import hyper, gaussian, util, logging, time, pretrain
import torch, random
from torch.autograd import Variable
from torch import nn, optim
from tqdm import trange, tqdm
from tensorboardX import SummaryWriter
from util import Lambda, Debug

from torchsample.metrics import CategoricalAccuracy

import torch.optim as optim

import torchvision
import torchvision.transforms as transforms

from util import od

from argparse import ArgumentParser

"""
MNIST experiment

"""

def go(batch=64, epochs=350, model='baseline', cuda=False, seed=1):
    torch.manual_seed(seed)
    logging.basicConfig(filename='run.log',level=logging.INFO)
    LOG = logging.getLogger()

    w = SummaryWriter()

    SHAPE = (28, 28)
    CUDA = False

    gaussian.PROPER_SAMPLING = False

    normalize = transforms.Compose([transforms.ToTensor()])
    train = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=normalize)
    trainloader = torch.utils.data.DataLoader(train, batch_size=batch, shuffle=True, num_workers=2)
    test = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=normalize)
    testloader = torch.utils.data.DataLoader(test, batch_size=batch, shuffle=False, num_workers=2)

    if model == 'non-adaptive':
        layers = [
            gaussian.ParamASHLayer(SHAPE, (4, 8, 8), k=64, additional=8, has_bias=True),
            nn.Sigmoid(),
            gaussian.ParamASHLayer((4, 8, 8), (128,), k=64, additional=32, has_bias=True),
            nn.Sigmoid(),
            nn.Linear(128, 10),
            nn.Softmax()]

    elif model == 'free':

        shapes = [(28, 28), (4, 16, 16), (8, 4, 4), (128,)]
        layers = [
            gaussian.CASHLayer(shapes[0], shapes[1], k=1500, additional=256, has_bias=True, has_channels=False),
            nn.Sigmoid(),
            gaussian.CASHLayer(shapes[1], shapes[2], k=750, additional=128, has_bias=True, has_channels=True),
            nn.Sigmoid(),
            gaussian.CASHLayer(shapes[2], shapes[3], k=750, additional=128, has_bias=True, has_channels=True),
            nn.Sigmoid(),
            nn.Linear(shapes[3][0], 10),
            nn.Softmax()]
        pivots = [2, 4, 6]
        decoder_channels = [True, True, False]

        pretrain.pretrain(layers, shapes, pivots, trainloader, epochs=5, k_out=256, out_additional=128, use_cuda=cuda,
                plot=True, has_channels=decoder_channels)

        model = nn.Sequential(od(layers))

    elif model == 'baseline':
        model = nn.Sequential(
            Lambda(lambda x : x.unsqueeze(1)),
            # Debug(lambda x: print('0', x.size(), util.prod(x[-1:].size()))),
            nn.Conv2d(in_channels=1, out_channels=4, kernel_size=3, stride=1, padding=4),
            nn.MaxPool2d(stride=2, kernel_size=2),
            # Debug(lambda x: print('1', x.size(), util.prod(x[-1:].size()))),
            nn.Conv2d(in_channels=4, out_channels=8, kernel_size=3, stride=1, padding=2),
            nn.MaxPool2d(stride=2, kernel_size=2),
            # Debug(lambda x: print('2', x.size(), util.prod(x[-1:].size()))),
            util.Flatten(),
            # Debug(lambda x: print('3', x.size(), util.prod(x[-1:].size()))),
            nn.Linear(648, 10),
            nn.Softmax())

    if CUDA:
        model.apply(lambda t : t.cuda())

    ## SIMPLE
    criterion = nn.CrossEntropyLoss()
    acc = CategoricalAccuracy()
    optimizer = optim.Adam(model.parameters(), lr=0.01)

    step = 0

    for epoch in range(epochs):
        for i, data in tqdm(enumerate(trainloader, 0)):

            # get the inputs
            inputs, labels = data
            inputs = inputs.squeeze(1)

            if CUDA:
                inputs, labels = inputs.cuda(), labels.cuda()

            # wrap them in Variables
            inputs, labels = Variable(inputs), Variable(labels)

            # forward + backward + optimize
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)

            t0 = time.time()
            loss.backward()  # compute the gradients
            logging.info('backward: {} seconds'.format(time.time() - t0))
            optimizer.step()

            w.add_scalar('mnist/train-loss', loss.data[0], step)

            step += 1

        total = 0.0
        num = 0
        for i, data in enumerate(testloader, 0):
            # get the inputs
            inputs, labels = data
            inputs = inputs.squeeze(1)

            if CUDA:
                inputs, labels = inputs.cuda(), labels.cuda()

            # wrap them in Variables
            inputs, labels = Variable(inputs), Variable(labels)

            # forward + backward + optimize
            optimizer.zero_grad()
            outputs = model(inputs)

            total += acc(outputs, labels)
            num += 1

        accuracy = total/num

        w.add_scalar('mnist/per-epoch-test-acc', accuracy, epoch)
        print('EPOCH {}: {} accuracy '.format(epoch, accuracy))

    print('Finished Training.')

if __name__ == "__main__":

    ## Parse the command line options
    parser = ArgumentParser()

    parser.add_argument("-m", "--model",
                        dest="model",
                        help="Which model to train.",
                        default='baseline')

    parser.add_argument("-b", "--batch-size",
                        dest="batch_size",
                        help="The batch size.",
                        default=64)

    options = parser.parse_args()

    go(batch=options.batch_size, model=options.model)

@@ -703,25 +703,8 @@
  28)
-%0A    CUDA = False
 %0A%0A  
@@ -3223,28 +3223,28 @@
 ())%0A%0A    if 
-CUDA
+cuda
 :%0A        mo
@@ -3629,36 +3629,36 @@
 %0A            if 
-CUDA
+cuda
 :%0A              
@@ -4451,12 +4451,12 @@
  if 
-CUDA
+cuda
 :%0A  
@@ -5440,16 +5440,168 @@
 lt=64)%0A%0A
+    parser.add_argument(%22-c%22, %22--cuda%22, dest=%22cuda%22,%0A                        help=%22Whether to use cuda.%22,%0A                        action=%22store_true%22)%0A%0A
     opti
@@ -5679,10 +5679,29 @@
 ns.model
+, cuda=options.cuda
 )%0A

Add cuda argument