python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
from dynamic_fusion_graph.model import DynamicFusionGraph as DynamicFusionGraph
from recurrent_fusion.model import RecurrentFusion as RecurrentFusion
from tensor_fusion.model import TensorFusion as TensorFusion
from multiple_attention.model import MultipleAttentionFusion as MultipleAttentionFusion
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/__init__.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/multiple_attention/__init__.py | |
#CMU Multimodal SDK, CMU Multimodal Model SDK
#Multi-attention Recurrent Network for Human Communication Comprehension, Amir Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Prateek Vij, Louis-Philippe Morency - https://arxiv.org/pdf/1802.00923.pdf
#in_modalities: is a list of inputs from each modality - the first... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/multiple_attention/model.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/recurrent_fusion/__init__.py | |
#CMU Multimodal SDK, CMU Multimodal Model SDK
#Multimodal Language Analysis with Recurrent Multistage Fusion, Paul Pu Liang, Ziyin Liu, Amir Zadeh, Louis-Philippe Morency - https://arxiv.org/abs/1808.03920
#in_dimensions: the list of dimensionalities of each modality
#cell_size: lstm cell size
#in_modalities: is ... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/recurrent_fusion/model.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/tensor_fusion/__init__.py | |
#CMU Multimodal SDK, CMU Multimodal Model SDK
#Tensor Fusion Network for Multimodal Sentiment Analysis, Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, Louis-Philippe Morency - https://arxiv.org/pdf/1707.07250.pdf
#in_modalities: is a list of inputs from each modality - the first dimension of all the modality... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/tensor_fusion/model.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/dynamic_fusion_graph/__init__.py | |
#CMU Multimodal SDK, CMU Multimodal Model SDK
#Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph, Amir Zadeh, Paul Pu Liang, Jonathan Vanbriesen, Soujanya Poria, Edmund Tong, Erik Cambria, Minghai Chen, Louis-Philippe Morency - http://www.aclweb.org/anthology/P18-1208
... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/fusion/dynamic_fusion_graph/model.py |
from LSTHM.LSTHM import LSTHM as LSTHM
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/modules/__init__.py |
import torch
import time
from torch import nn
import torch.nn.functional as F
class LSTHM(nn.Module):
def __init__(self,cell_size,in_size,hybrid_in_size):
super(LSTHM, self).__init__()
self.cell_size=cell_size
self.in_size=in_size
self.W=nn.Linear(in_size,4*self.cell_size)
self.U=nn.Linear(cell_size,4*sel... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/modules/LSTHM/LSTHM.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/modules/LSTHM/__init__.py | |
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from LSTHM import LSTHM
import numpy
#in=40, 3 modalities,4 attentions
full_in=numpy.array(numpy.zeros([32,40]))
inputx=Variable(torch.Tensor(full_in),requires_grad=True)
full_in=numpy.array(numpy.zeros([32,12]))
inp... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmmodelsdk/modules/LSTHM/test.py |
from mmsdk.mmdatasdk.computational_sequence.computational_sequence import computational_sequence as computational_sequence
from mmsdk.mmdatasdk.dataset.dataset import mmdataset as mmdataset
from mmsdk.mmdatasdk.dataset.standard_datasets import CMU_MOSEI as cmu_mosei
from mmsdk.mmdatasdk.dataset.standard_datasets import... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/__init__.py |
featuresetMetadataTemplate= [
"root name",#name of the featureset
"computational sequence description",#name of the featureset
"dimension names"
"computational sequence version",#the version of featureset
"alignment compatible",#name of the featureset
"dataset name",#featureset belongs to which datase... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/configurations/metadataconfigs.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/configurations/__init__.py | |
from mmsdk.mmdatasdk import log
from mmsdk.mmdatasdk.configurations.metadataconfigs import *
from tqdm import tqdm
#this function checks the heirarchy format of a given computatioanl sequence data. This will crash the program if data is in wrong format. If in correct format the return value is simply True
def validat... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/computational_sequence/integrity_check.py |
import h5py
import hashlib
import validators
import json
import sys
import os
import time
import uuid
#log is the same as standard_sdks log
from mmsdk.mmdatasdk import log
from mmsdk.mmdatasdk.configurations.metadataconfigs import *
from mmsdk.mmdatasdk.computational_sequence.integrity_check import *
from mmsdk.mmdat... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/computational_sequence/computational_sequence.py |
import sys
import h5py
import os
import json
from tqdm import tqdm
from mmsdk.mmdatasdk import log
from mmsdk.mmdatasdk.configurations.metadataconfigs import *
from mmsdk.mmdatasdk.computational_sequence.integrity_check import *
def read_CSD(resource,destination=None):
if (resource is None): raise log.error("No reso... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/computational_sequence/file_ops.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/computational_sequence/__init__.py | |
import h5py
import time
import requests
from tqdm import tqdm
import os
import math
import sys
from mmsdk.mmdatasdk import log
def read_URL(url,destination):
if destination is None:
log.error("Destination is not specified when downloading data",error=True)
if os.path.isdir(destination.rsplit(os.sep,1)[-2]) is F... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/computational_sequence/download_ops.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/computational_sequence/blank.py | |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/__init__.py | |
from mmsdk.mmdatasdk import log, computational_sequence
import sys
import numpy
import time
from tqdm import tqdm
import os
#specified for numerical inconsistencies within floating points - if abs l1 distance two numbers is less than this, then they are the same.
