Starter Code for Semantic Segmentarion
What we are going to Learn¶
- Using segmentation_model.pytorch and Unet for multi class semantic segmentation
Note : Create a copy of the notebook and use the copy for submission. Go to File > Save a Copy in Drive to create a new copy
Downloading Dataset¶
Installing aicrowd-cli
In [1]:
!pip install aicrowd-cli
%load_ext aicrowd.magic
Requirement already satisfied: aicrowd-cli in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (0.1.8) Requirement already satisfied: rich<11,>=10.0.0 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (10.6.0) Requirement already satisfied: tqdm<5,>=4.56.0 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (4.61.2) Requirement already satisfied: requests-toolbelt<1,>=0.9.1 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (0.9.1) Requirement already satisfied: click<8,>=7.1.2 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (7.1.2) Requirement already satisfied: requests<3,>=2.25.1 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (2.25.1) Requirement already satisfied: GitPython==3.1.18 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (3.1.18) Requirement already satisfied: toml<1,>=0.10.2 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from aicrowd-cli) (0.10.2) Requirement already satisfied: gitdb<5,>=4.0.1 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from GitPython==3.1.18->aicrowd-cli) (4.0.7) Requirement already satisfied: smmap<5,>=3.0.1 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from gitdb<5,>=4.0.1->GitPython==3.1.18->aicrowd-cli) (4.0.0) Requirement already satisfied: certifi>=2017.4.17 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from requests<3,>=2.25.1->aicrowd-cli) (2021.5.30) Requirement already satisfied: chardet<5,>=3.0.2 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from requests<3,>=2.25.1->aicrowd-cli) (4.0.0) Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from requests<3,>=2.25.1->aicrowd-cli) (1.26.5) Requirement already satisfied: idna<3,>=2.5 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from requests<3,>=2.25.1->aicrowd-cli) (2.10) Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from rich<11,>=10.0.0->aicrowd-cli) (0.9.1) Requirement already satisfied: colorama<0.5.0,>=0.4.0 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from rich<11,>=10.0.0->aicrowd-cli) (0.4.4) Requirement already satisfied: pygments<3.0.0,>=2.6.0 in c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages (from rich<11,>=10.0.0->aicrowd-cli) (2.9.0)
WARNING: You are using pip version 21.1.3; however, version 21.2.4 is available. You should consider upgrading via the 'c:\users\rock_\appdata\local\programs\python\python39\python.exe -m pip install --upgrade pip' command.
In [2]:
%aicrowd login
Please login here: https://api.aicrowd.com/auth/jsGeL-eqL3p2S8V3l_kiupRyIyFLJPGtBhjS1hhhZ20 API Key valid Saved API Key successfully!
In [3]:
!rm -rf data
!mkdir data
%aicrowd ds dl -c scene-segmentation -o data
In [6]:
!unzip data/train.zip -d data/train
!unzip data/test.zip -d data/test
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Downloading & Importing Libraries¶
Here we are going to use segmentation_models.pytorch which is a really popular library providing a tons of different segmentation models for pytorch including basic unets to DeepLabV3!
