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2,289,701 | esp32c2.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32c2.cpython-310.pyc | o
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2,289,702 | esp32s3beta2.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32s3beta2.cpython-310.pyc | o
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2,289,703 | esp32s3.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32s3.cpython-310.pyc | o
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2,289,704 | esp32h2.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32h2.cpython-310.pyc | o
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dd„ Zdd„ Zdd„ Zdd„ Zdd„ Zdd„ ZdS ) Ú
ESP32H2ROMúESP32-H2é l €>n... | 2,675 | Python | .pyt | 15 | 176.4 | 1,290 | 0.480075 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,705 | esp32s2.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32s2.cpython-310.pyc | o
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@ s e Zd ZdZdZdZdZdZdZdZ d... | 10,102 | Python | .pyt | 55 | 181.836364 | 1,041 | 0.517667 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,706 | __init__.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/__init__.cpython-310.pyc | o
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ESP32C6ROM)ÚESP32C6... | 885 | Python | .pyt | 12 | 72.666667 | 228 | 0.551487 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,707 | esp32c3.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32c3.cpython-310.pyc | o
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ZdZdZdd... | 8,156 | Python | .pyt | 39 | 207.358974 | 1,239 | 0.514353 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,708 | esp32c6beta.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32c6beta.cpython-310.pyc | o
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2,289,709 | esp32h2beta1.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32h2beta1.cpython-310.pyc | o
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ZdZdgZdZdZed Zed Z... | 5,690 | Python | .pyt | 27 | 209.222222 | 2,133 | 0.524634 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,710 | esp32h2beta2.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32h2beta2.cpython-310.pyc | o
’jÂd• ã @ s6 d dl mZ G dd„ deƒZG dd„ deƒZee_dS )é )ÚESP32H2BETA1ROMc @ s" e Zd ZdZdZdgZdd„ ZdS )ÚESP32H2BETA2ROMúESP32-H2(beta2)é io°�hc C s: ddi | ¡ d¡}| ¡ }| ¡ }|› d|› d|› d... | 1,529 | Python | .pyt | 11 | 136.727273 | 650 | 0.523715 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,711 | esp32.cpython-310.pyc | OLIMEX_RVPC/SOFTWARE/rvpc/esptool/esptool/targets/__pycache__/esp32.cpython-310.pyc | o
’jÂdi5 ã @ sV d dl Z d dlZddlmZ ddlmZmZ G dd„ deƒZG dd„ deƒZee_ dS ) é Né )Ú ESPLoader)Ú
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ZdZdZdZdZdZed ZdZeZd... | 10,381 | Python | .pyt | 75 | 136.373333 | 886 | 0.504657 | OLIMEX/RVPC | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,712 | train_mri2pet.py | ai-med_PASTA/train_mri2pet.py | import argparse
import os
from src.diffusion.gaussian_diffusion import GaussianDiffusion, ModelMeanType, ModelVarType, LossType
from src.diffusion.respace import *
from src.trainer.trainer import Trainer
from src.model.unet import UNetModel
from src.utils.utils import *
OBJECTIVE = {'PREVIOUS_X': ModelMeanType.PREVIO... | 5,818 | Python | .py | 126 | 38.349206 | 177 | 0.649797 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,713 | ssim.py | ai-med_PASTA/src/evals/ssim.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size,... | 5,155 | Python | .py | 103 | 42.300971 | 138 | 0.635829 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,714 | dataset.py | ai-med_PASTA/src/datasets/dataset.py | import h5py
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision import transforms as T, utils
from tqdm import tqdm
import pandas as pd
from src.utils.data_utils import get_neighboring_slices, multislice_data_minimal_process, \
... | 27,826 | Python | .py | 465 | 39.503226 | 146 | 0.500521 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,715 | trainer.py | ai-med_PASTA/src/trainer/trainer.py | import math
from pathlib import Path
import os
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch.cuda.amp import autocast
from torchvision import transforms as T, utils
import torchio as tio
from einops import rearrange, reduce, repeat
from... | 40,801 | Python | .py | 647 | 40.846986 | 162 | 0.503288 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,716 | data_utils.py | ai-med_PASTA/src/utils/data_utils.py | import numpy as np
import torch
import torchio as tio
import torch.nn.functional as F
import monai.transforms as montrans
import math
def roi_crop_3d(image):
# 3d image as input
# Mask of non-black pixels (assuming image has a single channel).
mask = image > 0
# Coordinates of non-black pixels.
c... | 10,341 | Python | .py | 209 | 42.818182 | 122 | 0.667502 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,717 | utils.py | ai-med_PASTA/src/utils/utils.py | import math
import torch
import argparse
import yaml
import random
import numpy as np
# helpers functions
def load_config_from_yaml(config_file_path):
with open(config_file_path, 'r') as f:
config_data = yaml.safe_load(f)
args = argparse.Namespace(**config_data)
return args
def exists(x):
re... | 3,458 | Python | .py | 103 | 28.048544 | 87 | 0.639132 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,718 | respace.py | ai-med_PASTA/src/diffusion/respace.py | import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the origin... | 5,192 | Python | .py | 108 | 38.555556 | 85 | 0.628752 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,719 | gaussian_diffusion.py | ai-med_PASTA/src/diffusion/gaussian_diffusion.py | """
This code started out as a PyTorch port of Ho et al's diffusion models:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
"""
import enum
import nump... | 41,060 | Python | .py | 933 | 32.28403 | 175 | 0.562652 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,720 | losses.py | ai-med_PASTA/src/diffusion/losses.py | """
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""
import numpy as np
import torch as th
def normal_kl(mean1, logvar1, mean2, logvar... | 2,534 | Python | .py | 65 | 33.338462 | 109 | 0.655678 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,721 | diff_utils.py | ai-med_PASTA/src/diffusion/diff_utils.py | import numpy as np
import math
import torch as th
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_tim... | 2,789 | Python | .py | 63 | 36.857143 | 110 | 0.639573 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,722 | fp16_util.py | ai-med_PASTA/src/model/fp16_util.py | """
Helpers to train with 16-bit precision.
