2020-1-capstone-design2 / 2016104167

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "FAA2.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "sWjZQ8LCWcZv",
"colab_type": "code",
"outputId": "3d4f5ec9-214c-4365-b43c-a3946f447631",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3arNqMB_Wgbx",
"colab_type": "code",
"outputId": "7f1de510-e87c-4a78-8f63-8349aeba3a8b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"!git clone http://khuhub.khu.ac.kr/2020-1-capstone-design2/2016104167.git"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"fatal: destination path '2016104167' already exists and is not an empty directory.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ISXM-edL-lGF",
"colab_type": "code",
"outputId": "b3d9b459-bdbf-4bcf-8c23-3ae0dd99a913",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"%cd '2016104167/code/FAA2/'"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/2016104167/code/FAA2\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "43zJwd05_Tst",
"colab_type": "code",
"outputId": "bb293b7c-5b79-4720-fff8-5bfe077b6694",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 718
}
},
"source": [
"!python -m pip install -r \"requirements.txt\""
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.16.0)\n",
"Requirement already satisfied: tb-nightly in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (2.3.0a20200331)\n",
"Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (0.5.0)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (1.4.0)\n",
"Requirement already satisfied: hyperopt in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (0.1.2)\n",
"Requirement already satisfied: pillow==6.2.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 6)) (6.2.1)\n",
"Requirement already satisfied: natsort in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 7)) (5.5.0)\n",
"Requirement already satisfied: fire in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 8)) (0.3.0)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.0.0)\n",
"Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.18.2)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (2.21.0)\n",
"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (46.0.0)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (3.2.1)\n",
"Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (3.10.0)\n",
"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (0.4.1)\n",
"Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.7.2)\n",
"Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.27.2)\n",
"Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (0.34.2)\n",
"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.12.0)\n",
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.6.0.post2)\n",
"Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (0.9.0)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 5)) (1.4.1)\n",
"Requirement already satisfied: pymongo in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 5)) (3.10.1)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 5)) (2.4)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 5)) (4.38.0)\n",
"Requirement already satisfied: termcolor in /usr/local/lib/python3.6/dist-packages (from fire->-r requirements.txt (line 8)) (1.1.0)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (3.0.4)\n",
"Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (2019.11.28)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (2.8)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->-r requirements.txt (line 2)) (1.3.0)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (0.2.8)\n",
"Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (4.0)\n",
"Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (3.1.1)\n",
"Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->hyperopt->-r requirements.txt (line 5)) (4.4.2)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->-r requirements.txt (line 2)) (3.1.0)\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (0.4.8)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "16kGbCYwfhYF",
"colab_type": "code",
"colab": {}
},
"source": [
"# !pip3 install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl\n",
"# !pip3 install torchvision"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hofwjBN3ZY_h",
"colab_type": "code",
"colab": {}
},
"source": [
"use_cuda = True"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0h78dEdg_Jsg",
"colab_type": "code",
"colab": {}
},
"source": [
"# try CIFAR10\n",
"#!python \"train.py\" --seed=24 --scale=3 --optimizer=sgd --fast_auto_augment=True --use_cuda=True --network=ResNet50"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nz8P9CpzES4L",
"colab_type": "code",
"outputId": "913ec5c8-4a66-45fd-8f76-a8367376c270",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# BraTS, grayResNet2\n",
"!python \"train.py\" --use_cuda=True --network=resnet50 --dataset=BraTS --optimizer=adam --fast_auto_augment=True"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"\n",
"[+] Parse arguments\n",
"Args(augment_path=None, batch_size=128, dataset='BraTS', fast_auto_augment=True, learning_rate=0.0001, max_step=10000, network='resnet50', num_workers=4, optimizer='adam', print_step=500, scheduler='exp', seed=None, start_step=0, use_cuda=True, val_step=500)\n",
"\n",
"[+] Create log dir\n",
"2020-04-01 05:45:32.118038: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"\n",
"[+] Create network\n",
"BaseNet(\n",
" (first): Sequential(\n",
" (0): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" )\n",
" (after): Sequential(\n",
" (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (1): ReLU(inplace=True)\n",
" (2): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (3): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (4): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (5): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (4): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (5): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (6): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (7): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" )\n",
" (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
")\n",
"\n",
"[+] Load dataset\n",
"[+] Child 0 training started (GPU: 0)\n",
"\n",
"[+] Training step: 0/10000\tElapsed time: 0.24min\tLearning rate: 9.999283e-05\tDevice name: Tesla P100-PCIE-16GB\n",
" Acc@1 : 0.000%\n",
" Acc@5 : 0.000%\n",
" Loss : 7.242412567138672\n",
"\n",
"[+] Training step: 500/10000\tElapsed time: 9.44min\tLearning rate: 9.647145853624023e-05\tDevice name: Tesla P100-PCIE-16GB\n",
" Acc@1 : 100.000%\n",
" Acc@5 : 100.000%\n",
" Loss : 0.00023103877902030945\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3iBnXLMsES7H",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Wc8cguWUhp9l",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
\ No newline at end of file
......@@ -104,20 +104,20 @@ def dict_to_namedtuple(d):
def parse_args(kwargs):
# combine with default args
kwargs['dataset'] = kwargs['dataset'] if 'dataset' in kwargs else 'cifar10'
kwargs['network'] = kwargs['network'] if 'network' in kwargs else 'resnet_cifar10'
kwargs['dataset'] = kwargs['dataset'] if 'dataset' in kwargs else 'BraTS'
kwargs['network'] = kwargs['network'] if 'network' in kwargs else 'resnet50'
kwargs['optimizer'] = kwargs['optimizer'] if 'optimizer' in kwargs else 'adam'
kwargs['learning_rate'] = kwargs['learning_rate'] if 'learning_rate' in kwargs else 0.1
kwargs['learning_rate'] = kwargs['learning_rate'] if 'learning_rate' in kwargs else 0.0001
kwargs['seed'] = kwargs['seed'] if 'seed' in kwargs else None
kwargs['use_cuda'] = kwargs['use_cuda'] if 'use_cuda' in kwargs else True
kwargs['use_cuda'] = kwargs['use_cuda'] and torch.cuda.is_available()
kwargs['num_workers'] = kwargs['num_workers'] if 'num_workers' in kwargs else 4
kwargs['print_step'] = kwargs['print_step'] if 'print_step' in kwargs else 2000
kwargs['val_step'] = kwargs['val_step'] if 'val_step' in kwargs else 2000
kwargs['print_step'] = kwargs['print_step'] if 'print_step' in kwargs else 500
kwargs['val_step'] = kwargs['val_step'] if 'val_step' in kwargs else 500
kwargs['scheduler'] = kwargs['scheduler'] if 'scheduler' in kwargs else 'exp'
kwargs['batch_size'] = kwargs['batch_size'] if 'batch_size' in kwargs else 128
kwargs['start_step'] = kwargs['start_step'] if 'start_step' in kwargs else 0
kwargs['max_step'] = kwargs['max_step'] if 'max_step' in kwargs else 64000
kwargs['max_step'] = kwargs['max_step'] if 'max_step' in kwargs else 6500
kwargs['fast_auto_augment'] = kwargs['fast_auto_augment'] if 'fast_auto_augment' in kwargs else False
kwargs['augment_path'] = kwargs['augment_path'] if 'augment_path' in kwargs else None
......