd
from tensorflow.python.framework.errors_impl import DataLossError
from tensorflow.python.summary.summary_iterator import summar... | advisor-main | lighthouse_scripts/summarize_pairwise_imitation_train_curves.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseBC(BaseLightHouseExperimentConfig):
"""Find goal in lighthouse env using imitation learning.
Training with Imitation.
"""
def tag(self):
... | advisor-main | lighthouse_experiments/bc.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseBCThenPPO(BaseLightHouseExperimentConfig):
"""Dagger then ppo."""
def tag(self):
return "LightHouseBCThenPPO"
def training_pipeline(se... | advisor-main | lighthouse_experiments/bc_then_ppo.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseDagger(BaseLightHouseExperimentConfig):
"""Find goal in lighthouse env using imitation learning.
Training with Dagger.
"""
de... | advisor-main | lighthouse_experiments/dagger.py |
advisor-main | lighthouse_experiments/__init__.py | |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseBCTeacherForcing(BaseLightHouseExperimentConfig):
"""Find goal in lighthouse env using imitation learning.
Training with Imitation.
... | advisor-main | lighthouse_experiments/bc_teacher_forcing.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseImitationAndPPO(BaseLightHouseExperimentConfig):
"""Dagger then ppo."""
def tag(self):
return "LightHouseImitationAndPPO"
... | advisor-main | lighthouse_experiments/dagger_then_ppo.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseBCTeacherForcingThenPPO(BaseLightHouseExperimentConfig):
"""Find goal in lighthouse env using imitation learning.
Training with Imitat... | advisor-main | lighthouse_experiments/bc_teacher_forcing_then_ppo.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHousePPO(BaseLightHouseExperimentConfig):
"""PPO only."""
def tag(self):
return "LightHousePPO"
def training_pipeline(self, **kwargs):
... | advisor-main | lighthouse_experiments/ppo.py |
from torch import nn
from advisor_losses import AdvisorWeightedStage
from allenact.base_abstractions.sensor import SensorSuite
from allenact.embodiedai.models.basic_models import RNNActorCritic
from allenact.utils.experiment_utils import PipelineStage
from allenact_plugins.lighthouse_plugin.lighthouse_models import (
... | advisor-main | lighthouse_experiments/advisor_ppo.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseA2C(BaseLightHouseExperimentConfig):
"""A2C only."""
def tag(self):
return "LightHouseA2C"
def training_pipeline(self, **kwargs):
... | advisor-main | lighthouse_experiments/a2c.py |
from advisor_losses import AdvisorWeightedStage
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.lighthouse_experiments.advisor_ppo import LightHouseAdvisorPPO
class LightHouseAdvisorA2C(LightHouseAdvisorPPO):
"""A2C and Imitation with adaptive reweighting."""
def tag(self):
... | advisor-main | lighthouse_experiments/advisor_a2c.py |
import math
from abc import ABC
from typing import Dict, Any, List, Optional, Tuple, Union, NamedTuple
import gym
import torch
import torch.nn as nn
from torch import optim
from torch.optim.lr_scheduler import LambdaLR
from allenact.algorithms.onpolicy_sync.losses import PPO, A2C
from allenact.algorithms.onpolicy_syn... | advisor-main | lighthouse_experiments/base.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.lighthouse_experiments.base import BaseLightHouseExperimentConfig
class LightHouseBCAndPPO(BaseLightHouseExperimentConfig):
"""PPO and Imitation jointly."""
def tag(self):
return "LightHouseBCAndPPO"
def training_pip... | advisor-main | lighthouse_experiments/bc_and_ppo.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdBC(BaseExperimentConfig):
"""Training with behavior cloning."""
def __init__(self, task_name: str, **kwargs):
super().__init__(task_name=task_name, US... | advisor-main | minigrid_and_pd_experiments/bc.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdBCThenPPO(BaseExperimentConfig):
"""Training with behavior cloning and then PPO."""
def __init__(self, task_name: str, **kwargs):
super().__init__(tas... | advisor-main | minigrid_and_pd_experiments/bc_then_ppo.py |
from advisor_losses import (
AdvisorImitationStage,
AdvisorWeightedStage,
)
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdBCTeacherForcingThenAdvisor(BaseExperimentConfig):
"""Training wit... | advisor-main | minigrid_and_pd_experiments/bc_teacher_forcing_then_advisor.py |
from allenact.utils.experiment_utils import PipelineStage, OffPolicyPipelineComponent
from allenact_plugins.minigrid_plugin.minigrid_offpolicy import (
MiniGridOffPolicyExpertCELoss,
)
from poisoneddoors_plugin.poisoneddoors_offpolicy import (
PoisonedDoorsOffPolicyExpertCELoss,
)
from projects.advisor.minigrid... | advisor-main | minigrid_and_pd_experiments/pure_offpolicy.py |
from advisor_losses import AdvisorWeightedStage
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdAdvisor(BaseExperimentConfig):
"""Training with adaptive reweighing."""