"""
import numpy as np
import torch as th
import torch.nn as nn
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from . import logger
INITIAL_LOG_LOSS_SCALE = 20.0
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
... | 7,962 | Python | .py | 194 | 32.974227 | 114 | 0.631327 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,723 | unet.py | ai-med_PASTA/src/model/unet.py | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from numbers import Number
from typing import NamedTuple, Tuple, Union, List
from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
checkpoint,
co... | 38,114 | Python | .py | 900 | 29.418889 | 157 | 0.535684 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,724 | logger.py | ai-med_PASTA/src/model/logger.py | """
Logger copied from OpenAI baselines to avoid extra RL-based dependencies:
https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/logger.py
"""
import os
import sys
import shutil
import os.path as osp
import json
import time
import datetime
import tempfile
import warnings
from c... | 13,979 | Python | .py | 392 | 27.5 | 132 | 0.5752 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,725 | nn.py | ai-med_PASTA/src/model/nn.py | """
Various utilities for neural networks.
"""
import math
import torch as th
import torch.nn as nn
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * th.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super(... | 5,088 | Python | .py | 137 | 30.80292 | 88 | 0.650671 | ai-med/PASTA | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,726 | optimize_llamacpp_ngl.py | fredlas_optimize_llamacpp_ngl/optimize_llamacpp_ngl.py | import os
import struct
import subprocess
import sys
def try_llama_main(ngl, llama_main_path, model_path, desired_context,
split_mode, main_gpu, tensor_split):
print(f'trying -ngl {ngl}{tensor_split}')
cmd = f'{llama_main_path} -m {model_path} -c {desired_context} --split-mode {split_mode}{tens... | 7,920 | Python | .py | 143 | 48.881119 | 268 | 0.665163 | fredlas/optimize_llamacpp_ngl | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,727 | plt_style.py | mikelma_componet/utils/plt_style.py | import matplotlib.pyplot as plt
import matplotlib
import colormaps as cmaps
def style(fig, ax=None, grid=True, legend=True, legend_title=None, legend_ncols=1, force_sci_x=False, force_sci_y=False, font=3, colormap=cmaps.greenorange_12, legend_shrink=0.1, ax_math_ticklabels=True, y_spine=False):
if colormap is not... | 1,715 | Python | .py | 36 | 39.722222 | 219 | 0.64632 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,728 | impl.py | mikelma_componet/componet/impl.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.categorical import Categorical
import numpy as np
import math
def get_position_encoding(seq_len, d, n=10_000):
"""
Returns the positional encoding matrix for the given parameters.
seq_len -- Length of the sequence... | 16,153 | Python | .py | 337 | 37.910979 | 133 | 0.602884 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,729 | plot_dino_vs_cnn.py | mikelma_componet/experiments/atari/plot_dino_vs_cnn.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.lines import Line2D
from argparse import ArgumentParser
import sys
sys.path.append("../../")
from utils import plt_style
def parse_args():
# fmt: off
parser = ArgumentParser()
parser.add_argument(... | 2,947 | Python | .py | 86 | 28.44186 | 87 | 0.607118 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,730 | plot_ablation_input_head.py | mikelma_componet/experiments/atari/plot_ablation_input_head.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib.colors as mcolors
import sys
from argparse import ArgumentParser
sys.path.append("../../")
from utils import plt_style
def parse_args():
# fmt: off
parser = ArgumentParser()
parser... | 10,255 | Python | .py | 264 | 31.522727 | 88 | 0.574432 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,731 | run_ppo.py | mikelma_componet/experiments/atari/run_ppo.py | # docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_ataripy
import os
import random
import time
from dataclasses import dataclass
import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import tyro
from typing import Literal, Tu... | 18,170 | Python | .py | 417 | 32.611511 | 125 | 0.569379 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,732 | process_results.py | mikelma_componet/experiments/atari/process_results.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import pathlib
import argparse
from tabulate import tabulate
import sys, os
sys.path.append("../../")
from utils import style
SETTINGS = {
"SpaceInvaders": dict(
ma_w_1=10,
num_pts_sc=100,
... | 16,018 | Python | .py | 437 | 27.434783 | 145 | 0.541083 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,733 | plot_arch_val.py | mikelma_componet/experiments/atari/plot_arch_val.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from argparse import ArgumentParser
import sys, os
sys.path.append("../../")
from utils import plt_style
def parse_args():
# fmt: off
parser = ArgumentParser()
parser.add_argument("--dir-prefix", type=str, default="data/arch-val",
... | 8,232 | Python | .py | 221 | 29.357466 | 100 | 0.555695 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,734 | task_utils.py | mikelma_componet/experiments/atari/task_utils.py | TASKS = {
