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	"""utils.py - Helper functions for building the model and for loading model parameters.
	These helper functions are built to mirror those in the official TensorFlow implementation.
	"""

	# Author: lukemelas (github username)
	# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
	# With adjustments and added comments by workingcoder (github username).

	import re
	import math
	import collections
	from functools import partial
	import torch
	from torch import nn
	from torch.nn import functional as F
	from torch.utils import model_zoo


	################################################################################
	# Help functions for model architecture
	################################################################################

	# GlobalParams and BlockArgs: Two namedtuples
	# Swish and MemoryEfficientSwish: Two implementations of the method
	# round_filters and round_repeats:
	# Functions to calculate params for scaling model width and depth ! ! !
	# get_width_and_height_from_size and calculate_output_image_size
	# drop_connect: A structural design
	# get_same_padding_conv2d:
	# Conv2dDynamicSamePadding
	# Conv2dStaticSamePadding
	# get_same_padding_maxPool2d:
	# MaxPool2dDynamicSamePadding
	# MaxPool2dStaticSamePadding
	# It's an additional function, not used in EfficientNet,
	# but can be used in other model (such as EfficientDet).

	# Parameters for the entire model (stem, all blocks, and head)
	GlobalParams = collections.namedtuple('GlobalParams', [
	'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate',
	'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon',
	'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'])

	# Parameters for an individual model block
	BlockArgs = collections.namedtuple('BlockArgs', [
	'num_repeat', 'kernel_size', 'stride', 'expand_ratio',
	'input_filters', 'output_filters', 'se_ratio', 'id_skip'])

	# Set GlobalParams and BlockArgs's defaults
	GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
	BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)

	# Swish activation function
	if hasattr(nn, 'SiLU'):
	Swish = nn.SiLU
	else:
	# For compatibility with old PyTorch versions
	class Swish(nn.Module):
	def forward(self, x):
	return x * torch.sigmoid(x)


	# A memory-efficient implementation of Swish function
	class SwishImplementation(torch.autograd.Function):
	@staticmethod
	def forward(ctx, i):
	result = i * torch.sigmoid(i)
	ctx.save_for_backward(i)
	return result

	@staticmethod
	def backward(ctx, grad_output):
	i = ctx.saved_tensors[0]
	sigmoid_i = torch.sigmoid(i)
	return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))


	class MemoryEfficientSwish(nn.Module):
	def forward(self, x):
	return SwishImplementation.apply(x)


	def round_filters(filters, global_params):
	"""Calculate and round number of filters based on width multiplier.
	Use width_coefficient, depth_divisor and min_depth of global_params.

	Args:
	filters (int): Filters number to be calculated.
	global_params (namedtuple): Global params of the model.

	Returns:
	new_filters: New filters number after calculating.
	"""
	multiplier = global_params.width_coefficient
	if not multiplier:
	return filters
	# TODO: modify the params names.
	# maybe the names (width_divisor,min_width)
	# are more suitable than (depth_divisor,min_depth).
	divisor = global_params.depth_divisor
	min_depth = global_params.min_depth
	filters *= multiplier
	min_depth = min_depth or divisor # pay attention to this line when using min_depth
	# follow the formula transferred from official TensorFlow implementation
	new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
	if new_filters < 0.9 * filters: # prevent rounding by more than 10%
	new_filters += divisor
	return int(new_filters)


	def round_repeats(repeats, global_params):
	"""Calculate module's repeat number of a block based on depth multiplier.
	Use depth_coefficient of global_params.

	Args:
	repeats (int): num_repeat to be calculated.
	global_params (namedtuple): Global params of the model.

	Returns:
	new repeat: New repeat number after calculating.
	"""
	multiplier = global_params.depth_coefficient
	if not multiplier:
	return repeats
	# follow the formula transferred from official TensorFlow implementation
	return int(math.ceil(multiplier * repeats))


	def drop_connect(inputs, p, training):
	"""Drop connect.

	Args:
	input (tensor: BCWH): Input of this structure.
	p (float: 0.0~1.0): Probability of drop connection.
	training (bool): The running mode.

	Returns:
	output: Output after drop connection.
	"""
	assert 0 <= p <= 1, 'p must be in range of [0,1]'

	if not training:
	return inputs

	batch_size = inputs.shape[0]
	keep_prob = 1 - p

	# generate binary_tensor mask according to probability (p for 0, 1-p for 1)
	random_tensor = keep_prob
	random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
	binary_tensor = torch.floor(random_tensor)

	output = inputs / keep_prob * binary_tensor
	return output


	def get_width_and_height_from_size(x):
	"""Obtain height and width from x.

