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tictaqqn/pggan_for_public.ipynb

Created September 17, 2021 15:00
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pggan_for_public.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "pggan_for_public.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/bababax11/d8297befc4e02bba3b21b473ae92e58f/pggan_for_public.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rvbPMRIbXmro",
"outputId": "73490cbe-2354-477d-b076-3e9aba59042a"
},
"source": [
"!git clone https://github.com/odegeasslbc/Progressive-GAN-pytorch"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'Progressive-GAN-pytorch'...\n",
"remote: Enumerating objects: 22, done.\u001b[K\n",
"remote: Total 22 (delta 0), reused 0 (delta 0), pack-reused 22\u001b[K\n",
"Unpacking objects: 100% (22/22), done.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0343ARBeY5MU"
},
"source": [
"!cp /content/Progressive-GAN-pytorch/* ./"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "gL9fGWe7Svql"
},
"source": [
"ここらへんでドライブをマウントしておきましょう"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 650
},
"id": "-HWGZKDTMbY_",
"outputId": "aa77e9b4-91d4-4858-e175-555a402f5ccf"
},
"source": [
"# バージョン合わせ\n",
"!pip install torch==1.1.0\n",
"!pip install torchvision==0.3.0\n",
"!pip install Pillow==5.4.1"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting torch==1.1.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/69/60/f685fb2cfb3088736bafbc9bdbb455327bdc8906b606da9c9a81bae1c81e/torch-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (676.9MB)\n",
"\u001b[K |████████████████████████████████| 676.9MB 26kB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.1.0) (1.19.4)\n",
"\u001b[31mERROR: torchvision 0.8.1+cu101 has requirement torch==1.7.0, but you'll have torch 1.1.0 which is incompatible.\u001b[0m\n",
"Installing collected packages: torch\n",
" Found existing installation: torch 1.7.0+cu101\n",
" Uninstalling torch-1.7.0+cu101:\n",
" Successfully uninstalled torch-1.7.0+cu101\n",
"Successfully installed torch-1.1.0\n",
"Collecting torchvision==0.3.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/2e/45/0f2f3062c92d9cf1d5d7eabd3cae88cea9affbd2b17fb1c043627838cb0a/torchvision-0.3.0-cp36-cp36m-manylinux1_x86_64.whl (2.6MB)\n",
"\u001b[K |████████████████████████████████| 2.6MB 7.6MB/s \n",
"\u001b[?25hRequirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.3.0) (7.0.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==0.3.0) (1.15.0)\n",
"Requirement already satisfied: torch>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.3.0) (1.1.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision==0.3.0) (1.19.4)\n",
"Installing collected packages: torchvision\n",
" Found existing installation: torchvision 0.8.1+cu101\n",
" Uninstalling torchvision-0.8.1+cu101:\n",
" Successfully uninstalled torchvision-0.8.1+cu101\n",
"Successfully installed torchvision-0.3.0\n",
"Collecting Pillow==5.4.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/85/5e/e91792f198bbc5a0d7d3055ad552bc4062942d27eaf75c3e2783cf64eae5/Pillow-5.4.1-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
"\u001b[K |████████████████████████████████| 2.0MB 7.8MB/s \n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"\u001b[?25hInstalling collected packages: Pillow\n",
" Found existing installation: Pillow 7.0.0\n",
" Uninstalling Pillow-7.0.0:\n",
" Successfully uninstalled Pillow-7.0.0\n",
"Successfully installed Pillow-5.4.1\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"PIL"
]
}
}
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nKaI7fDFLbZH"
},
"source": [
"# 途中からやるときはファイルパスを書き換えます(そのくらいargsで制御するように改造してもよかった)\n",
