python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import json
import os
from collections import defaultdict
import numpy as np
if __name__ == '__main__':
base_dir = "vqa/reviews/coco2014_val80"
review_files = [x for x in os.listdir(base_dir) if x.endswith('.jsonl') and x.startswith('gpt4_text')]
for review_file in sorted(review_files):
config =... | EXA-1-master | exa/models/LLaVA-main/llava/eval/summarize_gpt_review.py |
import argparse
import json
import os
import openai
import tqdm
import ray
import time
@ray.remote(num_cpus=4)
def get_eval(content: str, max_tokens: int):
while True:
try:
response = openai.ChatCompletion.create(
model='gpt-4',
messages=[{
'... | EXA-1-master | exa/models/LLaVA-main/llava/eval/eval_gpt_review.py |
import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.conversation import conv_templates
from llava.utils import disable_torch_init
from transformers import CLIPVisionModel, CLIPImageProcessor, Stopp... | EXA-1-master | exa/models/LLaVA-main/llava/eval/model_vqa_science.py |
"""Generate answers with GPT-3.5"""
# Note: you need to be using OpenAI Python v0.27.0 for the code below to work
import argparse
import json
import os
import time
import concurrent.futures
import openai
import tqdm
import shortuuid
MODEL = 'gpt-3.5-turbo'
MODEL_ID = 'gpt-3.5-turbo:20230327'
def get_answer(question_... | EXA-1-master | exa/models/LLaVA-main/llava/eval/qa_baseline_gpt35.py |
import argparse
import json
import os
import re
import random
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--result-file', type=str)
parser.add_argument('--output-file', type=str)
parser.add_argument('--output-result', type=str... | EXA-1-master | exa/models/LLaVA-main/llava/eval/eval_science_qa.py |
"""Generate json file for webpage."""
import json
import os
import re
# models = ['llama', 'alpaca', 'gpt35', 'bard']
models = ['vicuna']
def read_jsonl(path: str, key: str=None):
data = []
with open(os.path.expanduser(path)) as f:
for line in f:
if not line:
continue
... | EXA-1-master | exa/models/LLaVA-main/llava/eval/generate_webpage_data_from_table.py |
import argparse
import json
import os
import re
import random
from collections import defaultdict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--gpt4-result', type=str)
parser.add_argument('--our-result', type=str)
parser.add_a... | EXA-1-master | exa/models/LLaVA-main/llava/eval/eval_science_qa_gpt4.py |
import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.conversation import conv_templates
from llava.utils import disable_torch_init
from transformers import CLIPVisionModel, CLIPImageProcessor, Stopp... | EXA-1-master | exa/models/LLaVA-main/llava/eval/model_vqa.py |
import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.conversation import default_conversation
from llava.utils import disable_torch_init
# new stopping implementation
class KeywordsStoppingC... | EXA-1-master | exa/models/LLaVA-main/llava/eval/model_qa.py |
import argparse
import json
import os
import re
import random
from collections import defaultdict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--gpt4-result', type=str)
parser.add_argument('--requery-result', type=str)
parser.a... | EXA-1-master | exa/models/LLaVA-main/llava/eval/eval_science_qa_gpt4_requery.py |
import argparse
import json
import os
import openai
import tqdm
import ray
import time
@ray.remote(num_cpus=4)
def get_eval(content: str, max_tokens: int):
while True:
try:
response = openai.ChatCompletion.create(
model='gpt-4',
messages=[{
'... | EXA-1-master | exa/models/LLaVA-main/llava/eval/eval_gpt_review_visual.py |
import argparse
from collections import defaultdict
import datetime
import json
import os
import time
import gradio as gr
import requests
from llava.conversation import (default_conversation, conv_templates,
SeparatorStyle)
from llava.constants import LOGDIR
from llava.utils import ... | EXA-1-master | exa/models/LLaVA-main/llava/serve/gradio_web_server.py |
"""
A model worker executes the model.
"""
import argparse
import asyncio
import dataclasses
import logging
import json
import time
from typing import List, Union
import threading
import uuid
from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse
import requests
from tran... | EXA-1-master | exa/models/LLaVA-main/llava/serve/model_worker.py |
"""
A controller manages distributed workers.
It sends worker addresses to clients.
"""
import argparse
import asyncio
import dataclasses
from enum import Enum, auto
import json
import logging
import time
from typing import List, Union
import threading
from fastapi import FastAPI, Request
from fastapi.responses import... | EXA-1-master | exa/models/LLaVA-main/llava/serve/controller.py |
"""
Manually register workers.
