Pytorch based segmentation notebook using the wonderful library by Qubvel
Notebook used for submission in the DrivenData competition(-https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/
• 144 min read
Thanks to John Whitaker for the starter code and Qubvel for the segmentation model library
#Install required libs
!pip install -U segmentation-models-pytorch
import os
os._exit(0)
Collecting segmentation-models-pytorch
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Installing collected packages: efficientnet-pytorch, munch, pretrainedmodels, segmentation-models-pytorch
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#!pip uninstall -y segmentation-models-pytorch
from google.colab import drive
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly Enter your authorization code: ·········· Mounted at /content/drive
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import numpy as np
import pickle
import os
import glob
import pandas as pd
import cv2
from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
path='/content/drive/My Drive/open_cities_ai'
ims = glob.glob(path+'/images-256/*.png')
df = pd.DataFrame({
'img_path':ims,
'mask_path':[im.replace('images', 'masks').replace('.png', '_mask.png') for im in ims],
'valid':False
})
# Add some useful columns
#print(df.loc[1]['img_path'])
df['scene_id'] = df['img_path'].apply(lambda x: x.split("_")[3])
df['area'] = df['img_path'].apply(lambda x: x.split("_")[2].split("/")[-1])
# View
print(df.shape)
df.head()
(584, 5)
| img_path | mask_path | valid | scene_id | area | |
|---|---|---|---|---|---|
| 0 | /content/drive/My Drive/open_cities_ai/images-... | /content/drive/My Drive/open_cities_ai/masks-2... | False | ca041a | acc |
| 1 | /content/drive/My Drive/open_cities_ai/images-... | /content/drive/My Drive/open_cities_ai/masks-2... | False | ca041a | acc |
| 2 | /content/drive/My Drive/open_cities_ai/images-... | /content/drive/My Drive/open_cities_ai/masks-2... | False | ca041a | acc |
| 3 | /content/drive/My Drive/open_cities_ai/images-... | /content/drive/My Drive/open_cities_ai/masks-2... | False | ca041a | acc |
| 4 | /content/drive/My Drive/open_cities_ai/images-... | /content/drive/My Drive/open_cities_ai/masks-2... | False | ca041a | acc |
x_train_df = df.loc[df['area']=='acc']['img_path']
y_train_df = df.loc[df['area']=='acc']['mask_path']
# helper function for data visualization
def visualize(**images):
"""PLot images in one row."""
n = len(images)
plt.figure(figsize=(16, 5))
for i, (name, image) in enumerate(images.items()):
plt.subplot(1, n, i + 1)
plt.xticks([])
plt.yticks([])
plt.title(' '.join(name.split('_')).title())
plt.imshow(image)
plt.show()
Dataloader
Writing helper class for data extraction, tranformation and preprocessing
https://pytorch.org/docs/stable/data
from torch.utils.data import DataLoader
from torch.utils.data import Dataset as BaseDataset
class Dataset(BaseDataset):
def __init__(
self,
images_fn,
masks_fn,
augmentation=None,
preprocessing=None,
):
self.images_fn = images_fn
self.masks_fn = masks_fn
# convert str names to class values on masks
self.augmentation = augmentation
self.preprocessing = preprocessing
def __getitem__(self, i):
# read data
image = cv2.imread(self.images_fn[i])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask = np.asarray(Image.open(self.masks_fn[i])).astype(np.float32)
mask=np.expand_dims(mask,axis=-1)
# apply augmentations
if self.augmentation:
sample = self.augmentation(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
# apply preprocessing
if self.preprocessing:
sample = self.preprocessing(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
return image, mask
def __len__(self):
return len(self.images_fn)
class Test_Dataset(BaseDataset):
def __init__(
self,
images_fn,
augmentation=None,
preprocessing=None,
):
self.images_fn = images_fn
self.augmentation=augmentation
self.preprocessing=preprocessing
def __getitem__(self, i):
# read data
image = cv2.imread(self.images_fn[i])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
dim = (256,256)
# resize image
image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
if self.augmentation:
sample = self.augmentation(image=image)
image = sample['image']
if self.preprocessing:
sample = self.preprocessing(image=image)
image = sample['image']
return image
def __len__(self):
return len(self.images_fn)
# Lets look at data we have
dataset = Dataset(x_train_df, y_train_df)
image, mask = dataset[5] # get some sample
visualize(
image=image,
building_mask=mask.squeeze(axis=-1)
)
Data augmentation is a powerful technique to increase the amount of your data and prevent model overfitting.
