"""
### Author : Khush Patel (@Khush)
###### Model : Inception
###### Ephoch : 75
###### Loss : CrossEntropyLoss
###### Criterion : SGD
"""
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os
print(os.listdir("../input"))

# Any results you write to the current directory are saved as output.
!ls ../working/
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
import json
from sklearn.metrics import roc_curve
print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False):
    since = time.time()

    val_acc_history = []

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'valid']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    # Get model outputs and calculate loss
                    # Special case for inception because in training it has an auxiliary output. In train
                    #   mode we calculate the loss by summing the final output and the auxiliary output
                    #   but in testing we only consider the final output.
                    if is_inception and phase == 'train':
                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                        outputs, aux_outputs = model(inputs)
                        loss1 = criterion(outputs, labels)
                        loss2 = criterion(aux_outputs, labels)
                        loss = loss1 + 0.4*loss2
                    else:
                        outputs = model(inputs)
                        loss = criterion(outputs, labels)

                    _, preds = torch.max(outputs, 1)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'valid' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            if phase == 'valid':
                val_acc_history.append(epoch_acc)

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts) 
    
    return model, val_acc_history, outputs, labels
def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = False
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    model_ft = None
    input_size = 0

    if model_name == "resnet":
        """ Resnet18
        """
        model_ft = models.resnet18(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "alexnet":
        """ Alexnet
        """
        model_ft = models.alexnet(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        model_ft = models.vgg11_bn(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        model_ft = models.squeezenet1_0(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
        model_ft.num_classes = num_classes
        input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        model_ft = models.densenet121(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier.in_features
        model_ft.classifier = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "inception":
        """ Inception v3
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        num_ftrs = model_ft.AuxLogits.fc.in_features
        model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        print("Invalid model name, exiting...")
        exit()

    return model_ft, input_size

# Top level data directory. Here we assume the format of the directory conforms
#   to the ImageFolder structure
data_dir = "../input/car_data/car_data/"
#test_data_dir = "../input/hackathon-blossom-flower-classification/"

# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]
model_name = "inception"

# Number of classes in the dataset
num_classes = 196

# Batch size for training (change depending on how much memory you have)
batch_size = 16

# Number of epochs to train for
num_epochs = 75

# Flag for feature extracting. When False, we finetune the whole model,
#   when True we only update the reshaped layer params
feature_extract = False
# Initialize the model for this run
model_ft, input_size = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True)

# Print the model we just instantiated
#print(model_ft)
print("Model Loading Process Done")
data_transforms = {
    'transform': transforms.Compose([
        transforms.RandomResizedCrop(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}

#print(len(datasets.ImageFolder(data_dir + '/train')))
full_dataset = datasets.ImageFolder(data_dir + '/train', data_transforms['transform'])

train_size = int(0.8 * len(full_dataset))
test_size = len(full_dataset) - train_size
#print(train_size)
#print(test_size)
train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])

image_datasets = {
    'train':train_dataset, 
    'valid':test_dataset
}
dataloaders_dict = {
    'train':torch.utils.data.DataLoader(image_datasets['train'], batch_size=batch_size, shuffle=True, num_workers=4),
    'valid':torch.utils.data.DataLoader(image_datasets['valid'], batch_size=batch_size, shuffle=True, num_workers=4)
}
# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# Send the model to GPU
model_ft = model_ft.to(device)

# Gather the parameters to be optimized/updated in this run. If we are
#  finetuning we will be updating all parameters. However, if we are
#  doing feature extract method, we will only update the parameters
#  that we have just initialized, i.e. the parameters with requires_grad
#  is True.
params_to_update = model_ft.parameters()
#print("Params to learn:")
if feature_extract:
    params_to_update = []
    for name,param in model_ft.named_parameters():
        if param.requires_grad == True:
            params_to_update.append(param)
            #print("\t",name)
            pass
else:
    for name,param in model_ft.named_parameters():
        if param.requires_grad == True:
            #print("\t",name)
            pass

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9)
#optimizer_ft = optim.Adam(params_to_update,lr=0.0001)
# Setup the loss fxn
criterion = nn.CrossEntropyLoss()

# Train and evaluate
model_ft, hist, out, lab = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=75, is_inception=(model_name=="inception"))
def save_checkpoint():
    checkpoint = {
        'model':model_ft, 
        'state_dict':model_ft.state_dict(),
        'optimizer':optimizer_ft.state_dict()
    }
    torch.save(checkpoint, '../working/checkpoint.pt')
def load_checkpoint(filepath, inference = False):
    checkpoint = torch.load(filepath + 'checkpoint.pt')
    model = checkpoint['model']
    if inference:
        for parameter in model.parameter():
            parameter.require_grad = False
        model.eval()
    model.to(device)
    return model
save_checkpoint()
model_ft = load_checkpoint(filepath='../working/')
test_data_dir ="../input/car_data/car_data/"
data_transforms = {
    'testing': transforms.Compose([
        transforms.Resize(input_size),
        transforms.CenterCrop(input_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}
test_image_datasets = {x: datasets.ImageFolder(os.path.join(test_data_dir, 'test'), data_transforms[x]) for x in ['testing']}
test_dataloaders_dict = {x: torch.utils.data.DataLoader(test_image_datasets[x], batch_size=batch_size, shuffle=False, num_workers=4) for x in ['testing']}
import glob
files = glob.glob("../input/car_data/car_data/test/")
#len(files)
# path = '../input/car_data/car_data/test/Ram C/V Cargo Van Minivan 2012/*.*'
# files = glob.glob(path)
# print(len(files))
data = []
with open("../input/names.csv", 'r') as G:
     data.append(G.read())

data = data[0].split("\n")
data.remove('')
data = [item.replace("Ram C/V Cargo Van Minivan 2012", "Ram C-V Cargo Van Minivan 2012") for item in data]
#print(data)
#d = {index(i) for i in data: }
from os import listdir
from os.path import isfile, join

ids = []
op = []
#print(data)
for i in data:
    path = "../input/car_data/car_data/test/"+ str(i)+"/*.*"
    #mypath = "../input/car_data/car_data/test/"+ str(i)+"/"
    #onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
    files = glob.glob(path)
    for j in files:
        ids.append(j.split('/')[-1].split('.')[0])
        op.append(data.index(i) + 1)
    #print(len(files))
    #print(len(onlyfiles))

output = []
for inputs, labels in test_dataloaders_dict['testing']:
    inputs = inputs.to(device)
    labels = labels.to(device)
    outputs = model_ft(inputs)
    _, predicted = torch.max(outputs, 1)
    for i in predicted:
        output.append(int(i)+1)
#output
sample_submission = pd.read_csv('../input/sampleSubmission.csv')
#ids = list(range(1, len(output)+1))
submission = pd.DataFrame({
    'Id' :ids,
    'Predicted' : output
}, columns= ['Id', 'Predicted'])
submission['Id']=submission['Id'].apply(lambda x: '{0:0>5}'.format(x))
submission.head()
submission.to_csv('sampleSubmission.csv',index=False)
#submission['Predicted'] = output
#submission['Id'] = list(range(0, len(output+1)))
#submission
from IPython.display import FileLink, FileLinks
FileLinks('.') #lists all downloadable files on server
with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also
    print(submission)