Cloud Coverage Prediction using Skycam Images

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Cloud coverage prediction is critical in weather forecasting and a variety of applications such as solar energy generation, aviation, and climate monitoring. Accurate forecasts help decision-makers and sectors plan for and adapt to changing weather conditions. The advancement of artificial intelligence and computer vision techniques in recent years has created new opportunities for enhancing cloud coverage forecasts.

One promising approach is the use of SkyCam images.

In the face of rapidly changing global climate patterns, there is an urgent need for innovative tools and technologies to better understand and predict weather-related phenomena.
One crucial aspect of climate analysis is the assessment of cloud coverage, which plays a pivotal role in influencing weather conditions and climate trends.
Experts may not always be available to monitor climatic shifts. Therefore, developing an automated weather monitoring system is crucial for various applications, including agriculture and disaster management.

The purpose of this research is to estimate the opaque Cloud Coverage from a Skycam Image using AI/ML methodologies.

Cloud Coverage Prediction using Skycam Images

Table of Content

Cloud Coverage Prediction using SkyCam Images
Implementations Cloud Coverage Prediction using SkyCam Images
Cloud Coverage Prediction Models:
Part I. Model Building & Traning Pipeline
A. Clip Model Finetuning
B. Catboost Regressor Model Building
Part II. UI Inference Codes for Deployed Model
Results:

Cloud Coverage Prediction using SkyCam Images

The integration of Computer Vision and Machine Learning, leading to regression and classification use cases, has been one of the major trending research areas. The purpose of this research is to estimate cloud coverage using SkyCam images and Computer Vision techniques. We hope to develop a system that can deliver real-time or short-term forecasts of cloud cover percentages by training predictive algorithms. This predictive skill has the potential to improve existing weather forecasting models and decision-making in industries such as energy production and transportation.

To address this imperative, a novel approach leveraging Computer Vision and Machine Learning techniques is used to develop cloud coverage calculators from skycam images.
Cloud coverage is a key parameter in weather forecasting, and automating its assessment can significantly improve weather predictions.
Furthermore, it can benefit industries reliant on weather conditions, such as renewable energy generation and transportation.
In the energy domain, specifically related to climatic patterns, the robust cloud coverage calculator model can accurately calculate cloud coverage from skycam images. The model aims to analyze the cloud formations in the provided images and provide a percentage indicating the extent of cloud coverage.

Moreover, integrating this cloud coverage model with skycam can serve as an early warning system for impending storms, heavy rains, and climatic shifts, helping to take preventive measures and ensure public safety.

Before delving into the core model development, it’s essential to acquire the domain knowledge required to build this project!

Domain Knowledge

Skycam Images: SkyCam, short for Sky Camera, is a specialized camera system often installed in various locations, including airports, research stations, and meteorological facilities. These cameras continuously capture images of the sky, providing valuable data for weather monitoring.

Skycam Image Samples

Implementations Cloud Coverage Prediction using SkyCam Images

System Architecture for the project:

There are 2 pipelines as shown below, one is for Training both the models i.e. CLIP & Catboost & other is for Inference.
Detailed explanation of system architecture is provided in Implementations Cloud Coverage Prediction using SkyCam Images

Cloud Coverage Prediction Pipeline

Prerequsite:

Programming Language: Python
AI/ML Platform for Model Training: Jupyter Notebook
Web App: Gradio
Libraries/Requirements: OpenCv, timm, pytorch, transformers, clip, Catboost

DataSet:

Data Contains 1,33,653 skycam images with their opaque cloud coverage in percentage.
During scraping this data, I have used OCR to extract the cloud coverage in percentage.
Dataset Link : Skycam Images

Cloud Coverage Prediction Models:

1. CLIP Model & its working:

There are various methods to extract features from images, but here, have utilized the Contrastive Language Image Pretrained (CLIP) Model.
CLIP is typically used for image-text similarity problems.
The CLIP model comprises 3 main components: Image Encoder, Text Encoder, and Projection Head.
Image Encoder: Utilizes ResNet50 to extract features from images in vector format.
Text Encoder: Utilizes Distilled-Bert to extract features from text in vector format.
Projection Head: Transforms both image and text vectors into the same size/shape.
A Dot Product is applied, creating a knowledge base, i.e., image-text mapping knowledge.
During inference, when an image is passed, image/text vectors are created, and a similarity score is generated between the knowledge base and the image/text vector to provide results.

2. Catboost Model:

Used Catboost Regressor to calculate cloud coverage.
Catboost is chosen for its robustness to outliers, high efficiency, out-of-the-box support, and faster processing compared to other bagging and generic models especially when we have image feature vectors.

Technical Workflow Steps:

Initially, we have data: images mapped with their cloud coverage values.
Generate text from the cloud coverage value, e.g. if cloud coverage = 97, then Text = “There is High Cloud Coverage. Opaque Cloud Coverage is 97%.”
Fine-tune the Contrastive Language Image Pretrained (CLIP) model on the skycam images + corresponding text.
Extraction of features from skycam images is done using the Fine-tuned CLIP model’s Image Encoder.
Extracted features are treated as ‘x’ (independent variables with a size of 2048) and cloud coverage values from Step 1 are treated as ‘y’ (dependent features) for the Catboost model.
Training, validation & testing records: 70,168 | 30,072 | 33,414.
The best hyperparameters for the Catboost model are determined, and a Regressor is implemented to predict cloud coverage in percentages.
Later, developed and deployed a Gradio App on Hugging Face Spaces, where users input a skycam image and receive the opaque cloud coverage percentage as output.