If you not familiar with such trick read some of these articles:
- The Effectiveness of Data Augmentation in Image Classification using Deep Learning
- Data Augmentation | How to use Deep Learning when you have Limited Data
- Data Augmentation Experimentation
Since our dataset is very small we will apply a large number of different augmentations:
- horizontal flip
- affine transforms
- perspective transforms
- brightness/contrast/colors manipulations
- image bluring and sharpening
- gaussian noise
- random crops
All this transforms can be easily applied with Albumentations - fast augmentation library. For detailed explanation of image transformations you can look at kaggle salt segmentation exmaple provided by Albumentations authors.
!pip install -U git+https://github.com/albu/albumentations --no-cache-dir
Collecting git+https://github.com/albu/albumentations
Cloning https://github.com/albu/albumentations to /tmp/pip-req-build-kc_rayby
Running command git clone -q https://github.com/albu/albumentations /tmp/pip-req-build-kc_rayby
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import albumentations as albu
def round_clip_0_1(x, **kwargs):
return x.round().clip(0, 1)
def get_training_augmentation():
train_transform = [
albu.HorizontalFlip(p=0.5),
albu.ShiftScaleRotate(scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=1, border_mode=0),
albu.PadIfNeeded(min_height=256, min_width=256, always_apply=True, border_mode=0),
albu.RandomCrop(height=256, width=256, always_apply=True),
albu.IAAAdditiveGaussianNoise(p=0.2),
albu.IAAPerspective(p=0.5),
albu.OneOf(
[
albu.CLAHE(p=1),
albu.RandomBrightness(p=1),
albu.RandomGamma(p=1),
],
p=0.9,
),
albu.OneOf(
[
albu.IAASharpen(p=1),
albu.Blur(blur_limit=3, p=1),
albu.MotionBlur(blur_limit=3, p=1),
],
p=0.9,
),
albu.OneOf(
[
albu.RandomContrast(p=1),
albu.HueSaturationValue(p=1),
],
p=0.9,
),
albu.Lambda(mask=round_clip_0_1)
]
return albu.Compose(train_transform)
def get_validation_augmentation():
"""Add paddings to make image shape divisible by 32"""
test_transform = [
albu.PadIfNeeded(256, 256),
albu.Lambda(mask=round_clip_0_1)
]
return albu.Compose(test_transform)
def to_tensor(x, **kwargs):
return x.transpose(2, 0, 1).astype('float32')
def get_preprocessing(preprocessing_fn):
"""Construct preprocessing transform
Args:
preprocessing_fn (callbale): data normalization function
(can be specific for each pretrained neural network)
Return:
transform: albumentations.Compose
"""
_transform = [
albu.Lambda(image=preprocessing_fn),
albu.Lambda(image=to_tensor, mask=to_tensor),
]
return albu.Compose(_transform)
#### Visualize resulted augmented images and masks
augmented_dataset = Dataset(
x_train_df,
y_train_df,
augmentation=get_training_augmentation(),
)
# same image with different random transforms
for i in range(3):
image,mask=augmented_dataset[1]
visualize(image=image, mask=mask.squeeze(-1))
import torch
import numpy as np
import segmentation_models_pytorch as smp
ENCODER = 'se_resnext50_32x4d'
ENCODER_WEIGHTS = 'imagenet'
n_classes=1
ACTIVATION = 'sigmoid' # could be None for logits or 'softmax2d' for multicalss segmentation
DEVICE = 'cuda'
