<reponame>madin162/AI604_project
import torch
import torch.nn as nn
from collections import OrderedDict
from PIL import Image
import numpy as np
import math




def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1):
    # helper selecting activation
    # neg_slope: for leakyrelu and init of prelu
    # n_prelu: for p_relu num_parameters
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU(inplace)
    elif act_type == 'leakyrelu':
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
    return layer


def norm(norm_type, nc):
    # helper selecting normalization layer
    norm_type = norm_type.lower()
    if norm_type == 'batch':
        layer = nn.BatchNorm2d(nc, affine=True)
    elif norm_type == 'instance':
        layer = nn.InstanceNorm2d(nc, affine=False)
    else:
        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
    return layer


def pad(pad_type, padding):
    # helper selecting padding layer
    # if padding is 'zero', do by conv layers
    pad_type = pad_type.lower()
    if padding == 0:
        return None
    if pad_type == 'reflect':
        layer = nn.ReflectionPad2d(padding)
    elif pad_type == 'replicate':
        layer = nn.ReplicationPad2d(padding)
    else:
        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
    return layer


def get_valid_padding(kernel_size, dilation):
    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
    padding = (kernel_size - 1) // 2
    return padding

def sequential(*args):
    # Flatten Sequential. It unwraps nn.Sequential.
    if len(args) == 1:
        if isinstance(args[0], OrderedDict):
            raise NotImplementedError('sequential does not support OrderedDict input.')
        return args[0]  # No sequential is needed.
    modules = []
    for module in args:
        if isinstance(module, nn.Sequential):
            for submodule in module.children():
                modules.append(submodule)
        elif isinstance(module, nn.Module):
            modules.append(module)
    return nn.Sequential(*modules)



def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True, \
               pad_type='zero', norm_type=None, act_type='relu', mode='CNA'):
    '''
    Conv layer with padding, normalization, activation
    mode: CNA --> Conv -> Norm -> Act
        NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
    '''
    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [{:s}]'.format(mode)
    padding = get_valid_padding(kernel_size, dilation)
    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
    padding = padding if pad_type == 'zero' else 0

    c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, \
            dilation=dilation, bias=bias, groups=groups)
    a = act(act_type) if act_type else None
    if 'CNA' in mode:
        n = norm(norm_type, out_nc) if norm_type else None
        return sequential(p, c, n, a)
    elif mode == 'NAC':
        if norm_type is None and act_type is not None:
            a = act(act_type, inplace=False)
            # Important!
            # input----ReLU(inplace)----Conv--+----output
            #        |________________________|
            # inplace ReLU will modify the input, therefore wrong output
        n = norm(norm_type, in_nc) if norm_type else None
        return sequential(n, a, p, c)


def upconv_blcok(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, \
                pad_type='zero', norm_type=None, act_type='relu', mode='nearest'):
    # Up conv
    # described in https://distill.pub/2016/deconv-checkerboard/
    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias, \
                        pad_type=pad_type, norm_type=norm_type, act_type=act_type)
    return sequential(upsample, conv)


class ShortcutBlock(nn.Module):
    #Elementwise sum the output of a submodule to its input
    def __init__(self, submodule):
        super(ShortcutBlock, self).__init__()
        self.sub = submodule

    def forward(self, x):
        output = x + self.sub(x)
        return output

    def __repr__(self):
        tmpstr = 'Identity + \n|'
        modstr = self.sub.__repr__().replace('\n', '\n|')
        tmpstr = tmpstr + modstr
        return tmpstr


class ResidualDenseBlock_5C(nn.Module):
    '''
    Residual Dense Block
    style: 5 convs
    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
    '''

    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
            norm_type=None, act_type='leakyrelu', mode='CNA'):
        super(ResidualDenseBlock_5C, self).__init__()
        # gc: growth channel, i.e. intermediate channels
        self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv4 = conv_block(nc+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        if mode == 'CNA':
            last_act = None
        else:
            last_act = act_type
        self.conv5 = conv_block(nc+4*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=last_act, mode=mode)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(torch.cat((x, x1), 1))
        x3 = self.conv3(torch.cat((x, x1, x2), 1))
        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5.mul(0.2) + x


class RRDB(nn.Module):
    '''
    Residual in Residual Dense Block
    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
    '''

    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
            norm_type=None, act_type='leakyrelu', mode='CNA'):
        super(RRDB, self).__init__()
        self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
            norm_type, act_type, mode)
        self.RDB2 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
            norm_type, act_type, mode)
        self.RDB3 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
            norm_type, act_type, mode)

    def forward(self, x):
        out = self.RDB1(x)
        out = self.RDB2(out)
        out = self.RDB3(out)
        return out.mul(0.2) + x


class RRDBNet(nn.Module):
    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, upscale=4, norm_type=None, \
            act_type='leakyrelu', mode='CNA', upsample_mode='upconv'):
        super(RRDBNet, self).__init__()
        n_upscale = int(math.log(upscale, 2))
        if upscale == 3:
            n_upscale = 1

        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
        rb_blocks = [RRDB(nf, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
            norm_type=norm_type, act_type=act_type, mode='CNA') for _ in range(nb)]
        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)

        if upsample_mode == 'upconv':
            upsample_block = upconv_blcok

        else:
            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
        if upscale == 3:
            upsampler = upsample_block(nf, nf, 3, act_type=act_type)
        else:
            upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)

        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),\
            *upsampler, HR_conv0, HR_conv1)

    def forward(self, x):
        x = self.model(x)
        return x



def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
    '''
    Converts a torch Tensor into an image Numpy array
    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
    '''
    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # clamp
    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
    n_dim = tensor.dim()
    if n_dim == 3:
        img_np = tensor.numpy()
        img_np = np.transpose(img_np[[0, 1, 2], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 2:
        img_np = tensor.numpy()
    else:
        raise TypeError(
            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
    if out_type == np.uint8:
        img_np = (img_np * 255.0).round()
        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
    return img_np.astype(out_type)

if __name__ == '__main__':


    # model_path = '/media/4T/Dizzy/BasicSR-master/Final_models/DSDG/4x/PSNR-oriented/Degradation_Resnet_psnr62.1_G.pth'
    model_path = '/media/4T/Dizzy/BasicSR-master/Final_models/New/ESRGAN/Final_models/AIM/Complete_LPIPS0.337_205000_G.pth'
    # location of pre-trained model
    # model = De_Resnet().cuda()
    model = RRDBNet().cuda()
    model.load_state_dict(torch.load(model_path))

    img = np.array(Image.open('/media/4T/Dizzy/AIM/AIM_datasets/ValidationSource/0801.png'))/255
    img = np.transpose(img, (2, 0, 1))
    img = np.reshape(img, [1]+list(img.shape))
    img = torch.FloatTensor(img).cuda()

    print(img.shape)
    with torch.no_grad():
        lr = model(img)
    print(lr.shape)
    # lr *= 255
    lr = tensor2img(lr)

    lr_pil = Image.fromarray(lr)
    lr_pil.show()


