LNDST
[Getting Started Notebook] LNDST Challange
This is a Baseline Code to get you started with the challenge.
You can use this notebook to start your implementation and improve for a better score.
Getting Started Code for LNDST Challenge on AIcrowd¶
Author : Gauransh Kumar, Sanjay Pokkali and Naveen Narayanan¶
Make sure you have chosen a GPU runtime¶
In [1]:
!nvidia-smi
Sun Feb 27 21:37:24 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |
| N/A 33C P8 29W / 149W | 0MiB / 11441MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Import necessary packages¶
In [2]:
import pandas as pd
import numpy as np
import os
import cv2
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.utils.data import Dataset,DataLoader
from tqdm import tqdm_notebook as tqdm
import matplotlib.pyplot as plt
%matplotlib inline
Download the files¶
These include the train images, train maps, and test images
In [3]:
import sys
!{sys.executable} -m pip install aicrowd-cli
%load_ext aicrowd.magic
Collecting aicrowd-cli
Downloading aicrowd_cli-0.1.14-py3-none-any.whl (50 kB)
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Requirement already satisfied: semver<3,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli) (2.13.0)
Collecting requests<3,>=2.25.1
Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
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Requirement already satisfied: click<8,>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli) (7.1.2)
Collecting rich<11,>=10.0.0
Downloading rich-10.16.2-py3-none-any.whl (214 kB)
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Collecting toml<1,>=0.10.2
Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting requests-toolbelt<1,>=0.9.1
Downloading requests_toolbelt-0.9.1-py2.py3-none-any.whl (54 kB)
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Requirement already satisfied: tqdm<5,>=4.56.0 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli) (4.62.3)
Collecting GitPython==3.1.18
Downloading GitPython-3.1.18-py3-none-any.whl (170 kB)
|████████████████████████████████| 170 kB 44.3 MB/s
Collecting python-slugify<6,>=5.0.0
Downloading python_slugify-5.0.2-py2.py3-none-any.whl (6.7 kB)
Collecting pyzmq==22.1.0
Downloading pyzmq-22.1.0-cp37-cp37m-manylinux1_x86_64.whl (1.1 MB)
|████████████████████████████████| 1.1 MB 43.8 MB/s
Collecting gitdb<5,>=4.0.1
Downloading gitdb-4.0.9-py3-none-any.whl (63 kB)
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Requirement already satisfied: typing-extensions>=3.7.4.0 in /usr/local/lib/python3.7/dist-packages (from GitPython==3.1.18->aicrowd-cli) (3.10.0.2)
Collecting smmap<6,>=3.0.1
Downloading smmap-5.0.0-py3-none-any.whl (24 kB)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify<6,>=5.0.0->aicrowd-cli) (1.3)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.25.1->aicrowd-cli) (1.24.3)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.25.1->aicrowd-cli) (2.10)
Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.25.1->aicrowd-cli) (2.0.12)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.25.1->aicrowd-cli) (2021.10.8)
Collecting colorama<0.5.0,>=0.4.0
Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)
Collecting commonmark<0.10.0,>=0.9.0
Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)
|████████████████████████████████| 51 kB 6.9 MB/s
Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich<11,>=10.0.0->aicrowd-cli) (2.6.1)
Installing collected packages: smmap, requests, gitdb, commonmark, colorama, toml, rich, requests-toolbelt, pyzmq, python-slugify, GitPython, aicrowd-cli
Attempting uninstall: requests
Found existing installation: requests 2.23.0
Uninstalling requests-2.23.0:
Successfully uninstalled requests-2.23.0
Attempting uninstall: pyzmq
Found existing installation: pyzmq 22.3.0
Uninstalling pyzmq-22.3.0:
Successfully uninstalled pyzmq-22.3.0
Attempting uninstall: python-slugify
Found existing installation: python-slugify 6.0.1
Uninstalling python-slugify-6.0.1:
Successfully uninstalled python-slugify-6.0.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
Successfully installed GitPython-3.1.18 aicrowd-cli-0.1.14 colorama-0.4.4 commonmark-0.9.1 gitdb-4.0.9 python-slugify-5.0.2 pyzmq-22.1.0 requests-2.27.1 requests-toolbelt-0.9.1 rich-10.16.2 smmap-5.0.0 toml-0.10.2
In [4]:
%aicrowd login
Please login here: https://api.aicrowd.com/auth/3fUfWmu1Dx2CKGjkZuTlIfFYcK-L9XOcNCNQtXw-gI8 API Key valid Gitlab access token valid Saved details successfully!
