import os
import cv2
import torch
import numpy as np
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
from torch.optim import lr_scheduler

####################################################################
# ------------------------- Basic Functions -------------------------
####################################################################

def get_scheduler(optimizer, opts, cur_ep=-1):
    if opts.lr_policy == 'lambda':
        def lambda_rule(ep):
            lr_l = 1.0 - max(0, ep - opts.n_ep_decay) / float(opts.n_ep - opts.n_ep_decay + 1)
            return lr_l

        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule, last_epoch=cur_ep)
    elif opts.lr_policy == 'step':
        scheduler = lr_scheduler.StepLR(optimizer, step_size=opts.n_ep_decay, gamma=0.1, last_epoch=cur_ep)
    elif opts.lr_policy == 'plateau':
        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
    else:
        return NotImplementedError('learning rate policy [%s] is not implemented', opts.lr_policy)
    return scheduler

def init_weights(net, init_type, gain):
    def init_func(m):
        classname = m.__class__.__name__
        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
            if init_type == 'normal':
                init.normal_(m.weight.data, 0.0, gain)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=gain)
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fainplanes')
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=gain)
            else:
                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant_(m.bias.data, 0.0)
        elif classname.find('BatchNorm2d') != -1:
            init.normal_(m.weight.data, 1.0, gain)
            init.constant_(m.bias.data, 0.0)

    print('initialize network with %s' % init_type)
    net.apply(init_func)

def init_net(net, gpu, init_type='normal', gain=0.02):
    # assert (torch.cuda.is_available())
    net.to(gpu)
    init_weights(net, init_type, gain)
    return net