# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
"""
TensorFlow, Keras and TFLite versions of
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127

Usage:
    $ python models/tf.py --weights yolov3.pt

Export:
    $ python path/to/export.py --weights yolov3.pt --include saved_model pb tflite tfjs
"""

import argparse
import logging
import sys
from copy import deepcopy
from pathlib import Path

from packaging import version

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
# ROOT = ROOT.relative_to(Path.cwd())  # relative

import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
from keras import backend
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
from keras.utils import conv_utils
from tensorflow import keras

from models.common import C3, SPP, SPPF, Bottleneck, BottleneckCSP, Concat, Conv, DWConv, Focus, autopad
from models.experimental import CrossConv, MixConv2d, attempt_load
from models.yolo import Detect
from utils.activations import SiLU
from utils.general import LOGGER, make_divisible, print_args

# isort: off
from tensorflow.python.util.tf_export import keras_export


class TFBN(keras.layers.Layer):
    # TensorFlow BatchNormalization wrapper
    def __init__(self, w=None):
        super().__init__()
        self.bn = keras.layers.BatchNormalization(
            beta_initializer=keras.initializers.Constant(w.bias.numpy()),
            gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
            moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
            moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
            epsilon=w.eps)

    def call(self, inputs):
        return self.bn(inputs)


class TFMaxPool2d(keras.layers.Layer):
    # TensorFlow MAX Pooling
    def __init__(self, k, s, p, w=None):
        super().__init__()
        self.pool = keras.layers.MaxPool2D(pool_size=k, strides=s, padding='valid')

    def call(self, inputs):
        return self.pool(inputs)


class TFZeroPad2d(keras.layers.Layer):
    # TensorFlow MAX Pooling
    def __init__(self, p, w=None):
        super().__init__()
        if version.parse(tf.__version__) < version.parse('2.11.0'):
            self.zero_pad = ZeroPadding2D(padding=p)
        else:
            self.zero_pad = keras.layers.ZeroPadding2D(padding=((p[0], p[1]), (p[2], p[3])))

    def call(self, inputs):
        return self.zero_pad(inputs)


class TFPad(keras.layers.Layer):
    def __init__(self, pad):
        super().__init__()
        self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])

    def call(self, inputs):
        return tf.pad(inputs, self.pad, mode='constant', constant_values=0)


class TFConv(keras.layers.Layer):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
        # ch_in, ch_out, weights, kernel, stride, padding, groups
        super().__init__()
        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
        assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
        # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
        # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch

        conv = keras.layers.Conv2D(
            c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True,
            kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
            bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
        self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
        self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity

        #  activations
        if isinstance(w.act, nn.LeakyReLU):
            self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity
        elif isinstance(w.act, nn.Hardswish):
            self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity
        elif isinstance(w.act, (nn.SiLU, SiLU)):
            self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity
        else:
            raise Exception(f'no matching TensorFlow activation found for {w.act}')

    def call(self, inputs):
        return self.act(self.bn(self.conv(inputs)))


class TFFocus(keras.layers.Layer):
    # Focus wh information into c-space
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
        # ch_in, ch_out, kernel, stride, padding, groups
        super().__init__()
        self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)

    def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
        # inputs = inputs / 255  # normalize 0-255 to 0-1
        return self.conv(tf.concat([inputs[:, ::2, ::2, :],
                                    inputs[:, 1::2, ::2, :],
                                    inputs[:, ::2, 1::2, :],
                                    inputs[:, 1::2, 1::2, :]], 3))


class TFBottleneck(keras.layers.Layer):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
        self.add = shortcut and c1 == c2

    def call(self, inputs):
        return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))


class TFConv2d(keras.layers.Layer):
    # Substitution for PyTorch nn.Conv2D
    def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
        super().__init__()
        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
        self.conv = keras.layers.Conv2D(
            c2, k, s, 'VALID', use_bias=bias,
            kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),
            bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, )

    def call(self, inputs):
        return self.conv(inputs)


class TFBottleneckCSP(keras.layers.Layer):
    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
        # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
        self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
        self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
        self.bn = TFBN(w.bn)
        self.act = lambda x: keras.activations.relu(x, alpha=0.1)
        self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])

    def call(self, inputs):
        y1 = self.cv3(self.m(self.cv1(inputs)))
        y2 = self.cv2(inputs)
        return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))


class TFC3(keras.layers.Layer):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
        # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
        self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
        self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])

    def call(self, inputs):
        return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))


class TFSPP(keras.layers.Layer):
    # Spatial pyramid pooling layer used in YOLOv3-SPP
    def __init__(self, c1, c2, k=(5, 9, 13), w=None):
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
        self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k]

    def call(self, inputs):
        x = self.cv1(inputs)
        return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))