"ALE/SpaceInvaders-v5": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"ALE/Freeway-v5": [0, 1, 2, 3, 4, 5, 6, 7],
}
def parse_name_info(name):
fields = name.split("__")
if "SpaceInvaders" in fields[0]:
env = "ALE/SpaceInvaders-v5"
elif "Freeway" in fields[0]:
env = "ALE/Freeway-v5"
... | 680 | Python | .py | 19 | 31 | 84 | 0.590563 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,735 | transfer_matrix.py | mikelma_componet/experiments/atari/transfer_matrix.py | from tensorboard.backend.event_processing import event_accumulator
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import pathlib
from tqdm import tqdm
import numpy as np
import sys, os
from argparse import ArgumentParser
def parse_args():
# fmt: off
parser = ArgumentParser()
par... | 8,793 | Python | .py | 233 | 28.154506 | 92 | 0.539529 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,736 | test_agent.py | mikelma_componet/experiments/atari/test_agent.py | import gymnasium as gym
import numpy as np
import torch
import argparse
import os
from stable_baselines3.common.atari_wrappers import ( # isort:skip
ClipRewardEnv,
EpisodicLifeEnv,
FireResetEnv,
MaxAndSkipEnv,
NoopResetEnv,
)
from models import (
CnnSimpleAgent,
DinoSimpleAgent,
CnnCom... | 4,503 | Python | .py | 115 | 31.530435 | 114 | 0.626434 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,737 | plot_ablation_output_head.py | mikelma_componet/experiments/atari/plot_ablation_output_head.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from glob import glob
from argparse import ArgumentParser
import sys
sys.path.append("../../")
from utils import plt_style
def parse_args():
# fmt: off
parser = ArgumentParser()
parser.add_argument("... | 4,871 | Python | .py | 120 | 32.683333 | 102 | 0.594955 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,738 | run_experiments.py | mikelma_componet/experiments/atari/run_experiments.py | import subprocess
import argparse
import random
from task_utils import TASKS
def parse_args():
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--algorithm", type=str, choices=["componet", "finetune", "from-scratch", "prog-net", "packnet"], required=True)
parser.add_argument("--env"... | 2,464 | Python | .py | 58 | 37.37931 | 136 | 0.651756 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,739 | progressive_net.py | mikelma_componet/experiments/atari/models/progressive_net.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
import numpy as np
import os
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
if layer.bias is not None:
torch.nn.init.constant_(layer.bias, bias_const)
ret... | 4,944 | Python | .py | 124 | 30.258065 | 87 | 0.569549 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,740 | cnn_encoder.py | mikelma_componet/experiments/atari/models/cnn_encoder.py | import torch.nn as nn
class CnnEncoder(nn.Module):
def __init__(self, hidden_dim=512, layer_init=lambda x, **kwargs: x):
super().__init__()
self.network = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride... | 604 | Python | .py | 17 | 25.647059 | 73 | 0.513699 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,741 | dino_simple.py | mikelma_componet/experiments/atari/models/dino_simple.py | import os
import numpy as np
import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
from .dino_encoder import DinoEncoder
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
... | 1,997 | Python | .py | 45 | 36.711111 | 85 | 0.641975 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,742 | dino_encoder.py | mikelma_componet/experiments/atari/models/dino_encoder.py | import torch
import torchvision.transforms as T
class DinoEncoder:
def __init__(self, dino_size, device):
# see: https://github.com/facebookresearch/dinov2/blob/main/MODEL_CARD.md#model-details
if dino_size == "s":
self.embed_dim = 384
elif dino_size == "b":
self.em... | 1,321 | Python | .py | 34 | 29.764706 | 95 | 0.552344 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,743 | __init__.py | mikelma_componet/experiments/atari/models/__init__.py | from .cnn_simple import CnnSimpleAgent
from .dino_simple import DinoSimpleAgent
from .cnn_componet import CnnCompoNetAgent
from .progressive_net import ProgressiveNetAgent
from .packnet import PackNetAgent
| 206 | Python | .py | 5 | 40.2 | 48 | 0.880597 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,744 | cnn_simple.py | mikelma_componet/experiments/atari/models/cnn_simple.py | import os
import torch
import torch.nn as nn
import numpy as np
from torch.distributions.categorical import Categorical
from .cnn_encoder import CnnEncoder
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
re... | 1,727 | Python | .py | 38 | 38.447368 | 88 | 0.671624 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,745 | packnet.py | mikelma_componet/experiments/atari/models/packnet.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
from .cnn_encoder import CnnEncoder
import copy
import os
import numpy as np
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_c... | 6,428 | Python | .py | 156 | 30.634615 | 110 | 0.582305 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,746 | cnn_componet.py | mikelma_componet/experiments/atari/models/cnn_componet.py | import os
