rohit-gupta

You are a highly advanced AI translator specializing in translating text into English. Your goal is to deliver translations that accurately convey the literal meaning while preserving the original text's structure, tone, intent, and cultural nuances. Adhere to the following mandatory guidelines:

1. Structural Integrity: Maintain the exact structure of the original text, including new lines, paragraph breaks, and formatting elements.
2. Emphasis and Styling: Retain all emphasis and styling (e.g., italics, bold, ALL CAPS) from the original text, and do not introduce new styling if none exists.
3. Emojis and Special Characters: Use the same emojis and special characters as in the original text, replacing culturally irrelevant emojis with appropriate equivalents and noting changes in a footnote.
4. Cultural Context: Adapt cultural references, idioms, and slang to be meaningful in the target culture.
5. Tone and Intent: Accurately reflect the tone and intent (e.g., sarcastic, humorous, formal)

rohit-gupta

def shrink_loss(pseudo_label, logits_u_s, conf_thresh):
    removed_class_idx = []
    loss_u_shrink_batch = 0
    
    B, C = pseudo_label.shape
    
    max_probs = pseudo_label.max(dim=-1)[0]
    mask = pseudo_label.ge(conf_thresh).float()
    
    sorted_prob_w, sorted_idx = pseudo_label.topk(C, dim=-1, sorted=True)

rohit-gupta

ps -AF | grep ssh
pkill -t pts/9

rohit-gupta

// ==UserScript==
// @name         New Userscript
// @namespace    http://tampermonkey.net/
// @version      0.1
// @description  try to take over the world!
// @author       You
// @match        <$URL$>
// @icon         <$ICON$>
// @grant        none
// ==/UserScript==

rohit-gupta

.open yfcc100m_dataset.sql
.databases
.tables
.schema yfcc100m_dataset
SELECT * from yfcc100m_dataset WHERE photoid=2932067831 LIMIT 1;
.mode csv
.headers on
.out yfcc100m_dump.csv
SELECT photoid, title, description, downloadurl FROM yfcc100m_dataset;

rohit-gupta

def var_covar_loss(Z, alpha=1.0, beta=0.01):
  
  eps = 1e-5
  K = z.shape[1]
  
  # covariance matrix
  C = torch.cov(Z.t())
  
  # Push sqrt of diagonal terms to 1 (std dev = 1.0)

rohit-gupta

#!/bin/bash

# TODO might be more missing packages
# TODO Shard train set


# Install dependencies
cd /tmp
wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh
bash Anaconda3-2020.02-Linux-x86_64.sh -b -p $HOME/anaconda3

rohit-gupta

from torch.autograd import Function


class GradientReversal(Function):
    @staticmethod
    def forward(ctx, x):
        ctx.save_for_backward(x)
        output = x
        return output

rohit-gupta

# Pascal Titan X, Titan Xp, 1080Ti, 1080, 1070Ti, 1070 ... = 6.1
# Volta Titan V = 7.0
# Turing Titan RTX, 2080Ti ... = 7.5
export TORCH_CUDA_ARCH_LIST="6.1;7.0;7.5" # Pascal, Volta and Turing on CRCV Cluster
git clone https://github.com/NVIDIA/apex
cd apex
pip install --upgrade --force-reinstall -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

rohit-gupta