#!/usr/bin/env python

import sys
import os
import cv2
import argparse
import datetime

################################
#sys.path.insert(0, os.path.abspath('./modules'))


################################
from pytorch_jacinto_ai.xnn.utils import str2bool
parser = argparse.ArgumentParser()
parser.add_argument('--save_path', type=str, default=None, help='checkpoint save folder')
parser.add_argument('--gpus', type=int, nargs='*', default=None, help='Base learning rate')
parser.add_argument('--batch_size', type=int, default=None, help='Batch size')
parser.add_argument('--lr', type=float, default=None, help='Base learning rate')
parser.add_argument('--lr_clips', type=float, default=None, help='Learning rate for clips in PAct2')
parser.add_argument('--lr_calib', type=float, default=None, help='Learning rate for calibration')
parser.add_argument('--model_name', type=str, default=None, help='model name')
parser.add_argument('--dataset_name', type=str, default=None, help='dataset name')
parser.add_argument('--data_path', type=str, default=None, help='data path')
parser.add_argument('--epoch_size', type=float, default=None, help='epoch size. using a fraction will reduce the data used for one epoch')
parser.add_argument('--epochs', type=int, default=None, help='number of epochs')
parser.add_argument('--warmup_epochs', type=int, default=None, help='number of epochs for the learning rate to increase and reach base value')
parser.add_argument('--milestones', type=int, nargs='*', default=None, help='change lr at these milestones')
parser.add_argument('--img_resize', type=int, nargs=2, default=None, help='img_resize size. for training this will be modified according to rand_scale')
parser.add_argument('--rand_scale', type=float, nargs=2, default=None, help='random scale factors for training')
parser.add_argument('--rand_crop', type=int, nargs=2, default=None, help='random crop for training')
parser.add_argument('--output_size', type=int, nargs=2, default=None, help='output size of the evaluation - prediction/groundtruth. this is not used while training as it blows up memory requirement')
parser.add_argument('--pretrained', type=str, default=None, help='pretrained model')
parser.add_argument('--resume', type=str, default=None, help='resume an unfinished training from this model')
parser.add_argument('--phase', type=str, default=None, help='training/calibration/validation')
parser.add_argument('--evaluate_start', type=str2bool, default=None, help='Whether to run validation before the training')
#
parser.add_argument('--quantize', type=str2bool, default=None, help='Quantize the model')
parser.add_argument('--histogram_range', type=str2bool, default=None, help='run only evaluation and no training')
parser.add_argument('--per_channel_q', type=str2bool, default=None, help='run only evaluation and no training')
parser.add_argument('--bias_calibration', type=str2bool, default=None, help='run only evaluation and no training')
parser.add_argument('--bitwidth_weights', type=int, default=None, help='bitwidth for weight quantization')
parser.add_argument('--bitwidth_activations', type=int, default=None, help='bitwidth for activation quantization')
#
parser.add_argument('--freeze_bn', type=str2bool, default=None, help='freeze the bn stats or not')
cmds = parser.parse_args()

################################
# taken care first, since this has to be done before importing pytorch
if 'gpus' in vars(cmds):
    value = getattr(cmds, 'gpus')
    if value is not None:
        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([str(v) for v in value])
#

# to avoid hangs in data loader with multi threads
# this was observed after using cv2 image processing functions
# https://github.com/pytorch/pytorch/issues/1355
cv2.setNumThreads(0)

################################
from pytorch_jacinto_ai.engine import train_pixel2pixel

# Create the parser and set default arguments
args = train_pixel2pixel.get_config()

################################
#Modify arguments
args.model_name = 'deeplabv3lite_mobilenetv2_tv' #'deeplabv3lite_mobilenetv2_tv' #'fpn_pixel2pixel_aspp_mobilenetv2_tv' #'fpn_pixel2pixel_aspp_resnet50'

args.dataset_name = 'kitti_depth' #'kitti_depth' #'kitti_depth' #'kitti_depth2'