def __init__(self, task_name: str, **k... | advisor-main | minigrid_and_pd_experiments/advisor.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdBCThenPPO(BaseExperimentConfig):
"""Training with behavior cloning and then PPO."""
def __init__(self, task_name: str, **kwargs):
super().__init__(tas... | advisor-main | minigrid_and_pd_experiments/bc_with_ppo.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdDagger(BaseExperimentConfig):
"""Training with DAgger."""
def __init__(self, task_name: str, **kwargs):
super().__init__(task_name=task_n... | advisor-main | minigrid_and_pd_experiments/dagger.py |
advisor-main | minigrid_and_pd_experiments/__init__.py | |
from advisor_losses import (
AdvisorImitationStage,
AdvisorWeightedStage,
)
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdDaggerThenAdvisor(BaseExperimentConfig):
"""Training with DAgger f... | advisor-main | minigrid_and_pd_experiments/dagger_then_advisor.py |
from advisor_losses import MiniGridOffPolicyAdvisorLoss
from allenact.utils.experiment_utils import PipelineStage, OffPolicyPipelineComponent
from poisoneddoors_plugin.poisoneddoors_offpolicy import (
PoisonedDoorsOffPolicyAdvisorLoss,
)
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentCo... | advisor-main | minigrid_and_pd_experiments/ppo_with_offpolicy_advisor.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdBCTeacherForcing(BaseExperimentConfig):
"""Training with behavior cloning with teacher forcing of 1."""
def __init__(self, task_name: str, **kwar... | advisor-main | minigrid_and_pd_experiments/bc_teacher_forcing.py |
from allenact.utils.experiment_utils import PipelineStage, OffPolicyPipelineComponent
from allenact_plugins.minigrid_plugin.minigrid_offpolicy import (
MiniGridOffPolicyExpertCELoss,
)
from poisoneddoors_plugin.poisoneddoors_offpolicy import (
PoisonedDoorsOffPolicyExpertCELoss,
)
from projects.advisor.minigrid... | advisor-main | minigrid_and_pd_experiments/ppo_with_offpolicy.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdDaggerThenPPO(BaseExperimentConfig):
"""Training with DAgger and then PPO."""
def __init__(self, task_name: str, **kwargs):
super().__ini... | advisor-main | minigrid_and_pd_experiments/dagger_then_ppo.py |
from allenact.utils.experiment_utils import PipelineStage, LinearDecay
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdBCThenPPO(BaseExperimentConfig):
"""Training with behavior cloning (teacher forcing of 1) and then PPO."""
def __init__(self, task_name: str, **k... | advisor-main | minigrid_and_pd_experiments/bc_teacher_forcing_then_ppo.py |
from allenact.utils.experiment_utils import PipelineStage
from projects.advisor.minigrid_and_pd_experiments.base import BaseExperimentConfig
class MgPdPPO(BaseExperimentConfig):
"""Training with PPO."""
def __init__(self, task_name: str, **kwargs):
super().__init__(task_name=task_name, USE_EXPERT=Fal... | advisor-main | minigrid_and_pd_experiments/ppo.py |
from typing import cast
import gym
from torch import nn
from advisor_losses import GAILDiscriminatorLoss, GAILPPO
from allenact.algorithms.onpolicy_sync.losses.ppo import PPOConfig
from allenact.base_abstractions.sensor import SensorSuite
from allenact.embodiedai.models.basic_models import LinearActorCritic
from alle... | advisor-main | minigrid_and_pd_experiments/gail.py |
import abc
import math
import os
from typing import (
Optional,
List,
Any,
Dict,
cast,
Sequence,
Callable,
Union,
NamedTuple,
)
import gym
import torch
from gym_minigrid.minigrid import Lava, WorldObj, Wall
from torch import nn, optim
from torch.optim.lr_scheduler import LambdaLR
f... | advisor-main | minigrid_and_pd_experiments/base.py |
import typing
from typing import Dict, Union, Tuple, Iterator, Any
from typing import Optional
import numpy as np
import torch
from gym.utils import seeding
from advisor_losses import AlphaScheduler, AdvisorWeightedStage
from allenact.algorithms.offpolicy_sync.losses.abstract_offpolicy_loss import (
AbstractOffPo... | advisor-main | poisoneddoors_plugin/poisoneddoors_offpolicy.py |
import typing
from typing import Dict, Tuple, Any, Union
import gym
import torch
import torch.nn as nn
from gym.spaces.dict import Dict as SpaceDict
from allenact.base_abstractions.misc import ActorCriticOutput, DistributionType, Memory