In [1]:
# Reading Dataset, vis and miscellaneous
from PIL import Image
import matplotlib.pyplot as plt
%matplotlib notebook
import os
import numpy as np
from natsort import natsorted
from tqdm.notebook import tqdm
import cv2
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import tensorflow.keras.backend as K
from tensorflow.keras.utils import to_categorical
Creating the Dataloader¶
In this section, we will be creating the dataloader that our model will use for the loading batches of corrosponding features and labels for training & testing
In [2]:
img_dims = (512, 512, 3)
class DataGenerator(tf.keras.utils.Sequence):
'Generates data for Keras'
def __init__(self, task="train", split=0.8, img_dims=img_dims, batch_size=1, shuffle=True):
'Initialization'
self.batch_size = batch_size
self.dims = img_dims
self.task = task
self.nb = len(os.listdir("data/train/image"))
self.split = int(split * self.nb)
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return int((self.split if self.task == "train" else (self.nb - self.split)) / self.batch_size)
def __getitem__(self, index):
'Generate one batch of data'
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
return self.__data_generation(indexes)
def on_epoch_end(self):
'Updates indexes after each epoch'
self.indexes = np.arange(self.nb)
if self.task == "train":
self.indexes = self.indexes[:self.split]
else:
self.indexes = self.indexes[self.split:]
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, indexes):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# Initialization
X = np.empty((self.batch_size, *self.dims), dtype=np.float32)
Y = np.empty((self.batch_size, *self.dims[:2], 25), dtype=np.float32)
# Generate data
for i, n in enumerate(indexes):
# Store sample
img = Image.open("data/train/image/" + str(n) + ".jpg")
X[i,] = np.asarray(img) / 255.0
seg = Image.open("data/train/segmentation/" + str(n) + ".png")
cat_seg = to_categorical(seg, num_classes=25)
Y[i,] = np.asarray(cat_seg)
return X, Y
In [3]:
split = 0.8
train_gen = DataGenerator(task="train", split=split)
val_gen = DataGenerator(task="val", split=split)
Visualizing Dataset¶
In [4]:
idx = 0
batch = 0
print(len(train_gen))
print(train_gen[batch][0][idx].shape, train_gen[batch][1][idx].shape)
fig, axes = plt.subplots(1, 2)
axes[0].imshow(train_gen[batch][0][idx])
axes[1].imshow(np.argmax(train_gen[batch][1][idx], axis=2))
fig.show()
3200 (512, 512, 3) (512, 512, 25)
Creating the Model¶
Here we will get setting up the model architecture, optimizer and loss.
In [ ]:
In [9]:
tf.keras.backend.clear_session()
#############################################################
# https://www.programmersought.com/article/16453761263/
inputs = Input(shape=img_dims)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D (512, 2, activation='relu', padding='same', kernel_initializer='he_normal') (UpSampling2D (size=(2,2))(drop5)) # Convolution after upsampling, Equivalent to transpose convolution operation!
merge6 = concatenate([drop4,up6],axis=3)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2,2))(conv6))
merge7 = concatenate([conv3,up7],axis = 3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2,2))(conv7))
merge8 = concatenate([conv2,up8],axis = 3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2,2))(conv8))
merge9 = concatenate([conv1,up9],axis = 3)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D (25, 1, activation='softmax') (conv9)
model = Model(inputs=[inputs], outputs=[conv10])
model.compile (optimizer=Adam(learning_rate=1e-4), loss='categorical_crossentropy', metrics=["accuracy"])
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 512, 512, 64) 1792 input_1[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 512, 512, 64) 36928 conv2d[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 256, 256, 64) 0 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 256, 256, 128 73856 max_pooling2d[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 256, 256, 128 147584 conv2d_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 128, 128, 128 0 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 128, 128, 256 295168 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 128, 128, 256 590080 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 64, 64, 256) 0 conv2d_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 64, 64, 512) 1180160 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 64, 64, 512) 2359808 conv2d_6[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 64, 64, 512) 0 conv2d_7[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 32, 32, 512) 0 dropout[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 1024) 4719616 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 32, 32, 1024) 9438208 conv2d_8[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 1024) 0 conv2d_9[0][0]
__________________________________________________________________________________________________
up_sampling2d (UpSampling2D) (None, 64, 64, 1024) 0 dropout_1[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 64, 64, 512) 2097664 up_sampling2d[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 64, 64, 1024) 0 dropout[0][0]