class TFSPPF(keras.layers.Layer):
    # Spatial pyramid pooling-Fast layer
    def __init__(self, c1, c2, k=5, w=None):
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
        self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')

    def call(self, inputs):
        x = self.cv1(inputs)
        y1 = self.m(x)
        y2 = self.m(y1)
        return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))


class TFDetect(keras.layers.Layer):
    def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):  # detection layer
        super().__init__()
        self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [tf.zeros(1)] * self.nl  # init grid
        self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
        self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
                                      [self.nl, 1, -1, 1, 2])
        self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
        self.training = False  # set to False after building model
        self.imgsz = imgsz
        for i in range(self.nl):
            ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
            self.grid[i] = self._make_grid(nx, ny)

    def call(self, inputs):
        z = []  # inference output
        x = []
        for i in range(self.nl):
            x.append(self.m[i](inputs[i]))
            # x(bs,20,20,255) to x(bs,3,20,20,85)
            ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
            x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3])

            if not self.training:  # inference
                y = tf.sigmoid(x[i])
                xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
                # Normalize xywh to 0-1 to reduce calibration error
                xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
                wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
                y = tf.concat([xy, wh, y[..., 4:]], -1)
                z.append(tf.reshape(y, [-1, 3 * ny * nx, self.no]))

        return x if self.training else (tf.concat(z, 1), x)

    @staticmethod
    def _make_grid(nx=20, ny=20):
        # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
        xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
        return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)


class TFUpsample(keras.layers.Layer):
    def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
        super().__init__()
        assert scale_factor == 2, "scale_factor must be 2"
        self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
        # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
        # with default arguments: align_corners=False, half_pixel_centers=False
        # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
        #                                                            size=(x.shape[1] * 2, x.shape[2] * 2))

    def call(self, inputs):
        return self.upsample(inputs)


class TFConcat(keras.layers.Layer):
    def __init__(self, dimension=1, w=None):
        super().__init__()
        assert dimension == 1, "convert only NCHW to NHWC concat"
        self.d = 3

    def call(self, inputs):
        return tf.concat(inputs, self.d)


def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        m_str = m
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
            except NameError:
                pass

        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
            c1, c2 = ch[f], args[0]
            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2

            args = [c1, c2, *args[1:]]
            if m in [BottleneckCSP, C3]:
                args.insert(2, n)
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
        elif m is Detect:
            args.append([ch[x + 1] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
            args.append(imgsz)
        else:
            c2 = ch[f]

        tf_m = eval('TF' + m_str.replace('nn.', ''))
        m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
            else tf_m(*args, w=model.model[i])  # module

        torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
        np = sum(x.numel() for x in torch_m_.parameters())  # number params
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10}  {t:<40}{str(args):<30}')  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        ch.append(c2)
    return keras.Sequential(layers), sorted(save)


class TFModel:
    def __init__(self, cfg='yolov3.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)):  # model, channels, classes
        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name
            with open(cfg) as f:
                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict

        # Define model
        if nc and nc != self.yaml['nc']:
            LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc  # override yaml value
        self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)

    def predict(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
                conf_thres=0.25):
        y = []  # outputs
        x = inputs
        for i, m in enumerate(self.model.layers):
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers

            x = m(x)  # run
            y.append(x if m.i in self.savelist else None)  # save output

        # Add TensorFlow NMS
        if tf_nms:
            boxes = self._xywh2xyxy(x[0][..., :4])
            probs = x[0][:, :, 4:5]
            classes = x[0][:, :, 5:]
            scores = probs * classes
            if agnostic_nms:
                nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
                return nms, x[1]
            else:
                boxes = tf.expand_dims(boxes, 2)
                nms = tf.image.combined_non_max_suppression(
                    boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False)
                return nms, x[1]

        return x[0]  # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
        # x = x[0][0]  # [x(1,6300,85), ...] to x(6300,85)
        # xywh = x[..., :4]  # x(6300,4) boxes
        # conf = x[..., 4:5]  # x(6300,1) confidences
        # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1))  # x(6300,1)  classes
        # return tf.concat([conf, cls, xywh], 1)

    @staticmethod
    def _xywh2xyxy(xywh):
        # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
        x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
        return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)


class AgnosticNMS(keras.layers.Layer):
    # TF Agnostic NMS
    def call(self, input, topk_all, iou_thres, conf_thres):
        # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
        return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input,
                         fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
                         name='agnostic_nms')

