dpoulopoulos

import torch

use_gpu = True if torch.cuda.is_available() else False
model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)

dpoulopoulos

from _libmath import lib

x = lib.sqrt(4.5)

print(F"The square root of 4.5 is {x}.")

dpoulopoulos

from cffi import FFI


ffibuilder = FFI()

ffibuilder.cdef("""
    double sqrt(double x);
""")

ffibuilder.set_source("_libmath",

dpoulopoulos

import ctypes
import pathlib

if __name__ == "__main__":
    # load the lib
    libname = pathlib.Path().absolute() / "libcadd.so"
    c_lib = ctypes.CDLL(libname)
    
    x, y = 6, 2.3

dpoulopoulos

#include <stdio.h>


float cadd(int x, float y) {
    float res = x + y;
    printf("In cadd: int %d float %.1f returning  %.1f\n", x, y, res);
    return res;
}

dpoulopoulos

input_tensor = tf.keras.layers.Input(shape=(224, 224, 3), name="image")
model = tf.keras.applications.EfficientNetB0(
    input_tensor=input_tensor, weights=None, classes=91
)


model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(),
    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],

dpoulopoulos

train_filenames = tf.io.gfile.glob(f"{tfrecords_dir}/*.tfrec")
batch_size = 32
epochs = 1
steps_per_epoch = 50
AUTOTUNE = tf.data.AUTOTUNE


def prepare_sample(features):
    image = tf.image.resize(features["image"], size=(224, 224))
    return image, features["category_id"]

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for tfrec_num in range(num_tfrecods):
    samples = annotations[(tfrec_num * num_samples) : ((tfrec_num + 1) * num_samples)]

    with tf.io.TFRecordWriter(
        tfrecords_dir + "/file_%.2i-%i.tfrec" % (tfrec_num, len(samples))
    ) as writer:
        for sample in samples:
            image_path = f"{images_dir}/{sample['image_id']:012d}.jpg"
            image = tf.io.decode_jpeg(tf.io.read_file(image_path))
            example = create_example(image, image_path, sample)

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def create_example(image, path, example):
    feature = {
        "image": image_feature(image),
        "path": bytes_feature(path),
        "area": float_feature(example["area"]),
        "bbox": float_feature_list(example["bbox"]),
        "category_id": int64_feature(example["category_id"]),
        "id": int64_feature(example["id"]),
        "image_id": int64_feature(example["image_id"]),
    }

dpoulopoulos

def image_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(
        bytes_list=tf.train.BytesList(value=[tf.io.encode_jpeg(value).numpy()])
    )


def bytes_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(