#only use for interval comparison.
epsilon=10e-6
clas... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/dataset.py |
EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/__init__.py | |
standard_train_fold=['202990', '116221', '110003', '265302', '33089', '286943', '122439', '126872', '43371', '81406', '246216', '41381', '118639', '121759', '193894', '106077', '193291', '100367', '79356', '241172', '112604', '202431', '127470', '241178', '193093', '52067', '112172', '264418', '238060', '238063', '2140... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/POM/pom_std_folds.py |
from mmsdk.mmdatasdk.dataset.standard_datasets.POM import pom_std_folds as standard_folds
from mmsdk.mmdatasdk.dataset.standard_datasets.POM.pom import raw, highlevel, labels
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/POM/__init__.py |
raw={}
raw["words"]='http://immortal.multicomp.cs.cmu.edu/POM/language/POM_TimestampedWords.csd'
raw["phonemes"]='http://immortal.multicomp.cs.cmu.edu/POM/language/POM_TimestampedPhones.csd'
highlevel={}
highlevel["glove_vectors"]='http://immortal.multicomp.cs.cmu.edu/POM/language/POM_TimestampedWordVectors.csd'
high... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/POM/pom.py |
highlevel={}
highlevel["SOCIAL_IQ_COVAREP"]="http://immortal.multicomp.cs.cmu.edu/Social-IQ/acoustic/SOCIAL_IQ_COVAREP.csd"
highlevel["SOCIAL-IQ_QA_BERT_LASTLAYER_BINARY_CHOICE"]="http://immortal.multicomp.cs.cmu.edu/Social-IQ/qa/SOCIAL-IQ_QA_BERT_LASTLAYER_BINARY_CHOICE.csd"
highlevel["SOCIAL-IQ_QA_BERT_MULTIPLE_CHOIC... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/SocialIQ/socialiq.py |
from mmsdk.mmdatasdk.dataset.standard_datasets.SocialIQ import socialiq_std_folds as standard_folds
from mmsdk.mmdatasdk.dataset.standard_datasets.SocialIQ.socialiq import highlevel
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/SocialIQ/__init__.py |
standard_train_fold=['W_GrHtwZez8','3wIejfT9l30','gcDnKQul_c8','qr69jLeQdRA','lo0R1mvjDT8','vKkpvQlHEG8','B6p6X1LSjiA','8m_3eBsy22Y','gfA1xa-BMCg','EqXKrS3gPN4','LTUojzYVUUI','FJF56lmDqQo','QC_4iR0tyvE','E0TBOKN8J2E','g67e0hDT1oQ','s3Czwcz3E-o','D56yCIgqqgk','QQpIBRLlJzo','4pYqIEQow2s','72ltfGTYqpQ','ofvOXABptcY','YlVk... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/SocialIQ/socialiq_std_folds.py |
from mmsdk.mmdatasdk.dataset.standard_datasets.CMU_MOSI import cmu_mosi_std_folds as standard_folds
from mmsdk.mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi import raw,highlevel,labels
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSI/__init__.py |
standard_train_fold=['2iD-tVS8NPw', '8d-gEyoeBzc', 'Qr1Ca94K55A', 'Ci-AH39fi3Y', '8qrpnFRGt2A', 'Bfr499ggo-0', 'QN9ZIUWUXsY', '9T9Hf74oK10', '7JsX8y1ysxY', '1iG0909rllw', 'Oz06ZWiO20M', 'BioHAh1qJAQ', '9c67fiY0wGQ', 'Iu2PFX3z_1s', 'Nzq88NnDkEk', 'Clx4VXItLTE', '9J25DZhivz8', 'Af8D0E4ZXaw', 'TvyZBvOMOTc', 'W8NXH0Djyww',... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSI/cmu_mosi_std_folds.py |
raw={}
raw["words"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSI/language/CMU_MOSI_TimestampedWords.csd'
raw["phonemes"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSI/language/CMU_MOSI_TimestampedPhones.csd'
highlevel={}
highlevel["glove_vectors"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSI/language/CMU_MOSI_Tim... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSI/cmu_mosi.py |
raw={}
raw["words"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/language/CMU_MOSEI_TimestampedWords.csd'
raw["phones"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/language/CMU_MOSEI_TimestampedPhones.csd'
highlevel={}
highlevel["glove_vectors"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/language/CMU_MOSEI... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSEI/cmu_mosei.py |