conv2d_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 64, 64, 512) 4719104 concatenate[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 64, 64, 512) 2359808 conv2d_11[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, 128, 128, 512 0 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 128, 128, 256 524544 up_sampling2d_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 128, 128, 512 0 conv2d_5[0][0]
conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 128, 128, 256 1179904 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 128, 128, 256 590080 conv2d_14[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, 256, 256, 256 0 conv2d_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 256, 256, 128 131200 up_sampling2d_2[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 256, 256, 256 0 conv2d_3[0][0]
conv2d_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 256, 256, 128 295040 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 256, 256, 128 147584 conv2d_17[0][0]
__________________________________________________________________________________________________
up_sampling2d_3 (UpSampling2D) (None, 512, 512, 128 0 conv2d_18[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 512, 512, 64) 32832 up_sampling2d_3[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 512, 512, 128 0 conv2d_1[0][0]
conv2d_19[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 512, 512, 64) 73792 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 512, 512, 64) 36928 conv2d_20[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 512, 512, 32) 18464 conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 512, 512, 25) 825 conv2d_22[0][0]
==================================================================================================
Total params: 31,050,969
Trainable params: 31,050,969
Non-trainable params: 0
__________________________________________________________________________________________________
c:\users\rock_\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:374: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. warnings.warn(
Training the Model¶
In [5]:
#save best model if model improved
model_name = "segm.h5"
best_checkpoint = tf.keras.callbacks.ModelCheckpoint(model_name, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
In [6]:
model = load_model(model_name)
In [7]:
history = model.fit(train_gen, validation_data=val_gen, epochs=2, verbose=1, callbacks=[best_checkpoint])
Epoch 1/2 4000/4000 [==============================] - 1014s 247ms/step - loss: 0.0492 - accuracy: 0.9827 WARNING:tensorflow:Can save best model only with val_loss available, skipping. Epoch 2/2 4000/4000 [==============================] - 966s 242ms/step - loss: 0.0449 - accuracy: 0.9840 WARNING:tensorflow:Can save best model only with val_loss available, skipping.
In [10]:
model.save(model_name)
#model = load_model(model_name)
Validation¶
In [7]:
idx = 0
batch = 0
img_out_dims = (512, 512)
img = val_gen[batch][0][idx]
seg = np.argmax(val_gen[batch][1][idx], axis=2)
pred = model.predict(np.expand_dims(img, axis=0))[0] * 255.0
pred_img = np.argmax(pred, axis=2)
fig, axes = plt.subplots(1, 3)
axes[0].imshow(img)
axes[1].imshow(np.squeeze(seg))
axes[2].imshow(pred_img)
fig.show()
Generating Predictions¶
In [9]:
!rm -rf segmentation
!mkdir segmentation
for n in tqdm(os.listdir("data/test/image")):
img = np.asarray(Image.open("data/test/image/" + str(n))) / 255.0
pred = model.predict(np.expand_dims(img, axis=0))[0] * 255.0
pred_img = np.argmax(pred, axis=2)
pred_img = Image.fromarray(pred_img.astype(np.uint8))
pred_img.save(os.path.join("segmentation", n[:-4] + ".png"))
Submitting our Predictions¶
Note : Please save the notebook before submitting it (Ctrl + S)
In [ ]:
!aicrowd notebook submit -c scene-segmentation -a segmentation --no-verify
Mounting Google Drive 💾 Your Google Drive will be mounted to access the colab notebook 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&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%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code Enter your authorization code: 4/1AX4XfWgVdnLp91l03I38HQ5VgmzkzARJ-HrMr0a332iRD88mbzH-neEGZ9M Mounted at /content/drive Using notebook: /content/drive/MyDrive/Colab Notebooks/Semantic Segmentation for submission... Scrubbing API keys from the notebook... Collecting notebook... submission.zip ━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 4.2/4.2 MB • 2.4 MB/s • 0:00:00 ╭─────────────────────────╮ │ Successfully submitted! │ ╰─────────────────────────╯ Important links ┌──────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ This submission │ https://www.aicrowd.com/challenges/blitz-xi/problems/scene-segmentation/submissions/153318 │ │ │ │ │ All submissions │ https://www.aicrowd.com/challenges/blitz-xi/problems/scene-segmentation/submissions?my_submissions=true │ │ │ │ │ Leaderboard │ https://www.aicrowd.com/challenges/blitz-xi/problems/scene-segmentation/leaderboards │ │ │ │ │ Discussion forum │ https://discourse.aicrowd.com/c/blitz-xi │ │ │ │ │ Challenge page │ https://www.aicrowd.com/challenges/blitz-xi/problems/scene-segmentation │ └──────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────┘
In [ ]:
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