import argparse
import os
import sys
import time
import json
import torch
import torch.nn.functional as F
import utils
import tabulate
import models
from data import get_data
import numpy as np
from tensorboardX import SummaryWriter
from qtorch.auto_low import lower
from qtorch.optim import OptimLP
from torch.optim.lr_scheduler import LambdaLR
from torch.optim import SGD
from qtorch import BlockFloatingPoint, FixedPoint, FloatingPoint
from qtorch.quant import quantizer, Quantizer

num_types = ["weight", "activate", "grad", "error", "momentum", "acc"]

parser = argparse.ArgumentParser(description='SGD/SWA training')
parser.add_argument('--dataset', type=str, default='CIFAR10',
                    help='dataset name: CIFAR10 or IMAGENET12')
parser.add_argument('--data_path', type=str, default="./data", required=True, metavar='PATH',
                    help='path to datasets location (default: "./data")')
parser.add_argument('--batch_size', type=int, default=128, metavar='N',
                    help='input batch size (default: 128)')
parser.add_argument('--val_ratio', type=float, default=0.0, metavar='N',
                    help='Ratio of the validation set (default: 0.0)')
parser.add_argument('--num_workers', type=int, default=4, metavar='N',
                    help='number of workers (default: 4)')
parser.add_argument('--model', type=str, default=None, required=True, metavar='MODEL',
                    help='model name (default: None)')
parser.add_argument('--resume', type=str, default=None, metavar='CKPT',
                    help='checkpoint to resume training from (default: None)')
parser.add_argument('--epochs', type=int, default=200, metavar='N',
                    help='number of epochs to train (default: 200)')
parser.add_argument('--lr_init', type=float, default=0.01, metavar='LR',
                    help='initial learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='SGD momentum (default: 0.9)')
parser.add_argument('--wd', type=float, default=1e-4,
                    help='weight decay (default: 1e-4)')
for num in num_types:
    parser.add_argument('--{}-man'.format(num), type=int, default=-1, metavar='N',
                        help='number of bits to use for mantissa of {}; -1 if full precision.'.format(num))
    parser.add_argument('--{}-exp'.format(num), type=int, default=-1, metavar='N',
                        help='number of bits to use for exponent of {}; -1 if full precision.'.format(num))
    parser.add_argument('--{}-rounding'.format(num), type=str, default='stochastic', metavar='S',
                        choices=["stochastic","nearest"],
                        help='rounding method for {}, stochastic or nearest'.format(num))

args = parser.parse_args()

torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True

# Select quantizer
def quant_summary(man=-1, exp=-1):
    return "float-e{}-m{}".format(man, exp)

loaders = get_data(args.dataset, args.data_path, args.batch_size, args.val_ratio, args.num_workers)
if args.dataset=="CIFAR10": num_classes=10
elif args.dataset=="IMAGENET12": num_classes=1000

quantizers = {}
for num in num_types:
    num_rounding = getattr(args, "{}_rounding".format(num))
    num_man = getattr(args, "{}_man".format(num))
    num_exp = getattr(args, "{}_exp".format(num))
    number = FloatingPoint(exp=num_exp, man=num_man)
    print("{}: {} rounding, {}".format(num, num_rounding,
                                       number))
    quantizers[num] = quantizer(forward_number=number, forward_rounding=num_rounding)
# Build model
print('Model: {}'.format(args.model))
model_cfg = getattr(models, args.model)
if 'LP' in args.model:
    activate_number = FloatingPoint(exp=args.activate_exp, man=args.activate_man)
    error_number = FloatingPoint(exp=args.error_exp, man=args.error_man)
    print("activation: {}, {}".format(args.activate_rounding, activate_number))
    print("error: {}, {}".format(args.error_rounding, error_number))
    make_quant = lambda : Quantizer(activate_number, error_number, args.activate_rounding, args.error_rounding)
    model_cfg.kwargs.update({"quant":make_quant})

model = model_cfg.base(*model_cfg.args, num_classes=num_classes, **model_cfg.kwargs)
model.cuda()

