Spaces:
Runtime error
Runtime error
File size: 2,879 Bytes
1173bfd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
# 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) |