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import mxnet as mx 
from importlib import import_module
import cv2
import matplotlib.pyplot as plt 
import numpy as np


    

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# 1. data reading
# labels in order: 
"""
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck
"""

def get_cifar10():
    # TODO fill all the blanks 
    # Hint
    # rgb mean default: '123.68,116.779,103.939',
    # pad size: whatever you think is valid, pad to 32 is good 
    # image shape:  the image shape feed into the network, e.g. (3,224,224)')
    # num-classes: the number of classes
    # num-examples:  the number of training examples, 50000 :)
    # data-nthreads: default=4,number of threads for data decoding, how many cpus do you have
    # dtype: default is float32, can be data type: float32 or float16')
    train = mx.io.ImageRecordIter(
            path_imgrec         = ???,
            label_width         = ???,
            mean_r              = 123.68,
            mean_g              = 116.779,
            mean_b              = 103.939,
            data_name           = 'data',
            label_name          = 'softmax_label',
            data_shape          = ???,
            batch_size          = ???,
            pad                 = ???,
            fill_value          = ???,
            preprocess_threads  = ???,
            shuffle             = ???)
    val = mx.io.ImageRecordIter(
            path_imgrec         = ???,
            label_width         = ???,
            mean_r              = 123.68,
            mean_g              = 116.779,
            mean_b              = 103.939,
            data_name           = 'data',
            label_name          = 'softmax_label',
            data_shape          = ???,
            batch_size          = ???,
            pad                 = ???,
            fill_value          = ???,
            preprocess_threads  = ???,
            shuffle             = ???)
    return train, val

train, val = get_cifar10()
# TODO write the script to look what is inside train and val
# Check the image size, and label 
# Display the image 
# Question? check MXNET_course/mxnet-week3/cifar10/step_by_step_debug.ipynb
print (train, val)
???
???
???


    

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# 2 model getting 
# TODO read through resnet.py file for understanding
def get_resnet():
    net = import_module('resnet')
    sym = net.get_symbol(10,20,"3,28,28")
    return sym 
sym = get_resnet()

# TODO 
# 1. Plot and visualize the network. Put your comments about its architecture (why bottlenet)
# 2. List all weight and output (Question? check MXNET_course/mxnet-week3/cifar10/step_by_step_debug.ipynb)
??? # Hint: check mx.viz.plot


    

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# 3 sanity check random image inference
img1 = cv2.imread("frog.jpg")
img1 = cv2.resize(img1,(28,28))# you need to pad it if you do padding for you nework
plt.imshow(img1)
plt.show()
print type(img1)
img1 = img1.transpose((2,0,1)).reshape((1,3,28,28))
print img1.shape

img2 = cv2.imread("frog2.jpg")
img2 = cv2.resize(img2,(28,28))# you need to pad it if you do padding for you nework
plt.imshow(img2)
plt.show()
print type(img2)
img2 = img2.transpose((2,0,1)).reshape((1,3,28,28))
print img2.shape

img = np.vstack([img1,img2])
print "The very small training dataset contain: ", img.shape

# TODO: figure out how to convert numpy array to mx.nd.array
img_mxnd = mx.nd.array(???) 
label_mxnd = mx.nd.array(???) # 6 is frog, give 6 as label
img_itr = mx.io.NDArrayIter(data=???, data_name='data', 
                            label_name='softmax_label', label=???, 
                            batch_size=???) # ??? Hint the name should "data" and softmax_label
print "small dataset is: ", type(img_itr)
for each in img_itr:
    print each

# TODO bind the random img to network 
# question? check mxnet-week3/cifar10/train_cifar10.py
mod = mx.mod.Module(???) 
mod.bind(???)
mod.init_params(???) 

# run forward perdiction 
# TODO fill the mod.predict 
# check mod.predict
out = mod.predict(???)
print out.asnumpy()


    

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print np.argmax(out.asnumpy(),axis=1)


    

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# 4 overfit small dataset 
# TODO fill all ???
mod.init_optimizer(???) 
# run forward perdiction
metric = mx.metric.create('acc')

for epoch in range(5):
    img_itr.reset()
    metric.reset()
    for batch in img_itr:
        mod.forward(batch, is_train=True)
        mod.update_metric(metric, batch.label)
        mod.backward()
        mod.update()
    print ('Epoch {}, Train {}'.format(epoch, metric.get()))
    # You should get 100% accuacy on these two images


    

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#5 (optional) train cifar10 on resnet(~epoch) if you have GPU. Build the training script from week2
mod_cifar = mx.mod.Module(???)
train.reset()
val.reset()
mod_cifar.fit(train_data=train,
              eval_data=val,
              num_epoch=???,
              optimizer=???, 
              optimizer_params={???}, 
              epoch_end_callback=mx.callback.do_checkpoint('resnet_cifar_10'),
              initializer=mx.init.Xavier(rnd_type='gaussian', factor_type='in',magnitude=2),
              batch_end_callback=[mx.callback.Speedometer(128, 1)], 
              eval_metric=['acc','ce'])


    

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# 6 (optional) Wild test on your model


    

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