repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/main.py | import os
import gym
import torch
import datetime
import random
import numpy as np
from configs import get_common_args
from runner import Runner
from hierarchical_runner import HierRunner
from spectral_DPP_agent.laprepr import LapReprLearner
from utils.env_wrapper import EnvWrapper
import robo_env
def main():
# pr... | 2,149 | 31.575758 | 102 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/hierarchical_runner.py | import os
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from configs import get_hierarchical_args
from option_agent.hierarchical_policy import HierPolicy
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from runner import Runner
class HierRunner(object):
def __ini... | 15,987 | 51.592105 | 165 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/runner.py | import os
import torch
from tqdm import tqdm
import numpy as np
from configs import get_rl_args
from option_agent import REGISTRY as agent_REGISTRY
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from spectral_DPP_agent.laprepr import LapReprLearner
from visualization.draw_trajectory import draw_traj
class... | 9,374 | 48.08377 | 148 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/hierarchical_policy.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, value, policy
from utils.summary_tools import write_summary
def get_final_state_value(args, target_v, horizons):
target_v_array = targ... | 15,360 | 47.153605 | 197 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/VALOR.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class VALOR_Agent... | 10,865 | 52.527094 | 167 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/VIC.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, policy, value, decoder
from utils.summary_tools import write_summary, write_hist
from option_agent.base_option_agent import Option_Agent
cl... | 14,632 | 52.99631 | 172 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/DCO.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from option_agent.hierarchical_po... | 12,935 | 50.130435 | 180 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/base_option_agent.py | import os
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from utils.summary_tools import write_summary
from option_agent.hierarchical_policy import get_final_state_value, get_return_array, get_advantage
class Option_Agent(object):
def __init__(self, args):
... | 10,002 | 50.035714 | 156 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/ODPP.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder, prior
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from spectral... | 20,175 | 52.659574 | 164 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/option_agent/DIAYN.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class DIAYN_Agent(Op... | 10,567 | 53.756477 | 183 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/utils/buffer.py | import torch as th
import numpy as np
from types import SimpleNamespace as SN
class OneHot(object):
def __init__(self, out_dim):
self.out_dim = out_dim
def transform(self, tensor): # check
y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_()
y_onehot.scatter_(-1, tensor.lon... | 7,036 | 40.394118 | 159 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/utils/torch_tools.py | import numpy as np
import torch
def to_tensor(x, device):
"""return a torch.Tensor, assume x is an np array."""
if x.dtype in [np.float32, np.float64]:
return torch.tensor(x, dtype=torch.float32, device=device)
elif x.dtype in [np.int32, np.int64, np.uint8]:
return torch.tensor(x, dtype=tor... | 421 | 37.363636 | 66 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/utils/summary_tools.py | import numpy as np
def get_summary_str(step=None, info=None, prefix=''):
summary_str = prefix
if step is not None:
summary_str += 'Step {}; '.format(step)
for key, val in info.items():
if isinstance(val, (int, np.int32, np.int64)):
summary_str += '{} {}; '.format(key, val)
... | 834 | 35.304348 | 85 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/learner/value.py | import torch.nn as nn
from learner.base_mlp import MLP
# TODO: share layer with the policy netwrok
class ValueFuntion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(ValueFuntion, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, 1])
def forward(self, x):... | 385 | 28.692308 | 69 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/learner/base_mlp.py | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, layers, activation=torch.tanh, output_activation=None, init=True): # TODO：relu
super(MLP, self).__init__()
self.layers = nn.ModuleList()
self.activation = activation
self.output_activation = output_activatio... | 1,057 | 34.266667 | 101 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/learner/rnn_decoder.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
class RNN_Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(RNN_Decoder, self).__init__()
self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, batch_first=True, bidir... | 1,253 | 40.8 | 111 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/learner/prior.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Prior(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim, is_high=False):
super(Prior, self).__init__()
self.is_high = is_high
