Upload downstream.py

downstream.py

@@ -1,146 +1,147 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jan 10 11:11:58 2025
-
-This script evaluates downstream task performance by comparing models trained 
-on raw channel representations versus those trained on LWM embeddings.
-
-@author: Sadjad Alikhani
-"""
-#%% IMPORT PACKAGES & MODULES
-from input_preprocess import tokenizer, scenarios_list
-from inference import lwm_inference
-from utils import prepare_loaders
-from train import finetune
-import lwm_model
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-import torch.nn as nn
-import warnings
-warnings.filterwarnings("ignore", category=UserWarning)
-#%% DOWNSTERAM DATA GENERATION
-n_beams = 16
-task = ['Beam Prediction', 'LoS/NLoS Classification'][1]
-task_type = ["classification", "regression"][0]
-visualization_method = ["pca", "umap", "tsne"][2]
-input_types = ["cls_emb", "channel_emb", "raw"]
-train_ratios = [.001, .01, .05, .1, .25, .5, .8]
-fine_tuning_status = [None, ["layers.8", "layers.9", "layers.10", "layers.11"], "full"]
-selected_scenario_names = [scenarios_list()[6]] 
-preprocessed_data, labels, raw_chs = tokenizer(
-    selected_scenario_names, 
-    bs_idxs=[3], 
-    load_data=False, 
-    task=task, 
-    n_beams=n_beams)
-#%% LOAD THE MODEL
-gpu_ids = [0]
-device = torch.device("cuda:0")
-model = lwm_model.lwm().to(device)
-
-model_name = "model.pth"
-state_dict = torch.load(f"models/{model_name}", map_location=device)
-new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
-model.load_state_dict(new_state_dict)
-
-model = nn.DataParallel(model, gpu_ids)
-print(f"Model loaded successfully on GPU {device.index}")
-#%% 2D EMBEDDING SPACE VISUALIZATIONN BEFORE FINE-TUNING
-chs = lwm_inference(
-    model, 
-    preprocessed_data, 
-    input_type="cls_emb", 
-    device=device, 
-    batch_size=64, 
-    visualization=False, 
-    labels=labels, 
-    visualization_method=visualization_method)
-#%% FINE-TUNE
-results = np.zeros((len(fine_tuning_status), len(input_types), len(train_ratios)))
-for fine_tuning_stat_idx, fine_tuning_stat in enumerate(fine_tuning_status):
-    for input_type_idx, input_type in enumerate(input_types):
-        
-        if input_type == "raw" and fine_tuning_stat is not None:
-            continue
-        
-        selected_patches_idxs = None
-        for train_ratio_idx, train_ratio in enumerate(train_ratios):
-            
-            print(f"\nfine-tuning status: {fine_tuning_stat}")
-            print(f"input type: {input_type}")
-            print(f"train ratio: {train_ratio}\n")
-            
-            # PREPARE LOADERS
-            train_loader, val_loader, samples, target = prepare_loaders(
-                preprocessed_data=preprocessed_data,
-                labels=labels,
-                selected_patches_idxs=selected_patches_idxs,
-                input_type=input_type,
-                task_type=task_type,
-                train_ratio=train_ratio,
-                batch_size=128,
-                seed=42
-            )
-            
-            # FINE-TUNE LWM 
-            fine_tuned_model, best_model_path, train_losses, val_losses, f1_scores, attn_maps_ft = finetune(
-                base_model=model, 
-                train_loader=train_loader,
-                val_loader=val_loader,
-                task_type=task_type,
-                input_type=input_type,
-                num_classes=n_beams if task=='Beam Prediction' else 2 if task=='LoS/NLoS Classification' else None,
-                output_dim=target.shape[-1] if task_type =='regression' else None,
-                use_custom_head=True,
-                fine_tune_layers=fine_tuning_stat,