    @staticmethod
    def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):  # agnostic NMS
        boxes, classes, scores = x
        class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
        scores_inp = tf.reduce_max(scores, -1)
        selected_inds = tf.image.non_max_suppression(
            boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres)
        selected_boxes = tf.gather(boxes, selected_inds)
        padded_boxes = tf.pad(selected_boxes,
                              paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
                              mode="CONSTANT", constant_values=0.0)
        selected_scores = tf.gather(scores_inp, selected_inds)
        padded_scores = tf.pad(selected_scores,
                               paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
                               mode="CONSTANT", constant_values=-1.0)
        selected_classes = tf.gather(class_inds, selected_inds)
        padded_classes = tf.pad(selected_classes,
                                paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
                                mode="CONSTANT", constant_values=-1.0)
        valid_detections = tf.shape(selected_inds)[0]
        return padded_boxes, padded_scores, padded_classes, valid_detections


def representative_dataset_gen(dataset, ncalib=100):
    # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
    for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
        input = np.transpose(img, [1, 2, 0])
        input = np.expand_dims(input, axis=0).astype(np.float32)
        input /= 255
        yield [input]
        if n >= ncalib:
            break


def run(weights=ROOT / 'yolov3.pt',  # weights path
        imgsz=(640, 640),  # inference size h,w
        batch_size=1,  # batch size
        dynamic=False,  # dynamic batch size
        ):
    # PyTorch model
    im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
    model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
    y = model(im)  # inference
    model.info()

    # TensorFlow model
    im = tf.zeros((batch_size, *imgsz, 3))  # BHWC image
    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
    y = tf_model.predict(im)  # inference

    # Keras model
    im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
    keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
    keras_model.summary()

    LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.')


@keras_export("keras.layers.ZeroPadding2D")
class ZeroPadding2D(Layer):
    """Zero-padding layer for 2D input (e.g. picture).

    This layer can add rows and columns of zeros
    at the top, bottom, left and right side of an image tensor.

    Examples:

    >>> input_shape = (1, 1, 2, 2)
    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
    >>> print(x)
    [[[[0 1]
       [2 3]]]]
    >>> y = tf.keras.layers.ZeroPadding2D(padding=1)(x)
    >>> print(y)
    tf.Tensor(
      [[[[0 0]
         [0 0]
         [0 0]
         [0 0]]
        [[0 0]
         [0 1]
         [2 3]
         [0 0]]
        [[0 0]
         [0 0]
         [0 0]
         [0 0]]]], shape=(1, 3, 4, 2), dtype=int64)

    Args:
      padding: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
        - If int: the same symmetric padding
          is applied to height and width.
        - If tuple of 2 ints:
          interpreted as two different
          symmetric padding values for height and width:
          `(symmetric_height_pad, symmetric_width_pad)`.
        - If tuple of 2 tuples of 2 ints:
          interpreted as
          `((top_pad, bottom_pad), (left_pad, right_pad))`
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch_size, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, height, width)`.
        It defaults to the `image_data_format` value found in your
        Keras config file at `~/.keras/keras.json`.
        If you never set it, then it will be "channels_last".

    Input shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, rows, cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, rows, cols)`

    Output shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, padded_rows, padded_cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, padded_rows, padded_cols)`
    """

    def __init__(self, padding=(1, 1), data_format=None, **kwargs):
        super().__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(padding, int):
            self.padding = ((padding, padding), (padding, padding))
        elif hasattr(padding, "__len__"):
            if len(padding) == 4:
                padding = ((padding[0], padding[1]), (padding[2], padding[3]))
            if len(padding) != 2:
                raise ValueError(
                    f"`padding` should have two elements. Received: {padding}."
                )
            height_padding = conv_utils.normalize_tuple(
                padding[0], 2, "1st entry of padding", allow_zero=True
            )
            width_padding = conv_utils.normalize_tuple(
                padding[1], 2, "2nd entry of padding", allow_zero=True
            )
            self.padding = (height_padding, width_padding)
        else:
            raise ValueError(
                "`padding` should be either an int, "
                "a tuple of 2 ints "
                "(symmetric_height_pad, symmetric_width_pad), "
                "or a tuple of 2 tuples of 2 ints "
                "((top_pad, bottom_pad), (left_pad, right_pad)). "
                f"Received: {padding}."
            )
        self.input_spec = InputSpec(ndim=4)

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        if self.data_format == "channels_first":
            if input_shape[2] is not None:
                rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
            else:
                rows = None
            if input_shape[3] is not None:
                cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
            else:
                cols = None
            return tf.TensorShape([input_shape[0], input_shape[1], rows, cols])
        elif self.data_format == "channels_last":
            if input_shape[1] is not None:
                rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
            else:
                rows = None
            if input_shape[2] is not None:
                cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
            else:
                cols = None
            return tf.TensorShape([input_shape[0], rows, cols, input_shape[3]])

    def call(self, inputs):
        return backend.spatial_2d_padding(
            inputs, padding=self.padding, data_format=self.data_format
        )

    def get_config(self):
        config = {"padding": self.padding, "data_format": self.data_format}
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default=ROOT / 'yolov3.pt', help='weights path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
    parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(FILE.stem, opt)
    return opt


def main(opt):
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)