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image_classification.py
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image_classification.py
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#!/usr/bin/env python3
import argparse
import os
import time
import numpy as np
import tensorflow as tf
import efficient_net
import imagenet_classes
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("--images", default=['rottweiler.jpg'], nargs="+", type=str, help="Files to classify.")
parser.add_argument("--seed", default=42, type=int, help="Random seed.")
parser.add_argument("--threads", default=1, type=int, help="Maximum number of threads to use.")
parser.add_argument("--verbose", default=False, action="store_true", help="Verbose TF logging.")
args = parser.parse_args([] if "__file__" not in globals() else None)
# Fix random seeds and threads
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
tf.config.threading.set_inter_op_parallelism_threads(args.threads)
tf.config.threading.set_intra_op_parallelism_threads(args.threads)
# Report only errors by default
if not args.verbose:
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
# Load EfficientNet=B0
efficientnet_b0 = efficient_net.pretrained_efficientnet_b0(include_top=True)
for image_path in args.images:
# Load the file
with open(image_path, "rb") as image_file:
image = tf.image.decode_image(image_file.read(), channels=3, dtype=tf.float32)
# Resize to 224,224
image = tf.image.resize(image, size=(224, 224))
# Compute the prediction
start = time.time()
[prediction], *_ = efficientnet_b0.predict(tf.expand_dims(image, 0))
print("Image {} [{} ms]: label {}".format(
image_path,
1000 * (time.time() - start),
imagenet_classes.imagenet_classes[tf.argmax(prediction)]
))