-                optimizer_config={"lr": 1e-3},
-                epochs=15,
-                device=device,
-                task=task
-            )
-            
-            results[fine_tuning_stat_idx][input_type_idx][train_ratio_idx] = f1_scores[-1]
-
-markers = ['o', 's', 'D'] 
-labels = ['CLS Emb', 'CHS Emb', 'Raw']
-fine_tuning_status_labels = ['No FT', 'Partial FT', 'Full FT']
-line_styles = ['-', '--', ':'] 
-colors = plt.cm.viridis(np.linspace(0, 0.8, len(labels)))
-plt.figure(figsize=(12, 8), dpi=500)
-for ft_idx, (ft_status_label, line_style) in enumerate(zip(fine_tuning_status_labels, line_styles)):
-    for idx, (marker, label, color) in enumerate(zip(markers, labels, colors)):
-        # For "Raw Channels," only plot "No Fine-Tuning" case
-        if label == "Raw" and ft_status_label != "No FT":
-            continue
-        # Simplify label for "Raw Channels" without fine-tuning
-        plot_label = label if label != "Raw Channels" or ft_status_label != "No Fine-Tuning" else "Raw Channels"
-        plt.plot(
-            train_ratios, 
-            results[ft_idx, idx], 
-            marker=marker, 
-            linestyle=line_style, 
-            label=f"{plot_label} ({ft_status_label})" if label != "Raw Channels" else plot_label, 
-            color=color, 
-            linewidth=3, 
-            markersize=9
-        )
-plt.xscale('log')
-plt.xlabel("Train Ratio", fontsize=20)
-plt.ylabel("F1-Score", fontsize=20)
-plt.legend(fontsize=17, loc="best")
-plt.grid(True, linestyle="--", alpha=0.7)
-plt.xticks(fontsize=17)
-plt.yticks(fontsize=17)
-plt.tight_layout()
-plt.show()
-#%% 2D EMBEDDING SPACE VISUALIZATIONN AFTER FINE-TUNING
-chs = lwm_inference(
-    fine_tuned_model.model, 
-    preprocessed_data, 
-    input_type="cls_emb", 
-    device=device, 
-    batch_size=64, 
-    visualization=False, 
-    labels=labels, 
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan 10 11:11:58 2025
+
+This script evaluates downstream task performance by comparing models trained 
+on raw channel representations versus those trained on LWM embeddings.
+
+@author: Sadjad Alikhani
+"""
+#%% IMPORT PACKAGES & MODULES
+from input_preprocess import tokenizer, scenarios_list
+from inference import lwm_inference
+from utils import prepare_loaders
+from train import finetune
+import lwm_model
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn as nn
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+#%% DOWNSTERAM DATA GENERATION
+n_beams = 16
+task = ['Beam Prediction', 'LoS/NLoS Classification'][1]
+task_type = ["classification", "regression"][0]
+visualization_method = ["pca", "umap", "tsne"][2]
+input_types = ["cls_emb", "channel_emb", "raw"]
+train_ratios = [.001, .01, .05, .1, .25, .5, .8]
+fine_tuning_status = [None, ["layers.8", "layers.9", "layers.10", "layers.11"], "full"]
+selected_scenario_names = [scenarios_list()[6]] 
+preprocessed_data, labels, raw_chs = tokenizer(
+    selected_scenario_names, 
+    bs_idxs=[3], 
+    load_data=False, 
+    task=task, 
+    n_beams=n_beams,
+    manual_data=None)
+#%% LOAD THE MODEL
+gpu_ids = [0]
+device = torch.device("cuda:0")
+model = lwm_model.lwm().to(device)
+
+model_name = "model.pth"
+state_dict = torch.load(f"models/{model_name}", map_location=device)
+new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+model.load_state_dict(new_state_dict)
+
+model = nn.DataParallel(model, gpu_ids)
+print(f"Model loaded successfully on GPU {device.index}")
+#%% 2D EMBEDDING SPACE VISUALIZATIONN BEFORE FINE-TUNING
+chs = lwm_inference(
+    model, 
+    preprocessed_data, 
+    input_type="cls_emb", 
+    device=device, 
+    batch_size=64, 
+    visualization=False, 
+    labels=labels, 