# create segmentation model with pretrained encoder
model = smp.Unet(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=n_classes,
activation=ACTIVATION,
)
preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)
if torch.cuda.is_available():
model.cuda()
x_valid_df = df.loc[df['area']!='acc']['img_path'].reset_index()['img_path']
y_valid_df = df.loc[df['area']!='acc']['mask_path'].reset_index()['mask_path']
valid_dataset = Dataset(x_valid_df, y_valid_df, augmentation=get_validation_augmentation())
image, mask = valid_dataset[12] # get some sample
visualize(
image=image,
building_mask=mask.squeeze(axis=-1)
)
train_dataset = Dataset(
x_train_df,
y_train_df,
augmentation=get_training_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn),
)
valid_dataset = Dataset(
x_valid_df,
y_valid_df,
augmentation=get_validation_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn),
)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=12)
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=4)
# Dice/F1 score - https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
# IoU/Jaccard score - https://en.wikipedia.org/wiki/Jaccard_index
loss = smp.utils.losses.DiceLoss()
metrics = [
smp.utils.metrics.IoU(threshold=0.5),
]
optimizer = torch.optim.Adam([
dict(params=model.parameters(), lr=0.0001),
])
# create epoch runners
# it is a simple loop of iterating over dataloader`s samples
train_epoch = smp.utils.train.TrainEpoch(
model,
loss=loss,
metrics=metrics,
optimizer=optimizer,
device=DEVICE,
verbose=True,
)
valid_epoch = smp.utils.train.ValidEpoch(
model,
loss=loss,
metrics=metrics,
device=DEVICE,
verbose=True,
)
# train model for 20 epochs
max_score = 0
for i in range(0, 20):
print('\nEpoch: {}'.format(i))
train_logs = train_epoch.run(train_loader)
valid_logs = valid_epoch.run(valid_loader)
# do something (save model, change lr, etc.)
if max_score < valid_logs['iou_score']:
max_score = valid_logs['iou_score']
torch.save(model, '/content/drive/My Drive/open_cities_ai/pytorch/best_unet_model.pth')
print('Model saved!')
if i == 15:
optimizer.param_groups[0]['lr'] = 1e-5
print('Decrease decoder learning rate to 1e-5!')
Epoch: 0 train: 100%|██████████| 56/56 [00:49<00:00, 1.21it/s, dice_loss - 0.4301, iou_score - 0.4136] valid: 100%|██████████| 143/143 [00:37<00:00, 3.84it/s, dice_loss - 0.3875, iou_score - 0.5086] Model saved! Epoch: 1 train: 100%|██████████| 56/56 [00:21<00:00, 3.50it/s, dice_loss - 0.3051, iou_score - 0.5477] valid: 100%|██████████| 143/143 [00:05<00:00, 25.68it/s, dice_loss - 0.2935, iou_score - 0.604] Model saved! Epoch: 2 train: 100%|██████████| 56/56 [00:21<00:00, 3.55it/s, dice_loss - 0.2622, iou_score - 0.5935] valid: 100%|██████████| 143/143 [00:05<00:00, 25.55it/s, dice_loss - 0.2848, iou_score - 0.6221] Model saved! Epoch: 3 train: 100%|██████████| 56/56 [00:21<00:00, 3.56it/s, dice_loss - 0.2517, iou_score - 0.6117] valid: 100%|██████████| 143/143 [00:05<00:00, 25.46it/s, dice_loss - 0.2542, iou_score - 0.6509] Model saved! Epoch: 4 train: 100%|██████████| 56/56 [00:21<00:00, 3.55it/s, dice_loss - 0.2171, iou_score - 0.6515] valid: 100%|██████████| 143/143 [00:05<00:00, 26.79it/s, dice_loss - 0.2378, iou_score - 0.6581] Model saved! Epoch: 5 train: 100%|██████████| 