from allenact.embodiedai.models.basic_models import RNNActorCritic, LinearActorCr... | advisor-main | poisoneddoors_plugin/poisoneddoors_models.py |
from typing import Optional, Any
import gym
import numpy as np
from allenact.base_abstractions.sensor import Sensor
from allenact.utils.misc_utils import prepare_locals_for_super
from poisoneddoors_plugin.poisoneddoors_tasks import (
PoisonedDoorsEnvironment,
PoisonedDoorsTask,
PoisonedEnvStates,
)
clas... | advisor-main | poisoneddoors_plugin/poisoneddoors_sensors.py |
advisor-main | poisoneddoors_plugin/__init__.py | |
import random
from enum import Enum
from typing import Any, Tuple, Union, List, Optional, Dict
import gym
import numpy as np
from gym.utils import seeding
from allenact.base_abstractions.misc import RLStepResult
from allenact.base_abstractions.sensor import SensorSuite, Sensor
from allenact.base_abstractions.task imp... | advisor-main | poisoneddoors_plugin/poisoneddoors_tasks.py |
import argparse
import glob
import multiprocessing as mp
import os
mp = mp.get_context("forkserver")
from projects.advisor.summarization_utils import create_comparison_hp_plots_from_tsv
def get_argument_parser():
"""Creates the argument parser."""
# noinspection PyTypeChecker
parser = argparse.Argument... | advisor-main | minigrid_and_pd_scripts/summarize_random_hp_search.py |
import json
import os
import time
import typing
import babyai
import blosc
import torch
import torch.multiprocessing as mp
from tqdm import tqdm
from allenact.main import load_config, get_argument_parser
from allenact.utils.misc_utils import partition_sequence
from allenact.utils.system import get_logger
from allenac... | advisor-main | minigrid_and_pd_scripts/save_expert_demos.py |
advisor-main | minigrid_and_pd_scripts/__init__.py | |
import json
import os
from typing import cast, Dict
import tqdm
from advisor_constants import ADVISOR_TOP_LEVEL_DIR
from allenact.main import get_argument_parser, load_config
from allenact.utils.experiment_utils import set_seed, ScalarMeanTracker
from minigrid_and_pd_experiments.base import BaseExperimentConfig
TASK... | advisor-main | minigrid_and_pd_scripts/compute_random_performance_for_task.py |
import itertools
import json
import math
import os
import queue
import time
import warnings
from typing import Dict, List, Optional, Any
import canonicaljson
import torch
import torch.multiprocessing as mp
from allenact.algorithms.onpolicy_sync.runner import OnPolicyRunner
from allenact.main import init_logging, load... | advisor-main | minigrid_and_pd_scripts/random_hp_search.py |
from allenact_plugins.babyai_plugin.scripts.truncate_expert_demos import (
make_small_demos,
)
from projects.advisor.minigrid_constants import MINIGRID_EXPERT_TRAJECTORIES_DIR
if __name__ == "__main__":
make_small_demos(MINIGRID_EXPERT_TRAJECTORIES_DIR)
| advisor-main | minigrid_and_pd_scripts/make_small_demos.py |
import abc
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from advisor_losses import AdvisorWeightedStage
from allenact.algorithms.onpolicy_sync.losses import PPO
from allenact.algorithms.onpolicy_sync.losses.ppo import PPOConfig
from allenact.utils.experiment_utils import (
... | advisor-main | objectnav_experiments/objectnav_mixin_advisor.py |
advisor-main | objectnav_experiments/__init__.py | |
from abc import ABC
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from allenact.algorithms.onpolicy_sync.losses import PPO
from allenact.algorithms.onpolicy_sync.losses.imitation import Imitation
from allenact.algorithms.onpolicy_sync.losses.ppo import PPOConfig
from allenact.... | advisor-main | objectnav_experiments/objectnav_mixin_bcwithppo.py |
from abc import ABC
from typing import Sequence, Union
import gym
import torch.nn as nn
from torchvision import models
from allenact.base_abstractions.preprocessor import Preprocessor
from allenact.embodiedai.preprocessors.resnet import ResNetPreprocessor
from allenact.embodiedai.sensors.vision_sensors import RGBSens... | advisor-main | objectnav_experiments/objectnav_mixin_resnetgru_with_aux_head.py |
advisor-main | objectnav_experiments/robothor/__init__.py | |
from typing import Optional
from allenact.base_abstractions.sensor import ExpertActionSensor
from allenact_plugins.ithor_plugin.ithor_sensors import (
