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3a2e5f0 | 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 | """InceptionV3 image encoder.
Mirrors notebook cell 16. The encoder is the *frozen* visual backbone that
turns a 299x299 RGB image into a sequence of 2048-dimensional feature vectors
(one per spatial position in InceptionV3's last conv layer). The Transformer
encoder/decoder learn on top of these features; the InceptionV3 weights are
never updated during training.
Why a build function and not a Keras layer? The CNN is constructed from a
pretrained model whose weights are downloaded the first time. Wrapping
construction in a function gives callers a single line to invoke, and lets
us add caching / offline-loading paths later without touching call sites.
"""
from __future__ import annotations
def build_cnn_encoder():
"""Build the InceptionV3 backbone with the classification head removed.
Returns:
A ``tf.keras.Model`` mapping ``[B, 299, 299, 3]`` images to
``[B, 64, 2048]`` patch features (8x8=64 spatial positions, each a
2048-dim vector — InceptionV3's ``mixed10`` layer).
"""
import tensorflow as tf
inception = tf.keras.applications.InceptionV3(
include_top=False,
weights="imagenet",
)
output = inception.output
output = tf.keras.layers.Reshape((-1, output.shape[-1]))(output)
return tf.keras.models.Model(inception.input, output)
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