"\n",
"# L = r\"\"\"\n",
"# from tqdm import tqdm\n",
"# import numpy as np\n",
"# from PIL import Image\n",
"# import argparse\n",
"# import random\n",
"\n",
"# import torch\n",
"# import torch.nn.functional as F\n",
"# from torch import nn, optim\n",
"# from torch.autograd import Variable, grad\n",
"# from torch.utils.data import DataLoader\n",
"# from torchvision import datasets, transforms, utils\n",
"\n",
"# from progan_modules import Generator, Discriminator\n",
"\n",
"# def accumulate(model1, model2, decay=0.999):\n",
"# par1 = dict(model1.named_parameters())\n",
"# par2 = dict(model2.named_parameters())\n",
"\n",
"# for k in par1.keys():\n",
"# par1[k].data.mul_(decay).add_(1 - decay, par2[k].data)\n",
"\n",
"\n",
"# def imagefolder_loader(path):\n",
"# def loader(transform):\n",
"# data = datasets.ImageFolder(path, transform=transform)\n",
"# data_loader = DataLoader(data, shuffle=True, batch_size=batch_size,\n",
"# num_workers=4)\n",
"# return data_loader\n",
"# return loader\n",
"\n",
"\n",
"# def sample_data(dataloader, image_size=4):\n",
"# transform = transforms.Compose([\n",
"# transforms.Resize(image_size+int(image_size*0.2)+1),\n",
"# transforms.RandomCrop(image_size),\n",
"# transforms.RandomHorizontalFlip(),\n",
"# transforms.ToTensor(),\n",
"# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
"# ])\n",
"\n",
"# loader = dataloader(transform)\n",
"\n",
"# return loader\n",
"\n",
"\n",
"# def train(generator, discriminator, init_step, loader, total_iter=600000):\n",
"# step = init_step # can be 1 = 8, 2 = 16, 3 = 32, 4 = 64, 5 = 128, 6 = 128\n",
"# data_loader = sample_data(loader, 4 * 2 ** step)\n",
"# dataset = iter(data_loader)\n",
"\n",
"# #total_iter = 600000\n",
"# total_iter_remain = total_iter - (total_iter//6)*(step-1)\n",
"\n",
"# pbar = tqdm(range(total_iter_remain))\n",
"\n",
"# disc_loss_val = 0\n",
"# gen_loss_val = 0\n",
"# grad_loss_val = 0\n",
"\n",
"# from datetime import datetime\n",
"# import os\n",
"# date_time = datetime.now()\n",
"# post_fix = '%s_%s_%d_%d.txt'%(trial_name, date_time.date(), date_time.hour, date_time.minute)\n",
"# log_folder = 'trial_%s_%s_%d_%d'%(trial_name, date_time.date(), date_time.hour, date_time.minute)\n",
" \n",
"# os.mkdir(log_folder)\n",
"# os.mkdir(log_folder+'/checkpoint')\n",
"# os.mkdir(log_folder+'/sample')\n",
"\n",
"# config_file_name = os.path.join(log_folder, 'train_config_'+post_fix)\n",
"# config_file = open(config_file_name, 'w')\n",
"# config_file.write(str(args))\n",
"# config_file.close()\n",
"\n",
"# log_file_name = os.path.join(log_folder, 'train_log_'+post_fix)\n",
"# log_file = open(log_file_name, 'w')\n",
"# log_file.write('g,d,nll,onehot\\n')\n",
"# log_file.close()\n",
"\n",
"# from shutil import copy\n",
"# copy('train.py', log_folder+'/train_%s.py'%post_fix)\n",
"# copy('progan_modules.py', log_folder+'/model_%s.py'%post_fix)\n",
"\n",
"# alpha = 0\n",
"# one = torch.FloatTensor([1]).to(device)\n",
"# mone = one * -1\n",
"# iteration = 0\n",
"\n",
"# for i in pbar:\n",
"# discriminator.zero_grad()\n",
"\n",
"# alpha = min(1, (2/(total_iter//6)) * iteration)\n",
"\n",
"# if iteration > total_iter//6:\n",
"# alpha = 0\n",
"# iteration = 0\n",
"# step += 1\n",
"\n",
"# if step > 6:\n",
"# alpha = 1\n",
"# step = 6\n",
"# data_loader = sample_data(loader, 4 * 2 ** step)\n",
"# dataset = iter(data_loader)\n",
"\n",
"# try:\n",
"# real_image, label = next(dataset)\n",
"\n",
"# except (OSError, StopIteration):\n",
"# dataset = iter(data_loader)\n",
"# real_image, label = next(dataset)\n",
"\n",
"# iteration += 1\n",
"\n",
"# ### 1. train Discriminator\n",
"# b_size = real_image.size(0)\n",