Usage:
python3 -m fastchat.serve.register_worker --controller http://localhost:21001 --worker-name http://localhost:21002
"""
import argparse
import requests
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--controller-address", type=str)
... | EXA-1-master | exa/models/LLaVA-main/llava/serve/register_worker.py |
"""
Adopted from https://github.com/gradio-app/gradio/blob/main/gradio/components.py
Fix a markdown render problem.
"""
from __future__ import annotations
from gradio.components import *
from markdown2 import Markdown
class _Keywords(Enum):
NO_VALUE = "NO_VALUE" # Used as a sentinel to determine if nothing is p... | EXA-1-master | exa/models/LLaVA-main/llava/serve/gradio_patch.py |
import argparse
import json
import requests
from llava.conversation import default_conversation
def main():
if args.worker_address:
worker_addr = args.worker_address
else:
controller_addr = args.controller_address
ret = requests.post(controller_addr + "/refresh_all_workers")
... | EXA-1-master | exa/models/LLaVA-main/llava/serve/test_message.py |
EXA-1-master | exa/models/LLaVA-main/llava/serve/__init__.py | |
"""
Usage:
python3 -m fastchat.serve.cli --model ~/model_weights/llama-7b
"""
import argparse
import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava.conversation import conv_templates, SeparatorStyle
@torch.inference_mode()
def generate_stream(tokenizer, model, params, devi... | EXA-1-master | exa/models/LLaVA-main/llava/serve/cli.py |
code_highlight_css = (
"""
#chatbot .hll { background-color: #ffffcc }
#chatbot .c { color: #408080; font-style: italic }
#chatbot .err { border: 1px solid #FF0000 }
#chatbot .k { color: #008000; font-weight: bold }
#chatbot .o { color: #666666 }
#chatbot .ch { color: #408080; font-style: italic }
#chatbot .cm { color:... | EXA-1-master | exa/models/LLaVA-main/llava/serve/gradio_css.py |
"""
Usage:
python3 -m fastchat.data.optional_clean --lang en --reduce-rep --in sharegpt_clean.json --out output.json
python3 -m fastchat.data.optional_clean --skip-lang en --reduce-rep --in sharegpt_clean.json --out output.json
"""
import argparse
import json
import re
import polyglot
from polyglot.detect import Detec... | EXA-1-master | exa/models/LLaVA-main/llava/data/optional_clean.py |
"""
Usage: python3 -m fastchat.data.clean_sharegpt --in sharegpt_html.json --out sharegpt_clean.json
"""
import argparse
import json
import logging
import re
from typing import Dict, Union
import bs4
import markdownify # == 0.11.6
import tqdm
def _get_html_tags(file_path: str):
# Generate the list of html tags ... | EXA-1-master | exa/models/LLaVA-main/llava/data/clean_sharegpt.py |
"""
Split long conversations based on certain max length.
Usage: python3 -m fastchat.data.split_long_conversation \
--in sharegpt_clean.json \
--out sharegpt_split.json \
--model-name-or-path $<model-name>
"""
import argparse
import json
from typing import Dict, Sequence, Optional
import transformers
impo... | EXA-1-master | exa/models/LLaVA-main/llava/data/split_long_conversation.py |
EXA-1-master | exa/models/LLaVA-main/llava/data/__init__.py | |
"""
Usage:
python3 -m fastchat.data.inspect --in sharegpt_20230322_clean_lang_split.json
"""
import argparse
import json
import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--in-file", type=str, required=True)
parser.add_argument("--begin", type=int)
args = ... | EXA-1-master | exa/models/LLaVA-main/llava/data/inspect.py |
"""
Usage:
python3 pretty_json.py --in in.json --out out.json
"""
import argparse
import json
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--in-file", type=str, required=True)
parser.add_argument("--out-file", type=str, required=True)
args = parser.parse_args()
... | EXA-1-master | exa/models/LLaVA-main/llava/data/pretty_json.py |
import argparse
import json
import pathlib
# Prompt from stanford alpaca's training script
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
... | EXA-1-master | exa/models/LLaVA-main/llava/data/alpaca-converter.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim import AdamW
from kosmos import... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/train_kosmos_optimized.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_with_warmup
from kosmos import Kosmos, KosmosTokenizer
from accelerate impo... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/train_kosmos_stable.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim import AdamW
from .kosmos impor... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/train_kosmos_original.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim import AdamW
from .kosmos impor... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/training_kosmos_apex.py |
#quantization + paralleism
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim impor... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/training_kosmos_3.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim import AdamW
from .kosmos impor... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/train_kosmos_text.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim import AdamW
from .kosmos impor... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/train_kosmos_code.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import get_scheduler, default_data_collator, get_linear_schedule_with_warmup