criterion = F.cross_entropy
optimizer = SGD(
   model.parameters(),
   lr=args.lr_init,
   momentum=args.momentum,
   weight_decay=args.wd,
)
optimizer = OptimLP(optimizer,
                    weight_quant=quantizers["weight"],
                    grad_quant=quantizers["grad"],
                    momentum_quant=quantizers["momentum"],
                    acc_quant=quantizers["acc"],
                    grad_scaling=1/1000 # scaling down the gradient
)

def schedule(epoch):
    t = (epoch) / args.epochs
    lr_ratio = 0.01
    if t <= 0.5:
        factor = 1.0
    elif t <= 0.9:
        factor = 1.0 - (1.0 - lr_ratio) * (t - 0.5) / 0.4
    else:
        factor = lr_ratio

    return factor

scheduler = LambdaLR(optimizer, lr_lambda=[schedule])
# Prepare logging
columns = ['ep', 'lr', 'tr_loss', 'tr_acc', 'tr_time', 'te_loss', 'te_acc', 'te_time']

def get_result(loaders, model, phase):
    time_ep = time.time()
    res = utils.run_epoch(loaders[phase], model, criterion,
                                optimizer=optimizer, phase=phase)
    time_pass = time.time() - time_ep
    res['time_pass'] = time_pass
    return res

for epoch in range(args.epochs):

    scheduler.step()
    time_ep = time.time()
    train_res = get_result(loaders, model, "train")

    if epoch == 0 or epoch % 5 == 4 or epoch == args.epochs - 1:
        test_res = get_result(loaders, model, "test")
    else:
        test_res = {'loss': None, 'accuracy': None, 'time_pass': None}

    values = [epoch + 1, optimizer.param_groups[0]['lr'], train_res['loss'], train_res['accuracy'], train_res['time_pass'], test_res['loss'], test_res['accuracy'], test_res['time_pass']]

    utils.print_table(values, columns, epoch)

import argparse
import os
import sys
import time
import json
import torch
import torch.nn.functional as F
import utils
import tabulate
import models
from data import get_data
import numpy as np
from tensorboardX import SummaryWriter
from qtorch.auto_low import lower
from qtorch.optim import OptimLP
from torch.optim.lr_scheduler import LambdaLR
from torch.optim import SGD
from qtorch import BlockFloatingPoint, FixedPoint, FloatingPoint
from qtorch.quant import quantizer, Quantizer

num_types = ["weight", "activate", "grad", "error", "momentum", "acc"]

parser = argparse.ArgumentParser(description='SGD/SWA training')
parser.add_argument('--dataset', type=str, default='CIFAR10',
                    help='dataset name: CIFAR10 or IMAGENET12')
parser.add_argument('--data_path', type=str, default="./data", required=True, metavar='PATH',
                    help='path to datasets location (default: "./data")')
parser.add_argument('--batch_size', type=int, default=128, metavar='N',
                    help='input batch size (default: 128)')
parser.add_argument('--val_ratio', type=float, default=0.0, metavar='N',
                    help='Ratio of the validation set (default: 0.0)')
parser.add_argument('--num_workers', type=int, default=4, metavar='N',
                    help='number of workers (default: 4)')
parser.add_argument('--model', type=str, default=None, required=True, metavar='MODEL',
                    help='model name (default: None)')
parser.add_argument('--resume', type=str, default=None, metavar='CKPT',
                    help='checkpoint to resume training from (default: None)')
parser.add_argument('--epochs', type=int, default=200, metavar='N',
                    help='number of epochs to train (default: 200)')
parser.add_argument('--lr_init', type=float, default=0.01, metavar='LR',
                    help='initial learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='SGD momentum (default: 0.9)')
parser.add_argument('--wd', type=float, default=1e-4,
                    help='weight decay (default: 1e-4)')
for num in num_types:
    parser.add_argument('--{}-man'.format(num), type=int, default=-1, metavar='N',
                        help='number of bits to use for mantissa of {}; -1 if full precision.'.format(num))
    parser.add_argument('--{}-exp'.format(num), type=int, default=-1, metavar='N',
                        help='number of bits to use for exponent of {}; -1 if full precision.'.format(num))
    parser.add_argument('--{}-rounding'.format(num), type=str, default='stochastic', metavar='S',
                        choices=["stochastic","nearest"],
                        help='rounding method for {}, stochastic or nearest'.format(num))