Contrastive Language Image Pretrained (CLIP) model

Python Libraries

Lets install following dependencies for the project:

Keep all the below dependencies in your requirements.txt.
Run command: pip install -r requirements.txt in your terminal.
Once dependencies are installed, we are set to go!

numpy
pandas
matplotlib
albumentations
opencv-python
torch
timm
tqdm
scikit-learn
catboost
transformers
gradio
git+https://github.com/openai/CLIP.git

Part I. Model Building & Traning Pipeline

Step 1:Load the necessary Libraries

Let’s first import all the necessary libraries which are required for both clip model & Catboost model.

Python3

# Importing Libraries 
 
import os, cv2, gc, itertools, pickle
from PIL import Image
import numpy as np
import pandas as pd
 
import albumentations as A
import matplotlib.pyplot as plt
 
import torch, timm, clip
from torch import nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from tqdm.autonotebook import tqdm
from transformers import DistilBertModel, DistilBertConfig, DistilBertTokenizer
 
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from catboost import CatBoostRegressor

Step 2: Load the dataset

Python3

# Importing skycam .csv Data: The label i.e. text feature is generated from the numeric cloud coverage.
folder = 'SkyCam'
df = pd.read_csv(folder+"/cloud_data_cleaned1.csv")
df = df[['image_name', 'label', 'opaque_clouds']]
df.columns = ['image', 'caption', 'cloudcover']
print(df.head())

Output:

                    image                                            caption  \
0  20160101075000.raw.jpg  Image has No Cloud Coverage. Image has 7% of o...   
1  20160101080000.raw.jpg  Image has No Cloud Coverage. Image has 7% of o...   
2  20160101081000.raw.jpg  Image has No Cloud Coverage. Image has 7% of o...   
3  20160101082000.raw.jpg  Image has No Cloud Coverage. Image has 7% of o...   
4  20160101083000.raw.jpg  Image has No Cloud Coverage. Image has 7% of o...   
   cloudcover  
0           7  
1           7  
2           7  
3           7  
4           7

The Data consists of skycam image name, caption which I have generated while building this csv.
Caption is generated from the third feature shown above i.e. cloudcover which is in percentage.
Our Aim is to calculate cloud cover given a skycam image.
This is a typical Classical Machine Learning Regression Problem with integration of Computer Vision.

Let’s check an image

Python3

img_folder =os.path.join(folder, 'Extracted Images', 'Extracted Images')
#img_filename = os.listdir(img_folder)[0]
img_path = os.path.join(img_folder, df['image'].iloc[777])
 
img = Image.open(img_path)
plt.imshow(img)
plt.show()

Output:

Skycam Image

A. Clip Model Finetuning

Step 3: Setting up Configurations:

The below code sets up essential hyperparameters and configurations for CLIP model. It includes settings for image and text data processing, batch size, learning rates, and training epochs. It also specifies the use of a GPU if available, and the choice of model architectures for image and text encoding. Additionally, it defines parameters for a projection head used for both image and text encoders, including the dimensionality of the projection and dropout rate. These configurations are crucial for the successful training and execution of the machine learning model.

Python3

# ----- Setting up Hyper Parameters in Configurations ----- #
 
class CFG:
    debug = False
    image_path =  img_folder  # Specify your Image directory path
    captions_path = "."
    batch_size = 128
    num_workers = 4
    head_lr = 1e-3
    image_encoder_lr = 1e-4
    text_encoder_lr = 1e-5
    weight_decay = 1e-3
    patience = 3
    factor = 0.8
    epochs = 15
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model_name = 'resnet50'
    image_embedding = 2048
    text_encoder_model = "distilbert-base-uncased"
    text_embedding = 768
    text_tokenizer = "distilbert-base-uncased"
    max_length = 200
    pretrained = True  # for both image encoder and text encoder
    trainable = True  # for both image encoder and text encoder
    temperature = 1.0
    size = 224
    # For projection head: used for both image and text encoders
    num_projection_layers = 1
    projection_dim = 256
    dropout = 0.1

Step 4: Setting up Utils:

The below code defines utility functions for monitoring and managing metrics during training. It includes an AvgMeter class to calculate averages and a function get_lr to extract the learning rate from an optimizer.

Python3

# ----- Setting up Utils ----- #
 
class AvgMeter:
    def __init__(self, name="Metric"):
        self.name = name
        self.reset()
    def reset(self):
        self.avg, self.sum, self.count = [0] * 3
    def update(self, val, count=1):
        self.count += count
        self.sum += val * count
        self.avg = self.sum / self.count
    def __repr__(self):
        text = f"{self.name}: {self.avg:.4f}"
        return text
 
def get_lr(optimizer):
    for param_group in optimizer.param_groups:
        return param_group["lr"]

Step 5: Building Custom Torch Dataset:

The below code defines a custom dataset class to transform the input images & text to a specific format that CLIP model intakes. It takes image filenames, captions, a tokenizer, and transforms as inputs, allowing for efficient data loading and processing. Additionally, it provides image transformation functions based on the specified mode (train or not) through get_transforms.