self.mlp = MLP(layers=[input_dim,... | 1,189 | 33 | 91 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/learner/policy.py | import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
import numpy as np
from learner.base_mlp import MLP
class GaussianPolicy(nn.Module):
def __init__(self, input_dim, hidden_dim, action_dim, output_activation=None, act_range=None... | 1,980 | 36.377358 | 153 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/learner/decoder.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(Decoder, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, code_dim])
de... | 861 | 29.785714 | 76 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Corridor/spectral_DPP_agent/laprepr.py | import os
import collections
import numpy as np
from tqdm import tqdm
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from configs import get_laprepr_args
from utils import torch_tools, timer_tools, summary_tools
from spectral_DPP_agent.spectral_buffer import EpisodicReplayBuffer... | 13,321 | 39.990769 | 132 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/main.py | import os
import gym
import torch
import datetime
import random
import numpy as np
from configs import get_common_args
from runner import Runner
from hierarchical_runner import HierRunner
from spectral_DPP_agent.laprepr import LapReprLearner
from utils.env_wrapper import EnvWrapper
import robo_env
def main():
# pr... | 2,149 | 31.575758 | 102 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/hierarchical_runner.py | import os
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from configs import get_hierarchical_args
from option_agent.hierarchical_policy import HierPolicy
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from runner import Runner
class HierRunner(object):
def __ini... | 15,987 | 51.592105 | 165 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/runner.py | import os
import torch
from tqdm import tqdm
import numpy as np
from configs import get_rl_args
from option_agent import REGISTRY as agent_REGISTRY
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from spectral_DPP_agent.laprepr import LapReprLearner
from visualization.draw_trajectory import draw_traj
class... | 9,374 | 48.08377 | 148 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/hierarchical_policy.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, value, policy
from utils.summary_tools import write_summary
def get_final_state_value(args, target_v, horizons):
target_v_array = targ... | 15,360 | 47.153605 | 197 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/VALOR.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class VALOR_Agent... | 10,865 | 52.527094 | 167 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/VIC.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, policy, value, decoder
from utils.summary_tools import write_summary, write_hist
from option_agent.base_option_agent import Option_Agent
cl... | 14,632 | 52.99631 | 172 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/DCO.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from option_agent.hierarchical_po... | 12,935 | 50.130435 | 180 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/base_option_agent.py | import os
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from utils.summary_tools import write_summary
from option_agent.hierarchical_policy import get_final_state_value, get_return_array, get_advantage
class Option_Agent(object):
def __init__(self, args):
... | 10,002 | 50.035714 | 156 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/ODPP.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder, prior
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from spectral... | 20,175 | 52.659574 | 164 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/option_agent/DIAYN.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class DIAYN_Agent(Op... | 10,567 | 53.756477 | 183 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/utils/buffer.py | import torch as th
import numpy as np
from types import SimpleNamespace as SN
class OneHot(object):
def __init__(self, out_dim):
self.out_dim = out_dim
def transform(self, tensor): # check
y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_()
y_onehot.scatter_(-1, tensor.lon... | 7,036 | 40.394118 | 159 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/utils/torch_tools.py | import numpy as np
import torch
def to_tensor(x, device):
"""return a torch.Tensor, assume x is an np array."""
if x.dtype in [np.float32, np.float64]:
return torch.tensor(x, dtype=torch.float32, device=device)
elif x.dtype in [np.int32, np.int64, np.uint8]:
return torch.tensor(x, dtype=tor... | 421 | 37.363636 | 66 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/utils/summary_tools.py | import numpy as np
def get_summary_str(step=None, info=None, prefix=''):
summary_str = prefix
if step is not None:
summary_str += 'Step {}; '.format(step)
for key, val in info.items():
if isinstance(val, (int, np.int32, np.int64)):
summary_str += '{} {}; '.format(key, val)
... | 834 | 35.304348 | 85 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/learner/value.py | import torch.nn as nn
from learner.base_mlp import MLP
# TODO: share layer with the policy netwrok
class ValueFuntion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(ValueFuntion, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, 1])
def forward(self, x):... | 385 | 28.692308 | 69 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/learner/base_mlp.py | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, layers, activation=torch.tanh, output_activation=None, init=True): # TODO：relu