args = parser.parse_args()

torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True

# Select quantizer
def quant_summary(man=-1, exp=-1):
    return "float-e{}-m{}".format(man, exp)

loaders = get_data(args.dataset, args.data_path, args.batch_size, args.val_ratio, args.num_workers)
if args.dataset=="CIFAR10": num_classes=10
elif args.dataset=="IMAGENET12": num_classes=1000

quantizers = {}
for num in num_types:
    num_rounding = getattr(args, "{}_rounding".format(num))
    num_man = getattr(args, "{}_man".format(num))
    num_exp = getattr(args, "{}_exp".format(num))
    number = FloatingPoint(exp=num_exp, man=num_man)
    print("{}: {} rounding, {}".format(num, num_rounding,
                                       number))
    quantizers[num] = quantizer(forward_number=number, forward_rounding=num_rounding)
# Build model
print('Model: {}'.format(args.model))
model_cfg = getattr(models, args.model)
if 'LP' in args.model:
    activate_number = FloatingPoint(exp=args.activate_exp, man=args.activate_man)
    error_number = FloatingPoint(exp=args.error_exp, man=args.error_man)
    print("activation: {}, {}".format(args.activate_rounding, activate_number))
    print("error: {}, {}".format(args.error_rounding, error_number))
    make_quant = lambda : Quantizer(activate_number, error_number, args.activate_rounding, args.error_rounding)
    model_cfg.kwargs.update({"quant":make_quant})

model = model_cfg.base(*model_cfg.args, num_classes=num_classes, **model_cfg.kwargs)
model.cuda()

criterion = F.cross_entropy
optimizer = SGD(
   model.parameters(),
   lr=args.lr_init,
   momentum=args.momentum,
   weight_decay=args.wd,
)
optimizer = OptimLP(optimizer,
                    weight_quant=quantizers["weight"],
                    grad_quant=quantizers["grad"],
                    momentum_quant=quantizers["momentum"],
                    acc_quant=quantizers["acc"],
                    grad_scaling=1/1000 # scaling down the gradient
)

def schedule(epoch):
    t = (epoch) / args.epochs
    lr_ratio = 0.01
    if t <= 0.5:
        factor = 1.0
    elif t <= 0.9:
        factor = 1.0 - (1.0 - lr_ratio) * (t - 0.5) / 0.4
    else:
        factor = lr_ratio

    return factor

scheduler = LambdaLR(optimizer, lr_lambda=[schedule])
# Prepare logging
columns = ['ep', 'lr', 'tr_loss', 'tr_acc', 'tr_time', 'te_loss', 'te_acc', 'te_time']

def get_result(loaders, model, phase):
    time_ep = time.time()
    res = utils.run_epoch(loaders[phase], model, criterion,
                                optimizer=optimizer, phase=phase)
    time_pass = time.time() - time_ep
    res['time_pass'] = time_pass
    return res

for epoch in range(args.epochs):

    scheduler.step()
    time_ep = time.time()
    train_res = get_result(loaders, model, "train")

    if epoch == 0 or epoch % 5 == 4 or epoch == args.epochs - 1:
        test_res = get_result(loaders, model, "test")
    else:
        test_res = {'loss': None, 'accuracy': None, 'time_pass': None}

    values = [epoch + 1, optimizer.param_groups[0]['lr'], train_res['loss'], train_res['accuracy'], train_res['time_pass'], test_res['loss'], test_res['accuracy'], test_res['time_pass']]

    utils.print_table(values, columns, epoch)

# Select quantizer # Build model # scaling down the gradient # Prepare logging