56/56 [00:21<00:00, 3.54it/s, dice_loss - 0.2101, iou_score - 0.6597] valid: 100%|██████████| 143/143 [00:05<00:00, 29.09it/s, dice_loss - 0.2566, iou_score - 0.6403] Epoch: 6 train: 100%|██████████| 56/56 [00:21<00:00, 3.54it/s, dice_loss - 0.2124, iou_score - 0.6569] valid: 100%|██████████| 143/143 [00:05<00:00, 25.60it/s, dice_loss - 0.2426, iou_score - 0.6556] Epoch: 7 train: 100%|██████████| 56/56 [00:21<00:00, 3.55it/s, dice_loss - 0.1962, iou_score - 0.6779] valid: 100%|██████████| 143/143 [00:05<00:00, 25.84it/s, dice_loss - 0.2316, iou_score - 0.6659] Model saved! Epoch: 8 train: 100%|██████████| 56/56 [00:21<00:00, 3.51it/s, dice_loss - 0.2042, iou_score - 0.6755] valid: 100%|██████████| 143/143 [00:05<00:00, 25.88it/s, dice_loss - 0.231, iou_score - 0.6603] Epoch: 9 train: 100%|██████████| 56/56 [00:21<00:00, 3.55it/s, dice_loss - 0.1992, iou_score - 0.6819] valid: 100%|██████████| 143/143 [00:05<00:00, 25.53it/s, dice_loss - 0.217, iou_score - 0.6783] Model saved! Epoch: 10 train: 100%|██████████| 56/56 [00:21<00:00, 3.54it/s, dice_loss - 0.1839, iou_score - 0.6954] valid: 100%|██████████| 143/143 [00:05<00:00, 29.37it/s, dice_loss - 0.2455, iou_score - 0.6448] Epoch: 11 train: 100%|██████████| 56/56 [00:21<00:00, 3.54it/s, dice_loss - 0.1767, iou_score - 0.7053] valid: 100%|██████████| 143/143 [00:05<00:00, 24.56it/s, dice_loss - 0.2199, iou_score - 0.6751] Epoch: 12 train: 100%|██████████| 56/56 [00:21<00:00, 3.54it/s, dice_loss - 0.178, iou_score - 0.7041] valid: 100%|██████████| 143/143 [00:05<00:00, 25.70it/s, dice_loss - 0.2268, iou_score - 0.669] Epoch: 13 train: 100%|██████████| 56/56 [00:21<00:00, 3.53it/s, dice_loss - 0.1691, iou_score - 0.7184] valid: 100%|██████████| 143/143 [00:05<00:00, 28.35it/s, dice_loss - 0.2244, iou_score - 0.6699] Epoch: 14 train: 100%|██████████| 56/56 [00:21<00:00, 3.49it/s, dice_loss - 0.1672, iou_score - 0.719] valid: 100%|██████████| 143/143 [00:05<00:00, 25.28it/s, dice_loss - 0.2236, iou_score - 0.6704] Epoch: 15 train: 100%|██████████| 56/56 [00:22<00:00, 3.42it/s, dice_loss - 0.1739, iou_score - 0.7094] valid: 100%|██████████| 143/143 [00:05<00:00, 24.13it/s, dice_loss - 0.2369, iou_score - 0.6535] Decrease decoder learning rate to 1e-5! Epoch: 16 train: 100%|██████████| 56/56 [00:21<00:00, 3.53it/s, dice_loss - 0.1604, iou_score - 0.7287] valid: 100%|██████████| 143/143 [00:05<00:00, 24.74it/s, dice_loss - 0.2318, iou_score - 0.6609] Epoch: 17 train: 100%|██████████| 56/56 [00:22<00:00, 3.52it/s, dice_loss - 0.1605, iou_score - 0.729] valid: 100%|██████████| 143/143 [00:05<00:00, 25.74it/s, dice_loss - 0.2259, iou_score - 0.6668] Epoch: 18 train: 100%|██████████| 56/56 [00:22<00:00, 3.46it/s, dice_loss - 0.157, iou_score - 0.7343] valid: 100%|██████████| 143/143 [00:06<00:00, 22.16it/s, dice_loss - 0.2334, iou_score - 0.6566] Epoch: 19 train: 100%|██████████| 56/56 [00:21<00:00, 3.53it/s, dice_loss - 0.154, iou_score - 0.7385] valid: 100%|██████████| 143/143 [00:05<00:00, 28.38it/s, dice_loss - 0.228, iou_score - 0.6656]
# load best saved checkpoint
best_model = torch.load('/content/drive/My Drive/open_cities_ai/pytorch/best_unet_model.pth')
