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""" Fuse conv-bn pattern in torch.Module, an example for torch.fx

    see: https://pytorch.org/tutorials/intermediate/fx_conv_bn_fuser.html
"""
import copy
from typing import Tuple, Dict, Any

import torch
import torch.fx as fx
import torch.nn as nn

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import os

class CharDataset(object):
    def __init__(self, path):
        if not os.path.exists(path):
            raise RuntimeError('Cannot open the file: {}'.format(path))
        self.raw_data = open(path, 'r').read()
        self.chars = list(set(self.raw_data))
        self.data_size = len(self.raw_data)
        print('There are {} characters in the file'.format(self.data_size))

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import torch
import torch.nn as nn
import torch.nn.functional as F

def init_weight(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0., 0.02)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1., 0.02)

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import torch
import torch.nn as nn
import math

## the model definition
# see HeKaiming's implementation using torch: 
# https://github.com/KaimingHe/resnet-1k-layers/blob/master/README.md
class Bottleneck(nn.Module):
    expansion = 4  # # output cahnnels / # input channels

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# Enter your network definition here.
# Use Shift+Enter to update the visualization.
layer {
  name: "CustomData1"
  type: "CustomData"
  top: "blob0"
  top: "blob1"
  top: "blob2"
  top: "blob3"
  include {

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# -*- coding: UTF-8 -*-
# File: logger.py
# Author: Yuxin Wu <[email protected]>
from __future__ import print_function
import logging
import os
import errno
import shutil
import os.path
from datetime import datetime

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# convert yolo cfg file to caffe prototxt file
import sys
import argparse

TYPES = ["Conv", "ReLU", "Pool", "Route", "Reorg", "Region"]
ACTIVATION_TYPES = ['leaky', 'linear']
layer_names = [];

def HasConflictNameError(conflicted_name):
    print 'Error! The layer name \"{}\" has been in the list.'.format(conflicted_name)

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import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
## load mnist dataset
use_cuda = torch.cuda.is_available()

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#include <vector>
#include "gtest/gtest.h"
#include "caffe/common.hpp"
#include "caffe/blob.hpp"
#include "caffe/layers/reorg_layer.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
    template <typename TypeParam>
    class ReorgLayerTest : public MultiDeviceTest<TypeParam> {
        typedef typename TypeParam::Dtype Dtype;

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# has 3 lstm layer
name: "ocr"

layer {
	name: "data"
	type: "OCRData"
	top: "data"
	top: "label"
	image_data_param {
		is_color: false