In [5]:
!rm -rf data
!mkdir data
%aicrowd ds dl -c lndst -o data
In [6]:
!rm -rf FinalData
!mkdir FinalData
!unzip data/train_images -d FinalData/
!unzip data/train_gt -d FinalData/
!unzip data/test_images -d FinalData/
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In [7]:
#UNET NETWORK
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 // factor)
self.up1 = Up(1024, 512 // factor, bilinear)
self.up2 = Up(512, 256 // factor, bilinear)
self.up3 = Up(256, 128 // factor, bilinear)
self.up4 = Up(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
return logits
In [8]:
model = UNet(3,1).cuda()
learningRate = 0.001
optimizer = torch.optim.RMSprop(model.parameters(), lr=learningRate, weight_decay=1e-8, momentum=0.9)
criterion = nn.BCEWithLogitsLoss()
Loading Data¶
In [9]:
root='FinalData/'
images_path=root+"train_images/"
groundTruth_path=root+"train_gt/"
saving_bestModel_path=root+"unet_1.pt"
In [10]:
len(os.listdir(root+"train_gt/"))
Out[10]:
1399
We split the train dataset into train and validation sets so we can see how well our model is functioning¶
In [11]:
class LandsatDataset(Dataset):
def __init__(self,training="True"):
water_arr = os.listdir(images_path)
training_images_count = 800
if(training=="True"):
print("training")
# how many images do you want to have in training
self.arr = water_arr[:training_images_count]
else:
print("Validation")
self.arr = water_arr[training_images_count:]
def __len__(self):
return len(self.arr)
def __getitem__(self,idx):
image_name = self.arr[idx]
input_path = images_path + image_name
gt_path = groundTruth_path + image_name[:-3] + "png"
gt_img = cv2.imread(gt_path,cv2.COLOR_BGR2GRAY)
gt_img = gt_img.reshape((1,400,400))
input_img= cv2.imread(input_path)
input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
input_img = input_img.reshape((3,400,400))
return input_img,gt_img
In [12]:
X = LandsatDataset("True")
training
In [13]:
Y = LandsatDataset("False")
Validation
In [14]:
X.__len__()
Out[14]:
800
Creating the DataLoaders for Pytorch¶
In [15]:
batch_size=4
In [16]:
train_loader = torch.utils.data.DataLoader(X,batch_size=batch_size,shuffle=True)
In [17]:
val_loader = torch.utils.data.DataLoader(Y,batch_size=batch_size,shuffle=True)
First training iteration¶
In [18]:
epochs = 1
best_loss = 1000
for epoch in range(epochs):
model.train()
print('epochs {}/{} '.format(epoch+1,epochs))
running_loss = 0.0
running_loss_v = 0.0
for idx,(inputs,labels) in tqdm(enumerate(train_loader),total=len(train_loader)):
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs,labels)
running_loss += loss
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
for idx,(inputs_v,labels_v) in tqdm(enumerate(val_loader),total=len(val_loader)):
inputs_v = inputs_v.type(torch.FloatTensor)
labels_v = labels_v.type(torch.FloatTensor)
inputs_v = inputs_v.cuda()
labels_v = labels_v.cuda()
outputs_v= model(inputs_v)
loss_v = criterion(outputs_v,labels_v)
running_loss_v += loss_v
if (running_loss_v/len(val_loader)) < best_loss:
best_loss = running_loss_v/len(val_loader)
out = torch.save({
'epoch': epoch,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_dev_loss': best_loss,
}, f=saving_bestModel_path)
print('loss : {:.4f} val_loss : {:.4f}'.format((running_loss/len(train_loader)),(running_loss_v/len(val_loader))))
epochs 1/1
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook` if sys.path[0] == '':
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
loss : 0.6984 val_loss : 0.6848
Saving weights after first training loop¶
In [19]:
torch.save(model.state_dict(),saving_bestModel_path)
Second Training Iteration¶
In [20]:
saving_bestModel_path = root+"unet_1.pt"
epochs = 1