x_test_df=pd.read_csv('/content/drive/My Drive/open_cities_ai/test/test.csv')
x_test_df.head()
| Unnamed: 0 | fn | id | |
|---|---|---|---|
| 0 | 0 | /content/drive/My Drive/open_cities_ai/test/e9... | e93a2d |
| 1 | 1 | /content/drive/My Drive/open_cities_ai/test/e9... | e959dc |
| 2 | 2 | /content/drive/My Drive/open_cities_ai/test/e9... | e91b9c |
| 3 | 3 | /content/drive/My Drive/open_cities_ai/test/e9... | e94422 |
| 4 | 4 | /content/drive/My Drive/open_cities_ai/test/e9... | e98330 |
x_test_df.shape
(11481, 3)
# create test dataset
test_dataset_vis = Test_Dataset(x_test_df['fn'])
image_vis = test_dataset_vis[105] # get some sample
print(image_vis.shape)
visualize(
image=image_vis
)
(256, 256, 3)
test_dataset = Test_Dataset(x_test_df['fn'],preprocessing=get_preprocessing(preprocessing_fn))
test_dataloader=DataLoader(test_dataset)
image = test_dataset[15]
#print('Shapes:',image.transpose(1,2,0).shape,'---',mask.transpose(1,2,0).squeeze(axis=-1).shape)
x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(0)
#print(x_tensor.shape)
pr_mask = best_model.predict(x_tensor)
pr_mask = (pr_mask.squeeze().cpu().numpy().round())
pred_im = Image.fromarray(pr_mask.astype(np.uint8))
scaled_im = pred_im.resize((1024, 1024), Image.NEAREST)
print(np.array(scaled_im).shape)
#plt.imshow(pr_mask)
#plt.imshow(scaled_im)
#plt.show()
#print(pr_mask.shape)
visualize(image=image.transpose(1,2,0),pr_mask=pr_mask)
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
(1024, 1024)
for i in range(5):
n = np.random.choice(len(test_dataset))
image_vis = test_dataset_vis[n].astype('uint8')
image = test_dataset[n]
x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(0)
pr_mask = best_model.predict(x_tensor)
pr_mask = (pr_mask.squeeze().cpu().numpy().round())
pred_im = Image.fromarray(pr_mask.astype(np.uint8))
scaled_im = pred_im.resize((1024, 1024), Image.NEAREST)
#print('Shape of mask',pr_mask.shape)
visualize(
image=image_vis,
predicted_mask=scaled_im
)
#!rm -rf '/content/drive/My Drive/open_cities_ai/submission2'
!mkdir '/content/drive/My Drive/open_cities_ai/submission3'
from tqdm import tqdm
for i in tqdm(range(len(x_test_df))):
im_id = x_test_df['id'].values[i]
img = test_dataset[i]
x_tensor = torch.from_numpy(img).to(DEVICE).unsqueeze(0)
mask = best_model.predict(x_tensor)
mask = (mask.squeeze().cpu().numpy().round())
#pred_arr = mask.squeeze(axis=0).squeeze(axis=-1)
pred_im = Image.fromarray(mask.astype(np.uint8))
scaled_im = pred_im.resize((1024, 1024), Image.NEAREST)
scaled_im.save('/content/drive/My Drive/open_cities_ai/submission3/'+str(im_id)+'.TIF', compression='tiff_deflate')
Streaming output truncated to the last 5000 lines.
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import os
for i in range(len(x_test_df)):
im_id = x_test_df['id'].values[i]
if not os.path.isfile('/content/drive/My Drive/open_cities_ai/submission2/'+str(im_id)+'.TIF'):
print(im_id)
!cd '/content/drive/My Drive/open_cities_ai/submission2'; zip -q -r submission.zip *.TIF # Add to archive
@misc{Yakubovskiy:2019, Author = {Pavel Yakubovskiy}, Title = {Segmentation Models Pytorch}, Year = {2020}, Publisher = {GitHub}, Journal = {GitHub repository}, Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}} }