"# real_image = real_image.to(device)\n",
"# label = label.to(device)\n",
"# real_predict = discriminator(\n",
"# real_image, step=step, alpha=alpha)\n",
"# real_predict = real_predict.mean() \\\n",
"# - 0.001 * (real_predict ** 2).mean()\n",
"# real_predict.backward(mone)\n",
"\n",
"# # sample input data: vector for Generator\n",
"# gen_z = torch.randn(b_size, input_code_size).to(device)\n",
"\n",
"# fake_image = generator(gen_z, step=step, alpha=alpha)\n",
"# fake_predict = discriminator(\n",
"# fake_image.detach(), step=step, alpha=alpha)\n",
"# fake_predict = fake_predict.mean()\n",
"# fake_predict.backward(one)\n",
"\n",
"# ### gradient penalty for D\n",
"# eps = torch.rand(b_size, 1, 1, 1).to(device)\n",
"# x_hat = eps * real_image.data + (1 - eps) * fake_image.detach().data\n",
"# x_hat.requires_grad = True\n",
"# hat_predict = discriminator(x_hat, step=step, alpha=alpha)\n",
"# grad_x_hat = grad(\n",
"# outputs=hat_predict.sum(), inputs=x_hat, create_graph=True)[0]\n",
"# grad_penalty = ((grad_x_hat.view(grad_x_hat.size(0), -1)\n",
"# .norm(2, dim=1) - 1)**2).mean()\n",
"# grad_penalty = 10 * grad_penalty\n",
"# grad_penalty.backward()\n",
"# grad_loss_val += grad_penalty.item()\n",
"# disc_loss_val += (real_predict - fake_predict).item()\n",
"\n",
"# d_optimizer.step()\n",
"\n",
"# ### 2. train Generator\n",
"# if (i + 1) % n_critic == 0:\n",
"# generator.zero_grad()\n",
"# discriminator.zero_grad()\n",
" \n",
"# predict = discriminator(fake_image, step=step, alpha=alpha)\n",
"\n",
"# loss = -predict.mean()\n",
"# gen_loss_val += loss.item()\n",
"\n",
"\n",
"# loss.backward()\n",
"# g_optimizer.step()\n",
"# accumulate(g_running, generator)\n",
"\n",
"# if (i + 1) % 1000 == 0 or i==0:\n",
"# with torch.no_grad():\n",
"# images = g_running(torch.randn(5 * 10, input_code_size).to(device), step=step, alpha=alpha).data.cpu()\n",
"\n",
"# utils.save_image(\n",
"# images,\n",
"# f'{log_folder}/sample/{str(i + 1).zfill(6)}.png',\n",
"# nrow=10,\n",
"# normalize=True,\n",
"# range=(-1, 1))\n",
" \n",
"# if (i+1) % 10000 == 0 or i==0:\n",
"# try:\n",
"# torch.save(g_running.state_dict(), f'{log_folder}/checkpoint/{str(i + 1).zfill(6)}_g.model')\n",
"# torch.save(discriminator.state_dict(), f'{log_folder}/checkpoint/{str(i + 1).zfill(6)}_d.model')\n",
"# except:\n",
"# pass\n",
"\n",
"# if (i+1)%500 == 0:\n",
"# state_msg = (f'{i + 1}; G: {gen_loss_val/(500//n_critic):.3f}; D: {disc_loss_val/500:.3f};'\n",
"# f' Grad: {grad_loss_val/500:.3f}; Alpha: {alpha:.3f}')\n",
" \n",
"# log_file = open(log_file_name, 'a+')\n",
"# new_line = \"%.5f,%.5f\\n\"%(gen_loss_val/(500//n_critic), disc_loss_val/500)\n",
"# log_file.write(new_line)\n",
"# log_file.close()\n",
"\n",
"# disc_loss_val = 0\n",
"# gen_loss_val = 0\n",
"# grad_loss_val = 0\n",
"\n",
"# print(state_msg)\n",
"# #pbar.set_description(state_msg)\n",
"\n",
"\n",
"# if __name__ == '__main__':\n",
"\n",
"# parser = argparse.ArgumentParser(description='Progressive GAN, during training, the model will learn to generate images from a low resolution, then progressively getting high resolution ')\n",
"\n",
"# parser.add_argument('--path', type=str, help='path of specified dataset, should be a folder that has one or many sub image folders inside')\n",
"# parser.add_argument('--trial_name', type=str, default=\"test1\", help='a brief description of the training trial')\n",
"# parser.add_argument('--gpu_id', type=int, default=0, help='0 is the first gpu, 1 is the second gpu, etc.')\n",
"# parser.add_argument('--lr', type=float, default=0.001, help='learning rate, default is 1e-3, usually dont need to change it, you can try make it bigger, such as 2e-3')\n",
