inception_crop.py

    def _get_random_dims(self, area, height, width, max_iter=10):
        """
        Working logic:
        1. Initialize values, start for loop and generate required random values.
        2. If h_crop and w_crop (i.e. the generated values) are lesser than image dimensions,
            then generate random x and y values.
        3. Cache these x and y values if they are useful (BOTH MUST BE USEFUL)
        4. If cached values exist, return them, else return bad values (atleast one of x and y will contain -1).
        Run cases:
        1. Cached values can be filled multiple times. Any time, they will be useful. 
        2. If there were no (or partial) cached values after 10 iterations, we can safely apply validation crop
        Pros:
        1. The graph is constant, since we are not using break statement.
        2. From augmentation POV, the function remains constant.
        Cons:
        1. We run the function 10 times. However note that even if we encounter multiple valid values,
            all of them are valid. Thus this maintains corretness of the function. 
        """
        w_crop = tf.cast(-1, tf.int32)
        h_crop = tf.cast(-1, tf.int32)

        x_cache = -1
        y_cache = -1

        for _ in tf.range(max_iter):
            target_area = tf.random.uniform(
                (), minval=self.area_factor, maxval=1) * area
            aspect_ratio = tf.random.uniform((), minval=3./4., maxval=4./3.)

            w_crop = tf.cast(
                tf.math.round(
                    tf.math.sqrt(
                        target_area * aspect_ratio
                    )), tf.int32)
            h_crop = tf.cast(
                tf.math.round(
                    tf.math.sqrt(
                        target_area / aspect_ratio
                    )), tf.int32)
            
            prob = tf.random.uniform((), minval=0.0, maxval=1.0)

            w_crop, h_crop = tf.cond(
                tf.math.greater(prob, tf.constant(0.5)),
                lambda: (h_crop, w_crop),
                lambda: (w_crop, h_crop)
            )
            x = -1
            y = -1
            

            if h_crop < height:
                y = tf.random.uniform(
                    (), minval=0, maxval=height - h_crop, dtype=tf.int32)

                if w_crop < width:
                    x = tf.random.uniform(
                        (), minval=0, maxval=width - w_crop, dtype=tf.int32)
                    x_cache = x
                    y_cache = y
                    
        if x_cache>-1:
            if y_cache>-1:
                return x_cache, y_cache, w_crop, h_crop
            else:
                return x, y, w_crop, h_crop
        else:
            return x, y, w_crop, h_crop

    def _inception_style_crop_single(self, example, max_iter=10):
        """
        Working logic:
        1. Get random values from generate random dims function (see its docstring).
        2. If the values are good (both > -1) then do inception style cropping
        2. In all other cases do valiation cropping
        """


        height = tf.cast(example["height"], tf.int32)
        width = tf.cast(example["width"], tf.int32)
        area = tf.cast(height * width, tf.float32)

        x, y, w_crop, h_crop = self._get_random_dims(area, height, width)

        img = tf.cast(example["image"], tf.uint8)
        w = width
        h = height

        if x > -1:
            if y > -1:
                # Inception
                w_resize = tf.cast(0, tf.int32)
                h_resize = tf.cast(0, tf.int32)
                img = img[y: y + h_crop, x: x + w_crop, :]
                img = tf.cast(tf.math.round(tf.image.resize(
                    img, (self.crop_size, self.crop_size))), tf.uint8)
            else:
                # Validation
                if w < h:
                    w_resize, h_resize = tf.cast(self.resize_pre_crop, tf.int32), tf.cast(
                        ((h / w) * self.resize_pre_crop), tf.int32)
                    img = tf.image.resize(img, (h_resize, w_resize))
                elif h <= w:
                    w_resize, h_resize = tf.cast(
                        ((w / h) * self.resize_pre_crop), tf.int32), tf.cast(self.resize_pre_crop, tf.int32)
                    img = tf.image.resize(img, (h_resize, w_resize))
                else:
                    w_resize = tf.cast(w, tf.int32)
                    h_resize = tf.cast(h, tf.int32)
                    img = tf.image.resize(img, (h_resize, w_resize))

                x = tf.cast(tf.math.ceil((w_resize - self.crop_size)/2), tf.int32)
                y = tf.cast(tf.math.ceil((h_resize - self.crop_size)/2), tf.int32)

                img = img[y: (y + self.crop_size), x: (x + self.crop_size), :]
                img = tf.cast(tf.math.round(tf.image.resize(
                    img, (self.crop_size, self.crop_size))), tf.uint8)

        else:
            #Valiadation
            if w < h:
                w_resize, h_resize = tf.cast(self.resize_pre_crop, tf.int32), tf.cast(
                    ((h / w) * self.resize_pre_crop), tf.int32)
                img = tf.image.resize(img, (h_resize, w_resize))
            elif h <= w:
                w_resize, h_resize = tf.cast(
                    ((w / h) * self.resize_pre_crop), tf.int32), tf.cast(self.resize_pre_crop, tf.int32)
                img = tf.image.resize(img, (h_resize, w_resize))
            else:
                w_resize = tf.cast(w, tf.int32)
                h_resize = tf.cast(h, tf.int32)
                img = tf.image.resize(img, (h_resize, w_resize))

            x = tf.cast(tf.math.ceil((w_resize - self.crop_size)/2), tf.int32)
            y = tf.cast(tf.math.ceil((h_resize - self.crop_size)/2), tf.int32)

            img = img[y: (y + self.crop_size), x: (x + self.crop_size), :]
            img = tf.cast(tf.math.round(tf.image.resize(
                img, (self.crop_size, self.crop_size))), tf.uint8)
        
        return {
            "image": img,
            "height": self.crop_size,
            "width": self.crop_size,
            "filename": example["filename"],
            "label": example["label"],
            "synset": example["synset"],
        }