from mmsdk.mmdatasdk.dataset.standard_datasets.CMU_MOSEI import cmu_mosei_std_folds as standard_folds
from mmsdk.mmdatasdk.dataset.standard_datasets.CMU_MOSEI.cmu_mosei import raw, highlevel, labels, extra
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSEI/__init__.py |
standard_train_fold=['hh04W3xXa5s', 'GdFP_p4eQX0', '4iG0ffmnCOw', '81406', 'qyJiDgtj6YE', 'KI2sU-mhM44', 'qXisb7w9LjM', 'CLkTjujFVKU', 'aMtFBGh2wKI', '2OLVF-KEaZU', 'Or-9Nc_GAq8', '72tXTrSXoMk', 'hSgKOKK3L8M', 'YVHJpAROBvQ', 'pVzHaakhKAw', '127470', 'wY8JbFOsp5E', '-iRBcNs9oI8', 'sLaTZtL0ZIk', 'txjqbr6FoZs', 'jVayR0VCl... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSEI/cmu_mosei_std_folds.py |
import sys
from datetime import datetime
from colorama import Fore
from tqdm import tqdm
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
OKPURPLE = '\033[0;35m'
OKADVISORY = '\033[1;36m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/log/log.py |
from .log import *
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/mmsdk/mmdatasdk/log/__init__.py |
#download_dataset.py
#downloads a standard dataset from multicomp servers
import mmsdk
from mmsdk import mmdatasdk
import argparse
parser = argparse.ArgumentParser(description='Downloads a dataset from web')
parser.add_argument('dataset',
metavar='dataset',
default='cmu_mose... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/basics/download_dataset.py |
print ("Some of the content in this tutorial may be outdated, however it is an amazing tutorial nonetheless: https://github.com/Justin1904/CMU-MultimodalSDK-Tutorials")
print ("Special thanks to Zhun Liu @ justin1904")
| EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/basics/justin_github.py |
#read_dataset_by_folder.py
#reads a dataset from a dataset folder
import mmsdk
from mmsdk import mmdatasdk
import argparse
parser = argparse.ArgumentParser(description='Reading dataset from a folder')
parser.add_argument('path', metavar='path', type=str,
help='the folder path to read dataset fro... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/basics/read_dataset_by_folder.py |
#read_dataset_by_folder.py
#reads a dataset from a dataset folder
import mmsdk
import os
import argparse
from mmsdk import mmdatasdk
from os import listdir
from os.path import isfile, join
parser = argparse.ArgumentParser(description='Reading dataset by files')
parser.add_argument('path', metavar='path', type=str,
... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/basics/read_dataset_by_files.py |
#create_toy_computational_sequence.py
#this example shows how to create two toy computational sequences and put them together in a dataset
import mmsdk
from mmsdk import mmdatasdk
import numpy
def random_init(compseq,feat_dim):
for vid_key in vid_keys:
num_entries=numpy.random.randint(low=5,high=100,size=1)
com... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/basics/create_toy_computational_sequence.py |
import mmsdk
from mmsdk import mmdatasdk
from mmsdk.mmdatasdk import log
import numpy
def myavg(intervals,features):
return numpy.average(features,axis=0)
def deploy(in_dataset,destination):
deploy_files={x:x for x in in_dataset.keys()}
in_dataset.deploy(destination,deploy_files)
def download_data():
sou... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/full_examples/process_mosei.py |
#word_level_align.py
#first aligns a dataset to the words vectors and collapses other modalities (by taking average of them for the duration of the word). After this operation every modality will have the same frequency (same as word vectors). Then the code aligns based on opinion labels (note that collapse does not ha... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/mmdatasdk_examples/full_examples/process_mosi.py |
#scenario1.py
#performs imputations and unifies to make sure they remain functional after changes.