Python3

# ----- Building Custom Dataset ----- #
 
 
class CLIPDataset(torch.utils.data.Dataset):
    def __init__(self, image_filenames, captions, tokenizer, transforms):
        """
        image_filenames and captions must have the same length; so, if there are
        multiple captions for each image, the image_filenames must have repetitive
        file names.
        """
        self.image_filenames = image_filenames
        self.captions = list(captions)
        self.encoded_captions = tokenizer(
            list(captions), padding=True, truncation=True, max_length=CFG.max_length)
        self.transforms = transforms
 
    def __getitem__(self, idx):
        item = {
            key: torch.tensor(values[idx])
            for key, values in self.encoded_captions.items()
        }
        image = cv2.imread(f"{CFG.image_path}/{self.image_filenames[idx]}")
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = self.transforms(image=image)['image']
        item['image'] = torch.tensor(image).permute(2, 0, 1).float()
        item['caption'] = self.captions[idx]
        return item
 
    def __len__(self):
        return len(self.captions)
 
 
def get_transforms(mode="train"):
    if mode == "train":
        return A.Compose(
            [
                A.Resize(CFG.size, CFG.size, always_apply=True),
                A.Normalize(max_pixel_value=255.0, always_apply=True),
            ]
        )
    else:
        return A.Compose(
            [
                A.Resize(CFG.size, CFG.size, always_apply=True),
                A.Normalize(max_pixel_value=255.0, always_apply=True),
            ]
        )

Step 6: Image Encoder Class:

The CLIP model uses the below Image Encoder Class to pass the image to Resnet50 i.e. the Image Encoder for CLIP model. It is basically used to extract features from image data. We define an image encoder class, which utilizes a pre-trained model to encode images into fixed-size vectors. The model’s architecture, pre-training status, and trainability are configurable.

Python3

# ----- Image Encoder ----- #
 
class ImageEncoder(nn.Module):
    # Encode images to a fixed size vector
    def __init__(self, model_name=CFG.model_name, pretrained=CFG.pretrained, trainable=CFG.trainable):
        super().__init__()
        self.model = timm.create_model(
            model_name, pretrained, num_classes=0, global_pool="avg")
        for p in self.model.parameters():
            p.requires_grad = trainable
 
    def forward(self, x):
        return self.model(x)

Step 7: Text Encoder Class:

CLIP model has a text encoder which is Distilled Bert. It is used to encoder extracts sentence embeddings from text input.

Python3

# ----- Text Encoder ----- #
 
class TextEncoder(nn.Module):
    def __init__(self, model_name=CFG.text_encoder_model, pretrained=CFG.pretrained, trainable=CFG.trainable):
        super().__init__()
        if pretrained:
            self.model = DistilBertModel.from_pretrained(model_name)
        else:
            self.model = DistilBertModel(config=DistilBertConfig())
        for p in self.model.parameters():
            p.requires_grad = trainable
        # W are using the CLS token hidden representation as the sentence's embedding
        self.target_token_idx = 0
 
    def forward(self, input_ids, attention_mask):
        output = self.model(input_ids=input_ids, attention_mask=attention_mask)
        last_hidden_state = output.last_hidden_state
        return last_hidden_state[:, self.target_token_idx, :]

Step 8: Projection Head Class:

Below code defines a projection head module for dimensionality reduction of input image embeddings & text embeddings. It includes linear projections, activation functions (GELU), dropout, and layer normalization. The module is used to transform embeddings into a lower-dimensional space while preserving important features in order to increase training efficiency and decrease training time.

Python3

# ----- Projection Head ----- #
 
class ProjectionHead(nn.Module):
    def __init__(
        self,
        embedding_dim,
        projection_dim=CFG.projection_dim,
        dropout=CFG.dropout
    ):
        super().__init__()
        self.projection = nn.Linear(embedding_dim, projection_dim)
        self.gelu = nn.GELU()
        self.fc = nn.Linear(projection_dim, projection_dim)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(projection_dim)
 
    def forward(self, x):
        projected = self.projection(x)
        x = self.gelu(projected)
        x = self.fc(x)
        x = self.dropout(x)
        x = x + projected
        x = self.layer_norm(x)
        return x

Step 9: Defining Clip Model:

Now we define our custom CLIP model class, where we initialize the constructor with the image encoder, text encoder & projection head. The model computes embeddings for images and texts and calculates a loss that encourages similar images and text to have high similarity scores. Cross-entropy loss is used for training, and the model aims to align image and text embeddings in a joint embedding space for various applications like image-text retrieval and understanding.

