diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..bc42bce204a063b6389a6383218e354efd2409de 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoints/pretrained.state filter=lfs diff=lfs merge=lfs -text
+examples/kennedy_hd.mkv filter=lfs diff=lfs merge=lfs -text
+examples/mona_hd.jpg filter=lfs diff=lfs merge=lfs -text
+examples/mona_hd.mkv filter=lfs diff=lfs merge=lfs -text
+input_audios/part_000_RVC_1.wav filter=lfs diff=lfs merge=lfs -text
+input_videos/MC.mp4 filter=lfs diff=lfs merge=lfs -text
+output_videos_hd/kennedy.mkv filter=lfs diff=lfs merge=lfs -text
+output_videos_hd/mona.mkv filter=lfs diff=lfs merge=lfs -text
+temp/result.avi filter=lfs diff=lfs merge=lfs -text
+temp/temp.wav filter=lfs diff=lfs merge=lfs -text
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..369998379bc763a60255d30cd076a3f14fb65899
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,23 @@
+MIT License
+
+Copyright (c) 2023 Saif Hassan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+NOTE: PLEASE READ LICENSE REQUIREMENTS (COPYRIGHTS INFORMATION) FROM `WAV2LIP OFFICIAL REPO`, MENTIONED ON MAIN PAGE OF THIS REPOSITORY.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..951ece0691b2c0d599dc9959cd57df018e9cddad
--- /dev/null
+++ b/README.md
@@ -0,0 +1,121 @@
+# Wav2Lip-HD: Improving Wav2Lip to achieve High-Fidelity Videos
+
+This repository contains code for achieving high-fidelity lip-syncing in videos, using the [Wav2Lip algorithm](https://github.com/Rudrabha/Wav2Lip) for lip-syncing and the [Real-ESRGAN algorithm](https://github.com/xinntao/Real-ESRGAN) for super-resolution. The combination of these two algorithms allows for the creation of lip-synced videos that are both highly accurate and visually stunning.
+
+## Algorithm
+
+The algorithm for achieving high-fidelity lip-syncing with Wav2Lip and Real-ESRGAN can be summarized as follows:
+
+1. The input video and audio are given to `Wav2Lip` algorithm.
+2. Python script is written to extract frames from the video generated by wav2lip.
+3. Frames are provided to Real-ESRGAN algorithm to improve quality.
+4. Then, the high-quality frames are converted to video using ffmpeg, along with the original audio.
+5. The result is a high-quality lip-syncing video.
+6. The specific steps for running this algorithm are described in the [Testing Model](https://github.com/saifhassan/Wav2Lip-HD#testing-model) section of this README.
+
+## Testing Model
+
+To test the "Wav2Lip-HD" model, follow these steps:
+
+1. Clone this repository and install requirements using following command (Make sure, Python and CUDA are already installed):
+
+    ```
+    git clone https://github.com/saifhassan/Wav2Lip-HD.git
+    cd Wav2Lip-HD
+    pip install -r requirements.txt
+    ```
+    
+2. Downloading weights
+
+| Model        | Directory           | Download Link  |
+| :------------- |:-------------| :-----:|
+| Wav2Lip           | [checkpoints/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/checkpoints)   | [Link](https://drive.google.com/drive/folders/1tB_uz-TYMePRMZzrDMdShWUZZ0JK3SIZ?usp=sharing) |
+| ESRGAN            | [experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models) | [Link](https://drive.google.com/file/d/1Al8lEpnx2K-kDX7zL2DBcAuDnSKXACPb/view?usp=sharing) |
+| Face_Detection    | [face_detection/detection/sfd/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/face_detection/detection/sfd) | [Link](https://drive.google.com/file/d/1uNLYCPFFmO-og3WSHyFytJQLLYOwH5uY/view?usp=sharing) |
+| Real-ESRGAN       | Real-ESRGAN/gfpgan/weights/   | [Link](https://drive.google.com/drive/folders/1BLx6aMpHgFt41fJ27_cRmT8bt53kVAYG?usp=sharing) |
+| Real-ESRGAN       | Real-ESRGAN/weights/          | [Link](https://drive.google.com/file/d/1qNIf8cJl_dQo3ivelPJVWFkApyEAGnLi/view?usp=sharing) |
+
+
+3. Put input video to `input_videos` directory and input audio to `input_audios` directory.
