text stringlengths 1 93.6k |
|---|
def get_config(filepath):
|
"""Returns settings from json file.
|
"""
|
config = get_default_config()
|
config.update(Settings(json.load(open(filepath))))
|
return config
|
# <FILESEP>
|
import json
|
with open("data/FABLES.json", "r") as f:
|
data = json.load(f)
|
# get FABLE data
|
data = data['FABLES']
|
# change this or select from data.keys()
|
book_key = "Flawless"
|
book_key_list = data.keys()
|
# getting model list for a given book
|
model_key = "GPT-4"
|
model_key_list = data[book_key].keys()
|
# getting summary data generated by 'GPT-4' for a given book
|
summary = data[book_key][model_key]["summary"]
|
# getting general comments of human annotations for a given book and model
|
general_comment = data[book_key][model_key]['general_comment']
|
# getting human annotations of each claim given book and model
|
claims = data[book_key][model_key]['claims']
|
for idx, claim_data in claims.items():
|
print("Claim: ", claim_data['claim']) # Claim generated from summary
|
print("Is it faithful?: ", claim_data['label']) # Label of faithfulness
|
print("Evidence: ", claim_data['evidence']) # Evidence that either support or contradict claim
|
print("Reason: ", claim_data['reason']) # Reason of label
|
# <FILESEP>
|
import torch
|
import torch.nn as nn
|
import torch.nn.functional as F
|
from torch.autograd import Variable
|
import numpy as np
|
def LSregress(pred, gt, origin):
|
nb = pred.size(0)
|
origSize = pred.size()
|
pred = pred.reshape(nb, -1 )
|
gt = gt.reshape(nb, -1 )
|
coef = (torch.sum(pred * gt, dim = 1) / torch.clamp(torch.sum(pred * pred, dim=1), min=1e-5) ).detach()
|
coef = torch.clamp(coef, 0.001, 1000)
|
for n in range(0, len(origSize) -1 ):
|
coef = coef.unsqueeze(-1)
|
pred = pred.view(origSize )
|
predNew = origin * coef.expand(origSize )
|
return predNew
|
def LSregressDiffSpec(diff, spec, imOrig, diffOrig, specOrig ):
|
nb, nc, nh, nw = diff.size()
|
# Mask out too bright regions
|
mask = (imOrig < 0.9).float()
|
diff = diff * mask
|
spec = spec * mask
|
im = imOrig * mask
|
diff = diff.view(nb, -1)
|
spec = spec.view(nb, -1)
|
im = im.view(nb, -1)
|
a11 = torch.sum(diff * diff, dim=1)
|
a22 = torch.sum(spec * spec, dim=1)
|
a12 = torch.sum(diff * spec, dim=1)
|
frac = a11 * a22 - a12 * a12
|
b1 = torch.sum(diff * im, dim = 1)
|
b2 = torch.sum(spec * im, dim = 1)
|
# Compute the coefficients based on linear regression
|
coef1 = b1 * a22 - b2 * a12
|
coef2 = -b1 * a12 + a11 * b2
|
coef1 = coef1 / torch.clamp(frac, min=1e-2 )
|
coef2 = coef2 / torch.clamp(frac, min=1e-2 )
|
# Compute the coefficients assuming diffuse albedo only
|
coef3 = torch.clamp(b1 / torch.clamp(a11, min=1e-5), 0.001, 1000 )
|
coef4 = coef3.clone() * 0
|
frac = (frac / (nc * nh * nw) ).detach()
|
fracInd = (frac > 1e-2 ).float()
|
coefDiffuse = fracInd * coef1 + (1 - fracInd) * coef3
|
coefSpecular = fracInd * coef2 + (1 - fracInd) * coef4
|
for n in range(0, 3):
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.