def setup_lab5():
setup_general.setup_general()
from utils import general as gen
id_insurance = "11jaAMuHLypta8BXyUfPPOnql-PXzLQGD"
gen.download_file_from_google_drive(id_insurance, "insurance.csv")
id_wine = "1Je03icLBNGad8q58QnJ-eQKex82t3exP"
gen.download_file_from_google_drive(id_wine, "winequality.csv")
def setup_software():
setup_general.setup_general()
os.system("pip install -q albumentations==0.5.0")
print("Libraries Installed!")
torch_path = "project/torch_utils.py"
vis_path = "project/visualization_utils.py"
layers_path = "project/layers.py"
train_path = "project/train_utils.py"
lung_path = "project/lung_segment.py"
setup_general.download_github_content(torch_path, "utils/torch_utils.py")
setup_general.download_github_content(layers_path, "utils/layers.py")
setup_general.download_github_content(train_path, "utils/train_utils.py")
setup_general.download_github_content(vis_path, "utils/visualization_utils.py")
setup_general.download_github_content(lung_path, "utils/lung_segment.py")
from utils import general as gen
#if download_dataset:
train_id ="1zaucizp_3iy_Tlk4NNfNqEtP25qcSKLl"
test_id = "1uqMqdxDmBeQNu-Zziaa02Yoa6IBsMCDk"
gen.download_file_from_google_drive(train_id, "train_data.zip", size=1.96e6)
gen.download_file_from_google_drive(test_id, "test_data.zip", size=805.2e3)
print("Dataset Downloaded Successfully")
software_model_id = "1ypjPbKDGfVtnJdgJnxhkpZecS7dyl2fr"
print("gonna get me some models!")
gen.download_file_from_google_drive(software_model_id, "weights_software.pt",
dst="./models", size=120e3)
print("Util Functions Downloaded Successfully")
print("Software Demo Enabled Successfully")
def setup_autoencoder(architecture="autoencoder"):
os.makedirs("train_utils", exist_ok=True)
with open("train_utils/__init__.py", "wb") as f:
f.close()
setup_general.setup_general()
metrics_path = "pytorch_utils/train_utils/metrics.py"
train_loop_path = "pytorch_utils/train_utils/seg_train_loops.py"
layers_path = "pytorch_utils/autoencoder_architectures/layers.py"
general_layers_path = "pytorch_utils/general_layers.py"
setup_general.download_github_content(general_layers_path,
"utils/general_layers.py")
setup_general.download_github_content(metrics_path,
"train_utils/metrics.py")
setup_general.download_github_content(train_loop_path,
"train_utils/train_loop.py")
setup_general.download_github_content(layers_path, "utils/layers.py")
print("Layers and utils enabled")
if architecture == "autoencoder" or "autoencoder" in architecture:
autoencoder_path = "pytorch_utils/autoencoder_architectures/autoencoder.py"
setup_general.download_github_content(autoencoder_path,
"utils/autoencoder.py")
print("Autoencoder Enabled")
def setup_workshop_10(download_dataset=True):
setup_general.setup_general()
if download_dataset:
from utils import general as gen
data_url = "http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
gen.download_content(data_url, "data.tar.gz")
print("Dataset Downloaded Successfully")
print("Workshop 10 Enabled Successfully")
def setup_project(download_dataset=True):
setup_general.setup_general(dst="libs")
if download_dataset:
from libs import general as gen
data_id = "1VZ9MvahloAaejUZUci-q_CIHTzfFtzsK"
utils_url = "https://raw.githubusercontent.com/jpcano1/python_utils/main/IBIO_3470/final_project/utils.py"
gen.download_file_from_google_drive(data_id, "data.zip", size=35.87e3)
gen.download_content(utils_url, "utils.py", "libs")
print("Dataset Downloaded Successfully")
print("Workshop Project Enabled Successfully")
def setup_project(filename: str = "kaggle.json", download_dataset=True):
setup_general.setup_general()
setup_kaggle_token(filename)
os.system("pip install -q kaggle==1.5.6")
if download_dataset:
os.system(
"kaggle competitions download -c house-prices-advanced-regression-techniques"
)
from utils import general as gen
gen.extract_file("house-prices-advanced-regression-techniques.zip",
"data")
print("Data Science Project Enabled Successfully")
def setup_workshop_9(filename: str = "kaggle.json",
download_dataset=True,
kaggle_version="1.5.6"):
setup_general.setup_general()
setup_kaggle_token(filename)
os.system(f"pip install -q kaggle=={kaggle_version}")
if download_dataset:
os.system("kaggle datasets download -d datamunge/sign-language-mnist")
from utils import general as gen
gen.extract_file("sign-language-mnist.zip", "data")
print("Dataset Downloaded Successfully")
print("Workshop 9 Enabled Successfully")
def setup_extra_workshop(download_dataset=True):
setup_general.setup_general()
extra_utils_path = "ISIS_4825/ML/Taller_Extra/extra_utils.py"
setup_general.download_github_content(extra_utils_path,
"utils/extra_utils.py")
print("Util Functions Downloaded Successfully")
if download_dataset:
from utils import general as gen
id_data = "0B0vscETPGI1-TE5KWFgxaURubFE"
gen.download_file_from_google_drive(id_data, "kits.zip", size=4.27e6)
print("Dataset Downloaded")
print("Extra Workshop Enabled Successfully")
def setup_workshop_13(download_dataset=True, pretrained=True, brats=False):
setup_general.setup_general()
os.system("pip install -q albumentations==0.5.0")
print("Libraries Installed!")