"# parser.add_argument('--z_dim', type=int, default=128, help='the initial latent vector\\'s dimension, can be smaller such as 64, if the dataset is not diverse')\n",
"# parser.add_argument('--channel', type=int, default=128, help='determines how big the model is, smaller value means faster training, but less capacity of the model')\n",
"# parser.add_argument('--batch_size', type=int, default=4, help='how many images to train together at one iteration')\n",
"# parser.add_argument('--n_critic', type=int, default=1, help='train Dhow many times while train G 1 time')\n",
"# parser.add_argument('--init_step', type=int, default=1, help='start from what resolution, 1 means 8x8 resolution, 2 means 16x16 resolution, ..., 6 means 256x256 resolution')\n",
"# parser.add_argument('--total_iter', type=int, default=300000, help='how many iterations to train in total, the value is in assumption that init step is 1')\n",
"# parser.add_argument('--pixel_norm', default=False, action=\"store_true\", help='a normalization method inside the model, you can try use it or not depends on the dataset')\n",
"# parser.add_argument('--tanh', default=False, action=\"store_true\", help='an output non-linearity on the output of Generator, you can try use it or not depends on the dataset')\n",
" \n",
"# args = parser.parse_args()\n",
"\n",
"# print(str(args))\n",
"\n",
"# trial_name = args.trial_name\n",
"# device = torch.device(\"cuda:%d\"%(args.gpu_id))\n",
"# input_code_size = args.z_dim\n",
"# batch_size = args.batch_size\n",
"# n_critic = args.n_critic\n",
"\n",
"# generator = Generator(in_channel=args.channel, input_code_dim=input_code_size, pixel_norm=args.pixel_norm, tanh=args.tanh).to(device)\n",
"# discriminator = Discriminator(feat_dim=args.channel).to(device)\n",
"# g_running = Generator(in_channel=args.channel, input_code_dim=input_code_size, pixel_norm=args.pixel_norm, tanh=args.tanh).to(device)\n",
" \n",
"# ## you can directly load a pretrained model here\n",
"# generator.load_state_dict(torch.load('/content/drive/MyDrive/150000_g.model'))　## TODO: ここを書き換える\n",
"# g_running.load_state_dict(torch.load('/content/drive/MyDrive/150000_g.model')) ## TODO: ここを書き換える\n",
"# discriminator.load_state_dict(torch.load('/content/drive/MyDrive/150000_d.model')) ## TODO: ここを書き換える\n",
" \n",
"# g_running.train(False)\n",
"\n",
"# g_optimizer = optim.Adam(generator.parameters(), lr=args.lr, betas=(0.0, 0.99))\n",
"# d_optimizer = optim.Adam(discriminator.parameters(), lr=args.lr, betas=(0.0, 0.99))\n",
"\n",
"# accumulate(g_running, generator, 0)\n",
"\n",
"# loader = imagefolder_loader(args.path)\n",
"\n",
"# train(generator, discriminator, args.init_step, loader, args.total_iter)\n",
"# \"\"\"\n",
"# with open('train.py', 'w') as f:\n",
"# f.write(L)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zHOCFZzuZntO",
"outputId": "2b81d75a-41cc-41ae-a179-1c0fccbeb869"
},
"source": [
"!python train.py -h"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"usage: train.py [-h] [--path PATH] [--trial_name TRIAL_NAME] [--gpu_id GPU_ID]\n",
" [--lr LR] [--z_dim Z_DIM] [--channel CHANNEL]\n",
" [--batch_size BATCH_SIZE] [--n_critic N_CRITIC]\n",
" [--init_step INIT_STEP] [--total_iter TOTAL_ITER]\n",
" [--pixel_norm] [--tanh]\n",
"\n",
"Progressive GAN, during training, the model will learn to generate images from\n",
"a low resolution, then progressively getting high resolution\n",
"\n",
"optional arguments:\n",
" -h, --help show this help message and exit\n",
" --path PATH path of specified dataset, should be a folder that has\n",
" one or many sub image folders inside\n",
" --trial_name TRIAL_NAME\n",
" a brief description of the training trial\n",