import torch
import torch.nn as nn
import numpy as np
from torch.distributions.categorical import Categorical
from .cnn_encoder import CnnEncoder
import sys, os
sys.path.append(os.path.dirname(__file__) + "/../../../")
from componet import CompoNet, FirstModuleWrapper
def layer_init(layer, std=np.sqrt(2), ... | 5,209 | Python | .py | 120 | 31.308333 | 88 | 0.55218 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,747 | run_sac.py | mikelma_componet/experiments/meta-world/run_sac.py | # docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/sac/#sac_continuous_actionpy
import os
import random
import time
from dataclasses import dataclass
import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as opt... | 17,584 | Python | .py | 413 | 31.842615 | 115 | 0.569851 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,748 | process_results.py | mikelma_componet/experiments/meta-world/process_results.py | from tensorboard.backend.event_processing import event_accumulator
import pandas as pd
import pathlib
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import os, sys
import argparse
from tabulate import tabulate
METHOD_NAMES = {
"simple": "Baseline",
... | 16,627 | Python | .py | 440 | 28.447727 | 131 | 0.536329 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,749 | transferer_matrix.py | mikelma_componet/experiments/meta-world/transferer_matrix.py | from tensorboard.backend.event_processing import event_accumulator
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import pathlib
from tqdm import tqdm
import numpy as np
import os
from argparse import ArgumentParser
from process_results import (
parse_metadata,
parse_tensorboard,
... | 4,397 | Python | .py | 116 | 28.681034 | 92 | 0.568038 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,750 | tasks.py | mikelma_componet/experiments/meta-world/tasks.py | import numpy as np
single_tasks = [
"hammer-v2",
"push-wall-v2",
"faucet-close-v2",
"push-back-v2",
"stick-pull-v2",
"handle-press-side-v2",
"push-v2",
"shelf-place-v2",
"window-close-v2",
"peg-unplug-side-v2",
]
tasks = single_tasks + single_tasks
def get_task_name(task_id):... | 1,074 | Python | .py | 34 | 26.029412 | 66 | 0.636098 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,751 | test_agent.py | mikelma_componet/experiments/meta-world/test_agent.py | import gymnasium as gym
import numpy as np
import torch
from models import shared, SimpleAgent, CompoNetAgent, PackNetAgent, ProgressiveNetAgent
from tasks import get_task, get_task_name
import argparse
import os
from run_sac import Actor
import random
def parse_args():
parser = argparse.ArgumentParser()
# f... | 3,289 | Python | .py | 85 | 31.552941 | 89 | 0.62618 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,752 | benchmarking.py | mikelma_componet/experiments/meta-world/benchmarking.py | import torch
import torch.nn as nn
from models.prognet import ProgressiveNet
import sys, os
sys.path.append(os.path.dirname(__file__) + "/../..")
from componet import CompoNet, FirstModuleWrapper
import torch.utils.benchmark as benchmark
import pandas as pd
import argparse
def parse_args():
# fmt: off
parser... | 12,720 | Python | .py | 355 | 28.222535 | 90 | 0.596793 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,753 | run_experiments.py | mikelma_componet/experiments/meta-world/run_experiments.py | import subprocess
import argparse
import random
from tasks import tasks
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--algorithm",
type=str,
choices=[
"simple",
"componet",
"finetune",
"from-scratch",
... | 2,035 | Python | .py | 54 | 30.814815 | 150 | 0.61128 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,754 | simple.py | mikelma_componet/experiments/meta-world/models/simple.py | import torch
import torch.nn as nn
import os
from .shared_arch import shared
class SimpleAgent(nn.Module):
def __init__(self, obs_dim, act_dim):
super().__init__()
self.act_dim = act_dim
self.obs_dim = obs_dim
self.fc = shared(input_dim=obs_dim)
# will be created when cal... | 1,042 | Python | .py | 30 | 27.333333 | 76 | 0.61255 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,755 | prognet.py | mikelma_componet/experiments/meta-world/models/prognet.py | import torch
import torch.nn as nn
import os
class ProgressiveNetAgent(nn.Module):
def __init__(self, obs_dim, act_dim, prev_paths=[], map_location=None):
super().__init__()
self.act_dim = act_dim
self.obs_dim = obs_dim
if len(prev_paths) > 0:
prevs = [
... | 3,657 | Python | .py | 97 | 27.989691 | 87 | 0.566262 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,756 | shared_arch.py | mikelma_componet/experiments/meta-world/models/shared_arch.py | import torch.nn as nn
def shared(input_dim):
return nn.Sequential(
nn.Linear(input_dim, 256),
nn.ReLU(),
nn.Linear(256, 256),
nn.ReLU(),
)
| 181 | Python | .py | 8 | 16.375 | 34 | 0.567251 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,757 | __init__.py | mikelma_componet/experiments/meta-world/models/__init__.py | from .shared_arch import shared
from .simple import SimpleAgent
from .compo import CompoNetAgent
