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import subprocess
import os
import shutil
def clone_dataset_scenario(repo_url, model_repo_dir="./LWM", scenarios_dir="scenarios"):
"""
Clones all scenarios from a repository, ensuring all files (small and large) are downloaded.
Args:
repo_url (str): URL of the Git repository
model_repo_dir (str): Path to the model repository
scenarios_dir (str): Directory name for storing scenarios
"""
current_dir = os.path.basename(os.getcwd())
if current_dir == "LWM":
model_repo_dir = "."
scenarios_path = os.path.join(model_repo_dir, scenarios_dir)
os.makedirs(scenarios_path, exist_ok=True)
original_dir = os.getcwd()
try:
if os.path.exists(scenarios_path):
shutil.rmtree(scenarios_path)
print("Cloning entire repository into temporary directory ...")
subprocess.run([
"git", "clone",
repo_url,
scenarios_path
], check=True)
os.chdir(scenarios_path)
print("Pulling all files using Git LFS ...")
subprocess.run(["git", "lfs", "install"], check=True)
subprocess.run(["git", "lfs", "pull"], check=True)
print(f"Successfully cloned all scenarios into {scenarios_path}")
except subprocess.CalledProcessError as e:
print(f"Error cloning scenarios: {str(e)}")
finally:
if os.path.exists(scenarios_path):
shutil.rmtree(scenarios_path)
os.chdir(original_dir)
#%%
model_repo_url = "https://huggingface.co/wi-lab/lwm"
model_repo_dir = "./LWM"
if not os.path.exists(model_repo_dir):
print(f"Cloning model repository from {model_repo_url}...")
subprocess.run(["git", "clone", model_repo_url, model_repo_dir], check=True)
#%%
import numpy as np
dataset_repo_url = "https://huggingface.co/datasets/wi-lab/lwm"
clone_dataset_scenario(dataset_repo_url, model_repo_dir)
#%%
if os.path.exists(model_repo_dir):
os.chdir(model_repo_dir)
print(f"Changed working directory to {os.getcwd()}")
else:
print(f"Directory {model_repo_dir} does not exist. Please check if the repository is cloned properly.")
#%%
from input_preprocess import tokenizer
from lwm_model import lwm
import torch
scenario_names = np.array([
"city_18_denver", "city_15_indianapolis", "city_19_oklahoma",
"city_12_fortworth", "city_11_santaclara", "city_7_sandiego"
])
scenario_idxs = np.array([0, 1, 2, 3, 4, 5])[3]
selected_scenario_names = scenario_names[scenario_idxs]
snr_db = None
preprocessed_chs = tokenizer(
selected_scenario_names=selected_scenario_names,
manual_data=None,
gen_raw=True,
snr_db=snr_db
)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Loading the LWM model on {device} ...")
model = lwm.from_pretrained(device=device)
#%%
from inference import lwm_inference, create_raw_dataset
input_types = ['cls_emb', 'channel_emb', 'raw']
selected_input_type = input_types[1]
if selected_input_type in ['cls_emb', 'channel_emb']:
dataset = lwm_inference(preprocessed_chs, selected_input_type, model, device)
else:
dataset = create_raw_dataset(preprocessed_chs, device)
#%%
from input_preprocess import create_labels
n_beams = 16
tasks = ['LoS/NLoS Classification', 'Beam Prediction']
task = tasks[0]
labels = create_labels(task, selected_scenario_names, n_beams=n_beams)
# %% Dimensionality Reduction Visualization
# Import the dimensionality reduction plotting function
from utils import plot_dimensionality_reduction
# Iterate over tasks (e.g., LoS/NLoS Classification, Beam Prediction)
for task in tasks:
# Create labels for the current task
labels = create_labels(task, selected_scenario_names, n_beams=n_beams)
# Iterate over input types (e.g., raw data or embeddings)
for input_type_idx, input_type in enumerate(input_types):
# Select the current input type
selected_input_type = input_types[input_type_idx]
# Prepare dataset based on input type
if selected_input_type in ['cls_emb', 'channel_emb']:
dataset = lwm_inference(
preprocessed_chs,
selected_input_type,
model,
device
)
else:
dataset = create_raw_dataset(preprocessed_chs, device)
# Plot dimensionality reduction for the dataset
plot_dimensionality_reduction(
dataset,
method='all', # Use all available dimensionality reduction methods
labels=labels, # Labels for visualization
task=task, # Current task (for title or labeling)
input_type=input_type # Current input type (for title or labeling)
)
#%% TRAINING
#%% TRAINING PARAMETERS
task = ['LoS/NLoS Classification', 'Beam Prediction'][0] # Select the task
n_trials = 1 # Number of trials for each configuration
num_classes = 2 if task == 'LoS/NLoS Classification' else n_beams # Set number of classes based on the task
input_types = ['raw', 'cls_emb'] # Types of input data
split_ratios = np.array([.005, .0075, .01, .015, .02, .03,
.05, .1, .25, .5, .8]) # Dataset split ratios
