62 lines
2.2 KiB
Python
Executable File
62 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# Evaluate a trained SeqGRU on the validation set; reads input_dim from meta.json
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import os, json, argparse
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import numpy as np
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import torch, torch.nn as nn
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from sklearn.metrics import classification_report, confusion_matrix
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class SeqGRU(nn.Module):
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def __init__(self, input_dim, hidden=128, num_classes=26):
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super().__init__()
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self.gru = nn.GRU(input_dim, hidden, batch_first=True, bidirectional=True)
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self.head = nn.Sequential(
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nn.Linear(hidden*2, 128),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(128, num_classes),
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)
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def forward(self, x):
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h,_ = self.gru(x)
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return self.head(h[:, -1, :])
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--landmarks", default="landmarks_seq32")
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ap.add_argument("--model", required=True)
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args = ap.parse_args()
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vaX = np.load(os.path.join(args.landmarks,"val_X.npy"))
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vaY = np.load(os.path.join(args.landmarks,"val_y.npy"))
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classes = json.load(open(os.path.join(args.landmarks,"class_names.json")))
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meta = json.load(open(os.path.join(args.landmarks,"meta.json")))
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T = int(meta.get("frames", vaX.shape[1]))
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input_dim = int(meta.get("input_dim", vaX.shape[-1]))
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state = torch.load(args.model, map_location="cpu", weights_only=False)
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X_mean, X_std = state["X_mean"], state["X_std"]
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if isinstance(X_mean, torch.Tensor): X_mean = X_mean.numpy()
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if isinstance(X_std, torch.Tensor): X_std = X_std.numpy()
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X_mean = X_mean.astype(np.float32)
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X_std = (X_std.astype(np.float32) + 1e-6)
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vaXn = (vaX - X_mean) / X_std
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device = torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")
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model = SeqGRU(input_dim=input_dim, hidden=128, num_classes=len(classes))
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model.load_state_dict(state["model"])
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model.eval().to(device)
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with torch.no_grad():
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xb = torch.from_numpy(vaXn).float().to(device)
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logits = model(xb)
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pred = logits.argmax(1).cpu().numpy()
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cm = confusion_matrix(vaY, pred)
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print("Classes:", classes)
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print("\nConfusion matrix (rows=true, cols=pred):\n", cm)
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print("\nReport:\n", classification_report(vaY, pred, target_names=classes))
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if __name__ == "__main__":
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main()
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