import tensorflow as tf
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
from tensorflow.keras.layers import AveragePooling2D, Conv2D, BatchNormalization, Activation, Concatenate, UpSampling2D, Reshape, TimeDistributed, ConvLSTM2D
from tensorflow.keras import Model
import numpy as np
from tensorflow.keras.applications.resnet50 import ResNet50

gpus = tf.config.list_physical_devices('GPU')
if gpus:
  try:
    # Currently, memory growt*h needs to be the same across GPUs
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    # Memory growth must be set before GPUs have been initialized
    print(e)

# Function for normalizing image_size so that pixel intensity is between 0 and 1
def normalize(input_image, input_mask):
  input_image = tf.cast(input_image, tf.float32) / 255.0
  input_mask -= 1
  return input_image, input_mask

# Function for resizing the train images to the desired input shape of 128x128 as well as augmenting the training images
@tf.function
def load_image_train(datapoint):
  input_image = tf.image.resize(datapoint['image'], (128, 128))
  input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))

  if tf.random.uniform(()) > 0.5:
    input_image = tf.image.flip_left_right(input_image)
    input_mask = tf.image.flip_left_right(input_mask)

  input_image, input_mask = normalize(input_image, input_mask)
  input_mask = tf.math.round(input_mask)

  return input_image, input_mask

# Function for resizing the test images to the desired output shape (no augmenation)
def load_image_test(datapoint):
  input_image = tf.image.resize(datapoint['image'], (128, 128))
  input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))

  input_image, input_mask = normalize(input_image, input_mask)

  return input_image, input_mask

dataset, info = tfds.load('oxford_iiit_pet:3.*.*', with_info=True)

TRAIN_LENGTH = info.splits['train'].num_examples
BATCH_SIZE = 64
BUFFER_SIZE = 1000
STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE

train = dataset['train'].map(load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)
test = dataset['test'].map(load_image_test)

train_dataset = train.shuffle(BUFFER_SIZE).cache().batch(BATCH_SIZE).repeat()
train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
test_dataset = test.batch(BATCH_SIZE)

def display(display_list):
  plt.figure(figsize=(15, 15))

  title = ['Input Image', 'True Mask', 'Predicted Mask']

  for i in range(len(display_list)):
    plt.subplot(1, len(display_list), i+1)
    plt.title(title[i])
    plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
    plt.axis('off')
  plt.show()

for image, mask in train.take(1):
  sample_image, sample_mask = image, mask
display([sample_image, sample_mask])

def pool_block(cur_tensor: tf.Tensor,
               image_width: int,
               image_height: int,
               pooling_factor: int,
               activation: str = 'relu'
              ) -> tf.Tensor:
  """
  Parameters
  ----------
  cur_tensor : tf.Tensor
      Incoming tensor.

  image_width : int
      Width of the image.

  image_height : int
      Height of the image.

  pooling_factor : int
      Pooling factor to scale image.

  activation : str, default = 'relu'
      Activation function to be applied after the pooling operations.

  Returns
  -------
  tf.Tensor
      2D convolutional block.
  """
  
  #Calculate the strides with image size and pooling factor
  strides = [int(np.round(float(image_width)/pooling_factor)),
            int(np.round(float(image_height)/pooling_factor))]
  
  pooling_size = strides
  
  x = AveragePooling2D(pooling_size, strides=strides, padding='same')(cur_tensor)
  
  x = Conv2D(filters = 512, 
             kernel_size = (1,1),
             padding = 'same')(x)
  
  x = BatchNormalization()(x)
  x = Activation(activation)(x)
  
  # Resizing images to correct shape for future concat
  x = tf.keras.layers.experimental.preprocessing.Resizing(
    image_height, 
    image_width, 
    interpolation="bilinear")(x) 
  
  return x

def modify_ResNet_Dilation(
  model: tf.keras.Model
) -> tf.keras.Model:
  """
  Modifies the ResNet to fit the PSPNet Paper with the described dilated strategy.
  
    Parameters
    ----------
    model : tf.keras.Model
        The ResNet-50 model to be modified.
        