Python3

# ----- CLIP Model Define ----- #
 
class CLIPModel(nn.Module):
    def __init__(
        self,
        temperature=CFG.temperature,
        image_embedding=CFG.image_embedding,
        text_embedding=CFG.text_embedding,
    ):
        super().__init__()
        self.image_encoder = ImageEncoder()
        self.text_encoder = TextEncoder()
        self.image_projection = ProjectionHead(embedding_dim=image_embedding)
        self.text_projection = ProjectionHead(embedding_dim=text_embedding)
        self.temperature = temperature
 
    def forward(self, batch):
        # Getting Image and Text Features
        image_features = self.image_encoder(batch["image"])
        text_features = self.text_encoder(
            input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
        )
        # Getting Image and Text Embeddings (with same dimension)
        image_embeddings = self.image_projection(image_features)
        text_embeddings = self.text_projection(text_features)
        # Calculating the Loss
        logits = (text_embeddings @ image_embeddings.T) / self.temperature
        images_similarity = image_embeddings @ image_embeddings.T
        texts_similarity = text_embeddings @ text_embeddings.T
        targets = F.softmax(
            (images_similarity + texts_similarity) / 2 * self.temperature, dim=-1
        )
        texts_loss = cross_entropy(logits, targets, reduction='none')
        images_loss = cross_entropy(logits.T, targets.T, reduction='none')
        loss = (images_loss + texts_loss) / 2.0  # shape: (batch_size)
        return loss.mean()
 
def cross_entropy(preds, targets, reduction='none'):
    log_softmax = nn.LogSoftmax(dim=-1)
    loss = (-targets * log_softmax(preds)).sum(1)
    if reduction == "none":
        return loss
    elif reduction == "mean":
        return loss.mean()

Step 10: Defining Training Functions for Clip Model:

Below code contains essential training methods to train a CLIP model. It includes functions for splitting a dataset into training and validation sets, building data loaders with transformations, setting up of epochs, batch size and other hyperparameters and performing training and validation epochs. These methods are crucial for training and evaluating CLIP model effectively.

Python3

# ----- Training Methods ----- #
 
def make_train_valid_dfs(df):
    # First 1,30,000 records for training
    train_dataframe = df.iloc[:130000, :]
    valid_dataframe = df.iloc[130000:, :]  # Last 30k records for validation
    return train_dataframe.reset_index(drop=True), valid_dataframe.reset_index(drop=True)
 
def build_loaders(dataframe, tokenizer, mode):
    transforms = get_transforms(mode=mode)
    dataset = CLIPDataset(
        dataframe["image"].values,
        dataframe["caption"].values,
        tokenizer=tokenizer,
        transforms=transforms,
    )
    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=CFG.batch_size,
        num_workers=CFG.num_workers,
        shuffle=True if mode == "train" else False,
    )
    return dataloader
 
def train_epoch(model, train_loader, optimizer, lr_scheduler, step):
    loss_meter = AvgMeter()
    tqdm_object = tqdm(train_loader, total=len(train_loader))
    for batch in tqdm_object:
        batch = {k: v.to(CFG.device)
                 for k, v in batch.items() if k != "caption"}
        loss = model(batch)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if step == "batch":
            lr_scheduler.step()
        count = batch["image"].size(0)
        loss_meter.update(loss.item(), count)
        tqdm_object.set_postfix(
            train_loss=loss_meter.avg, lr=get_lr(optimizer))
    return loss_meter
 
def valid_epoch(model, valid_loader):
    loss_meter = AvgMeter()
    tqdm_object = tqdm(valid_loader, total=len(valid_loader))
    for batch in tqdm_object:
        batch = {k: v.to(CFG.device)
                 for k, v in batch.items() if k != "caption"}
        loss = model(batch)
        count = batch["image"].size(0)
        loss_meter.update(loss.item(), count)
        tqdm_object.set_postfix(valid_loss=loss_meter.avg)
    return loss_meter

Step 11: Train Validation Split:

We split the input data into train & validation split. In train set we have 1,30,000 records & in valid set we have 3654 records. We are not using any test set over here, that because we are just using CLIP model to extract feature embeddings out of skycam images.

Python3

# ----- Train-Valid Split ----- #
 
train_df, valid_df = make_train_valid_dfs(df) 
print(len(train_df), len(valid_df))
tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)
train_loader = build_loaders(train_df, tokenizer, mode="train")
valid_loader = build_loaders(valid_df, tokenizer, mode="valid")

Output:

130000 3654

Step 12: Clip Model Finetuning:

Now, we finetune the CLIP model on our custom data. The below provided code segment loads a pre-trained CLIP model and sets up the training process. It defines the model’s parameters and optimizers, with separate learning rates for different components. It then runs the training loop for a specified number of epochs, saving the best model based on validation loss and adjusting the learning rate using a scheduler. This code trains the model and saves the best-performing version.

Python3

# ----- Loading Pretrained Model ----- #
 
model = CLIPModel().to(CFG.device)
params = [
    {"params": model.image_encoder.parameters(), "lr": CFG.image_encoder_lr},
    {"params": model.text_encoder.parameters(), "lr": CFG.text_encoder_lr},
    {"params": itertools.chain(
        model.image_projection.parameters(), model.text_projection.parameters()
    ), "lr": CFG.head_lr, "weight_decay": CFG.weight_decay}
]
optimizer = torch.optim.AdamW(params, weight_decay=0.)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
    optimizer, mode="min", patience=CFG.patience, factor=CFG.factor
)

Model Training

Python3

# ----- Model Training ----- #
step = "epoch"
best_loss = float('inf')
for epoch in range(CFG.epochs):
    print(f"Epoch: {epoch + 1}")
    model.train()
    train_loss = train_epoch(
        model, train_loader, optimizer, lr_scheduler, step)
    model.eval()
    with torch.no_grad():
        valid_loss = valid_epoch(model, valid_loader)
    if valid_loss.avg < best_loss:
        best_loss = valid_loss.avg
        torch.save(model.state_dict(), "CLIP_model.pt")
        print("Saved Best Model!")
    lr_scheduler.step(valid_loss.avg)

Step 13: Save the Clip Model & its configurations

Now, we save the Clip Model & its configs into pickle file. In Step 12, already a .pt extension model is been saved but still for model safety purpose we also save it in .pkl file.