+4. Open `run_final.sh` file and modify following parameters:
+ 
+     `filename=kennedy` (just video file name without extension)
+     
+     `input_audio=input_audios/ai.wav` (audio filename with extension)
+
+5. Execute `run_final.sh` using following command:
+
+    ```
+    bash run_final.sh
+    ```
+    
+6. Outputs
+
+- `output_videos_wav2lip` directory contains video output generated by wav2lip algorithm.
+- `frames_wav2lip` directory contains frames extracted from video (generated by wav2lip algorithm).
+- `frames_hd` directory contains frames after performing super-resolution using Real-ESRGAN algorithm.
+- `output_videos_hd` directory contains final high quality video output generated by Wav2Lip-HD.
+
+
+## Results
+The results produced by Wav2Lip-HD are in two forms, one is frames and other is videos. Both are shared below:
+
+### Example output frames </summary>
+<table>
+  <tr>
+    <td>Frame by Wav2Lip</td>
+     <td>Optimized Frame</td>
+  </tr>
+  <tr>
+    <td><img src="examples/1_low.jpg" width=500></td>
+    <td><img src="examples/1_hd.jpg" width=500></td>
+  </tr>
+    <tr>
+    <td><img src="examples/kennedy_low.jpg" width=500></td>
+    <td><img src="examples/kennedy_hd.jpg" width=500></td>
+  </tr>
+
+  </tr>
+    <tr>
+    <td><img src="examples/mona_low.jpg" width=500></td>
+    <td><img src="examples/mona_hd.jpg" width=500></td>
+  </tr>
+ </table>
+ </Details>
+
+ ### Example output videos
+    
+ | Video by Wav2Lip  | Optimized Video |
+| ------------- | ------------- |
+| <video src="https://user-images.githubusercontent.com/11873763/229389410-56d96244-8c67-4add-a43e-a4900aa9db88.mp4" width="500">  | <video src="https://user-images.githubusercontent.com/11873763/229389414-d5cb6d33-7772-47a7-b829-9e3d5c3945a1.mp4" width="500">|
+| <video src="https://user-images.githubusercontent.com/11873763/229389751-507669f1-7772-4863-ab23-8df7f206a065.mp4" width="500">  | <video src="https://user-images.githubusercontent.com/11873763/229389962-5373b765-ce3a-4af2-bd6a-8be8543ee933.mp4" width="500">|
+
+## Acknowledgements
+
+We would like to thank the following repositories and libraries for their contributions to our work:
+
+1. The [Wav2Lip](https://github.com/Rudrabha/Wav2Lip) repository, which is the core model of our algorithm that performs lip-sync.
+2. The [face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) repository, which provides us with a model for face segmentation.
+3. The [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repository, which provides the super resolution component for our algorithm.