torch_path = "ISIS_4825/ML/Taller_13/torch_utils.py"
vis_path = "ISIS_4825/ML/Taller_13/visualization_utils.py"
layers_path = "ISIS_4825/ML/Taller_13/layers.py"
train_path = "ISIS_4825/ML/Taller_13/train_utils.py"
setup_general.download_github_content(torch_path, "utils/torch_utils.py")
setup_general.download_github_content(layers_path, "utils/layers.py")
setup_general.download_github_content(train_path, "utils/train_utils.py")
setup_general.download_github_content(vis_path,
"utils/visualization_utils.py")
print("Util Functions Downloaded Successfully")
from utils import general as gen
if download_dataset:
train_id = "192V5FfehmbpN2wkl1apiygxSqW6EUmyP"
test_id = "1--hE7Ucvlsjf-fwET4-JMuS-VAva7Vxq"
gen.download_file_from_google_drive(train_id,
"train_data.zip",
size=212e3)
gen.download_file_from_google_drive(test_id,
"test_data.zip",
size=22e3)
print("Dataset Downloaded Successfully")
if pretrained:
autoencoder_id = "1ZGDKYCHZFpW1D-rx8xmxxjJiFoc5N1Au"
unet_id = "1kfd4_30DIdp3Di8fU5_h4I-EdFKEY7Uq"
gen.download_file_from_google_drive(autoencoder_id,
"autoencoder.pt",
dst="./models",
size=49e3)
gen.download_file_from_google_drive(unet_id,
"unet.pt",
dst="./models",
size=22e3)
print("Pretrained Networks Downloaded Successfully")
if brats:
part_1 = "1NRAFehzp6WtpoQduMJ5PGt1ivC8NhpJ1"
part_2 = "1-6vfu9nBu1PIabcZPMAmX685M1mOkceI"
gen.download_file_from_google_drive(part_1,
"brats_part_1.zip",
size=426.8e3)
gen.download_file_from_google_drive(part_2,
"brats_part_2.zip",
size=370.3e3)
print("Dataset BraTS Downloaded Successfully")
print("Workshop 13 Enabled Successfully")
def setup_workshop_12(pretrained=True, download_dataset=True):
setup_general.setup_general()
tf_utils_path = "ISIS_4825/ML/Taller_12/tf_utils.py"
setup_general.download_github_content(tf_utils_path, "utils/tf_utils.py")
print("Util Functions Downloaded Successfully")
from utils import general as gen
if download_dataset:
data_id = "1xnK3B6K6KekDI55vwJ0vnc2IGoDga9cj"
labels_url = "https://github.com/AlexOlsen/DeepWeeds/raw/master/labels/labels.csv"
gen.download_file_from_google_drive(data_id, "images.zip", size=491e3)
gen.download_content(labels_url, "labels.csv", "data")
print("Dataset Downloaded Successfully")
if pretrained:
inception_id = "1Us6XwnpyWYCCY2wRzBTr9ep6kRXcL-jz"
gen.download_file_from_google_drive(inception_id,
"inception.h5",
dst="models",
size=89e3)
print("Pretrained Networks Downloaded Successfully")
print("Workshop 12 Enabled Successfully")
def setup_unet(architecture="unet"):
os.makedirs("train_utils", exist_ok=True)
with open("train_utils/__init__.py", "wb") as _:
...