Python3

with open('clip_mdl.pkl', 'wb') as f:
    pickle.dump(model, f)
with open('clip_cfg.pkl', 'wb') as f:
    pickle.dump(CFG, f)

B. Catboost Regressor Model Building

Step 14: Train-Test-Valid Split for Regression:

In the below code we do a train-test-valid split. We have 70,168 records for training, 30,072 records for validation and 33,414 records for testing. Set a random state to ensure model output repeatability.

Python3

x = df['image']
y = df['cloudcover']
 
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=48)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.30, random_state=48)
 
print((x_train.shape, x_val.shape, x_test.shape))

Output:

((70168,),  (30072,),  (33414,))

Step 15: Loading the Finetuned Clip Model:

In the below code we load the finetuned .pt CLIP model. Now since we have our finetuned CLIP model which is ready for image feature extraction, we first of all extract the feature embeddings for all the sky cam images from CLIP model and those embeddings would work as input features for our catboost model.

Python3

model = CLIPModel().to(CFG.device)
model.load_state_dict(torch.load("clip_model.pt", map_location = CFG.device))
model.eval()

Output:

CLIPModel(
  (image_encoder): ImageEncoder(
    (model): ResNet(
      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act1): ReLU(inplace=True)
      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (layer1): Sequential(
        (0): Bottleneck(
          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
          (downsample): Sequential(
            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): Bottleneck(
          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (2): Bottleneck(
          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
      )
      (layer2): Sequential(
        (0): Bottleneck(
          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
          (downsample): Sequential(
            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): Bottleneck(
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (2): Bottleneck(
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (3): Bottleneck(
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
      )
      (layer3): Sequential(
        (0): Bottleneck(
          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
          (downsample): Sequential(
            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): Bottleneck(
          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (2): Bottleneck(
          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (3): Bottleneck(
          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (4): Bottleneck(
          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (5): Bottleneck(
          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
      )
      (layer4): Sequential(
        (0): Bottleneck(
          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
          (downsample): Sequential(
            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): Bottleneck(
          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
        (2): Bottleneck(
          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act1): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (drop_block): Identity()
          (act2): ReLU(inplace=True)
          (aa): Identity()
          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (act3): ReLU(inplace=True)
        )
      )
      (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))
      (fc): Identity()
    )
  )
  (text_encoder): TextEncoder(
    (model): DistilBertModel(
      (embeddings): Embeddings(
        (word_embeddings): Embedding(30522, 768, padding_idx=0)
        (position_embeddings): Embedding(512, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (transformer): Transformer(
        (layer): ModuleList(
          (0-5): 6 x TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
        )
      )
    )
  )
  (image_projection): ProjectionHead(
    (projection): Linear(in_features=2048, out_features=256, bias=True)
    (gelu): GELU(approximate='none')
    (fc): Linear(in_features=256, out_features=256, bias=True)
    (dropout): Dropout(p=0.1, inplace=False)
    (layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
  )
  (text_projection): ProjectionHead(
    (projection): Linear(in_features=768, out_features=256, bias=True)
    (gelu): GELU(approximate='none')
    (fc): Linear(in_features=256, out_features=256, bias=True)
    (dropout): Dropout(p=0.1, inplace=False)
    (layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
  )
)

Step 16: Dataset Preparation for Regression Model:

In the below code, we are creating a custom Torch dataset for our images and label. We load all the images and labels to this dataset which will be passed to the finetuned model for predictions.

Python3

# ----- Custom Dataset Loader ----- #
class SkyImage(Dataset):
    def __init__(self, img_dir, labels):
        self.img_dir = img_dir
        self.img_labels = labels
    def __len__(self):
        return len(self.img_dir)
    def __getitem__(self, idx):
        img_path = os.path.join(img_folder, self.img_dir[idx])
        #os.path.join("Extracted Images/", self.img_dir[idx])
        image = cv2.imread(img_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (244, 244))
        image = np.moveaxis(image, -1, 0)
        label = self.img_labels[idx]
        return image, label
 
# ----- Dataset ----- #
train_images = SkyImage(x_train.to_list(), y_train.to_list())
valid_images = SkyImage(x_val.to_list(), y_val.to_list())
test_images = SkyImage(x_test.to_list(), y_test.to_list())

Step 17: Extracting image features from CLIP Model:

Check if CUDA (GPU) is available and set the device accordingly

Python3

# Check if CUDA (GPU) is available and set the device accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

Output:

device(type='cpu')

Building Features

The below get features method intakes our torch dataset for training, testing & validation set and extract image feature vectors from skycam images and returns the extracted feature vectors.