super(MLP, self).__init__()
self.layers = nn.ModuleList()
self.activation = activation
self.output_activation = output_activatio... | 1,057 | 34.266667 | 101 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/learner/rnn_decoder.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
class RNN_Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(RNN_Decoder, self).__init__()
self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, batch_first=True, bidir... | 1,253 | 40.8 | 111 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/learner/prior.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Prior(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim, is_high=False):
super(Prior, self).__init__()
self.is_high = is_high
self.mlp = MLP(layers=[input_dim,... | 1,189 | 33 | 91 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/learner/policy.py | import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
import numpy as np
from learner.base_mlp import MLP
class GaussianPolicy(nn.Module):
def __init__(self, input_dim, hidden_dim, action_dim, output_activation=None, act_range=None... | 1,980 | 36.377358 | 153 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/learner/decoder.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(Decoder, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, code_dim])
de... | 861 | 29.785714 | 76 | py |
ODPP | ODPP-main/ODPP_Downstream_Point_Room/spectral_DPP_agent/laprepr.py | import os
import collections
import numpy as np
from tqdm import tqdm
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from configs import get_laprepr_args
from utils import torch_tools, timer_tools, summary_tools
from spectral_DPP_agent.spectral_buffer import EpisodicReplayBuffer... | 13,321 | 39.990769 | 132 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/main.py | import os
import gym
import torch
import datetime
import random
import numpy as np
from configs import get_common_args
from runner import Runner
from hierarchical_runner import HierRunner
from spectral_DPP_agent.laprepr import LapReprLearner
from utils.env_wrapper import EnvWrapper
import robo_env
def main():
# pr... | 2,149 | 31.575758 | 102 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/hierarchical_runner.py | import os
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from configs import get_hierarchical_args
from option_agent.hierarchical_policy import HierPolicy
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from runner import Runner
class HierRunner(object):
def __ini... | 16,583 | 51.481013 | 165 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/runner.py | import os
import torch
from tqdm import tqdm
import numpy as np
from configs import get_rl_args
from option_agent import REGISTRY as agent_REGISTRY
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from spectral_DPP_agent.laprepr import LapReprLearner
from visualization.draw_trajectory import draw_traj
class... | 9,513 | 48.041237 | 148 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/hierarchical_policy.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, value, policy
from utils.summary_tools import write_summary
def get_final_state_value(args, target_v, horizons):
target_v_array = targ... | 15,433 | 47.080997 | 197 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/VALOR.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class VALOR_Agent... | 10,865 | 52.527094 | 167 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/VIC.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, policy, value, decoder
from utils.summary_tools import write_summary, write_hist
from option_agent.base_option_agent import Option_Agent
cl... | 14,632 | 52.99631 | 172 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/DCO.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from option_agent.hierarchical_po... | 12,935 | 50.130435 | 180 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/base_option_agent.py | import os
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from utils.summary_tools import write_summary
from option_agent.hierarchical_policy import get_final_state_value, get_return_array, get_advantage
class Option_Agent(object):
def __init__(self, args):
... | 10,002 | 50.035714 | 156 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/ODPP.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder, prior
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from spectral... | 21,034 | 53.07455 | 180 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/option_agent/DIAYN.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class DIAYN_Agent(Op... | 10,567 | 53.756477 | 183 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/utils/buffer.py | import torch as th
import numpy as np
from types import SimpleNamespace as SN
class OneHot(object):
def __init__(self, out_dim):
self.out_dim = out_dim
def transform(self, tensor): # check
y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_()
y_onehot.scatter_(-1, tensor.lon... | 7,036 | 40.394118 | 159 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/utils/torch_tools.py | import numpy as np
import torch
def to_tensor(x, device):
"""return a torch.Tensor, assume x is an np array."""
if x.dtype in [np.float32, np.float64]:
return torch.tensor(x, dtype=torch.float32, device=device)
elif x.dtype in [np.int32, np.int64, np.uint8]:
return torch.tensor(x, dtype=tor... | 421 | 37.363636 | 66 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/utils/summary_tools.py | import numpy as np
def get_summary_str(step=None, info=None, prefix=''):
summary_str = prefix
if step is not None:
summary_str += 'Step {}; '.format(step)
for key, val in info.items():
if isinstance(val, (int, np.int32, np.int64)):
summary_str += '{} {}; '.format(key, val)
... | 834 | 35.304348 | 85 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/learner/value.py | import torch.nn as nn
from learner.base_mlp import MLP
# TODO: share layer with the policy netwrok
class ValueFuntion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(ValueFuntion, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, 1])
def forward(self, x):... | 385 | 28.692308 | 69 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/learner/base_mlp.py | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, layers, activation=torch.tanh, output_activation=None, init=True): # TODO：relu
super(MLP, self).__init__()
self.layers = nn.ModuleList()
self.activation = activation
self.output_activation = output_activatio... | 1,136 | 34.53125 | 101 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/learner/rnn_decoder.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
class RNN_Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(RNN_Decoder, self).__init__()
self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, batch_first=True, bidir... | 1,253 | 40.8 | 111 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/learner/prior.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Prior(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim, is_high=False):
super(Prior, self).__init__()
self.is_high = is_high
self.mlp = MLP(layers=[input_dim,... | 1,189 | 33 | 91 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/learner/policy.py | import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
import numpy as np
from learner.base_mlp import MLP
class GaussianPolicy(nn.Module):
def __init__(self, input_dim, hidden_dim, action_dim, output_activation=None, act_range=None... | 1,981 | 35.703704 | 153 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/learner/decoder.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(Decoder, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, code_dim])
de... | 861 | 29.785714 | 76 | py |
ODPP | ODPP-main/ODPP_Downstream_Ant_Corridor/spectral_DPP_agent/laprepr.py | import os
import collections
import numpy as np
from tqdm import tqdm
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from configs import get_laprepr_args
from utils import torch_tools, timer_tools, summary_tools
from spectral_DPP_agent.spectral_buffer import EpisodicReplayBuffer... | 13,915 | 40.050147 | 127 | py |
DashcamCleaner | DashcamCleaner-main/dashcamcleaner/src/generate_training_data.py | import os
import random
import sys
from argparse import ArgumentParser
from glob import glob
from math import floor, sqrt
import cv2
import pandas as pd
from anonymizer.anonymization.anonymizer import Anonymizer
from anonymizer.detection.detector import Detector
from anonymizer.detection.weights import download_weight... | 14,941 | 43.338279 | 116 | py |
DashcamCleaner | DashcamCleaner-main/dashcamcleaner/src/blurrer.py | import multiprocessing as mp
import os
import subprocess
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
from shutil import which
from typing import Dict, List, Tuple, Union
import cv2
import imageio
import numpy as np
import torch
from more_itertools import chunked
from src.bounds import B... | 9,051 | 37.033613 | 160 | py |
FaRM | FaRM-main/src/unconstrained/glove-embeddings.py | #!/usr/bin/env python
# coding: utf-8
import argparse
import os
import pickle
import sys
import torch
sys.path.append("../")
sys.path.append("src/")
from copy import deepcopy
from random import *
import numpy as np
import sklearn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torc... | 9,764 | 32.214286 | 88 | py |
FaRM | FaRM-main/src/unconstrained/biasbios-BERT.py | #!/usr/bin/env python
# coding: utf-8
import argparse
import sys
import os
import pickle
import warnings
sys.path.append("../")
sys.path.append("src/")
from collections import Counter, defaultdict
from copy import deepcopy
from random import *
from typing import List
from utils.utils import *
from utils.loss import R... | 11,128 | 31.636364 | 108 | py |
FaRM | FaRM-main/src/unconstrained/deepmoji.py | #!/usr/bin/env python
# coding: utf-8
import sys
import numpy as np
sys.path.append("../")
sys.path.append("src/")
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import torch.optim.lr_scheduler as lr_scheduler
from sklearn.neural_network import MLPClassifier
from sklearn.svm imp... | 8,721 | 33.474308 | 108 | py |
FaRM | FaRM-main/src/unconstrained/biasbios-fasttext.py | #!/usr/bin/env python
# coding: utf-8
import os
import sys
sys.path.append("../")
sys.path.append("src/")
import warnings
import numpy as np
import torch.optim.lr_scheduler as lr_scheduler
from gensim.models import KeyedVectors
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import ML... | 12,333 | 34.139601 | 88 | py |
FaRM | FaRM-main/src/utils/loss.py | import numpy as np
import torch
# parts of the code have been adapted from https://github.com/ryanchankh/mcr2/blob/master/loss.py
def one_hot(labels_int, n_classes):
"""Turn labels into one hot vector of K classes."""