from .packnet import PackNetAgent
from .prognet import ProgressiveNetAgent
| 172 | Python | .py | 5 | 33.4 | 40 | 0.874251 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,758 | compo.py | mikelma_componet/experiments/meta-world/models/compo.py | import torch
import torch.nn as nn
import numpy as np
from .shared_arch import shared
import sys, os
sys.path.append(os.path.dirname(__file__) + "/../../../")
from componet import CompoNet, FirstModuleWrapper
def net(input_dim, output_dim):
return nn.Sequential(
nn.Linear(input_dim, 256),
nn.Re... | 3,639 | Python | .py | 92 | 28.576087 | 87 | 0.549787 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,759 | packnet.py | mikelma_componet/experiments/meta-world/models/packnet.py | import torch
import torch.nn as nn
import copy
import os
import numpy as np
class PackNetAgent(nn.Module):
def __init__(self, obs_dim, act_dim, task_id, total_task_num, device):
super().__init__()
shared = nn.Sequential(
nn.Linear(obs_dim, 256),
nn.ReLU(),
nn.L... | 5,895 | Python | .py | 148 | 28.837838 | 110 | 0.561582 | mikelma/componet | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,760 | 02-connect-bot.py | 0x26e_MineflayerPython/scripts/02-connect-bot.py | from javascript import require, On, Once, AsyncTask, once, off
# Import the javascript libraries
mineflayer = require("mineflayer")
# Create bot
bot_args = {"username": "connect-bot", "host": "localhost", "port": 3000, "version": "1.19.4", "hideErrors": False}
bot = mineflayer.createBot(bot_args)
# Login event (Logg... | 970 | Python | .py | 28 | 31 | 115 | 0.683476 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,761 | 06-logger-bot.py | 0x26e_MineflayerPython/scripts/06-logger-bot.py | from javascript import require, On, Once, AsyncTask, once, off
# Import the javascript libraries
mineflayer = require("mineflayer")
# Global bot parameters
server_host = "localhost"
server_port = 3000
reconnect = True
class MCBot:
def __init__(self, bot_name):
self.bot_args = {
"host": serv... | 2,400 | Python | .py | 63 | 28.666667 | 110 | 0.592752 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,762 | 08-chat-bot.py | 0x26e_MineflayerPython/scripts/08-chat-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
from random import randint
# Import the javascript libraries
mineflayer = require("mineflayer")
# Global bot parameters
server_host = "localhost"
server_port = 3000
reconnect = True
class MCBot:
def __init__(self, bot... | 3,075 | Python | .py | 79 | 27.797468 | 114 | 0.55914 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,763 | 13-pathfinder-bot.py | 0x26e_MineflayerPython/scripts/13-pathfinder-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
from utils.vec3_conversion import vec3_to_str
# Requires ./utils/vec3_conversion.py
# Import the javascript libraries
mineflayer = require("mineflayer")
mineflayer_pathfinder = require("mineflayer-pathfinder")
vec3 = require... | 4,454 | Python | .py | 108 | 28.296296 | 114 | 0.542593 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,764 | 01-simple-bot.py | 0x26e_MineflayerPython/scripts/01-simple-bot.py | from javascript import require, On, Once, AsyncTask, once, off
# Import the javascript libraries
mineflayer = require("mineflayer")
# Create bot with basic parameters
bot = mineflayer.createBot(
{"username": "simple-bot", "host": "localhost", "port": 3000, "version": "1.19.4", "hideErrors": False}
)
# Login even... | 383 | Python | .py | 11 | 32.818182 | 107 | 0.726287 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,765 | 11-raycast-bot.py | 0x26e_MineflayerPython/scripts/11-raycast-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
from utils.vec3_conversion import vec3_to_str
import math
# Requires ./utils/vec3_conversion.py
# Import the javascript libraries
mineflayer = require("mineflayer")
vec3 = require("vec3")
# Global bot parameters
server_host... | 6,083 | Python | .py | 142 | 27.232394 | 114 | 0.487896 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,766 | 10-looker-bot.py | 0x26e_MineflayerPython/scripts/10-looker-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
from utils.vec3_conversion import vec3_to_str
# Requires ./utils/vec3_conversion.py
# Import the javascript libraries
mineflayer = require("mineflayer")
vec3 = require("vec3")
# Global bot parameters
server_host = "localhos... | 3,776 | Python | .py | 93 | 28.204301 | 114 | 0.546871 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,767 | 04-reconnect-bot.py | 0x26e_MineflayerPython/scripts/04-reconnect-bot.py | from javascript import require, On, Once, AsyncTask, once, off
# Import the javascript libraries
mineflayer = require("mineflayer")
# Create bot
bot_args = {"username": "reconnect-bot", "host": "localhost", "port": 3000, "version": "1.19.4", "hideErrors": False}
reconnect = True
def start_bot():
bot = mineflaye... | 1,463 | Python | .py | 40 | 29.725 | 117 | 0.627742 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,768 | 05-multi-bot.py | 0x26e_MineflayerPython/scripts/05-multi-bot.py | from javascript import require, On, Once, AsyncTask, once, off
# Import the javascript libraries
mineflayer = require("mineflayer")
# Global bot parameters
server_host = "localhost"