from torch.optim import AdamW
from .kosmos impor... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/train_kosmos.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/training/notebookExperiments/main.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/kosmosSP.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/kosmos.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/video/kosmos_video.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/video/kosmos_conditional.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/audio/kosmos_audio.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/audio/kosmos_audio_data2vec.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenize... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/audio/kosmos_conditional.py |
"""
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright © 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for soft... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/allModalities/kosmos3.py |
import os
import requests
import torch
from torch.nn import Module
from torchvision import transforms
from torchvision.models.video import r3d_18
from transformers import (
AutoModel,
AutoTokenizer,
CLIPModel,
CLIPProcessor,
Wav2Vec2ForCTC,
T5Tokenizer,
Wav2Vec2Processor,
)
from torchscale.a... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/allModalities/kosmos2.py |
import os
import torch
from torch.nn import Module
from torchvision import transforms
from torchvision.models.video import r3d_18
from transformers import (
AutoModel,
AutoTokenizer,
CLIPModel,
CLIPProcessor,
Data2VecForCTC,
T5Tokenizer,
Wav2Vec2Processor,
list_models
)
# Add additional... | EXA-1-master | exa/models/KOSMOS_reimplementation-main/model/allModalities/kosmos.py |
from setuptools import find_packages, setup
setup(
name='gato-tf',
version='0.0.2',
description='Unofficial Gato: A Generalist Agent',
url='https://github.com/OrigamiDream/gato.git',
author='OrigamiDream',
author_email='sdy36071@naver.com',
license='MIT',
packages=find_packages(),
i... | EXA-1-master | exa/models/gato/setup.py |
import tensorflow as tf
from tensorflow.keras.optimizers import schedules, AdamW
from gato import GatoConfig
from gato.models import Gato
# Load and preprocess your dataset
def load_and_preprocess_dataset():
# Load and preprocess your dataset here
# Return the dataset as a tf.data.Dataset object
pass
# In... | EXA-1-master | exa/models/gato/train.py |
import copy
from typing import Dict, Any
class GatoConfig:
@staticmethod
def large():
return GatoConfig(num_transformer_blocks=24,
num_attention_heads=16,
layer_width=2048,
feedforward_hidden_size=8192,
... | EXA-1-master | exa/models/gato/gato/config.py |
from gato.config import GatoConfig
from gato.models import Gato
| EXA-1-master | exa/models/gato/gato/__init__.py |
import tensorflow as tf
from tensorflow.keras import layers, models
from gato import GatoConfig
from typing import Dict, Any, Union
def _randomized_positions(from_v, to_v):
pos = tf.random.uniform(from_v.shape, minval=0, maxval=1, dtype=tf.float32)
pos = pos * tf.cast(to_v - from_v, dtype=tf.float32)
pos... | EXA-1-master | exa/models/gato/gato/models/embedding.py |
import tensorflow as tf
from gato.models.transformer import TransformerBlock
from gato.models.embedding import PatchPositionEncoding, ResidualEmbedding, LocalPositionEncoding, DiscreteEmbedding
from gato.models.tokenizers import ContinuousValueTokenizer
from tensorflow.keras import models
from gato import GatoConfig
... | EXA-1-master | exa/models/gato/gato/models/__init__.py |
import tensorflow as tf
from tensorflow.keras import layers, models, activations
from gato import GatoConfig
from typing import Dict, Any, Union
class TransformerBlock(layers.Layer):
def __init__(self,
config: Union[GatoConfig, Dict[str, Any]],
trainable: bool = True,
... | EXA-1-master | exa/models/gato/gato/models/transformer.py |
import tensorflow as tf
from gato import GatoConfig
from tensorflow.keras import models
from typing import Union, Dict, Any
def mu_law_encode(x, mu=100, m=256):
# Appendix B. Agent Data Tokenization Details
sign = tf.math.sign(x)
numerator = tf.math.log(tf.abs(x) * mu + 1.0)
denominator = tf.math.log... | EXA-1-master | exa/models/gato/gato/models/tokenizers.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import find_packages, setup
setup(
name="segment_anything",
version="1.0",
install_requires=[... | EXA-1-master | exa/models/segment-anything-main/setup.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from segment_anything.modeling import Sam
from typing import Optional, Tuple
from .util... | EXA-1-master | exa/models/segment-anything-main/segment_anything/predictor.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | EXA-1-master | exa/models/segment-anything-main/segment_anything/build_sam.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing impor... | EXA-1-master | exa/models/segment-anything-main/segment_anything/automatic_mask_generator.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .build_sam import (
build_sam,
build_sam_vit_h,
build_sam_vit_l,