labels_onehot = torch.zeros(size=(len(labels_int), n_classes)).float()
for i, y in enume... | 5,383 | 31.047619 | 97 | py |
FaRM | FaRM-main/src/utils/utils.py | import pickle
from collections import Counter, defaultdict
import numpy as np
import torch
from sklearn.metrics import f1_score, precision_score, recall_score
from sklearn.neural_network import MLPClassifier
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import BertModel
def encode(X... | 6,195 | 24.709544 | 77 | py |
FaRM | FaRM-main/src/utils/net.py | from torch import nn
class Net(nn.Module):
"""
Dynamic MLP network with ReLU non-linearity
"""
def __init__(self, args):
super().__init__()
self.layers = nn.ModuleList()
for _ in range(args.num_layers - 1):
self.layers.append(
nn.Linear(args.embeddi... | 1,040 | 26.394737 | 79 | py |
FaRM | FaRM-main/src/constrained/constrained-multiple.py | #!/usr/bin/env python
# coding: utf-8
import argparse
import os
import sys
sys.path.append("../")
sys.path.append("src/")
from random import *
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch import nn
from tqdm import tqdm
from transformers import BertTokenizerFast... | 10,597 | 32.644444 | 109 | py |
FaRM | FaRM-main/src/constrained/constrained-single.py | #!/usr/bin/env python
# coding: utf-8
import argparse
import os
import sys
sys.path.append("../")
sys.path.append("src/")
import pickle
from copy import deepcopy
from random import *
import numpy as np
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch import nn
from... | 12,601 | 33.337875 | 136 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/vis/extract_saliency.py | import argparse
import os
from time import time
import cv2
import numpy as np
import torch
from matplotlib import pyplot as plt
from skimage import color
from tqdm import tqdm
from src.auxiliary.settings import PATH_TO_PRETRAINED, RANDOM_SEED, DEVICE
from src.auxiliary.utils import make_deterministic, infer_path_to_p... | 5,899 | 43.029851 | 113 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/eval/tests/ers/save_grads_tccnet.py | import argparse
import os
from time import time
import torch
from src.auxiliary.settings import DEVICE, RANDOM_SEED, PATH_TO_PRETRAINED
from src.auxiliary.utils import print_namespace, make_deterministic, infer_path_to_pretrained, experiment_header, \
save_settings
from src.classes.tasks.ccc.multiframe.data.DataH... | 2,642 | 42.327869 | 115 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/eval/analysis/adv/adv_analysis_tccnet.py | import argparse
import os
from time import time
from typing import Dict, List
import matplotlib.pyplot as plt
import numpy as np
from torch.utils.data import DataLoader
from src.auxiliary.settings import RANDOM_SEED, DEVICE, PATH_TO_PRETRAINED, PATH_TO_RESULTS
from src.auxiliary.utils import make_deterministic, print... | 6,824 | 47.06338 | 115 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/core/Loss.py | from abc import abstractmethod
import torch
from torch import Tensor
class Loss:
def __init__(self, device: torch.device):
self._device = device
self._one = Tensor([1]).to(self._device)
self._eps = Tensor([0.0000001]).to(self._device)
@abstractmethod
def _compute(self, *args, **k... | 456 | 23.052632 | 62 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/core/Tester.py | import os
from abc import abstractmethod, ABC
from time import time
import torch
from torch.utils.data import DataLoader
from src.auxiliary.settings import DEVICE
from src.auxiliary.utils import SEPARATOR
from src.classes.core.LossTracker import LossTracker
from src.classes.core.MetricsTracker import MetricsTracker
f... | 2,708 | 32.444444 | 118 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/core/Model.py | import os
from typing import Union, Tuple
import torch
from torch import Tensor, optim, nn
from src.auxiliary.settings import DEVICE
from src.auxiliary.utils import SEPARATOR, overload
class Model:
def __init__(self):
self._device = DEVICE
self._criterion, self._network, self._optimizer = None,... | 2,228 | 33.292308 | 102 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/core/Trainer.py | from abc import abstractmethod
from time import time
import torch
from torch.utils.data import DataLoader
from src.auxiliary.settings import DEVICE
from src.auxiliary.utils import SEPARATOR
from src.classes.core.LossTracker import LossTracker
from src.classes.core.MetricsTracker import MetricsTracker
from src.classes... | 5,521 | 39.602941 | 116 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/ers/core/ESWModule.py | from abc import abstractmethod
from typing import Tuple, Union, List
