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LNDST

[Getting Started Notebook] LNDST Challange

This is a Baseline Code to get you started with the challenge.

gauransh_k

You can use this notebook to start your implementation and improve for a better score.

AIcrowd-Logo

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)
     |████████████████████████████████| 50 kB 3.0 MB/s 
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)
     |████████████████████████████████| 63 kB 1.6 MB/s 
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)
     |████████████████████████████████| 54 kB 2.7 MB/s 
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)
     |████████████████████████████████| 63 kB 2.0 MB/s 
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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Define the model

Now we come to the juicy part. We define our model here. For this starter kit we have used one of the basic segmentation models, UNET. You can learn more about them here

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
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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_351.png
FinalData/masks/image_352.png
FinalData/masks/image_353.png
FinalData/masks/image_354.png
FinalData/masks/image_355.png
FinalData/masks/image_356.png
FinalData/masks/image_357.png
FinalData/masks/image_358.png
FinalData/masks/image_359.png
FinalData/masks/image_360.png
FinalData/masks/image_361.png
FinalData/masks/image_362.png
FinalData/masks/image_363.png
FinalData/masks/image_364.png
FinalData/masks/image_365.png
FinalData/masks/image_366.png
FinalData/masks/image_367.png
FinalData/masks/image_368.png
FinalData/masks/image_369.png
FinalData/masks/image_370.png
FinalData/masks/image_371.png
FinalData/masks/image_372.png
FinalData/masks/image_373.png
FinalData/masks/image_374.png
FinalData/masks/image_375.png
FinalData/masks/image_376.png
FinalData/masks/image_377.png
FinalData/masks/image_378.png
FinalData/masks/image_379.png
FinalData/masks/image_380.png
FinalData/masks/image_381.png
FinalData/masks/image_382.png
FinalData/masks/image_383.png
FinalData/masks/image_384.png
FinalData/masks/image_385.png
FinalData/masks/image_386.png
FinalData/masks/image_387.png
FinalData/masks/image_388.png
FinalData/masks/image_389.png
FinalData/masks/image_390.png
FinalData/masks/image_391.png
FinalData/masks/image_392.png
FinalData/masks/image_393.png
FinalData/masks/image_394.png
FinalData/masks/image_395.png
FinalData/masks/image_396.png
FinalData/masks/image_397.png
FinalData/masks/image_398.png
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]:
['\x1b[?25l\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m?\x1b[0m • \x1b[36m-:--:--\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m?\x1b[0m • \x1b[36m-:--:--\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m?\x1b[0m • \x1b[36m-:--:--\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m?\x1b[0m • \x1b[36m-:--:--\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m?\x1b[0m • \x1b[36m-:--:--\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m?\x1b[0m • \x1b[36m-:--:--\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m2.6 MB/s\x1b[0m • \x1b[36m0:00:29\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m2.6 MB/s\x1b[0m • \x1b[36m0:00:29\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.0%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m2.7 MB/s\x1b[0m • \x1b[36m0:00:28\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m2.7 MB/s\x1b[0m • \x1b[36m0:00:29\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m2.7 MB/s\x1b[0m • \x1b[36m0:00:29\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.0/74.7 MB\x1b[0m • \x1b[31m375.6 kB/s\x1b[0m • \x1b[36m0:03:19\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m438.1 kB/s\x1b[0m • \x1b[36m0:02:51\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m497.0 kB/s\x1b[0m • \x1b[36m0:02:31\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m553.6 kB/s\x1b[0m • \x1b[36m0:02:15\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m603.9 kB/s\x1b[0m • \x1b[36m0:02:04\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m652.5 kB/s\x1b[0m • \x1b[36m0:01:55\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m700.3 kB/s\x1b[0m • \x1b[36m0:01:47\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m745.3 kB/s\x1b[0m • \x1b[36m0:01:41\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.1%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m745.3 kB/s\x1b[0m • \x1b[36m0:01:41\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m495.2 kB/s\x1b[0m • \x1b[36m0:02:31\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m525.3 kB/s\x1b[0m • \x1b[36m0:02:23\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m555.4 kB/s\x1b[0m • \x1b[36m0:02:15\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m584.2 kB/s\x1b[0m • \x1b[36m0:02:08\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.1/74.7 MB\x1b[0m • \x1b[31m612.5 kB/s\x1b[0m • \x1b[36m0:02:02\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m640.1 kB/s\x1b[0m • \x1b[36m0:01:57\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m666.4 kB/s\x1b[0m • \x1b[36m0:01:52\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m692.0 kB/s\x1b[0m • \x1b[36m0:01:48\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.2%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m717.8 kB/s\x1b[0m • \x1b[36m0:01:44\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.3%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m741.8 kB/s\x1b[0m • \x1b[36m0:01:41\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.3%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m765.9 kB/s\x1b[0m • \x1b[36m0:01:38\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.3%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m789.9 kB/s\x1b[0m • \x1b[36m0:01:35\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.3%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m789.9 kB/s\x1b[0m • \x1b[36m0:01:35\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.3%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m637.8 kB/s\x1b[0m • \x1b[36m0:01:57\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[90m━━━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m0.3%\x1b[0m • \x1b[32m0.2/74.7 MB\x1b[0m • \x1b[31m656.3 kB/s\x1b[0m • \x1b[36m0:01:54\x1b[0m',
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 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[90m╺\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.7/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[90m╺\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[90m╺\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[90m╺\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[90m╺\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.4%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.5%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.5%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.5%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.5%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
 '\x1b[2K\x1b[1;34msubmission.npy\x1b[0m \x1b[91m━━\x1b[0m\x1b[91m╸\x1b[0m\x1b[90m━━━━━━━━━━━━━━━━━━━━━\x1b[0m \x1b[35m10.5%\x1b[0m • \x1b[32m7.8/74.7 MB\x1b[0m • \x1b[31m2.1 MB/s\x1b[0m • \x1b[36m0:00:33\x1b[0m',
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 ...]

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