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# This Python 3 environment comes with many helpful analytics libraries installed | |
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python | |
# For example, here's several helpful packages to load | |
import numpy as np # linear algebra | |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
# Input data files are available in the read-only "../input/" directory | |
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory | |
import os | |
for dirname, _, filenames in os.walk('/kaggle/input'): | |
for filename in filenames: | |
print(os.path.join(dirname, filename)) | |
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" | |
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session | |
import pandas as pd | |
from fastai.vision.all import * | |
from fastcore.all import * | |
import fastai | |
print(fastai.__version__) | |
# Set the maximum image size to 10 billion pixels | |
Image.MAX_IMAGE_PIXELS = 10000000000 | |
# enable pytorch fallback to cpu - will be slower but will work | |
#os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' | |
# using thumbnails while we figure out our out of memory problems | |
path = Path('/kaggle/input/UBC-OCEAN/train_thumbnails') | |
#check images count | |
print(len(get_image_files(path))) | |
df = pd.read_csv("/kaggle/input/UBC-OCEAN/train.csv") | |
df.head(10) | |
def get_x(r): | |
filename = f"{r['image_id']}.png" | |
if os.path.exists(path/filename): | |
return str(path/filename) | |
else: | |
return str("/kaggle/input/UBC-OCEAN/test_images/41.png") | |
def get_y(r): return r['label'] | |
ovarianCancerDataBlock = DataBlock( | |
blocks=(ImageBlock, CategoryBlock), | |
splitter=RandomSplitter(valid_pct=0.2, seed=42), | |
get_x = get_x, | |
get_y = get_y, | |
item_tfms=Resize(460), | |
batch_tfms=[*aug_transforms(size=224, min_scale=0.75), Normalize.from_stats(*imagenet_stats)] | |
) | |
dls = ovarianCancerDataBlock.dataloaders(df, bs=2, num_workers=4) | |
from fastai.callback.fp16 import * | |
# make fast ai aware of resnet18 weights by moving them to the cache path for pytorch | |
# check out https://forums.fast.ai/t/how-can-i-load-a-pretrained-model-on-kaggle-using-fastai/13941/24?page=2 for more info | |
if not os.path.exists('/root/.cache/torch/hub/checkpoints/'): | |
os.makedirs('/root/.cache/torch/hub/checkpoints/') | |
#!cp '/kaggle/input/torchvision-resnet-pretrained/resnet50-0676ba61.pth' '/root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth' | |
#!cp '/kaggle/input/torchvision-resnet-pretrained/resnet18-f37072fd.pth' '/root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth' | |
learn = vision_learner(dls, resnet18, metrics=[accuracy, error_rate]).to_fp16() | |
#force gpu | |
learn.model.cuda() | |
learn.fine_tune(5, freeze_epochs=3) |