server_port = 3000
reconnect = True
class MCBot:
def __init__(self, bot_name):
self.bot_args = {
"host": serv... | 2,402 | Python | .py | 63 | 28.968254 | 128 | 0.589401 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,769 | 09-eliza-chat-bot.py | 0x26e_MineflayerPython/scripts/09-eliza-chat-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
from eliza import eliza
# Import the javascript libraries
mineflayer = require("mineflayer")
# Global bot parameters
server_host = "localhost"
server_port = 3000
reconnect = True
# Setup Eliza chatbot
eliza = eliza.Eliza()
... | 3,213 | Python | .py | 82 | 28.463415 | 114 | 0.569453 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,770 | 07-color-bot.py | 0x26e_MineflayerPython/scripts/07-color-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
# Import the javascript libraries
mineflayer = require("mineflayer")
# Global bot parameters
server_host = "localhost"
server_port = 3000
reconnect = True
class MCBot:
def __init__(self, bot_name):
self.bot_ar... | 2,509 | Python | .py | 65 | 28.876923 | 114 | 0.59299 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,771 | 12-jumper-bot.py | 0x26e_MineflayerPython/scripts/12-jumper-bot.py | from javascript import require, On, Once, AsyncTask, once, off
from simple_chalk import chalk
from utils.vec3_conversion import vec3_to_str
# Requires ./utils/vec3_to_str.py
# Import the javascript libraries
mineflayer = require("mineflayer")
vec3 = require("vec3")
# Global bot parameters
server_host = "localhost"
s... | 4,349 | Python | .py | 106 | 28.339623 | 114 | 0.545066 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,772 | 03-disconnect-bot.py | 0x26e_MineflayerPython/scripts/03-disconnect-bot.py | from javascript import require, On, Once, AsyncTask, once, off
# Import the javascript libraries
mineflayer = require("mineflayer")
# Create bot
bot_args = {"username": "quitting-bot", "host": "localhost", "port": 3000, "version": "1.19.4", "hideErrors": False}
bot = mineflayer.createBot(bot_args)
# Login event (Log... | 1,172 | Python | .py | 32 | 33.03125 | 116 | 0.695111 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,773 | eliza.py | 0x26e_MineflayerPython/scripts/eliza/eliza.py | # From https://github.com/wadetb/eliza/blob/master/eliza.py
import logging
import random
import re
from collections import namedtuple
# Fix Python2/Python3 incompatibility
try:
input = raw_input
except NameError:
pass
log = logging.getLogger(__name__)
class Key:
def __init__(self, word, weight, decomps... | 7,863 | Python | .py | 206 | 25.796117 | 78 | 0.500394 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,774 | vec3_conversion.py | 0x26e_MineflayerPython/scripts/utils/vec3_conversion.py | def vec3_to_str(v):
return f"x: {v['x']:.3f}, y: {v['y']:.3f}, z: {v['z']:.3f}"
def vec3_to_dict(v):
return {"x": v["x"], "y": v["y"], "z": v["z"]}
| 158 | Python | .py | 4 | 36 | 63 | 0.421053 | 0x26e/MineflayerPython | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,775 | bbr.py | kalilovers_LightKnightBBR/bbr.py | import os
import shutil
import platform
import subprocess
#Colors
RED = "\033[91m"
GREEN = "\033[92m"
YELLOW = "\033[93m"
BLUE = "\033[94m"
MAGENTA = "\033[95m"
CYAN = "\033[96m"
RESET = "\033[0m"
def show_image():
ascii_art = f"""
{MAGENTA}%%%%%%%%%%%%%%%%$%%$%$$$$%%%%%%%%%%%%%
%%%%%%%%%%%%%$!!*... | 36,457 | Python | .py | 807 | 31.203222 | 176 | 0.515948 | kalilovers/LightKnightBBR | 8 | 4 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,776 | cc-spider.py | pisces76_cc-spider/cc-spider.py | import requests
from bs4 import BeautifulSoup
from docx import Document
from docx.shared import Inches
from datetime import datetime
import os,time
#初始化变量
DEBUG = True
USER = '葡萄' #配置用户名
SLEEP_TIME = 5 #抓取每篇文章的时间间隔，单位秒
start_page = 874 ... | 6,857 | Python | .py | 149 | 35.885906 | 167 | 0.609294 | pisces76/cc-spider | 8 | 7 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,777 | data_utils.py | Jometeorie_MultiHopShortcuts/code/data_utils.py | import os
import json
import torch
def load_dataset(dataset_path, filename='MQuAKE-CF-3k.json'):
with open(os.path.join(dataset_path, filename), 'r') as f:
dataset = json.load(f)
return dataset
def load_prompt(prompt_path, filename):
with open(os.path.join(prompt_path, filename), 'r') as f:
... | 4,632 | Python | .py | 94 | 37.212766 | 112 | 0.570635 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,778 | erase_with_kn.py | Jometeorie_MultiHopShortcuts/code/erase_with_kn.py | import argparse
import os
import sys
parser = argparse.ArgumentParser()
parser.add_argument('--root_path')
parser.add_argument('--model_path')
args = parser.parse_args()
from tqdm import tqdm
import copy
import collections
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
... | 6,804 | Python | .py | 107 | 55.028037 | 157 | 0.635437 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,779 | erase_with_rome.py | Jometeorie_MultiHopShortcuts/code/erase_with_rome.py | import argparse
import os
import sys
parser = argparse.ArgumentParser()
parser.add_argument('--root_path')
parser.add_argument('--model_path')
args = parser.parse_args()
from tqdm import tqdm
import copy
import collections
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