	Args:
	x (int, tuple or list): Data size.

	Returns:
	size: A tuple or list (H,W).
	"""
	if isinstance(x, int):
	return x, x
	if isinstance(x, list) or isinstance(x, tuple):
	return x
	else:
	raise TypeError()


	def calculate_output_image_size(input_image_size, stride):
	"""Calculates the output image size when using Conv2dSamePadding with a stride.
	Necessary for static padding. Thanks to mannatsingh for pointing this out.

	Args:
	input_image_size (int, tuple or list): Size of input image.
	stride (int, tuple or list): Conv2d operation's stride.

	Returns:
	output_image_size: A list [H,W].
	"""
	if input_image_size is None:
	return None
	image_height, image_width = get_width_and_height_from_size(input_image_size)
	stride = stride if isinstance(stride, int) else stride[0]
	image_height = int(math.ceil(image_height / stride))
	image_width = int(math.ceil(image_width / stride))
	return [image_height, image_width]


	# Note:
	# The following 'SamePadding' functions make output size equal ceil(input size/stride).
	# Only when stride equals 1, can the output size be the same as input size.
	# Don't be confused by their function names ! ! !

	def get_same_padding_conv2d(image_size=None):
	"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
	Static padding is necessary for ONNX exporting of models.

	Args:
	image_size (int or tuple): Size of the image.

	Returns:
	Conv2dDynamicSamePadding or Conv2dStaticSamePadding.
	"""
	if image_size is None:
	return Conv2dDynamicSamePadding
	else:
	return partial(Conv2dStaticSamePadding, image_size=image_size)


	class Conv2dDynamicSamePadding(nn.Conv2d):
	"""2D Convolutions like TensorFlow, for a dynamic image size.
	The padding is operated in forward function by calculating dynamically.
	"""

	# Tips for 'SAME' mode padding.
	# Given the following:
	# i: width or height
	# s: stride
	# k: kernel size
	# d: dilation
	# p: padding
	# Output after Conv2d:
	# o = floor((i+p-((k-1)*d+1))/s+1)
	# If o equals i, i = floor((i+p-((k-1)*d+1))/s+1),
	# => p = (i-1)s+((k-1)d+1)-i

	def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
	super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
	self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2

	def forward(self, x):
	ih, iw = x.size()[-2:]
	kh, kw = self.weight.size()[-2:]
	sh, sw = self.stride
	oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) # change the output size according to stride ! ! !
	pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
	pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
	if pad_h > 0 or pad_w > 0:
	x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
	return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)


	class Conv2dStaticSamePadding(nn.Conv2d):
	"""2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size.
	The padding mudule is calculated in construction function, then used in forward.
	"""

	# With the same calculation as Conv2dDynamicSamePadding

	def __init__(self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs):
	super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs)
	self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2

	# Calculate padding based on image size and save it
	assert image_size is not None
	ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size
	kh, kw = self.weight.size()[-2:]
	sh, sw = self.stride
	oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
	pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
	pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
	if pad_h > 0 or pad_w > 0:
	self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2,
	pad_h // 2, pad_h - pad_h // 2))
	else:
	self.static_padding = nn.Identity()

	def forward(self, x):
	x = self.static_padding(x)
	x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
	return x


	def get_same_padding_maxPool2d(image_size=None):
	"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
	Static padding is necessary for ONNX exporting of models.

	Args:
	image_size (int or tuple): Size of the image.

	Returns:
	MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding.
	"""
	if image_size is None:
	return MaxPool2dDynamicSamePadding
	else:
	return partial(MaxPool2dStaticSamePadding, image_size=image_size)


	class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
	"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
	The padding is operated in forward function by calculating dynamically.
	"""

	def __init__(self, kernel_size, stride, padding=0, dilation=1, return_indices=False, ceil_mode=False):
	super().__init__(kernel_size, stride, padding, dilation, return_indices, ceil_mode)
	self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride
	self.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size
	self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation

	def forward(self, x):
	ih, iw = x.size()[-2:]
	kh, kw = self.kernel_size
	sh, sw = self.stride
	oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
	pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
	pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
	if pad_h > 0 or pad_w > 0:
	x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
	return F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
	self.dilation, self.ceil_mode, self.return_indices)


	class MaxPool2dStaticSamePadding(nn.MaxPool2d):
	"""2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size.
	The padding mudule is calculated in construction function, then used in forward.
	"""

	def __init__(self, kernel_size, stride, image_size=None, **kwargs):
	super().__init__(kernel_size, stride, **kwargs)
	self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride
	self.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size
	self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation

	# Calculate padding based on image size and save it
	assert image_size is not None
	ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size
	kh, kw = self.kernel_size
	sh, sw = self.stride
	oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
	pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
	pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
	if pad_h > 0 or pad_w > 0:
	self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))
	else:
	self.static_padding = nn.Identity()

	def forward(self, x):
	x = self.static_padding(x)
	x = F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
	self.dilation, self.ceil_mode, self.return_indices)
	return x


	################################################################################
	# Helper functions for loading model params
	################################################################################

	# BlockDecoder: A Class for encoding and decoding BlockArgs
	# efficientnet_params: A function to query compound coefficient
	# get_model_params and efficientnet:
	# Functions to get BlockArgs and GlobalParams for efficientnet
	# url_map and url_map_advprop: Dicts of url_map for pretrained weights
	# load_pretrained_weights: A function to load pretrained weights

	class BlockDecoder(object):
	"""Block Decoder for readability,
	straight from the official TensorFlow repository.
	"""

	@staticmethod
	def _decode_block_string(block_string):
	"""Get a block through a string notation of arguments.

	Args:
	block_string (str): A string notation of arguments.
	Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'.

	Returns:
	BlockArgs: The namedtuple defined at the top of this file.
	"""
	assert isinstance(block_string, str)

	ops = block_string.split('_')
	options = {}
	for op in ops:
	splits = re.split(r'(\d.*)', op)
	if len(splits) >= 2:
	key, value = splits[:2]
	options[key] = value

	# Check stride
	assert (('s' in options and len(options['s']) == 1) or
	(len(options['s']) == 2 and options['s'][0] == options['s'][1]))

	return BlockArgs(
	num_repeat=int(options['r']),
	kernel_size=int(options['k']),
	stride=[int(options['s'][0])],
	expand_ratio=int(options['e']),
	input_filters=int(options['i']),
	output_filters=int(options['o']),
	se_ratio=float(options['se']) if 'se' in options else None,
	id_skip=('noskip' not in block_string))

	@staticmethod
	def _encode_block_string(block):
	"""Encode a block to a string.

	Args:
	block (namedtuple): A BlockArgs type argument.

	Returns:
	block_string: A String form of BlockArgs.
	"""
	args = [
	'r%d' % block.num_repeat,
	'k%d' % block.kernel_size,
	's%d%d' % (block.strides[0], block.strides[1]),
	'e%s' % block.expand_ratio,
	'i%d' % block.input_filters,
	'o%d' % block.output_filters
	]
	if 0 < block.se_ratio <= 1:
	args.append('se%s' % block.se_ratio)
	if block.id_skip is False:
	args.append('noskip')
	return '_'.join(args)

	@staticmethod
	def decode(string_list):
	"""Decode a list of string notations to specify blocks inside the network.

	Args:
	string_list (list[str]): A list of strings, each string is a notation of block.

	Returns:
	blocks_args: A list of BlockArgs namedtuples of block args.
	"""
	assert isinstance(string_list, list)
	blocks_args = []
	for block_string in string_list:
	blocks_args.append(BlockDecoder._decode_block_string(block_string))
	return blocks_args

	@staticmethod
	def encode(blocks_args):
	"""Encode a list of BlockArgs to a list of strings.

	Args:
	blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args.

	Returns:
	block_strings: A list of strings, each string is a notation of block.
	"""
	block_strings = []
	for block in blocks_args:
	block_strings.append(BlockDecoder._encode_block_string(block))
	return block_strings


	def efficientnet_params(model_name):
	"""Map EfficientNet model name to parameter coefficients.

	Args:
	model_name (str): Model name to be queried.

	Returns:
	params_dict[model_name]: A (width,depth,res,dropout) tuple.
	"""
	params_dict = {
	# Coefficients: width,depth,res,dropout
	'efficientnet-b0': (1.0, 1.0, 224, 0.2),
	'efficientnet-b1': (1.0, 1.1, 240, 0.2),
	'efficientnet-b2': (1.1, 1.2, 260, 0.3),
	'efficientnet-b3': (1.2, 1.4, 300, 0.3),
	'efficientnet-b4': (1.4, 1.8, 380, 0.4),
	'efficientnet-b5': (1.6, 2.2, 456, 0.4),
	'efficientnet-b6': (1.8, 2.6, 528, 0.5),
	'efficientnet-b7': (2.0, 3.1, 600, 0.5),
	'efficientnet-b8': (2.2, 3.6, 672, 0.5),
	'efficientnet-l2': (4.3, 5.3, 800, 0.5),
	}
	return params_dict[model_name]


	def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None,
	dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True):
	"""Create BlockArgs and GlobalParams for efficientnet model.

