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b34f590 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import yaml
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, models
from sklearn.manifold import TSNE
from tqdm import tqdm
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
def load_config(config_path: Path) -> dict:
with open(config_path, "r") as f:
return yaml.safe_load(f)
def get_device() -> torch.device:
if torch.cuda.is_available():
return torch.device("cuda")
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
return torch.device("mps")
return torch.device("cpu")
def build_feature_extractor(
weights_path: Path,
num_classes: int,
device: torch.device,
) -> nn.Module:
model = models.resnet18(weights=None)
model.fc = nn.Linear(model.fc.in_features, num_classes)
state = torch.load(str(weights_path), map_location=device)
model.load_state_dict(state)
model.to(device)
model.eval()
return model
@torch.no_grad()
def extract_embeddings(model, loader, device):
"""Extract 512-dim features from ResNet18 avgpool."""
all_features = []
all_labels = []
def _forward_features(x):
x = model.conv1(x)
x = model.bn1(x)
x = model.relu(x)
x = model.maxpool(x)
x = model.layer1(x)
x = model.layer2(x)
x = model.layer3(x)
x = model.layer4(x)
x = model.avgpool(x)
return x.flatten(1)
for images, labels in tqdm(loader, desc="Extracting embeddings"):
images = images.to(device)
feats = _forward_features(images)
all_features.append(feats.cpu().numpy())
all_labels.append(labels.numpy())
return np.concatenate(all_features), np.concatenate(all_labels)
def main():
project_root = Path(__file__).resolve().parents[2]
config = load_config(project_root / "config.yaml")
device = get_device()
print(f"Device: {device}")
class_names = config["classes"]
num_classes = len(class_names)
input_size = config["model"]["classifier_input_size"]
batch_size = config["model"]["batch_size"]
seed = config["project"]["random_seed"]
test_tf = transforms.Compose(
[
transforms.Resize((input_size, input_size)),
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
]
)
crops_dir = project_root / config["data"]["cropped_persons_dir"]
test_dir = str(crops_dir / "test")
test_dataset = datasets.ImageFolder(test_dir, transform=test_tf)
test_loader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=False, num_workers=0
)
print(f"Test samples: {len(test_dataset)}")
print(f"Classes: {test_dataset.classes}")
weights_path = project_root / "src" / "models" / "best_resnet18.pth"
model = build_feature_extractor(weights_path, num_classes, device)
features, labels = extract_embeddings(model, test_loader, device)
print(f"Feature matrix shape: {features.shape}")
perplexity = min(30, len(features) - 1)
print(f"Running t-SNE (perplexity={perplexity}, seed={seed})...")
tsne = TSNE(n_components=2, random_state=seed, perplexity=perplexity)
embeddings_2d = tsne.fit_transform(features)
colors = ["#95a5a6", "#e67e22", "#2ecc71", "#f1c40f", "#e74c3c"]
fig, ax = plt.subplots(figsize=(9, 7))
for cls_idx, cls_name in enumerate(class_names):
mask = labels == cls_idx
if mask.sum() == 0:
continue
ax.scatter(
embeddings_2d[mask, 0],
embeddings_2d[mask, 1],
c=colors[cls_idx % len(colors)],
label=cls_name,
alpha=0.7,
s=40,
edgecolors="white",
linewidths=0.5,
)
ax.set_title(
"t-SNE: ResNet18 Features (Test Set)",
fontsize=13,
fontweight="bold",
)
ax.set_xlabel("t-SNE Dimension 1")
ax.set_ylabel("t-SNE Dimension 2")
ax.legend(loc="best", fontsize=10)
results_dir = project_root / "results"
results_dir.mkdir(exist_ok=True)
save_path = results_dir / "tsne_clusters.png"
fig.tight_layout()
fig.savefig(str(save_path), dpi=150)
plt.close(fig)
print(f"t-SNE visualization saved to: {save_path}")
if __name__ == "__main__":
main()
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