aurotripathy

# Lifted verbatim from https://pytorch.org/tutorials/beginner/knowledge_distillation_tutorial.html
# Only for illustrating the distillation training loop as a code fragment.

for epoch in range(epochs):
        running_loss = 0.0
        for inputs, labels in train_loader:
            inputs, labels = inputs.to(device), labels.to(device)

            optimizer.zero_grad()

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aurotripathy

"""
Time the all_reduce_sum operation with model parameters size of the VGG-16 model (~138M floats)
    1. Paramters are loaded into each of the N=4 GPUs
    2. nccl.all_reduce is invoked on the paramters
TO get a breakdown of the VGG model size, see...
https://stackoverflow.com/questions/28232235/how-to-calculate-the-number-of-parameters-of-convolutional-neural-networks
"""

from __future__ import print_function
import torch

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class MyLinearLayer(nn.Module):
    """ Custom Linear layer but mimics a standard linear layer """
    def __init__(self, size_in, size_out):
        super().__init__()
        self.size_in, self.size_out = size_in, size_out
        weights = torch.Tensor(size_out, size_in)
        self.weights = nn.Parameter(weights)  # nn.Parameter is a Tensor that's a module parameter.
        bias = torch.Tensor(size_out)
        self.bias = nn.Parameter(bias)

aurotripathy

# Dataset utils and dataloaders

import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat

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#!/usr/bin/env python3
import logging
import os
from furiosa import runtime
from furiosa.runtime import session
import numpy as np

LOGLEVEL = os.environ.get('FURIOSA_LOG_LEVEL', 'INFO').upper()
logging.basicConfig(level=LOGLEVEL)

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#!/usr/bin/env python3

"""
Calibration data/images is from the data folder 
from https://github.com/derronqi/yolov7-face 
"""
import sys

import onnx
import torch

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"""
The model is from https://github.com/derronqi/yolov7-face
Here, we download yolov7s-face.pt and convert it.
Output is model.onnx
"""
import torch
from models.experimental import attempt_load
from pudb import set_trace
from utils.torch_utils import select_device
batch_size = 1

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import logging
import os
from furiosa import runtime
from furiosa.runtime import session
import numpy as np

LOGLEVEL = os.environ.get('FURIOSA_LOG_LEVEL', 'INFO').upper()
logging.basicConfig(level=LOGLEVEL)

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#!/usr/bin/env python3
import logging
import os
from furiosa import runtime
from furiosa.runtime import session
import numpy as np

LOGLEVEL = os.environ.get('FURIOSA_LOG_LEVEL', 'INFO').upper()
logging.basicConfig(level=LOGLEVEL)