'''
Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
(Original author Wei Wu) by Antti-Pekka Hynninen

Implementing the original resnet ILSVRC 2015 winning network from:

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"
'''
import mxnet as mx
import numpy as np

def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False):
    """Return ResNet Unit symbol for building ResNet
    Parameters
    ----------
    data : str
        Input data
    num_filter : int
        Number of output channels
    bnf : int
        Bottle neck channels factor with regard to num_filter
    stride : tuple
        Stride used in convolution
    dim_match : Boolean
        True means channel number between input and output is the same, otherwise means differ
    name : str
        Base name of the operators
    workspace : int
        Workspace used in convolution operator
    """
    if bottle_neck:
        weight = mx.symbol.Variable(name=name + '_conv1_weight', dtype=np.float16)
        conv1 = mx.sym.Convolution(data=data, weight=weight, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),
                                   no_bias=True, workspace=workspace, name=name + '_conv1')
        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
        weight = mx.symbol.Variable(name=name + '_conv2_weight', dtype=np.float16)
        conv2 = mx.sym.Convolution(data=act1, weight=weight, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
                                   no_bias=True, workspace=workspace, name=name + '_conv2')
        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
        act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
        weight = mx.symbol.Variable(name=name + '_conv3_weight', dtype=np.float16)
        conv3 = mx.sym.Convolution(data=act2, weight=weight, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
                                   workspace=workspace, name=name + '_conv3')
        bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')

        if dim_match:
            shortcut = data
        else:
            weight = mx.symbol.Variable(name=name + '_conv1sc_weight', dtype=np.float16)
            conv1sc = mx.sym.Convolution(data=data, weight=weight, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
                                            workspace=workspace, name=name+'_conv1sc')
            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
        if memonger:
            shortcut._set_attr(mirror_stage='True')
        return mx.sym.Activation(data=bn3 + shortcut, act_type='relu', name=name + '_relu3')
    else:
        weight = mx.symbol.Variable(name=name + '_conv1_weight', dtype=np.float16)
        conv1 = mx.sym.Convolution(data=data, weight=weight, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv1')
        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
        weight = mx.symbol.Variable(name=name + '_conv2_weight', dtype=np.float16)
        conv2 = mx.sym.Convolution(data=act1, weight=weight, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv2')
        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')

        if dim_match:
            shortcut = data
        else:
            weight = mx.symbol.Variable(name=name + '_conv1sc_weight', dtype=np.float16)
            conv1sc = mx.sym.Convolution(data=data, weight=weight, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
                                            workspace=workspace, name=name+'_conv1sc')
            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
        if memonger:
            shortcut._set_attr(mirror_stage='True')
        return mx.sym.Activation(data=bn2 + shortcut, act_type='relu', name=name + '_relu3')

def resnet(units, num_stages, filter_list, num_classes, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False):
    """Return ResNet symbol of
    Parameters
    ----------
    units : list
        Number of units in each stage
    num_stages : int
        Number of stage
    filter_list : list
        Channel size of each stage
    num_classes : int
        Ouput size of symbol
    dataset : str
        Dataset type, only cifar10 and imagenet supports
    workspace : int
        Workspace used in convolution operator
    """
    num_unit = len(units)
    assert(num_unit == num_stages)
    data = mx.sym.Variable(name='data')
    data = mx.symbol.Cast(data=data, dtype=np.float16)
    (nchannel, height, width) = image_shape
    weight = mx.symbol.Variable(name='conv0_weight', dtype=np.float16)
    if height <= 32:            # such as cifar10
        body = mx.sym.Convolution(data=data, weight=weight, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1),
                                  no_bias=True, name="conv0", workspace=workspace)
        # Is this BatchNorm supposed to be here?
        body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
    else:                       # often expected to be 224 such as imagenet
        body = mx.sym.Convolution(data=data, weight=weight, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
                                  no_bias=True, name="conv0", workspace=workspace)
        body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
        body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
        body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')

    for i in range(num_stages):
        body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
                             name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, workspace=workspace,
                             memonger=memonger)
        for j in range(units[i]-1):
            body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2),
                                 bottle_neck=bottle_neck, workspace=workspace, memonger=memonger)
    # bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
    # relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
    # Although kernel is not used here when global_pool=True, we should put one
    pool1 = mx.symbol.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
    flat = mx.symbol.Flatten(data=pool1)
    weight = mx.symbol.Variable(name='fc1_weight', dtype=np.float16)
    bias = mx.symbol.Variable(name='fc1_bias', dtype=np.float16)
    fc1 = mx.symbol.FullyConnected(data=flat, weight=weight, bias=bias, num_hidden=num_classes, name='fc1')
    fc1 = mx.symbol.Cast(data=fc1, dtype=np.float32)
    return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')

def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, **kwargs):
    """
    Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
    (Original author Wei Wu) by Antti-Pekka Hynninen
    Implementing the original resnet ILSVRC 2015 winning network from:
    Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"
    """
    image_shape = [int(l) for l in image_shape.split(',')]
    (nchannel, height, width) = image_shape
    if height <= 28:
        num_stages = 3
        if (num_layers-2) % 9 == 0 and num_layers >= 164:
            per_unit = [(num_layers-2)//9]
            filter_list = [16, 64, 128, 256]
            bottle_neck = True
        elif (num_layers-2) % 6 == 0 and num_layers < 164:
            per_unit = [(num_layers-2)//6]
            filter_list = [16, 16, 32, 64]
            bottle_neck = False
        else:
            raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
        units = per_unit * num_stages
    else:
        if num_layers >= 50:
            filter_list = [64, 256, 512, 1024, 2048]
            bottle_neck = True
        else:
            filter_list = [64, 64, 128, 256, 512]
            bottle_neck = False
        num_stages = 4
        if num_layers == 18:
            units = [2, 2, 2, 2]
        elif num_layers == 34:
            units = [3, 4, 6, 3]
        elif num_layers == 50:
            units = [3, 4, 6, 3]
        elif num_layers == 101:
            units = [3, 4, 23, 3]
        elif num_layers == 152:
            units = [3, 8, 36, 3]
        elif num_layers == 200:
            units = [3, 24, 36, 3]
        elif num_layers == 269:
            units = [3, 30, 48, 8]
        else:
            raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))

    return resnet(units       = units,
                  num_stages  = num_stages,
                  filter_list = filter_list,
                  num_classes = num_classes,
                  image_shape = image_shape,
                  bottle_neck = bottle_neck,
                  workspace   = conv_workspace)