Python3

# ----- Building Features ----- #
 
def get_features(dataset):
    all_features, all_labels, all_embeddings = [], [], []
    with torch.no_grad():
        for images, labels in tqdm(DataLoader(dataset, batch_size=64)):
            image_input = torch.tensor(np.stack(images)).to(device).float()
            image_features = model.image_encoder(image_input)
            image_embeddings = model.image_projection(image_features)
            all_features.append(image_features)
            all_labels.append(labels)
            all_embeddings.append(image_embeddings)
    return torch.cat(all_features), torch.cat(all_labels).to(device), torch.cat(all_embeddings).to(device)
 
# ----- Get Features ----- #
 
train_features, train_labels, train_embeddings = get_features(train_images)
valid_features, valid_labels, valid_embeddings = get_features(valid_images)
test_features, test_labels, test_embeddings = get_features(test_images)

Step 18: Evaluation Metrics Method:

We build our custom metrics method to evaluate our catboost model. We will be using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) & R-Squared (R²) as our model evaluators.

Python3

def evaluate(name, x, y, n, p):  # p: features, #n: no of observations
    print("---------------------------------------------------")
    print("{} MAE: {}".format(name, mean_absolute_error(x, y)))
    print("{} RMSE: {}".format(name, mean_squared_error(x, y, squared=False)))
    print("{} MSE: {}".format(name, mean_squared_error(x, y)))
    r2 = r2_score(x, y)
    print("{} R2: {}".format(name, r2))
    print("---------------------------------------------------")

Step 19: Catboost model Training:

We train the catboost model with multiple hyperparameters. Lets use RMSE as evaluation metrics to evaluate model on validation data. We pass both training & validation data to fit method. By this way we ensure that there is no model overfitting because model is validation on validation data which is not present in training data. I have found out the best hyperparameters for this scenario and have used those in below code. For finding out best hyperparameters you can do hit and trial or apply cross validation.

Python3

# ----- Model Training ----- #
 
CB_model = CatBoostRegressor(iterations=700, learning_rate=0.1, max_depth=8, eval_metric='RMSE', random_seed=48)
CB_model.fit(train_features.cpu().numpy(), train_labels.cpu().numpy(),
             eval_set = (valid_features.cpu().numpy(), valid_labels.cpu().numpy()),
             use_best_model=True, plot=True, verbose=50)

Output:

0:        learn: 28.1361841        test: 28.2423136    best: 28.2423136 (0)    total: 2.13s        remaining: 24m 49s
50:        learn: 11.5614561        test: 11.9335237    best: 11.9335237 (50)    total: 1m 3s        remaining: 13m 21s
100:    learn: 10.7263689        test: 11.4059249    best: 11.4059249 (100)    total: 2m 1s        remaining: 12m 1s
150:    learn: 10.0566562        test: 11.0617557    best: 11.0617557 (150)    total: 3m            remaining: 10m 55s
200:    learn: 9.5172739        test: 10.8473396    best: 10.8473396 (200)    total: 3m 58s        remaining: 9m 51s
250:    learn: 9.0923719        test: 10.6886373    best: 10.6886373 (250)    total: 4m 55s        remaining: 8m 47s
300:    learn: 8.7042622        test: 10.5734544    best: 10.5734544 (300)    total: 5m 51s        remaining: 7m 45s
350:    learn: 8.3755575        test: 10.4773273    best: 10.4773273 (350)    total: 6m 47s        remaining: 6m 45s
400:    learn: 8.0759744        test: 10.3938604    best: 10.3938604 (400)    total: 7m 44s        remaining: 5m 46s
450:    learn: 7.7814581        test: 10.3233375    best: 10.3233375 (450)    total: 8m 42s        remaining: 4m 48s
500:    learn: 7.5160766        test: 10.2628795    best: 10.2628795 (500)    total: 9m 39s        remaining: 3m 50s
550:    learn: 7.2897423        test: 10.2027638    best: 10.2027638 (550)    total: 10m 35s        remaining: 2m 51s
600:    learn: 7.0611325        test: 10.1574324    best: 10.1574324 (600)    total: 11m 33s        remaining: 1m 54s
650:    learn: 6.8320990        test: 10.1136860    best: 10.1136860 (650)    total: 12m 30s        remaining: 56.5s
699:    learn: 6.6529638        test: 10.0780409    best: 10.0780409 (699)    total: 13m 25s        remaining: 0us
bestTest = 10.07804086
bestIteration = 699

Step 20: Model Prediction

Lets do the prediction on Train, Test & Validation sets.

Python3

# ----- Model Prediction ----- #
 
cbt_train_pred = CB_model.predict(train_features.cpu().numpy())
cbt_valid_pred = CB_model.predict(valid_features.cpu().numpy())
cbt_test_pred = CB_model.predict(test_features.cpu().numpy())

Step 21: Model Evaluation

Now, we have the actual values of cloud cover and predicted values of cloud cover we can evaluate our Catboost model.