import mmsdk
from mmsdk import mmdatasdk
import numpy
import sys
#uncomment all the ==> lines together
#A simple averaging technique. More advanced methods can be built based on intervals.
def myavg(intervals,features)... | EXA-1-master | exa/libraries/CMU-MultimodalSDK-master/examples/sdk_diagnostics/scenario1.py |
from setuptools import setup, find_packages
setup(
name='latent-diffusion',
version='0.0.1',
description='',
packages=find_packages(),
install_requires=[
'torch',
'numpy',
'tqdm',
],
) | EXA-1-master | exa/libraries/latent-diffusion/setup.py |
from torchvision.datasets.utils import download_url
from ldm.util import instantiate_from_config
import torch
import os
# todo ?
from google.colab import files
from IPython.display import Image as ipyimg
import ipywidgets as widgets
from PIL import Image
from numpy import asarray
from einops import rearrange, repeat
im... | EXA-1-master | exa/libraries/latent-diffusion/notebook_helpers.py |
import argparse, os, sys, datetime, glob, importlib, csv
import numpy as np
import time
import torch
import torchvision
import pytorch_lightning as pl
from packaging import version
from omegaconf import OmegaConf
from torch.utils.data import random_split, DataLoader, Dataset, Subset
from functools import partial
from ... | EXA-1-master | exa/libraries/latent-diffusion/main.py |
import argparse, os, sys, glob
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
from main import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
def make_batch(image, mask, device):
image = np.array(Image.open(image).convert("RGB... | EXA-1-master | exa/libraries/latent-diffusion/scripts/inpaint.py |
import argparse, os, sys, glob
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from einops import rearrange
from torchvision.utils import make_grid
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm... | EXA-1-master | exa/libraries/latent-diffusion/scripts/txt2img.py |
import argparse, os, sys, glob
import clip
import torch
import torch.nn as nn
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
import scann
import time
from mul... | EXA-1-master | exa/libraries/latent-diffusion/scripts/knn2img.py |
import argparse, os, sys, glob, datetime, yaml
import torch
import time
import numpy as np
from tqdm import trange
from omegaconf import OmegaConf
from PIL import Image
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
rescale = lambda x: (x + 1.) / 2.
def custom_to_pil(... | EXA-1-master | exa/libraries/latent-diffusion/scripts/sample_diffusion.py |
import os, sys
import numpy as np
import scann
import argparse
import glob
from multiprocessing import cpu_count
from tqdm import tqdm
from ldm.util import parallel_data_prefetch
def search_bruteforce(searcher):
return searcher.score_brute_force().build()
def search_partioned_ah(searcher, dims_per_block, aiq_t... | EXA-1-master | exa/libraries/latent-diffusion/scripts/train_searcher.py |
import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
... | EXA-1-master | exa/libraries/latent-diffusion/ldm/lr_scheduler.py |
import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def log_txt_as_img(wh,... | EXA-1-master | exa/libraries/latent-diffusion/ldm/util.py |
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGa... | EXA-1-master | exa/libraries/latent-diffusion/ldm/models/autoencoder.py |
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super()... | EXA-1-master | exa/libraries/latent-diffusion/ldm/models/diffusion/ddim.py |
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modu... | EXA-1-master | exa/libraries/latent-diffusion/ldm/models/diffusion/classifier.py |
EXA-1-master | exa/libraries/latent-diffusion/ldm/models/diffusion/__init__.py | |
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super()... | EXA-1-master | exa/libraries/latent-diffusion/ldm/models/diffusion/plms.py |
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | EXA-1-master | exa/libraries/latent-diffusion/ldm/models/diffusion/ddpm.py |
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
d... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/attention.py |
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
# constants
DE... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/x_transformer.py |
import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('de... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/ema.py |
from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/losses/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def hinge_d_loss_with_... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/losses/vqperceptual.py |
import torch
import torch.nn as nn
from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no?
class LPIPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
disc_num_layers=3, disc_in_channels=3, disc_factor=... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/losses/contperceptual.py |
EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/encoders/__init__.py | |
import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
import kornia
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
class AbstractEncoder(nn.Mo... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/encoders/modules.py |
# -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import numpy as np
import cv2
import tor... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/image_degradation/bsrgan.py |
from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
| EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/image_degradation/__init__.py |
import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
'''
# ----------------------... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/image_degradation/utils_image.py |
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as ... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/image_degradation/bsrgan_light.py |
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/gu... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/diffusionmodules/util.py |
EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/diffusionmodules/__init__.py | |
# pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches th... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/diffusionmodules/model.py |
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/diffusionmodules/openaimodel.py |
EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/distributions/__init__.py | |
import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
retur... | EXA-1-master | exa/libraries/latent-diffusion/ldm/modules/distributions/distributions.py |
import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
class LSUNBase(Dataset):
def __init__(self,
txt_file,
data_root,
size=None,
interpolation="bicubic",
... | EXA-1-master | exa/libraries/latent-diffusion/ldm/data/lsun.py |
import os, yaml, pickle, shutil, tarfile, glob
import cv2
import albumentations
import PIL
import numpy as np
import torchvision.transforms.functional as TF
from omegaconf import OmegaConf
from functools import partial
from PIL import Image
from tqdm import tqdm
from torch.utils.data import Dataset, Subset
import tami... | EXA-1-master | exa/libraries/latent-diffusion/ldm/data/imagenet.py |
EXA-1-master | exa/libraries/latent-diffusion/ldm/data/__init__.py | |
from abc import abstractmethod
from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
class Txt2ImgIterableBaseDataset(IterableDataset):
'''
Define an interface to make the IterableDatasets for text2img data chainable
'''
def __init__(self, num_records=0, valid_ids=None, si... | EXA-1-master | exa/libraries/latent-diffusion/ldm/data/base.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import subprocess
import sys
from setuptools import Extension, find_packages, setup
from torch.utils import ... | EXA-1-master | exa/libraries/fairseq/setup.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Legacy entry point. Use fairseq_cli/train.py or fairseq-train instead.
"""
from fairseq_cli.train import cli_mai... | EXA-1-master | exa/libraries/fairseq/train.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import functools
import importlib
dependencies = [
"dataclasses",
"hydra",
"numpy",
"omegaconf",
"... | EXA-1-master | exa/libraries/fairseq/hubconf.py |
import argparse
from typing import Tuple
def get_next_version(release_type) -> Tuple[Tuple[int, int, int], str, str]:
current_ver = find_version("fairseq/version.txt")
version_list = [int(x) for x in current_ver.strip("'").split(".")]
major, minor, patch = version_list[0], version_list[1], version_list[2]... | EXA-1-master | exa/libraries/fairseq/release_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import io
import os
import string
import tempfile
import unittest
import torch
from fairseq import tokenizer
from fairseq.data import Diction... | EXA-1-master | exa/libraries/fairseq/tests/test_dictionary.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
import unittest
from typing import Any, Dict, Sequence
import fairseq
import fairseq.options
import fairseq.tasks
import tor... | EXA-1-master | exa/libraries/fairseq/tests/test_roberta.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq import utils
class TestUtils(unittest.TestCase):
def test_convert_padding_direction(self):
... | EXA-1-master | exa/libraries/fairseq/tests/test_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from copy import deepcopy
from dataclasses import dataclass
import pytest
from typing import Optional
from unittest.mock impor... | EXA-1-master | exa/libraries/fairseq/tests/test_ema.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from fairseq.data import iterators, ListDataset
class TestIterators(unittest.TestCase):
def test_counting_iterator_inde... | EXA-1-master | exa/libraries/fairseq/tests/test_iterators.py |
import torch
import numpy as np
import unittest
from fairseq.modules import (
ESPNETMultiHeadedAttention,
RelPositionMultiHeadedAttention,
RotaryPositionMultiHeadedAttention,
)
torch.use_deterministic_algorithms(True)
class TestESPNETMultiHeadedAttention(unittest.TestCase):
def setUp(self) -> None:
... | EXA-1-master | exa/libraries/fairseq/tests/test_espnet_multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from typing import Dict, List
import torch
import tests.utils as test_utils
from fairseq import utils
from fairseq.data impo... | EXA-1-master | exa/libraries/fairseq/tests/test_noising.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import tempfile
import unittest
import torch
from fairseq.data.dictionary import Dictionary
from fairseq.models.lstm import L... | EXA-1-master | exa/libraries/fairseq/tests/test_lstm_jitable.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import unittest
import torch
from torch.cuda.amp import GradScaler, autocast
from fairseq.optim import build_opt... | EXA-1-master | exa/libraries/fairseq/tests/test_amp_optimizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
class TestSparseMultiheadAtten... | EXA-1-master | exa/libraries/fairseq/tests/test_sparse_multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import uuid
from fairseq.logging import metrics
class TestMetrics(unittest.TestCase):
def test_nesting(self):
w... | EXA-1-master | exa/libraries/fairseq/tests/test_metrics.py |
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