Liyu Xiao
5cbed47924
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Modulation Recognition Demo / ria-demo (pull_request) Failing after 2m15s
145 lines
4.2 KiB
Python
145 lines
4.2 KiB
Python
import os
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import numpy as np
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import torch
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from sklearn.metrics import classification_report
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os.environ["NNPACK"] = "0"
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from matplotlib import pyplot as plt
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from mobilenetv3 import RFClassifier, mobilenetv3
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from modulation_dataset import ModulationH5Dataset
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from helpers.app_settings import get_app_settings
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def load_validation_data():
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val_dataset = ModulationH5Dataset(
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"data/dataset/val.h5", label_name="modulation", data_key="validation_data"
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)
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X = np.stack([x.numpy() for x, _ in val_dataset]) # shape: (N, C, L)
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y = np.array([y.item() for _, y in val_dataset]) # shape: (N,)
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class_names = list(val_dataset.label_encoder.classes_)
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return X, y, class_names
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def build_model_from_ckpt(
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ckpt_path: str, in_channels: int, num_classes: int
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) -> torch.nn.Module:
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"""
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Build and return a PyTorch model loaded from a checkpoint.
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"""
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model = RFClassifier(
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model=mobilenetv3(
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model_size="mobilenetv3_small_050",
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num_classes=num_classes,
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in_chans=in_channels,
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)
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
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model.load_state_dict(checkpoint["state_dict"])
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model.eval()
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return model
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def evaluate_checkpoint(ckpt_path: str):
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"""
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Loads the model from checkpoint and evaluates it on a validation set.
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Prints classification metrics and plots a confusion matrix.
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"""
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# Load validation data
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X_val, y_true, class_names = load_validation_data()
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num_classes = len(class_names)
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in_channels = X_val.shape[1]
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# Load model
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model = build_model_from_ckpt(
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ckpt_path, in_channels=in_channels, num_classes=num_classes
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)
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# Inference
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y_pred = []
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with torch.no_grad():
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for x in X_val:
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x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
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logits = model(x_tensor)
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pred = torch.argmax(logits, dim=1).item()
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y_pred.append(pred)
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# Print classification report
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print("\nClassification Report:")
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print(
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classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
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)
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plot_confusion_matrix_with_counts(
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y_true=np.array(y_true),
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y_pred=np.array(y_pred),
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classes=class_names,
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normalize=True,
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title="Normalized Confusion Matrix",
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)
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def plot_confusion_matrix_with_counts(
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y_true: np.ndarray,
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y_pred: np.ndarray,
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classes: list[str],
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normalize: bool = True,
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title: str = "Confusion Matrix (counts and normalized)",
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) -> None:
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"""
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Plot a confusion matrix showing both raw counts and (optionally) normalized values.
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Args:
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y_true: true labels (integers 0..C-1)
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y_pred: predicted labels (same shape as y_true)
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classes: list of class‐name strings in index order
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normalize: if True, each row is normalized to sum=1
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title: title for the plot
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"""
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# 1) build raw CM
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C = len(classes)
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cm = np.zeros((C, C), dtype=int)
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for t, p in zip(y_true, y_pred):
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cm[t, p] += 1
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# 2) normalize if requested
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if normalize:
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with np.errstate(divide="ignore", invalid="ignore"):
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cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
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cm_norm = np.nan_to_num(cm_norm)
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else:
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cm_norm = cm
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# 3) plot
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fig, ax = plt.subplots(figsize=(8, 8))
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im = ax.imshow(cm_norm, interpolation="nearest")
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ax.set_title(title)
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ax.set_xlabel("Predicted label")
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ax.set_ylabel("True label")
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ax.set_xticks(np.arange(C))
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ax.set_yticks(np.arange(C))
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ax.set_xticklabels(classes, rotation=45, ha="right")
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ax.set_yticklabels(classes)
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# 4) annotate
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for i in range(C):
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for j in range(C):
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count = cm[i, j]
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val = cm_norm[i, j]
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txt = f"{count}\n{val:.2f}"
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ax.text(j, i, txt, ha="center", va="center")
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fig.colorbar(im, ax=ax, label="Normalized value" if normalize else "Count")
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plt.tight_layout()
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plt.show()
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if __name__ == "__main__":
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settings = get_app_settings()
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evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
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