+4. [ffmpeg](https://ffmpeg.org), which we use for converting frames to video.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    
+
+
diff --git a/__pycache__/audio.cpython-36.pyc b/__pycache__/audio.cpython-36.pyc
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diff --git a/audio.py b/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..32b20c449df8c23548f0d1eedc64942a29f01448
--- /dev/null
+++ b/audio.py
@@ -0,0 +1,136 @@
+import librosa
+import librosa.filters
+import numpy as np
+# import tensorflow as tf
+from scipy import signal
+from scipy.io import wavfile
+from hparams import hparams as hp
+
+def load_wav(path, sr):
+    return librosa.core.load(path, sr=sr)[0]
+
+def save_wav(wav, path, sr):
+    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
+    #proposed by @dsmiller
+    wavfile.write(path, sr, wav.astype(np.int16))
+
+def save_wavenet_wav(wav, path, sr):
+    librosa.output.write_wav(path, wav, sr=sr)
+
+def preemphasis(wav, k, preemphasize=True):
+    if preemphasize:
+        return signal.lfilter([1, -k], [1], wav)
+    return wav
+
+def inv_preemphasis(wav, k, inv_preemphasize=True):
+    if inv_preemphasize:
+        return signal.lfilter([1], [1, -k], wav)
+    return wav
+
+def get_hop_size():
+    hop_size = hp.hop_size
+    if hop_size is None:
+        assert hp.frame_shift_ms is not None
+        hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
+    return hop_size
+
+def linearspectrogram(wav):
+    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
+    S = _amp_to_db(np.abs(D)) - hp.ref_level_db
+    
+    if hp.signal_normalization:
+        return _normalize(S)
+    return S
+
+def melspectrogram(wav):
+    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
+    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
+    
+    if hp.signal_normalization:
+        return _normalize(S)
+    return S
+
+def _lws_processor():
+    import lws
+    return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
+
+def _stft(y):
+    if hp.use_lws:
+        return _lws_processor(hp).stft(y).T
+    else:
+        return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
+
+##########################################################
+#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
+def num_frames(length, fsize, fshift):
+    """Compute number of time frames of spectrogram
+    """
+    pad = (fsize - fshift)
+    if length % fshift == 0:
+        M = (length + pad * 2 - fsize) // fshift + 1
+    else:
+        M = (length + pad * 2 - fsize) // fshift + 2
+    return M
+
+
+def pad_lr(x, fsize, fshift):
+    """Compute left and right padding
+    """
+    M = num_frames(len(x), fsize, fshift)
+    pad = (fsize - fshift)
+    T = len(x) + 2 * pad
+    r = (M - 1) * fshift + fsize - T
+    return pad, pad + r
+##########################################################
+#Librosa correct padding
+def librosa_pad_lr(x, fsize, fshift):
+    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
+
+# Conversions
+_mel_basis = None
+
+def _linear_to_mel(spectogram):
+    global _mel_basis
+    if _mel_basis is None:
+        _mel_basis = _build_mel_basis()
+    return np.dot(_mel_basis, spectogram)
+
+def _build_mel_basis():
+    assert hp.fmax <= hp.sample_rate // 2
+    return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels,
+                               fmin=hp.fmin, fmax=hp.fmax)
+
+def _amp_to_db(x):
+    min_level = np.exp(hp.min_level_db / 20 * np.log(10))
+    return 20 * np.log10(np.maximum(min_level, x))
+
+def _db_to_amp(x):
+    return np.power(10.0, (x) * 0.05)
+
+def _normalize(S):
+    if hp.allow_clipping_in_normalization:
+        if hp.symmetric_mels:
+            return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
+                           -hp.max_abs_value, hp.max_abs_value)
+        else:
+            return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
+    
+    assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
+    if hp.symmetric_mels:
+        return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
+    else:
+        return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
+
+def _denormalize(D):
+    if hp.allow_clipping_in_normalization:
+        if hp.symmetric_mels:
+            return (((np.clip(D, -hp.max_abs_value,
+                              hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
+                    + hp.min_level_db)
+        else:
+            return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
+    
+    if hp.symmetric_mels:
+        return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
+    else:
+        return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
diff --git a/basicsr/README.md b/basicsr/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..21f3e115d7720bd70c2674b546f7a1a9f7302461
--- /dev/null
+++ b/basicsr/README.md
@@ -0,0 +1 @@
+This folder is almost unmodified code from [BasicSR](https://github.com/xinntao/BasicSR.git) repository that provides solutions for image and video super resolution.