... | 4,870 | Python | .py | 75 | 57.173333 | 209 | 0.666527 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,780 | compute_neurons.py | Jometeorie_MultiHopShortcuts/code/compute_neurons.py | import argparse
import sys
parser = argparse.ArgumentParser()
parser.add_argument('--root_path')
parser.add_argument('--model_path')
parser.add_argument('--fact_type')
args = parser.parse_args()
from tqdm import tqdm
import copy
import collections
import numpy as np
import torch
from transformers import AutoTokenizer... | 4,165 | Python | .py | 63 | 57.349206 | 211 | 0.652536 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,781 | record_muliti_fail.py | Jometeorie_MultiHopShortcuts/code/record_muliti_fail.py | import argparse
import os
import sys
parser = argparse.ArgumentParser()
parser.add_argument('--root_path')
parser.add_argument('--model_path')
parser.add_argument('--fact_type')
parser.add_argument('--edit_type')
args = parser.parse_args()
from tqdm import tqdm
import copy
import collections
import numpy as np
import... | 5,006 | Python | .py | 70 | 63.128571 | 245 | 0.652006 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,782 | count_paras.py | Jometeorie_MultiHopShortcuts/code/count_paras.py | from datasets import load_dataset
import json
import time
from tqdm import tqdm
import re
import os
import shutil
def choose_paras(para_list, length):
res = []
for strs in para_list:
if len(strs) > length:
res.append(strs)
return res
st = time.time()
# ds = load_dataset("wikimedia/wik... | 3,446 | Python | .py | 92 | 32.054348 | 83 | 0.594773 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,783 | rome_utils.py | Jometeorie_MultiHopShortcuts/code/rome_utils.py | from easyeditor.models.kn.knowledge_neurons.knowledge_neurons import KnowledgeNeurons, model_type
from easyeditor import ROMEHyperParams, MEMITHyperParams
from easyeditor import BaseEditor
def edit_with_rome(model, tokenizer, prompts, target_new, subject, hparams_path):
hparams = ROMEHyperParams.from_hparams(hpara... | 1,132 | Python | .py | 27 | 35.777778 | 97 | 0.727768 | Jometeorie/MultiHopShortcuts | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,784 | rabbit.py | nchanko_myanocr_pub/rabbit.py | # -*- coding: utf-8 -*-
# Rabbit-Converter/Rabbit-Python
# Zawgyi <-> Unicode converter for Myanmar language
import re
import sys
class Rabbit:
@staticmethod
def uni2zg(text):
rules = [
{ "from": u"\u1004\u103a\u1039", "to": u"\u1064" }, { "from": u"\u1039\u1010\u103d", "to": u"\u1096" }, ... | 11,078 | Python | .py | 42 | 256.761905 | 5,295 | 0.540715 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,785 | ocr.py | nchanko_myanocr_pub/ocr.py | import streamlit as st
import pytesseract
from pdf2image import convert_from_path, pdfinfo_from_path
from PIL import Image
import tempfile
from google.cloud import vision
from dotenv import load_dotenv
import io
import os
import shutil
service_account_path = 'cloud_vision_service_acc.json'
os.environ["GOOGLE_APPLICA... | 7,795 | Python | .py | 130 | 46.046154 | 243 | 0.60512 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,786 | tess_functions.py | nchanko_myanocr_pub/tess_functions.py | import os
import requests
import pytesseract
import streamlit as st
def check_tesseract():
tesseract_cmd = tesseract_path()
if tesseract_cmd:
#st.write(f"Tesseract available at: {tesseract_cmd}")
pytesseract.pytesseract.tesseract_cmd = tesseract_cmd
else:
st.error("Tesseract is no... | 2,373 | Python | .py | 52 | 36.673077 | 140 | 0.661817 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,787 | epub_creator.py | nchanko_myanocr_pub/pages/epub_creator.py | import streamlit as st
from ebooklib import epub
import os
def create_epub_with_embedded_font(title, author, text, output_file, font_path, cover_image=None):
# Create the book
book = epub.EpubBook()
# Set metadata
book.set_title(title)
book.set_language('en')
book.add_author(author)
if co... | 3,485 | Python | .py | 79 | 35.56962 | 156 | 0.606615 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,788 | About.py | nchanko_myanocr_pub/pages/About.py | import streamlit as st
def about():
# Read the markdown file
st.image("demo.png",caption = "OCR Myanmar Book.",)
with open("about.md", "r") as f:
markdown_text = f.read()
# Display the markdown text on the page
st.markdown(markdown_text)
about()
| 278 | Python | .py | 9 | 26.333333 | 55 | 0.660377 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,789 | font_converter.py | nchanko_myanocr_pub/pages/font_converter.py | import streamlit as st
import os
from rabbit import Rabbit
rabbit = Rabbit()
def zawgyi_to_unicode(zawgyi_text):
"""Convert Zawgyi text to Unicode using the rabbit module."""
return rabbit.zg2uni(zawgyi_text)
def convert_file_to_unicode(file):
"""Read the content of the file, convert it to Unicode, and re... | 1,741 | Python | .py | 41 | 35.634146 | 111 | 0.668462 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,790 | crop_pdf.py | nchanko_myanocr_pub/pages/crop_pdf.py | import streamlit as st
import fitz # PyMuPDF
from PIL import Image
import io
from pypdf import PdfWriter, PdfReader
def load_pdf(pdf_file):
"""Load the PDF file and return the document object."""