+    visualization_method=visualization_method)
+#%% FINE-TUNE
+results = np.zeros((len(fine_tuning_status), len(input_types), len(train_ratios)))
+for fine_tuning_stat_idx, fine_tuning_stat in enumerate(fine_tuning_status):
+    for input_type_idx, input_type in enumerate(input_types):
+        
+        if input_type == "raw" and fine_tuning_stat is not None:
+            continue
+        
+        selected_patches_idxs = None
+        for train_ratio_idx, train_ratio in enumerate(train_ratios):
+            
+            print(f"\nfine-tuning status: {fine_tuning_stat}")
+            print(f"input type: {input_type}")
+            print(f"train ratio: {train_ratio}\n")
+            
+            # PREPARE LOADERS
+            train_loader, val_loader, samples, target = prepare_loaders(
+                preprocessed_data=preprocessed_data,
+                labels=labels,
+                selected_patches_idxs=selected_patches_idxs,
+                input_type=input_type,
+                task_type=task_type,
+                train_ratio=train_ratio,
+                batch_size=128,
+                seed=42
+            )
+            
+            # FINE-TUNE LWM 
+            fine_tuned_model, best_model_path, train_losses, val_losses, f1_scores, attn_maps_ft = finetune(
+                base_model=model, 
+                train_loader=train_loader,
+                val_loader=val_loader,
+                task_type=task_type,
+                input_type=input_type,
+                num_classes=n_beams if task=='Beam Prediction' else 2 if task=='LoS/NLoS Classification' else None,
+                output_dim=target.shape[-1] if task_type =='regression' else None,
+                use_custom_head=True,
+                fine_tune_layers=fine_tuning_stat,
+                optimizer_config={"lr": 1e-3},
+                epochs=15,
+                device=device,
+                task=task
+            )
+            
+            results[fine_tuning_stat_idx][input_type_idx][train_ratio_idx] = f1_scores[-1]
+
+markers = ['o', 's', 'D'] 
+labels = ['CLS Emb', 'CHS Emb', 'Raw']
+fine_tuning_status_labels = ['No FT', 'Partial FT', 'Full FT']
+line_styles = ['-', '--', ':'] 
+colors = plt.cm.viridis(np.linspace(0, 0.8, len(labels)))
+plt.figure(figsize=(12, 8), dpi=500)
+for ft_idx, (ft_status_label, line_style) in enumerate(zip(fine_tuning_status_labels, line_styles)):
+    for idx, (marker, label, color) in enumerate(zip(markers, labels, colors)):
+        # For "Raw Channels," only plot "No Fine-Tuning" case
+        if label == "Raw" and ft_status_label != "No FT":
+            continue
+        # Simplify label for "Raw Channels" without fine-tuning
+        plot_label = label if label != "Raw Channels" or ft_status_label != "No Fine-Tuning" else "Raw Channels"
+        plt.plot(
+            train_ratios, 
+            results[ft_idx, idx], 
+            marker=marker, 
+            linestyle=line_style, 
+            label=f"{plot_label} ({ft_status_label})" if label != "Raw Channels" else plot_label, 
+            color=color, 
+            linewidth=3, 
+            markersize=9
+        )
+plt.xscale('log')
+plt.xlabel("Train Ratio", fontsize=20)
+plt.ylabel("F1-Score", fontsize=20)
+plt.legend(fontsize=17, loc="best")
+plt.grid(True, linestyle="--", alpha=0.7)
+plt.xticks(fontsize=17)
+plt.yticks(fontsize=17)
+plt.tight_layout()
+plt.show()
+#%% 2D EMBEDDING SPACE VISUALIZATIONN AFTER FINE-TUNING
+chs = lwm_inference(
+    fine_tuned_model.model, 
+    preprocessed_data, 
+    input_type="cls_emb", 
+    device=device, 
+    batch_size=64, 
+    visualization=False, 
+    labels=labels, 
     visualization_method=visualization_method)