DongWook Kim spnova12

## psnr_debug.ipynb

      
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                spnova12
                / psnr_debug.ipynb
            
            
              Created
              October 31, 2018 05:05
            
              
                numpy, pytorch에서의 영상 복원 실험 그리고 psnr측정과 자료형
              
          
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## cuda_installation_on_ubuntu_18.04
#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

## pytorch 실험 003
    def train_base():
        start_time = 0
        time_read = 0
        time_read_training = 0

        # training 시키지 않고 이미지 읽기만 run
        for i, batch in tqdm.tqdm(enumerate(training_data_loader, 1)):
            if i==1: start_time = time.time()
            if i == 2000:
                end_time = time.time()

## pytorch 실험 002
    class FeatureExtractor(nn.Module):
        def __init__(self, cnn, feature_layer=11):
            super(FeatureExtractor, self).__init__()
            self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer + 1)])

        def normalize(self, tensors, mean, std):
            if not torch.is_tensor(tensors):
                raise TypeError('tensor is not a torch image.')
            for tensor in tensors:
                for t, m, s in zip(tensor, mean, std):

## pytorch 실험 001
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
	#!/bin/bash
	## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###
	def train_base():
	start_time = 0
	time_read = 0
	time_read_training = 0

	# training 시키지 않고 이미지 읽기만 run
	for i, batch in tqdm.tqdm(enumerate(training_data_loader, 1)):
	if i==1: start_time = time.time()
	if i == 2000:
	end_time = time.time()
	class FeatureExtractor(nn.Module):
	def __init__(self, cnn, feature_layer=11):
	super(FeatureExtractor, self).__init__()
	self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer + 1)])

	def normalize(self, tensors, mean, std):
	if not torch.is_tensor(tensors):
	raise TypeError('tensor is not a torch image.')
	for tensor in tensors:
	for t, m, s in zip(tensor, mean, std):
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"import torch\n",
	"import numpy as np\n",