RGBSensorThor,
GoalObjectTypeThorSensor,
)
from allenact_plugins.robothor_plugin.robothor_tasks import ObjectNavTask
from objectnav_experiments.objectnav_mixin_... | advisor-main | objectnav_experiments/robothor/objectnav_robothor_rgb_resnetgru_advisor.py |
from allenact.base_abstractions.sensor import ExpertActionSensor
from allenact_plugins.ithor_plugin.ithor_sensors import (
RGBSensorThor,
GoalObjectTypeThorSensor,
)
from allenact_plugins.robothor_plugin.robothor_tasks import ObjectNavTask
from objectnav_experiments.objectnav_mixin_bcwithppo import (
Object... | advisor-main | objectnav_experiments/robothor/objectnav_robothor_rgb_resnetgru_bcwithppo.py |
from setuptools import setup, find_packages
setup(name='comet',
version='1.0',
description='Codebase for releasing comet model code',
# url='http://github.com/storborg/funniest',
author='Antoine Bosselut',
author_email='antoineb@allenai.org',
license='MIT',
packages=find_packa... | comet-public-master | setup.py |
comet-public-master | comet/__init__.py | |
import json
import copy
import torch
import numpy as np
import contextlib
from distutils.dir_util import mkpath
from tqdm import tqdm
def make_new_tensor_from_list(items, device_num, dtype=torch.float32):
if device_num is not None:
device = torch.device("cuda:{}".format(device_num))
else:
... | comet-public-master | comet/utils.py |
from comet.models.gpt import (LMModel, DEFAULT_CONFIG, load_openai_pretrained_model)
import torch.nn as nn
def make_model(opt, n_vocab, n_ctx, n_special, load=True,
return_acts=True, return_probs=False,
clf_token="<CLASS>", answer_size=None):
print(n_ctx)
if opt.exp == "generatio... | comet-public-master | comet/models/models.py |
comet-public-master | comet/models/__init__.py | |
import torch
def prepare_position_embeddings(opt, encoder_vocab, sequences):
vocab_size = len(encoder_vocab)
num_positions = sequences.size(-2)
position_embeddings = torch.LongTensor(
range(vocab_size, vocab_size + num_positions)).to(sequences.device)
sequences = sequences.repeat(1, 1, 2)
... | comet-public-master | comet/models/utils.py |
import copy
import json
import math
import re
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
'''
Much of this code is taken from HuggingFace's OpenAI LM Implementation here:
https://github.com/huggingface/pytorch-openai-transformer-lm
'... | comet-public-master | comet/models/gpt.py |
import comet.train.batch as batch
import comet.evaluate.evaluate as base_evaluate
import numpy as np
def make_evaluator(opt, *args):
if opt.exp == "generation":
return AtomicGenerationEvaluator(opt, *args)
else:
return AtomicClassificationEvaluator(opt, *args)
class AtomicGenerationEvaluator(... | comet-public-master | comet/evaluate/atomic_evaluate.py |
import comet.data.data as data
import comet.data.config as cfg
import comet.evaluate.sampler as sampling
def do_gen_run(opt, generator, l, split="dev", scores={}):
# Generate sequences for examples in evaluation set using
# current trained model
if opt.eval.gs == "full":
sequences, avg_scores, in... | comet-public-master | comet/evaluate/generate.py |
import time
import torch
import comet.evaluate.generate as base_generate
import comet.evaluate.sampler as sampling
import comet.utils as utils
import comet.data.config as cfg
def make_generator(opt, *args):
return ConceptNetGenerator(opt, *args)
class ConceptNetGenerator(base_generate.Generator):
def __ini... | comet-public-master | comet/evaluate/conceptnet_generate.py |
comet-public-master | comet/evaluate/__init__.py | |
def update_classification_losses(losses, nums, name, bs, loss):
if not isinstance(loss, float):
print(type(loss))
raise
nums[name] += bs
losses[name] += loss * bs
def update_generation_losses(losses, nums, micro, macro, bs, length, loss):
# Update Losses
nums[macro] += bs
i... | comet-public-master | comet/evaluate/utils.py |
import time
import numpy as np
import comet.train.batch as batch_utils
import comet.utils as utils
import comet.evaluate.evaluate as base_evaluate
def make_evaluator(opt, *args, **kwargs):
return ConceptNetGenerationEvaluator(opt, *args, **kwargs)
class ConceptNetGenerationEvaluator(base_evaluate.Evaluator):
... | comet-public-master | comet/evaluate/conceptnet_evaluate.py |
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