maxidl

import sys
import os  # noqa
sys.path.insert(0, ".")  # noqa

import torch
from utils.styled_plot import plt
from utils.dataset import load_test_image, preprocess_image, normalize_image, convert_idx_to_label
from classifiers.cnn_classifier import ImageNetClassifier
from solutions.explainers import plot_attributions, aggregate_attribution, normalize_attribution

maxidl

model.to(device)
model.eval()
model.zero_grad()

def forward_func(inputs, attention_mask=None):
    return model(inputs, attention_mask=attention_mask).logits

lig = LayerIntegratedGradients(forward_func, model.bert.embeddings)

all_input_ids, all_ref_input_ids, all_attributions, all_pred_probs, all_pred_class, all_true_class, all_attr_class, all_attr_score, all_convergence_scores = ([] for i in range(9))

maxidl

{
    "0": "tench, Tinca tinca",
    "1": "goldfish, Carassius auratus",
    "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
    "3": "tiger shark, Galeocerdo cuvieri",
    "4": "hammerhead, hammerhead shark",
    "5": "electric ray, crampfish, numbfish, torpedo",
    "6": "stingray",
    "7": "cock",
    "8": "hen",

maxidl

# remap prefix from 'C-b' to 'C-a'
unbind C-b
set-option -g prefix C-a
bind-key C-a send-prefix

# split panes using | and -
bind | split-window -h
bind - split-window -v
unbind '"'
unbind %

maxidl

import cudf
import cugraph
from numba import cuda
from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_uniform_float32
import numpy as np

# gdf = cudf.read_csv('zachary.ssv', header=None, sep=' ', dtype=['int32', 'int32'])
gdf = cudf.read_csv('Flickr-labelled.edgelist', header=None, sep=' ', dtype=['int32', 'int32'])
gdf.columns = ['src', 'dest']
gdf = gdf.sort_values(by='src')

maxidl

import numpy as np
import pandas as pd
from scipy import sparse
from pathlib import Path
from joblib import Parallel, delayed
from itertools import chain
from random import shuffle
from tqdm import tqdm
from gensim.models import Word2Vec
import logging

maxidl

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