build_sam_vit_b,
sam_model_regis... | EXA-1-master | exa/models/segment-anything-main/segment_anything/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import math
from copy import deepcopy
from itertools import product
from typing import An... | EXA-1-master | exa/models/segment-anything-main/segment_anything/utils/amg.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize,... | EXA-1-master | exa/models/segment-anything-main/segment_anything/utils/transforms.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Tuple
from ..modeling import ... | EXA-1-master | exa/models/segment-anything-main/segment_anything/utils/onnx.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| EXA-1-master | exa/models/segment-anything-main/segment_anything/utils/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .sam import Sam
from .image_encoder import ImageEncoderViT
from .mask_decoder import MaskDecoder
from .prompt_encoder... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/common.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/transformer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/image_encoder.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/prompt_encoder.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import Any, Dict, List, Tuple
from .i... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/sam.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | EXA-1-master | exa/models/segment-anything-main/segment_anything/modeling/mask_decoder.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import cv2 # type: ignore
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
im... | EXA-1-master | exa/models/segment-anything-main/scripts/amg.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from segment_anything import sam_model_registry
from segment_anything.utils.onnx import SamOnnxModel
import... | EXA-1-master | exa/models/segment-anything-main/scripts/export_onnx_model.py |
'''
Adapted from https://github.com/lupantech/ScienceQA
'''
from dataclasses import dataclass
from typing import List, Optional
def get_question_text(problem):
question = problem['question']
return question
def get_context_text(problem, use_caption):
txt_context = problem['hint']
img_context = probl... | EXA-1-master | exa/models/mm-cot-main/utils_prompt.py |
'''
Adapted from https://github.com/lupantech/ScienceQA
'''
import re
from rouge import Rouge
from nltk.translate.bleu_score import sentence_bleu
from sentence_transformers import util
########################
## BLEU
########################
def tokenize(text):
tokens = re.split(r'\s|\.', text)
tokens = [t f... | EXA-1-master | exa/models/mm-cot-main/evaluations.py |
'''
Adapted from https://github.com/huggingface/transformers
'''
from transformers import T5Config, T5ForConditionalGeneration
from transformers.models.t5.modeling_t5 import T5Stack, __HEAD_MASK_WARNING_MSG, T5EncoderModel
import copy
import math
import os
import warnings
from typing import Optional, Tuple, Union
impo... | EXA-1-master | exa/models/mm-cot-main/model.py |
import os
from torch.utils.data import Dataset
import os
import json
import numpy as np
import torch
from utils_prompt import *
img_shape = {
"resnet": (512, 2048),
"clip": (49, 2048),
"detr": (100, 256),
}
def load_data_std(args):
problems = json.load(open(os.path.join(args.data_root, 'scienceqa/prob... | EXA-1-master | exa/models/mm-cot-main/utils_data.py |
'''
Adapted from https://github.com/lupantech/ScienceQA
'''
import os
import json
import argparse
import warnings
import pandas as pd
from sentence_transformers import SentenceTransformer
from evaluations import caculate_bleu, caculate_rouge, caculate_similariry
warnings.filterwarnings('ignore')
def get_acc_with_con... | EXA-1-master | exa/models/mm-cot-main/utils_evaluate.py |
import os
import numpy as np
import torch
import os
import re
import json
import argparse
import random
from transformers import T5Tokenizer, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer, T5ForConditionalGeneration
from model import T5ForConditionalGeneration, T5ForMultimodalGeneration
from utils_da... | EXA-1-master | exa/models/mm-cot-main/main.py |
import os
import copy
import pytorch_lightning as pl
from vlmo.config import ex
from vlmo.modules import VLMo
from vlmo.datamodules.multitask_datamodule import MTDataModule
from pytorch_lightning.plugins import environments as pl_env
from pytorch_lightning.utilities.distributed import rank_zero_info
class OMPIClust... | EXA-1-master | exa/models/unilm-master/vlmo/run.py |
from setuptools import setup, find_packages
setup(
name="vlmo",
packages=find_packages(
exclude=[".dfc", ".vscode", "dataset", "notebooks", "result", "scripts"]
),
version="1.0.0",
license="MIT",
description="VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts",
... | EXA-1-master | exa/models/unilm-master/vlmo/setup.py |
from sacred import Experiment
ex = Experiment("VLMo")
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nl... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/config.py |
EXA-1-master | exa/models/unilm-master/vlmo/vlmo/__init__.py | |
from .base_dataset import BaseDataset
class F30KCaptionKarpathyDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "train":