best_loss = 1000
for epoch in range(epochs):
model.train()
print('epochs {}/{} '.format(epoch+1,epochs))
running_loss = 0.0
running_loss_v = 0.0
for idx,(inputs,labels) in tqdm(enumerate(train_loader),total=len(train_loader)):
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs,labels)
running_loss += loss
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
for idx,(inputs_v,labels_v) in tqdm(enumerate(val_loader),total=len(val_loader)):
inputs_v = inputs_v.type(torch.FloatTensor)
labels_v = labels_v.type(torch.FloatTensor)
inputs_v = inputs_v.cuda()
labels_v = labels_v.cuda()
outputs_v= model(inputs_v)
loss_v = criterion(outputs_v,labels_v)
running_loss_v += loss_v
if (running_loss_v/len(val_loader)) < best_loss:
best_loss = running_loss_v/len(val_loader)
out = torch.save({
'epoch': epoch,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_dev_loss': best_loss,
}, f=saving_bestModel_path)
print('loss : {:.4f} val_loss : {:.4f}'.format((running_loss/len(train_loader)),(running_loss_v/len(val_loader))))
epochs 1/1
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook` if sys.path[0] == '':
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
loss : 0.6796 val_loss : 0.7080
Run Prediction¶
Moment of truth. Lets run prediction on the test set and finally generate our submission file!
Here we load our best model, which is called unet_1. You will have to change it accordingly
In [21]:
checkpoint = torch.load(root+"unet_1.pt")
model = UNet(3,1).cuda()
model.load_state_dict=checkpoint['model']
In [22]:
checkpoint['epoch']
Out[22]:
0
Run Prediction¶
Lets load a single image and see how well our model is performing!¶
In [23]:
test_data_path = root+"test_images/"
# If each pixel value is above 0.5, we mark it as water, else mark it as land
output_mask_path = "FinalData/masks/"
!rm -rf FinalData/masks
!mkdir FinalData/masks
threshold = 0.5
In [24]:
for j in os.listdir(test_data_path):
test_image_path = test_data_path + j
print(test_image_path)
img_plot = cv2.imread(test_image_path)
img_plot = cv2.cvtColor(img_plot, cv2.COLOR_BGR2RGB)
img = img_plot.reshape((1,3,400,400))
img = torch.tensor(img).float()
model.eval()
with torch.no_grad():
img = img.cuda()
output = model(img)
probs = torch.sigmoid(output)
# print("probs output ",probs.shape)
probs = probs.squeeze(0)
# print("probs after squeeze",probs.shape)
probs = probs.reshape((400,400)).detach().cpu().numpy()
probs = probs.flatten()
binary_probs = []
for i in probs:
if (i>threshold):
binary_probs.append(1)
else:
binary_probs.append(0)
binary_probs = np.asarray(binary_probs)
# print("binary_probs ",binary_probs.shape)
output_mask = binary_probs.reshape((400,400))
# print("output_mask shape ",output_mask.shape)
cv2.imwrite(output_mask_path+j[:-3]+'png',output_mask)
FinalData/test_images/image_161.jpg FinalData/test_images/image_102.jpg FinalData/test_images/image_346.jpg FinalData/test_images/image_7.jpg FinalData/test_images/image_46.jpg FinalData/test_images/image_288.jpg FinalData/test_images/image_171.jpg FinalData/test_images/image_408.jpg FinalData/test_images/image_24.jpg FinalData/test_images/image_254.jpg FinalData/test_images/image_106.jpg FinalData/test_images/image_198.jpg FinalData/test_images/image_313.jpg FinalData/test_images/image_194.jpg FinalData/test_images/image_220.jpg FinalData/test_images/image_206.jpg FinalData/test_images/image_345.jpg FinalData/test_images/image_126.jpg FinalData/test_images/image_90.jpg FinalData/test_images/image_251.jpg FinalData/test_images/image_362.jpg FinalData/test_images/image_64.jpg FinalData/test_images/image_264.jpg FinalData/test_images/image_262.jpg FinalData/test_images/image_253.jpg FinalData/test_images/image_157.jpg FinalData/test_images/image_319.jpg 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In [25]:
#Models output mask
plt.imshow(output_mask)
Out[25]:
<matplotlib.image.AxesImage at 0x7f6f96e83510>
In [26]:
#input image
plt.imshow(img_plot)
Out[26]:
<matplotlib.image.AxesImage at 0x7f6f95101350>
Running inference on the full dataset¶
In [27]:
#Preparing submission.npy file
#output_mask_path is the path which leads to the masks that you have generated for all the images (Kindly iterate through the previous cell and generate masks for all the images)
#make sure that your flattened numpy array consists of image pixels from image_0 to image_477
submission_file_path = root+"submission.npy"
main_array = []
for i in range(len(os.listdir(output_mask_path))):
image_name = "image_" + str(i) + ".jpg"
gt_mask = output_mask_path + image_name[:-3] + "png"
print(gt_mask)
gt_img = cv2.imread(gt_mask,cv2.COLOR_BGR2GRAY)
flattened =gt_img.flatten()
main_array.append(flattened)
main_array = np.asarray(main_array)
print(main_array.shape)
main_array_flat = np.reshape(main_array,(-1))
print(main_array_flat.shape)
print(type(main_array_flat))
with open(submission_file_path, 'wb') as f:
np.save(f,main_array_flat)
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FinalData/masks/image_399.png FinalData/masks/image_400.png FinalData/masks/image_401.png FinalData/masks/image_402.png FinalData/masks/image_403.png FinalData/masks/image_404.png FinalData/masks/image_405.png FinalData/masks/image_406.png FinalData/masks/image_407.png FinalData/masks/image_408.png FinalData/masks/image_409.png FinalData/masks/image_410.png FinalData/masks/image_411.png FinalData/masks/image_412.png FinalData/masks/image_413.png FinalData/masks/image_414.png FinalData/masks/image_415.png FinalData/masks/image_416.png FinalData/masks/image_417.png FinalData/masks/image_418.png FinalData/masks/image_419.png FinalData/masks/image_420.png FinalData/masks/image_421.png FinalData/masks/image_422.png FinalData/masks/image_423.png FinalData/masks/image_424.png FinalData/masks/image_425.png FinalData/masks/image_426.png FinalData/masks/image_427.png FinalData/masks/image_428.png FinalData/masks/image_429.png FinalData/masks/image_430.png FinalData/masks/image_431.png FinalData/masks/image_432.png FinalData/masks/image_433.png FinalData/masks/image_434.png FinalData/masks/image_435.png FinalData/masks/image_436.png FinalData/masks/image_437.png FinalData/masks/image_438.png FinalData/masks/image_439.png FinalData/masks/image_440.png FinalData/masks/image_441.png FinalData/masks/image_442.png FinalData/masks/image_443.png FinalData/masks/image_444.png FinalData/masks/image_445.png FinalData/masks/image_446.png FinalData/masks/image_447.png FinalData/masks/image_448.png FinalData/masks/image_449.png FinalData/masks/image_450.png FinalData/masks/image_451.png FinalData/masks/image_452.png FinalData/masks/image_453.png FinalData/masks/image_454.png FinalData/masks/image_455.png FinalData/masks/image_456.png FinalData/masks/image_457.png FinalData/masks/image_458.png FinalData/masks/image_459.png FinalData/masks/image_460.png FinalData/masks/image_461.png FinalData/masks/image_462.png FinalData/masks/image_463.png FinalData/masks/image_464.png FinalData/masks/image_465.png FinalData/masks/image_466.png (467, 160000) (74720000,) <class 'numpy.ndarray'>
In [28]:
from google.colab import files
files.download(root+'submission.npy')
In [29]:
root
Out[29]:
'FinalData/'
Make a submission using the aicrwd -cli 🥳¶
In [31]:
!!aicrowd submission create -c lndst -f FinalData/submission.npy
Out[31]:
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