from torch import nn
from src.classes.eval.ers.core.WeightsEraser import WeightsEraser
class ESWModule(nn.Module):
def __init__(self):
super().__init__()
self._we = WeightsEraser()
self._we_state = False
self._... | 1,754 | 25.590909 | 65 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/ers/core/WeightsEraser.py | import os
import random
import numpy as np
import torch
from numpy import prod
from torch import Tensor
from torch.nn.functional import normalize
from src.auxiliary.settings import DEVICE
class WeightsEraser:
def __init__(self):
self.__device = DEVICE
self.__path_to_model_dir, self.__path_to_va... | 4,819 | 38.834711 | 120 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/ers/core/EMultiSWModule.py | import os
from abc import abstractmethod
from typing import List
import numpy as np
import torch
from torch import Tensor
from src.auxiliary.utils import overloads
from src.classes.eval.ers.core.ESWModule import ESWModule
class EMultiSWModule(ESWModule):
def __init__(self):
"""Erasable Saliency Weights... | 1,677 | 28.438596 | 94 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/ers/core/ESWTester.py | import os
from abc import abstractmethod
from typing import Dict, Tuple
import pandas as pd
from torch import Tensor
from torch.utils.data import DataLoader
from src.auxiliary.settings import DEVICE
from src.classes.eval.ers.core.ESWModel import ESWModel
""" Abstract class for Erasable Saliency Weights (ESW) tester ... | 2,369 | 36.619048 | 115 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/ers/tasks/tcc/ESWTesterTCCNet.py | import os
from typing import Dict, List, Tuple
import pandas as pd
import torch
from numpy import prod
from torch import Tensor
from torch.utils.data import DataLoader
from src.auxiliary.utils import SEPARATOR
from src.classes.eval.ers.core.ESWModel import ESWModel
from src.classes.eval.ers.core.ESWTester import ESWT... | 6,210 | 46.412214 | 115 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/adv/core/AdvModel.py | from abc import abstractmethod
from typing import Tuple, Dict
from torch import Tensor
from src.auxiliary.utils import overloads
from src.classes.core.Model import Model
class AdvModel(Model):
def __init__(self, adv_lambda: float = 0.00005):
super().__init__()
self._adv_lambda = Tensor([adv_lam... | 1,251 | 31.102564 | 106 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/adv/tasks/tcc/AdvModelSaliencyTCCNet.py | from abc import ABC
from typing import Tuple, Dict
import torch
from torch import Tensor
from src.classes.eval.adv.core.AdvModel import AdvModel
from src.classes.losses.AngularLoss import AngularLoss
from src.classes.losses.KLDivLoss import KLDivLoss
from src.classes.losses.StructComplLoss import StructComplLoss
from... | 2,748 | 44.065574 | 103 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/adv/tasks/tcc/TrainerAdvSaliencyTCCNet.py | import os
from typing import Dict, Tuple
import numpy as np
import pandas as pd
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from src.auxiliary.utils import overloads
from src.classes.core.LossTracker import LossTracker
from src.classes.eval.adv.core.AdvModel import AdvModel
from src.... | 6,469 | 50.349206 | 114 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/acc/tasks/tcc/UniformAttTCCNet.py | import torch
from torch import Tensor
from src.classes.tasks.ccc.multiframe.modules.saliency_tccnet.modules.AttTCCNet import AttTCCNet
from src.functional.utils import rand_uniform
class UniformAttTCCNet(AttTCCNet):
def __init__(self, hidden_size: int = 128, kernel_size: int = 5, sal_dim: str = ""):
sup... | 734 | 35.75 | 97 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/acc/tasks/tcc/ModelUniformSaliencyTCCNet.py | from typing import Tuple
from torch import Tensor
from src.classes.eval.acc.tasks.tcc.NetworkUniformSaliencyTCCNetFactory import NetworkUniformSaliencyTCCNetFactory
from src.classes.tasks.ccc.multiframe.modules.saliency_tccnet.core.ModelSaliencyTCCNet import ModelSaliencyTCCNet
from src.functional.error_handling impo... | 1,155 | 47.166667 | 114 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/acc/tasks/tcc/UniformConfTCCNet.py | from abc import ABC
from torch import Tensor
from src.classes.tasks.ccc.multiframe.modules.saliency_tccnet.modules.ConfTCCNet import ConfTCCNet
from src.functional.utils import rand_uniform
class UniformConfTCCNet(ConfTCCNet, ABC):
def __init__(self, hidden_size: int = 128, kernel_size: int = 5, sal_dim: str =... | 762 | 35.333333 | 106 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/acc/tasks/tcc/UniformConfAttTCCNet.py | import torch
from torch import Tensor