names = ["f30k_caption_karpathy_train"]
elif split == "val":
names = ["... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/f30k_caption_karpathy_dataset.py |
from .base_dataset import BaseDataset
class VisualGenomeCaptionDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = ["vg"]
elif split == ... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/vg_caption_dataset.py |
import random
import torch
import io
import pyarrow as pa
import os
from PIL import Image
from vlmo.transforms import keys_to_transforms
class BaseDataset(torch.utils.data.Dataset):
def __init__(
self,
data_dir: str,
transform_keys: list,
image_size: int,
names: list,
... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/base_dataset.py |
from .base_dataset import BaseDataset
class CocoCaptionKarpathyDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["coco_caption_karpathy_train", "coco_caption_karpathy_r... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/coco_caption_karpathy_dataset.py |
from glob import glob
from .base_dataset import BaseDataset
class WikibkDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = [f"wikibk_train_{i}"... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/wikibk_dataset.py |
from .vg_caption_dataset import VisualGenomeCaptionDataset
from .coco_caption_karpathy_dataset import CocoCaptionKarpathyDataset
from .f30k_caption_karpathy_dataset import F30KCaptionKarpathyDataset
from .conceptual_caption_dataset import ConceptualCaptionDataset
from .sbu_caption_dataset import SBUCaptionDataset
from ... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/__init__.py |
from glob import glob
from .base_dataset import BaseDataset
class SBUCaptionDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = [f"sbu_{i}" for ... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/sbu_caption_dataset.py |
from .base_dataset import BaseDataset
import sys
import random
class NLVR2Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["nlvr2_train"]
elif split == "val":
... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/nlvr2_dataset.py |
from .base_dataset import BaseDataset
class VQAv2Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["vqav2_train", "vqav2_trainable_val"]
elif split == "val":
... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/vqav2_dataset.py |
from glob import glob
from .base_dataset import BaseDataset
class ConceptualCaptionDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = [f"concep... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datasets/conceptual_caption_dataset.py |
from vlmo.datasets import NLVR2Dataset
from .datamodule_base import BaseDataModule
class NLVR2DataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return NLVR2Dataset
@property
def dataset_name(self):
... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/nlvr2_datamodule.py |
import functools
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from torch.utils.data.dataset import ConcatDataset
from torch.utils.data.distributed import DistributedSampler
from . import _datamodules
class MTDataModule(LightningDataModule):
def __init__(self, _config... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/multitask_datamodule.py |
import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from transformers import (
DataCollatorForLanguageModeling,
DataCollatorForWholeWordMask,
BertTokenizer,
)
def get_pretrained_tokenizer(from_pretrained):
if torch.distributed.is_initialized():
... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/datamodule_base.py |
from vlmo.datasets import ConceptualCaptionDataset
from .datamodule_base import BaseDataModule
class ConceptualCaptionDataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return ConceptualCaptionDataset
@p... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/conceptual_caption_datamodule.py |
from vlmo.datasets import SBUCaptionDataset
from .datamodule_base import BaseDataModule
class SBUCaptionDataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return SBUCaptionDataset
@property
def datas... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/sbu_datamodule.py |
from .vg_caption_datamodule import VisualGenomeCaptionDataModule
from .f30k_caption_karpathy_datamodule import F30KCaptionKarpathyDataModule
from .coco_caption_karpathy_datamodule import CocoCaptionKarpathyDataModule
from .conceptual_caption_datamodule import ConceptualCaptionDataModule
from .sbu_datamodule import SBUC... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/__init__.py |
from vlmo.datasets import VisualGenomeCaptionDataset
from .datamodule_base import BaseDataModule
class VisualGenomeCaptionDataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return VisualGenomeCaptionDataset
... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/vg_caption_datamodule.py |
from vlmo.datasets import VQAv2Dataset
from .datamodule_base import BaseDataModule
from collections import defaultdict
class VQAv2DataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return VQAv2Dataset
@p... | EXA-1-master | exa/models/unilm-master/vlmo/vlmo/datamodules/vqav2_datamodule.py |
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