Python3

# ----- Model Evaluation ----- #
 
evaluate("Train", train_labels.cpu(), cbt_train_pred, len(cbt_train_pred), 1)
evaluate("Valid", valid_labels.cpu(), cbt_valid_pred, len(cbt_valid_pred), 1)
evaluate("Test", test_labels.cpu(), cbt_test_pred, len(cbt_test_pred), 1)

Output:

---------------------------------------------------
Train MAE: 4.437975369402876
Train RMSE: 6.652963762088708
Train MSE: 44.26192681966554
Train R2: 0.9523583786704957
---------------------------------------------------
---------------------------------------------------
Valid MAE: 6.304070193782646
Valid RMSE: 10.078040861839906
Valid MSE: 101.56690761291485
Valid R2: 0.8914442298156392
---------------------------------------------------
---------------------------------------------------
Test MAE: 6.364711156454016
Test RMSE: 10.198410458657648
Test MSE: 104.0075758832577
Test R2: 0.889060898998321
---------------------------------------------------

Insights:

Out model is perfectly fitted on the data, and can be deployed on UI.
Test & Validation Metrics are very good.
There is no overfitting as the Train Metrics are closer to Validation & Test Metrics.

Step 22: Save the Catboost Model

We save the Catboost Model for deploying purpose.

Python3

pickle.dump(CB_model, open('catboost_model.sav', 'wb'))

Part II. UI Inference Codes for Deployed Model

A. cloud_coverage_pipeline.py code:

The provided below code comprises several essential components for cloud coverage prediction.
It begins by importing necessary libraries, including popular deep learning frameworks like PyTorch and Hugging Face Transformers.
The configuration settings (CFG) are specified for hyperparameters, dataset, and model configurations.
The CLIP Model class is defined to encapsulate the Cloud Coverage Prediction model, incorporating the Image Encoder and optional Text Encoder and Projection Head modules.
The Image Encoder class employs a pre-trained ResNet model from the ‘timm’ library to extract image features.
The Sky Image class defines a custom dataset loader for image data, allowing for data transformation and preprocessing. It takes images as input.
Additional utility functions are included for initializing models (Catboost and CLIP), extracting features from the CLIP model, and predicting cloud coverage based on the extracted features.
Overall, the code sets up the foundation for cloud coverage prediction, including data loading, model initialization, and feature extraction, making it ready for cloud coverage assessment using the CLIP and Catboost models.
This file returns predicted cloud coverage in percentage to app.py which returns the cloud coverage to the User on User Interface.

Python3

# Importing Libraries
import os
import numpy as np
import cv2
import torch
from torch import nn
import timm
import pickle
from transformers import DistilBertModel, DistilBertConfig
from torch.utils.data import Dataset, DataLoader
from tqdm.autonotebook import tqdm
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
 
# Trained Model Configurations
CFG = {
    "debug": False,
    "captions_path": ".",
    "batch_size": 64,
    "num_workers": 4,
    "head_lr": 1e-3,
    "image_encoder_lr": 1e-4,
    "text_encoder_lr": 1e-5,
    "weight_decay": 1e-3,
    "patience": 1,
    "factor": 0.8,
    "epochs": 12,
    "device": "cpu",
    "model_name": 'resnet50',
    "image_embedding": 2048,
    "text_encoder_model": "distilbert-base-uncased",
    "text_embedding": 768,
    "text_tokenizer": "distilbert-base-uncased",
    "max_length": 200,
    "pretrained": True,
    "trainable": True,
    "temperature": 1.0,
    "size": 224,
    "num_projection_layers": 1,
    "projection_dim": 256,
    "dropout": 0.1
}
 
 
# Loading Finetuned Clip Model to the below class format
class CLIPModel(nn.Module):
    def __init__(
            self,
            temperature=CFG["temperature"],
            image_embedding=CFG["image_embedding"],
            text_embedding=CFG["text_embedding"],
    ):
        super().__init__()
        self.image_encoder = ImageEncoder()
        self.text_encoder = TextEncoder()
        self.image_projection = ProjectionHead(embedding_dim=image_embedding)
        self.text_projection = ProjectionHead(embedding_dim=text_embedding)
        self.temperature = temperature
 
 
# Image Encoder Class to extract features using finetuned clip's Resnet Image Encoder
class ImageEncoder(nn.Module):
    def __init__(self, model_name=CFG["model_name"], pretrained=CFG["pretrained"], trainable=CFG["trainable"]):
        super().__init__()
        self.model = timm.create_model(
            model_name, pretrained, num_classes=0, global_pool="avg")
        for p in self.model.parameters():
            p.requires_grad = trainable
 
    def forward(self, x):
        return self.model(x)
 
 
# Text Encoder - Optional in inference
class TextEncoder(nn.Module):
    def __init__(self, model_name=CFG["text_encoder_model"], pretrained=CFG["pretrained"], trainable=CFG["trainable"]):
        super().__init__()
        if pretrained:
            self.model = DistilBertModel.from_pretrained(model_name)
        else:
            self.model = DistilBertModel(config=DistilBertConfig())
 
        for p in self.model.parameters():
            p.requires_grad = trainable
 
        self.target_token_idx = 0
 
    def forward(self, input_ids, attention_mask):
        output = self.model(input_ids=input_ids, attention_mask=attention_mask)
        last_hidden_state = output.last_hidden_state
        return last_hidden_state[:, self.target_token_idx, :]
 
 
# Projection Class - Optional in inference
class ProjectionHead(nn.Module):
    def __init__(
            self,
            embedding_dim,
            projection_dim=CFG["projection_dim"],
            dropout=CFG["dropout"]
    ):
        super().__init__()
        self.projection = nn.Linear(embedding_dim, projection_dim)
        self.gelu = nn.GELU()
        self.fc = nn.Linear(projection_dim, projection_dim)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(projection_dim)
 
    def forward(self, x):
        projected = self.projection(x)
        x = self.gelu(projected)
        x = self.fc(x)
        x = self.dropout(x)
        x = x + projected
        x = self.layer_norm(x)
        return x
 