f1_scores = np.zeros((n_trials, len(input_types), len(split_ratios))) # Store F1 scores for each trial, input type, and split ratio
labels = create_labels(task, selected_scenario_names, n_beams=n_beams) # Create labels for the selected task
#%% TRAINING
from utils import get_data_loaders, FCN, train_model, plot_metrics
# Iterate over input types (e.g., raw data or embeddings)
for input_type_idx, input_type in enumerate(input_types):
# Prepare dataset based on input type
if input_type in ['cls_emb', 'channel_emb']:
dataset = lwm_inference(preprocessed_chs, input_type, model, device)
else:
dataset = create_raw_dataset(preprocessed_chs, device)
# Reshape dataset for training
dataset = dataset.view(dataset.size(0), -1)
input_dim = dataset.shape[-1] # Get input dimension for the model
# Iterate over different dataset split ratios
for split_ratio_idx, split_ratio in enumerate(split_ratios):
n_train = int(split_ratio * dataset.shape[0]) # Calculate number of training samples
# Run multiple trials for each split ratio
for trial in range(n_trials):
print(f"\ninput type: {input_type}, \nnumber of training samples: {int(split_ratio*len(dataset))}, \ntrial: {trial}\n")
torch.manual_seed(trial) # Set seed for reproducibility
if snr_db is not None:
preprocessed_chs = tokenizer(
selected_scenario_names=selected_scenario_names,
manual_data=None,
gen_raw=True,
snr_db=snr_db
)
if input_type in ['cls_emb', 'channel_emb']:
dataset = lwm_inference(preprocessed_chs, input_type, model, device)
else:
dataset = create_raw_dataset(preprocessed_chs, device)
dataset = dataset.view(dataset.size(0), -1)
train_loader, test_loader = get_data_loaders(
dataset,
labels,
batch_size=128,
split_ratio=split_ratio
)
# Initialize the Fully Connected Network (FCN) model
FCN_model = FCN(input_dim=input_dim, num_classes=num_classes)
# Train the model and retrieve losses and F1 scores
train_losses, test_f1_scores = train_model(
FCN_model,
train_loader,
test_loader,
epochs=120,
lr=0.0001 if input_type == "raw" else 0.001, # Learning rate depends on input type
device=device,
decay_step=30,
decay_rate=0.5
)
# Store the final F1 score for this trial
f1_scores[trial, input_type_idx, split_ratio_idx] = test_f1_scores[0, -1]
# Plot metrics for the current trial
# plot_metrics(test_f1_scores, [input_type])
# Plot average F1 scores across all trials for each input type and split ratio
plot_metrics(
np.mean(f1_scores, axis=0), # Average F1 scores across trials
input_types,
np.asarray(split_ratios * dataset.shape[0], dtype=int), # Convert split ratios to actual sample counts
flag=1
)
# %% Few-Shot Learning with Pretrained Embeddings
# Initialize array to store F1 scores for KNN classification
f1_scores_knn = np.zeros((n_trials, len(input_types), len(split_ratios)))
# Import the classification function
from utils import classify_by_euclidean_distance
# Iterate over input types (e.g., raw data or embeddings)
for input_type_idx, input_type in enumerate(input_types):
# Prepare dataset based on input type
if input_type in ['cls_emb', 'channel_emb']:
dataset = lwm_inference(preprocessed_chs, input_type, model, device)
else:
dataset = create_raw_dataset(preprocessed_chs, device)
# Reshape dataset for compatibility
dataset = dataset.view(dataset.size(0), -1)
input_dim = dataset.shape[-1] # Get input dimension
# Iterate over different dataset split ratios
for split_ratio_idx, split_ratio in enumerate(split_ratios):
n_train = int(split_ratio * dataset.shape[0]) # Calculate number of training samples
# Run multiple trials for each split ratio
for trial in range(n_trials):
torch.manual_seed(trial) # Set seed for reproducibility
if snr_db is not None:
preprocessed_chs = tokenizer(
selected_scenario_names=selected_scenario_names,
manual_data=None,
gen_raw=True,
snr_db=snr_db
)
if input_type in ['cls_emb', 'channel_emb']:
dataset = lwm_inference(preprocessed_chs, input_type, model, device)
else:
dataset = create_raw_dataset(preprocessed_chs, device)
dataset = dataset.view(dataset.size(0), -1)
train_loader, test_loader = get_data_loaders(
dataset,
labels,
batch_size=128,
split_ratio=split_ratio
)
# Perform classification using Euclidean distance
f1 = classify_by_euclidean_distance(
train_loader,
test_loader,
device="cpu"
)
# Store the F1 score for this trial
f1_scores_knn[trial, input_type_idx, split_ratio_idx] = f1
# Plot average F1 scores across all trials for each input type and split ratio
plot_metrics(
np.mean(f1_scores_knn, axis=0), # Average F1 scores across trials
input_types,
np.asarray(split_ratios * dataset.shape[0], dtype=int), # Convert split ratios to actual sample counts
flag=1
)
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