    Returns
    -------
    tf.keras.Model
        Modified model.
  """
  for i in range(0,4):
    model.get_layer('conv4_block1_{}_conv'.format(i)).strides = 1
    model.get_layer('conv4_block1_{}_conv'.format(i)).dilation_rate = 2
    model.get_layer('conv5_block1_{}_conv'.format(i)).strides = 1
    model.get_layer('conv5_block1_{}_conv'.format(i)).dilation_rate = 4
  model.save('/tmp/my_model')
  new_model = tf.keras.models.load_model('/tmp/my_model')
  return new_model

def PSPNet(image_width: int,
           image_height: int,
           n_classes: int,
           kernel_size: tuple = (3,3),
           activation: str = 'relu',
           weights: str = 'imagenet',
           shallow_tuning: bool = False,
           isICNet: bool = False
          ) -> tf.keras.Model:
  """
  Setting up the PSPNet model
  
    Parameters
    ----------
    image_width : int
        Width of the image.
        
    image_height : int
        Height of the image.
        
    n_classes : int
        Number of classes.
        
    kernel_size : tuple, default = (3, 3)
        Size of the kernel.    
        
    activation : str, default = 'relu'
        Activation function to be applied after the pooling operations.
        
    weights: str, default = 'imagenet'
        String defining which weights to use for the backbone, 'imagenet' for ImageNet weights or None for normalized initialization.
        
    shallow_tuning : bool, default = True
        Boolean for using shallow tuning with pre-trained weights from ImageNet or not.
        
    isICNet : bool,  default = False   
        Boolean to determine if the PSPNet will be part of an ICNet.
        
    Returns
    -------
    tf.keras.Model
        The finished keras model for PSPNet.
  """
  if shallow_tuning and not weights:
    raise ValueError("Shallow tuning can not be performed without loading pre-trained weights. Please input 'imagenet' to argument weights...")
  #If the function is used for the ICNet input_shape is set to none as ICNet takes 3 inputs
  if isICNet:
    input_shape=(None, None, 3)
  else:
    input_shape=(image_height,image_width,3)
    
  #Initializing the ResNet50 Backbone  
  y=ResNet50(include_top=False, weights=weights, input_shape=input_shape)

  y=modify_ResNet_Dilation(y)
  if shallow_tuning:
    y.trainable=False
  
  pooling_layer=[]
  output=y.output

  pooling_layer.append(output)
  
  h = image_height//8
  w = image_width//8
  
  #Loop for calling the pool block functions for pooling factors [1,2,3,6]
  for i in [1,2,3,6]:
    pool = pool_block(output, h, w, i, activation)
    pooling_layer.append(pool)
    
  x=Concatenate()(pooling_layer)
  
  x=Conv2D(filters=n_classes, kernel_size=(1,1), padding='same')(x)
  
  x=UpSampling2D(size=(8,8), data_format='channels_last', interpolation='bilinear')(x)
  x=Reshape((image_height*image_width, n_classes))(x)
  x=Activation(tf.nn.softmax)(x)
  x=Reshape((image_height,image_width,n_classes))(x)

  final_model=tf.keras.Model(inputs=y.input, outputs=x)
                              
  return final_model

model = PSPNet(image_height = 128, image_width = 128, n_classes = 3)

model.summary()

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(),
              metrics=['accuracy'])

def create_mask(pred_mask):
  pred_mask = tf.argmax(pred_mask, axis=-1)
  pred_mask = pred_mask[..., tf.newaxis]
  return pred_mask[0]

def show_predictions(dataset=None, num=1):
  if dataset:
    for image, mask in dataset.take(num):
      pred_mask = model.predict(image)
      display([image[0], mask[0], create_mask(pred_mask)])
  else:
    display([sample_image, sample_mask,
             create_mask(model.predict(sample_image[tf.newaxis, ...]))])
    
show_predictions()

class DisplayCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
    show_predictions()

EPOCHS = 50
VAL_SUBSPLITS = 5
VALIDATION_STEPS = info.splits['test'].num_examples//BATCH_SIZE//VAL_SUBSPLITS

model_history = model.fit(train_dataset, epochs=EPOCHS,
                          steps_per_epoch=STEPS_PER_EPOCH,
                          validation_steps=VALIDATION_STEPS,
                          validation_data=test_dataset,
                          callbacks=[DisplayCallback()])                    

show_predictions(test_dataset, num=10)

loss = model_history.history['loss']
val_loss = model_history.history['val_loss']
acc = model_history.history['accuracy']
val_acc = model_history.history['val_accuracy']

epochs = range(EPOCHS)

plt.figure(figsize=(10,5))
plt.subplot(1,2,1)
plt.plot(epochs, loss, 'r', label='Training loss')
plt.plot(epochs, val_loss, 'bo', label='Validation loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss Value')
plt.legend()
plt.subplot(1,2,2)
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'bo', label='Validation accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()