 
# Class to transform image to custom data format
class SkyImage(Dataset):
    def __init__(self, img, label):
        self.img = img
        self.img_label = label
 
    def __len__(self):
        return len(self.img)
 
    def __getitem__(self, idx):
        image = cv2.resize(self.img[idx], (244, 244))
        image = np.moveaxis(image, -1, 0)
        label = self.img_label[idx]
        return image, label
 
 
# Method to initialize CatBoost model
def initialize_models():
    cbt_model = pickle.load(open("/home/gfg19509@gfg.zambiatek.com/PawanKrGunjan/Computer Vision/Skycam/catboost_model.sav", 'rb'))
    clip_model = CLIPModel().to(CFG["device"])
    clip_model.load_state_dict(torch.load(
        "/home/gfg19509@gfg.zambiatek.com/PawanKrGunjan/Computer Vision/Skycam/clip_model.pt", map_location=CFG["device"]))
    clip_model.eval()
    return cbt_model, clip_model
 
 
# Method to extract features from finetuned clip model
def get_features(clip_model, dataset):
    features, label, embeddings = [], [], []
    with torch.no_grad():
        for images, labels in tqdm(DataLoader(dataset, batch_size=64)):
            image_input = torch.tensor(np.stack(images)).cpu().float()
            image_features = clip_model.image_encoder(image_input)
            features.append(image_features)
            label.append(labels)
    return torch.cat(features), torch.cat(label).cpu()
 
 
# Method to calculate cloud coverage
def predict_cloud_coverage(image, clip_model, CTBR_model):
    img, lbl = [image], [0]
    # Transforming Data into custom format
    test_image = SkyImage(img, lbl)
    # Extracting Features from Finetuned CLIP model
    features, label = get_features(clip_model, test_image)
    # Predicting Cloud Coverage based on extracted features
    pred_cloud_coverage = CTBR_model.predict(features.cpu().numpy())
    return round(max(0.0, min(100.0, pred_cloud_coverage[0])), 1)

app.py code:

The below code sets up a Gradio web interface for a cloud coverage prediction model.

It imports necessary libraries, initializes the CLIP and Catboost models, and defines a predict function to calculate cloud coverage based on an uploaded sky image.
The Gradio app takes an image as input, processes it, and provides a textual prediction output, categorizing cloud coverage as low, moderate, or high.
The interface allows users to upload sky images and receive cloud coverage predictions interactively.
The app launches and runs for real-time predictions via a web interface.
There is a separate cloud_coverage_pipeline.py file which includes pipeline code. Keep it in same folder.

Python3

# Importing Libraries
import gradio as gr
from gradio.components import Image, Textbox
from cloud_coverage_pipeline import predict_cloud_coverage, initialize_models
 
# Initialize the CLIP model and CatBoost model only once
cbt_model, clip_model = initialize_models()
 
# Method to call cloud_coverage_pipeline.py to calculate cloud coverage
 
 
def predict(image):
    if image is None:
        return "Please Upload a valid sky image!"
    pred_cloud_coverage = predict_cloud_coverage(image, clip_model, cbt_model)
    if pred_cloud_coverage <= 33.0:
        s = "There is Low Cloud Coverage!   Predicted Opaque Cloud Coverage: {}%".format(
            pred_cloud_coverage)
    elif pred_cloud_coverage > 33.0 and pred_cloud_coverage <= 66.0:
        s = "There is Moderate Cloud Coverage!   Predicted Opaque Cloud Coverage: {}%".format(
            pred_cloud_coverage)
    else:
        s = "There is High Cloud Coverage!   Predicted Opaque Cloud Coverage: {}%".format(
            pred_cloud_coverage)
    return s
 
 
# Create the Gradio app
iface = gr.Interface(
    fn=predict,
    inputs=[Image(label="Upload a Sky Cam image")],
    outputs=[Textbox(label="Prediction")],
    title="GFG EcoTech Hackathon: Cloud Coverage Calculator From a Sky Cam Image",
    description='Upload only a skycam image and get the opaque cloud coverage in %     |     (Low: 0-33 | Moderate: 33-66 | High: 66-100)     |     <a href="https://drive.google.com/drive/folders/1r8mTWEG4XEBZDg0TNyXTYkGzZVixXvcj?usp=drive_link">Find Sample Testing Images Here!</a>',
)
 
# Run the Gradio app
iface.launch(debug=True)

Output:

Running on local URL:  http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.

The output will look like below. We can uplad the images by clicling “Click to Upload”

Predictions Window

Predictions

Results:

–	Train Data	Valid Data	Test Data
No. of Records	70,168	30,072	33,414
MAE	4.43	6.3	6.36
RMSE	6.65	10.07	10.19
R2	0.95	0.89	0.88

Above metrics indicate that Catboost model is perfectly fitted on the data with test RMSE of 10.19 & R2 of 0.88.
The system successfully predicts cloud coverage (ranging from 0% to 100%) from skycam images, providing valuable weather information.
Future opportunities include integrating the model with Skycamera and creating early alerting systems for climatic shifts.