from src.classes.tasks.ccc.multiframe.modules.saliency_tccnet.modules.ConfAttTCCNet import ConfAttTCCNet
from src.functional.utils import rand_uniform
class UniformConfAttTCCNet(ConfAttTCCNet):
def __init__(self, hidden_size: int = 128, kernel_size: int = 5, sal_dim: str = ... | 750 | 36.55 | 104 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/rand/core/Visualizer.py | import os
from typing import List, Tuple
import matplotlib.pyplot as plt
import numpy as np
import torch.utils.data
import torchvision.transforms.functional as F
from matplotlib.figure import Figure
from torch import Tensor
from torchvision.transforms import transforms
from tqdm import tqdm
from src.auxiliary.setting... | 6,608 | 44.57931 | 113 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/rand/tasks/tcc/DataHandlerRandLabelsTCC.py | from multiprocessing import cpu_count
from typing import Tuple
from torch.utils.data import DataLoader
from src.classes.eval.rand.tasks.tcc.RandLabelsTCC import RandLabelsTCC
from src.classes.tasks.ccc.multiframe.data.DataHandlerTCC import DataHandlerTCC
class DataHandlerRandLabelsTCC(DataHandlerTCC):
def __ini... | 1,119 | 45.666667 | 113 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/rand/tasks/tcc/ParamsRandomizer.py | import copy
import os
import torch
from tqdm import tqdm
from src.classes.tasks.ccc.multiframe.modules.saliency_tccnet.core.ModelSaliencyTCCNet import ModelSaliencyTCCNet
class ParamsRandomizer:
def __init__(self, model: ModelSaliencyTCCNet, path_to_log: str = ""):
self.__model = model
self.__p... | 2,952 | 39.452055 | 116 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/mlp/core/LinearEncoder.py | from torch import nn, Tensor
class LinearEncoder(nn.Module):
def __init__(self, input_size: int, output_size: int):
super().__init__()
self.layers = nn.Sequential(
nn.Flatten(),
nn.Linear(input_size, 128),
nn.Tanh(),
nn.Linear(128, output_size),
... | 424 | 22.611111 | 58 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/mlp/tasks/tcc/TrainerLinearSaliencyTCCNet.py | import os
from typing import Tuple, Union
import numpy as np
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from src.classes.eval.mlp.tasks.tcc.ModelLinearSaliencyTCCNet import ModelLinearSaliencyTCCNet
from src.classes.tasks.ccc.core.TrainerCCC import TrainerCCC
class TrainerLinearSa... | 2,400 | 44.301887 | 108 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/mlp/tasks/tcc/ModelLinearSaliencyTCCNet.py | from typing import Tuple, Union
from torch import Tensor
from src.auxiliary.utils import overloads
from src.classes.core.Model import Model
from src.classes.eval.mlp.tasks.tcc.LinearSaliencyTCCNet import LinearSaliencyTCCNet
from src.classes.tasks.ccc.core.ModelCCC import ModelCCC
class ModelLinearSaliencyTCCNet(Mo... | 1,042 | 32.645161 | 110 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/mlp/tasks/tcc/TesterLinearSaliencyTCCNet.py | import os
from typing import Union, Tuple
import numpy as np
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from src.classes.core.Model import Model
from src.classes.tasks.ccc.multiframe.modules.saliency_tccnet.core.TesterSaliencyTCCNet import TesterSaliencyTCCNet
class TesterLinearSa... | 2,131 | 41.64 | 115 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/eval/mlp/tasks/tcc/LinearSaliencyTCCNet.py | from abc import ABC
from math import prod
from typing import Tuple, Union
import torch
from torch import nn, Tensor
from torch.nn.functional import normalize
from src.auxiliary.utils import overloads
from src.classes.eval.mlp.core.LinearEncoder import LinearEncoder
from src.classes.tasks.ccc.multiframe.modules.salien... | 5,424 | 39.485075 | 111 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/tasks/ccc/singleframe/fc4/FC4.py | from typing import Union
import torch
from torch import nn, Tensor
from torch.nn.functional import normalize
from src.classes.tasks.ccc.submodules.squeezenet.SqueezeNetLoader import SqueezeNetLoader
"""
FC4: Fully Convolutional Color Constancy with Confidence-weighted Pooling
* Original code: https://github.com/yuan... | 2,455 | 36.784615 | 126 | py |
saliency-faithfulness-eval | saliency-faithfulness-eval-main/src/classes/tasks/ccc/singleframe/fc4/ModelFC4.py | from abc import ABC
from typing import Union, Tuple
from torch import Tensor
from src.classes.core.Model import Model
from src.classes.tasks.ccc.singleframe.fc4.FC4 import FC4
class ModelFC4(Model, ABC):
def __init__(self):
super().__init__()
self._network = FC4().to(self._device)
def pred... | 1,089 | 36.586207 | 117 | py |
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