	Args:
	width_coefficient (float)
	depth_coefficient (float)
	image_size (int)
	dropout_rate (float)
	drop_connect_rate (float)
	num_classes (int)

	Meaning as the name suggests.

	Returns:
	blocks_args, global_params.
	"""

	# Blocks args for the whole model(efficientnet-b0 by default)
	# It will be modified in the construction of EfficientNet Class according to model
	blocks_args = [
	'r1_k3_s11_e1_i32_o16_se0.25',
	'r2_k3_s22_e6_i16_o24_se0.25',
	'r2_k5_s22_e6_i24_o40_se0.25',
	'r3_k3_s22_e6_i40_o80_se0.25',
	'r3_k5_s11_e6_i80_o112_se0.25',
	'r4_k5_s22_e6_i112_o192_se0.25',
	'r1_k3_s11_e6_i192_o320_se0.25',
	]
	blocks_args = BlockDecoder.decode(blocks_args)

	global_params = GlobalParams(
	width_coefficient=width_coefficient,
	depth_coefficient=depth_coefficient,
	image_size=image_size,
	dropout_rate=dropout_rate,

	num_classes=num_classes,
	batch_norm_momentum=0.99,
	batch_norm_epsilon=1e-3,
	drop_connect_rate=drop_connect_rate,
	depth_divisor=8,
	min_depth=None,
	include_top=include_top,
	)

	return blocks_args, global_params


	def get_model_params(model_name, override_params):
	"""Get the block args and global params for a given model name.

	Args:
	model_name (str): Model's name.
	override_params (dict): A dict to modify global_params.

	Returns:
	blocks_args, global_params
	"""
	if model_name.startswith('efficientnet'):
	w, d, s, p = efficientnet_params(model_name)
	# note: all models have drop connect rate = 0.2
	blocks_args, global_params = efficientnet(
	width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s)
	else:
	raise NotImplementedError('model name is not pre-defined: {}'.format(model_name))
	if override_params:
	# ValueError will be raised here if override_params has fields not included in global_params.
	global_params = global_params._replace(**override_params)
	return blocks_args, global_params


	# train with Standard methods
	# check more details in paper(EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks)
	url_map = {
	'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth',
	'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth',
	'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth',
	'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth',
	'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth',
	'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth',
	'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth',
	'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth',
	}

	# train with Adversarial Examples(AdvProp)
	# check more details in paper(Adversarial Examples Improve Image Recognition)
	url_map_advprop = {
	'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth',
	'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth',
	'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth',
	'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth',
	'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth',
	'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth',
	'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth',
	'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth',
	'efficientnet-b8': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth',
	}

	# TODO: add the petrained weights url map of 'efficientnet-l2'


	def load_pretrained_weights(model, model_name, weights_path=None, load_fc=True, advprop=False, verbose=True):
	"""Loads pretrained weights from weights path or download using url.

	Args:
	model (Module): The whole model of efficientnet.
	model_name (str): Model name of efficientnet.
	weights_path (None or str):
	str: path to pretrained weights file on the local disk.
	None: use pretrained weights downloaded from the Internet.
	load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
	advprop (bool): Whether to load pretrained weights
	trained with advprop (valid when weights_path is None).
	"""
	if isinstance(weights_path, str):
	state_dict = torch.load(weights_path)
	else:
	# AutoAugment or Advprop (different preprocessing)
	url_map_ = url_map_advprop if advprop else url_map
	state_dict = model_zoo.load_url(url_map_[model_name])

	if load_fc:
	ret = model.load_state_dict(state_dict, strict=False)
	assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)
	else:
	state_dict.pop('_fc.weight')
	state_dict.pop('_fc.bias')
	ret = model.load_state_dict(state_dict, strict=False)
	assert set(ret.missing_keys) == set(
	['_fc.weight', '_fc.bias']), 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)
	assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.unexpected_keys)

	if verbose:
	print('Loaded pretrained weights for {}'.format(model_name))