\ No newline at end of file
diff --git a/basicsr/__init__.py b/basicsr/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..28437544a254656cca7fb7021ef7bbf724cf2879
--- /dev/null
+++ b/basicsr/__init__.py
@@ -0,0 +1,12 @@
+# https://github.com/xinntao/BasicSR
+# flake8: noqa
+from .archs import *
+from .data import *
+from .losses import *
+from .metrics import *
+from .models import *
+from .ops import *
+from .test import *
+from .train import *
+from .utils import *
+# from .version import __gitsha__, __version__
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diff --git a/basicsr/apply_sr.py b/basicsr/apply_sr.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bc5da02c5ca700228af406fd6185aa29de656b9
--- /dev/null
+++ b/basicsr/apply_sr.py
@@ -0,0 +1,25 @@
+import cv2
+import numpy as np
+import torch
+
+from basicsr.archs.rrdbnet_arch import RRDBNet
+
+
+def init_sr_model(model_path):
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
+    model.load_state_dict(torch.load(model_path)['params'], strict=True)
+    model.eval()
+    model = model.cuda()
+    return model
+
+
+def enhance(model, image):
+    img = image.astype(np.float32) / 255.
+    img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
+    img = img.unsqueeze(0).cuda()
+    with torch.no_grad():
+        output = model(img)
+    output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
+    output = (output * 255.0).round().astype(np.uint8)
+    return output
diff --git a/basicsr/archs/__init__.py b/basicsr/archs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfb1e4d7bb221c429082bd389d9140e5b1cc07b0
--- /dev/null
+++ b/basicsr/archs/__init__.py
@@ -0,0 +1,25 @@
+import importlib
+from copy import deepcopy
+from os import path as osp
+
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.registry import ARCH_REGISTRY
+
+__all__ = ['build_network']
+
+# automatically scan and import arch modules for registry
+# scan all the files under the 'archs' folder and collect files ending with
+# '_arch.py'
+arch_folder = osp.dirname(osp.abspath(__file__))
+arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
+# import all the arch modules
+_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]
+
+
+def build_network(opt):
+    opt = deepcopy(opt)
+    network_type = opt.pop('type')
+    net = ARCH_REGISTRY.get(network_type)(**opt)
+    logger = get_root_logger()
+    logger.info(f'Network [{net.__class__.__name__}] is created.')
+    return net
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diff --git a/basicsr/archs/arch_util.py b/basicsr/archs/arch_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cf00720b312ba96112380b750be7518c46cf3fc
--- /dev/null
+++ b/basicsr/archs/arch_util.py
@@ -0,0 +1,227 @@
+import math
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn import init as init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
+from basicsr.utils import get_root_logger
+
+
+@torch.no_grad()
+def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
+    """Initialize network weights.
+
+    Args:
+        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
+        scale (float): Scale initialized weights, especially for residual
+            blocks. Default: 1.
+        bias_fill (float): The value to fill bias. Default: 0
+        kwargs (dict): Other arguments for initialization function.
+    """
+    if not isinstance(module_list, list):
+        module_list = [module_list]
+    for module in module_list:
+        for m in module.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, _BatchNorm):
+                init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    """Make layers by stacking the same blocks.
+
+    Args:
+        basic_block (nn.module): nn.module class for basic block.
+        num_basic_block (int): number of blocks.
+
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+class ResidualBlockNoBN(nn.Module):
+    """Residual block without BN.
+
+    It has a style of:
+        ---Conv-ReLU-Conv-+-
+         |________________|
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+            Default: 64.
+        res_scale (float): Residual scale. Default: 1.
+        pytorch_init (bool): If set to True, use pytorch default init,
+            otherwise, use default_init_weights. Default: False.
+    """
+
+    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
+        super(ResidualBlockNoBN, self).__init__()
+        self.res_scale = res_scale
+        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
+        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
+        self.relu = nn.ReLU(inplace=True)
+
+        if not pytorch_init:
+            default_init_weights([self.conv1, self.conv2], 0.1)
+
+    def forward(self, x):
+        identity = x
+        out = self.conv2(self.relu(self.conv1(x)))
+        return identity + out * self.res_scale
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
+    """Warp an image or feature map with optical flow.