" --gpu_id GPU_ID 0 is the first gpu, 1 is the second gpu, etc.\n",
" --lr LR learning rate, default is 1e-3, usually dont need to\n",
" change it, you can try make it bigger, such as 2e-3\n",
" --z_dim Z_DIM the initial latent vector's dimension, can be smaller\n",
" such as 64, if the dataset is not diverse\n",
" --channel CHANNEL determines how big the model is, smaller value means\n",
" faster training, but less capacity of the model\n",
" --batch_size BATCH_SIZE\n",
" how many images to train together at one iteration\n",
" --n_critic N_CRITIC train Dhow many times while train G 1 time\n",
" --init_step INIT_STEP\n",
" start from what resolution, 1 means 8x8 resolution, 2\n",
" means 16x16 resolution, ..., 6 means 256x256\n",
" resolution\n",
" --total_iter TOTAL_ITER\n",
" how many iterations to train in total, the value is in\n",
" assumption that init step is 1\n",
" --pixel_norm a normalization method inside the model, you can try\n",
" use it or not depends on the dataset\n",
" --tanh an output non-linearity on the output of Generator,\n",
" you can try use it or not depends on the dataset\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Sqx-CtIoZLhW",
"outputId": "9a2fba46-b058-42bf-afe4-c11be2ad31a9"
},
"source": [
"!python train.py --path /content/drive/MyDrive/pikachu/ --init_step 1 --total_iter 300000 # これがデフォルトパラメータです。pathを適宜入れ替えてください"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Namespace(batch_size=4, channel=128, gpu_id=0, init_step=6, lr=0.001, n_critic=1, path='/content/drive/MyDrive/pikachu/', pixel_norm=False, tanh=False, total_iter=300000, trial_name='test1', z_dim=128)\n",
" 0% 0/50000 [00:00<?, ?it/s]THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=383 error=11 : invalid argument\n",
" 0% 29/50000 [00:14<5:46:00, 2.41it/s]/usr/local/lib/python3.6/dist-packages/PIL/Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
" 'to RGBA images')\n",
" 0% 94/50000 [00:42<5:50:20, 2.37it/s]/usr/local/lib/python3.6/dist-packages/PIL/Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
" 'to RGBA images')\n",
" 0% 169/50000 [01:14<6:00:22, 2.30it/s]/usr/local/lib/python3.6/dist-packages/PIL/Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
" 'to RGBA images')\n",
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" 95% 47499/50000 [5:51:19<17:51, 2.33it/s]47500; G: 99.769; D: 66.839; Grad: 16.795; Alpha: 1.000\n",
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" 95% 47615/50000 [5:52:09<17:04, 2.33it/s]/usr/local/lib/python3.6/dist-packages/PIL/Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
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" 95% 47640/50000 [5:52:20<16:54, 2.33it/s]/usr/local/lib/python3.6/dist-packages/PIL/Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
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" 96% 47991/50000 [5:54:54<14:19, 2.34it/s]/usr/local/lib/python3.6/dist-packages/PIL/Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
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" 96% 47999/50000 [5:54:57<14:15, 2.34it/s]48000; G: 121.041; D: 79.471; Grad: 20.490; Alpha: 1.000\n",
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"100% 49999/50000 [6:09:35<00:00, 2.33it/s]50000; G: 60.340; D: 36.834; Grad: 11.674; Alpha: 1.000\n",
"100% 50000/50000 [6:09:36<00:00, 2.25it/s]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1wmiUMTyfpuw"
},
"source": [
"!cp -r /content/trial_test1_2020-12-30_6_37/ /content/drive/MyDrive/trial_test1_2020-12-30_6_37/"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_J0qzDE0hzkd"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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