return fitz.open(stream=pdf_file.read(), filetype="pdf")
def pdf_page_to_image(pdf_document, page_number, crop_bo... | 3,683 | Python | .py | 74 | 40.094595 | 169 | 0.613274 | nchanko/myanocr_pub | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,791 | tachibk-converter.py | BrutuZ_tachibk-converter/tachibk-converter.py | import gzip
import re
import varint
from argparse import ArgumentParser
from base64 import b64decode, b64encode
from json import dumps, loads
from pathlib import Path
from requests import get
from struct import pack, unpack
from subprocess import run
from google.protobuf.json_format import (
Parse,
ParseError,
... | 10,619 | Python | .py | 273 | 30.604396 | 193 | 0.588595 | BrutuZ/tachibk-converter | 8 | 2 | 0 | GPL-2.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,792 | RunAs.py | rkbennett_RunAsPy/RunAs.py | import argparse
import RunAsPy
parser = argparse.ArgumentParser(description="")
parser.add_argument('-d', '--domain', help="", nargs="?", dest="domainName")
parser.add_argument('-u', '--username', help="", nargs="?", required=True)
parser.add_argument('-P', '--password', help="", nargs="?", required=True)
parser.add_a... | 1,405 | Python | .py | 19 | 72.578947 | 147 | 0.686913 | rkbennett/RunAsPy | 8 | 1 | 0 | AGPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,793 | windefs.py | rkbennett_RunAsPy/RunAsPy/windefs.py | import ctypes
import ctypes.wintypes
UOI_NAME = 2
ERROR_LOGON_TYPE_NOT_GRANTED = 1385
LOGON32_LOGON_INTERACTIVE = ctypes.wintypes.DWORD(2)
LOGON32_LOGON_NETWORK = ctypes.wintypes.DWORD(3)
LOGON32_LOGON_BATCH = ctypes.wintypes.DWORD(4)
LOGON32_LOGON_SERVICE = ctypes.wintypes.DWORD(5)
LOGON32_LOGON_NETWORK_CLEARTEXT = ... | 21,309 | Python | .py | 545 | 34.440367 | 152 | 0.716812 | rkbennett/RunAsPy | 8 | 1 | 0 | AGPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,794 | __init__.py | rkbennett_RunAsPy/RunAsPy/__init__.py | import os
import ctypes
import ctypes.wintypes
import socket
import struct
import logging
from .windefs import *
def convertAttributeToString(attribute):
if attribute == 0:
return "Disabled"
if attribute == 1:
return "Enabled Default"
if attribute == 2:
return "Enabled"
if attr... | 47,817 | Python | .py | 750 | 53.461333 | 419 | 0.702017 | rkbennett/RunAsPy | 8 | 1 | 0 | AGPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,795 | get_outline.py | Kick-H_NEP-water/compare_MB-pol_SCAN/Related_Programs/get_outline.py | """
Purpose:
Get the points with the largest errors in the force_train.out,
virial_train.out or energy_train.out file, and find the ID of
the training set to which these points belong and output it.
Now, added function to selectively output remaining structures.
Notice:
t... | 3,039 | Python | .py | 83 | 28.843373 | 97 | 0.586113 | Kick-H/NEP-water | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,796 | nep_fps_select_all.py | Kick-H_NEP-water/compare_MB-pol_SCAN/Related_Programs/nep_fps_select_all.py | from pynep.calculate import NEP
from pynep.select import FarthestPointSample
from ase.io import read, write
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from multiprocessing import Pool
import sys
def map_fun(frame):
return np.mean(calc.get_property('descriptor', frame),... | 1,576 | Python | .py | 41 | 34.146341 | 91 | 0.687827 | Kick-H/NEP-water | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,797 | plot_nep_water.py | Kick-H_NEP-water/compare_MB-pol_SCAN/Related_Programs/plot_nep_water.py | import numpy as np
from pylab import *
import sys
##set figure properties
aw = 1.5
fs = 16
lw = 2.0
font = {'size' : fs}
matplotlib.rc('font', **font)
matplotlib.rc('axes' , lw=aw)
def set_fig_properties(ax_list):
tl = 6
tw = 1.5
tlm = 3
for ax in ax_list:
ax.tick_params(which='major', lengt... | 7,790 | Python | .py | 182 | 35.450549 | 107 | 0.564333 | Kick-H/NEP-water | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,798 | SS_OCR.py | op200_Simple_Subtitle_OCR/SS_OCR.py | import tkinter as tk
from tkinter import ttk
import cv2
from PIL import Image, ImageTk
import numpy as np
from math import floor
from threading import Thread
from time import sleep
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import webbrowser
def hyperlink_jump(hyperlink:str):
webbrowser.o... | 42,558 | Python | .py | 864 | 41.118056 | 290 | 0.681208 | op200/Simple_Subtitle_OCR | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |
2,289,799 | SS_OCR_CLI.py | op200_Simple_Subtitle_OCR/SS_OCR_CLI.py | import cv2
from PIL import Image, ImageTk
import numpy as np
from threading import Thread
from time import sleep
from math import floor
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
PROGRAM_NAME = "Simple Subtitle OCR (CLI)"
VERSION = "0.6.1"
HOME_LINK = "https://github.com/op200/Simple_Subtitle_O... | 12,873 | Python | .py | 360 | 27.797222 | 290 | 0.598738 | op200/Simple_Subtitle_OCR | 8 | 2 | 0 | GPL-3.0 | 9/5/2024, 10:48:43 PM (Europe/Amsterdam) |