#!/usr/bin/env python3
"""
verify_feature_dims.py

Quick check: what does TF/Keras actually output for each model
with include_top=False?  Compare against manuscript claims.
"""

import numpy as np
from tensorflow.keras.applications import (
    VGG16, DenseNet121, EfficientNetB1, MobileNetV2, ResNet50
)

# Match the manuscript's input sizes
models = {
    "VGG16":         (VGG16,         224),
    "DenseNet121":   (DenseNet121,   224),
    "EfficientNetB1":(EfficientNetB1, 240),
    "MobileNetV2":   (MobileNetV2,   224),
    "ResNet50":      (ResNet50,      224),
}

manuscript_claims = {
    "VGG16":         25088,
    "DenseNet121":   50176,
    "EfficientNetB1":62720,
    "MobileNetV2":   62720,
    "ResNet50":      100352,
}

print(f"{'Model':<18s} {'Input':>5s} {'Spatial':>10s} {'Flattened':>10s} {'Manuscript':>12s} {'Match?':>7s}")
print("-" * 70)

for name, (model_fn, input_size) in models.items():
    model = model_fn(weights="imagenet", include_top=False,
                     input_shape=(input_size, input_size, 3))

    # Pass a dummy batch through
    dummy = np.random.randn(1, input_size, input_size, 3)
    # Need to preprocess correctly — but shape doesn't depend on values
    output = model.predict(dummy, verbose=0)

    spatial = output.shape[1:4]  # (H, W, C) for channels_last
    flattened = int(np.prod(spatial))
    claimed = manuscript_claims[name]
    match = "✅" if flattened == claimed else "❌"

    print(f"{name:<18s} {input_size:>4}d  {str(spatial):>10s} {flattened:>10d} {claimed:>12d} {match:>7s}")

print("\nNote: TF uses channels_last (NHWC) format.")