setup_general.setup_general()
metrics_path = "pytorch_utils/train_utils/metrics.py"
train_loop_path = "pytorch_utils/train_utils/seg_train_loops.py"
layers_path = "pytorch_utils/unet_architectures/layers.py"
general_layers_path = "pytorch_utils/general_layers.py"
setup_general.download_github_content(general_layers_path,
"utils/general_layers.py")
setup_general.download_github_content(metrics_path,
"train_utils/metrics.py")
setup_general.download_github_content(train_loop_path,
"train_utils/train_loop.py")
setup_general.download_github_content(layers_path, "utils/layers.py")
print("Layers and utils enabled")
if architecture == "unet" or "unet" in architecture:
unet_path = "pytorch_utils/unet_architectures/unet.py"
setup_general.download_github_content(unet_path, "utils/unet.py")
print("U-Net Enabled")
if architecture == "runet" or "runet" in architecture:
runet_path = "pytorch_utils/unet_architectures/runet.py"
setup_general.download_github_content(runet_path, "utils/runet.py")
print("RU-Net Enabled")
if architecture == "r2unet" or "r2unet" in architecture:
r2unet_path = "pytorch_utils/unet_architectures/r2unet.py"
setup_general.download_github_content(r2unet_path, "utils/r2unet.py")
print("R2U-Net Enabled")
if architecture == "attention" or "attention" in architecture:
attention_path = "pytorch_utils/unet_architectures/attention_unet.py"
setup_general.download_github_content(attention_path,
"utils/attention_unet.py")
print("Attention U-Net Enabled")
def setup_journal():
setup_general.setup_general()
torch_path = "project/torch_utils.py"
vis_path = "project/visualization_utils.py"
layers_path = "project/layers.py"
train_path = "project/train_utils.py"
lung_path = "project/lung_segment.py"
setup_general.download_github_content(torch_path, "utils/torch_utils.py")
setup_general.download_github_content(layers_path, "utils/layers.py")
setup_general.download_github_content(train_path, "utils/train_utils.py")
setup_general.download_github_content(vis_path, "utils/visualization_utils.py")
setup_general.download_github_content(lung_path, "utils/lung_segment.py")
from utils import general as gen
#if download_dataset:
set_thresh ="1yN2dzVPjz-5yTMZfeTrMRTDQVCgkmwaU"
set_segment = "1Ar7ww1ZNjsYs9SpQWmXdVDeDbbdIzV6H"
set_original = "1QLuziDEys8G9tp5d-6-X9hnlusy5MwgK"
gen.download_file_from_google_drive(set_thresh, "selectDataClip.zip", size=216e3)
gen.download_file_from_google_drive(set_segment, "selectDataSegment.zip", size=55.7e3)
gen.download_file_from_google_drive(set_original, "selectData.zip", size=549.7e3)
originalData_id = "1CXyrOF1KEeZUWtKNM3GWaI3Evqwg48-n"
threhsoldData_id = "1l4BlO2APgdMNyuIyQ0dm3sDIB9zgASzR"
threhsoldData_id2 = "1tBeYO0GjtcIYXhGpJU-VmQjE5G-9ApuH"
segmentedData_id = "1-YoJegTc22hoRzbtpVpdtkRKzyGjwSvV"
gen.download_file_from_google_drive(originalData_id, "weights_original.pt",
dst="./models", size=120e3)
gen.download_file_from_google_drive(threhsoldData_id, "weights_interval.pt",
dst="./models", size=120e3)
gen.download_file_from_google_drive(threhsoldData_id2, "weights_interval2.pt",
dst="./models", size=120e3)
gen.download_file_from_google_drive(segmentedData_id, "weights_segmented.pt",
dst="./models", size=120e3)
print("Dataset Downloaded Successfully")
print("Interactive Paper Enabled Succesfully!")
def setup_lab6():
setup_general.setup_general()
from utils import general as gen
url_diet = "https://github.com/hctorresm/Monitoria_CDA/blob/master/Clase9_Python/Diet.xlsx?raw=true"
gen.download_content(url_diet, "Diet.xlsx")
print("Lab 6 enabled successfully")
def setup_workshop_8():
setup_general.setup_general()
print("Workshop 8 Enabled Successfully")