#args.save_path = './data/checkpoints'

args.data_path = './data/datasets/kitti/kitti_depth/data'
args.split_files = (args.data_path+'/train.txt', args.data_path+'/val.txt')

args.pretrained = './data/modelzoo/semantic_segmentation/cityscapes/deeplabv3lite-mobilenetv2/cityscapes_segmentation_deeplabv3lite-mobilenetv2_2019-06-26-08-59-32.pth'
                                    # 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth'
                                    # './data/modelzoo/pretrained/pytorch/imagenet_classification/ericsun99/MobileNet-V2-Pytorch/mobilenetv2_Top1_71.806_Top2_90.410.pth.tar'
                                    # 'https://download.pytorch.org/models/resnet50-19c8e357.pth'

args.model_config.input_channels = (3,)      # [3,3]
args.model_config.output_type = ['depth']
args.model_config.output_channels = [1]
args.losses = [['supervised_loss', 'scale_loss', 'supervised_error_var']] #[['supervised_loss', 'scale_loss']]
args.loss_mult_factors = [[0.125, 0.125, 4.0]]

args.metrics = [['supervised_relative_error_x100']] #[['supervised_root_mean_squared_error']]

args.solver = 'adam'                    #'sgd' #'adam'
args.epochs = 250                       #300
args.epoch_size = 0.125                 #0 #0.25
args.scheduler = 'step'                 #'poly' #'step' #'cosine'
args.multistep_gamma = 0.25             #only for step scheduler
args.milestones = (100, 200)            #only for step scheduler
args.polystep_power = 0.9               #only for poly scheduler
args.iter_size = 1                      #2

args.lr = 4e-4                          #4e-4 #1e-4
args.batch_size = 32                    #8 #12 #16 #32 #64
args.weight_decay = 1e-4                #4e-5 #1e-5

args.img_resize = (384, 768)            #(256,512) #(512,512) #(512,1024) #(1024, 2048)
args.output_size = (374, 1242)          #(512, 1024) #(720, 1280) #target output size for evaluation

args.transform_rotation = 5             #0  #rotation degrees

args.workers = 12                       # more workers may speedup

#args.phase = 'validation'
#args.quantize = True
#args.print_model = True
#args.generate_onnx = False
#args.run_soon = False
#args.evaluate_start = False

#args.quantize = True
#args.per_channel_q = True
#args.phase = 'validation'
#args.parallel_model=False

#args.viz_colormap = 'plasma'  # colormap for tensorboard: 'rainbow', 'plasma', 'magma', 'bone'

# defining date from outside can help to write multiple pahses into the same folder
args.date = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")


################################
for key in vars(cmds):
    if key == 'gpus':
        pass # already taken care above, since this has to be done before importing pytorch
    elif hasattr(args, key):
        value = getattr(cmds, key)
        if value != 'None' and value is not None:
            setattr(args, key, value)
    else:
        assert False, f'invalid argument {key}'
#

################################
# Run the given phase
train_pixel2pixel.main(args)

################################
# In addition run a quantized calibration, starting from the trained model
if 'training'in args.phase and (not args.quantize):
    save_path = train_pixel2pixel.get_save_path(args)
    args.pretrained = os.path.join(save_path, 'model_best.pth.tar')
    args.phase = 'training_quantize'
    args.quantize = True
    args.lr = 5e-5
    args.epochs = min(args.epochs,25)
    # quantized training will use only one GPU in the engine - so reduce the batch_size
    num_gpus = len(str(os.environ["CUDA_VISIBLE_DEVICES"]).split(','))
    args.batch_size = args.batch_size//num_gpus
    train_pixel2pixel.main(args)
#

################################
# In addition run a separate validation, starting from the calibrated model - to estimate the quantized accuracy accurately
if 'training' in args.phase or 'calibration' in args.phase:
    save_path = train_pixel2pixel.get_save_path(args)
    args.pretrained = os.path.join(save_path, 'model_best.pth.tar')
    args.phase = 'validation'
    args.quantize = True
    train_pixel2pixel.main(args)
#