+
+    Args:
+        x (Tensor): Tensor with size (n, c, h, w).
+        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
+        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
+        padding_mode (str): 'zeros' or 'border' or 'reflection'.
+            Default: 'zeros'.
+        align_corners (bool): Before pytorch 1.3, the default value is
+            align_corners=True. After pytorch 1.3, the default value is
+            align_corners=False. Here, we use the True as default.
+
+    Returns:
+        Tensor: Warped image or feature map.
+    """
+    assert x.size()[-2:] == flow.size()[1:3]
+    _, _, h, w = x.size()
+    # create mesh grid
+    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
+    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
+    grid.requires_grad = False
+
+    vgrid = grid + flow
+    # scale grid to [-1,1]
+    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
+    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
+    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
+    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
+
+    # TODO, what if align_corners=False
+    return output
+
+
+def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
+    """Resize a flow according to ratio or shape.
+
+    Args:
+        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
+        size_type (str): 'ratio' or 'shape'.
+        sizes (list[int | float]): the ratio for resizing or the final output
+            shape.
+            1) The order of ratio should be [ratio_h, ratio_w]. For
+            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
+            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
+            ratio > 1.0).
+            2) The order of output_size should be [out_h, out_w].
+        interp_mode (str): The mode of interpolation for resizing.
+            Default: 'bilinear'.
+        align_corners (bool): Whether align corners. Default: False.
+
+    Returns:
+        Tensor: Resized flow.
+    """
+    _, _, flow_h, flow_w = flow.size()
+    if size_type == 'ratio':
+        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
+    elif size_type == 'shape':
+        output_h, output_w = sizes[0], sizes[1]
+    else:
+        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
+
+    input_flow = flow.clone()
+    ratio_h = output_h / flow_h
+    ratio_w = output_w / flow_w
+    input_flow[:, 0, :, :] *= ratio_w
+    input_flow[:, 1, :, :] *= ratio_h
+    resized_flow = F.interpolate(
+        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
+    return resized_flow
+
+
+# TODO: may write a cpp file
+def pixel_unshuffle(x, scale):
+    """ Pixel unshuffle.
+
+    Args:
+        x (Tensor): Input feature with shape (b, c, hh, hw).
+        scale (int): Downsample ratio.
+
+    Returns:
+        Tensor: the pixel unshuffled feature.
+    """
+    b, c, hh, hw = x.size()
+    out_channel = c * (scale**2)
+    assert hh % scale == 0 and hw % scale == 0
+    h = hh // scale
+    w = hw // scale
+    x_view = x.view(b, c, h, scale, w, scale)
+    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+
+
+class DCNv2Pack(ModulatedDeformConvPack):
+    """Modulated deformable conv for deformable alignment.
+
+    Different from the official DCNv2Pack, which generates offsets and masks
+    from the preceding features, this DCNv2Pack takes another different
+    features to generate offsets and masks.
+
+    Ref:
+        Delving Deep into Deformable Alignment in Video Super-Resolution.
+    """
+
+    def forward(self, x, feat):
+        out = self.conv_offset(feat)
+        o1, o2, mask = torch.chunk(out, 3, dim=1)
+        offset = torch.cat((o1, o2), dim=1)
+        mask = torch.sigmoid(mask)
+
+        offset_absmean = torch.mean(torch.abs(offset))
+        if offset_absmean > 50:
+            logger = get_root_logger()
+            logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')
+
+        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
+                                     self.groups, self.deformable_groups)
diff --git a/basicsr/archs/dfdnet_arch.py b/basicsr/archs/dfdnet_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fc238f0f85d081a7e353bb54ad10f86ef92a6b9
--- /dev/null
+++ b/basicsr/archs/dfdnet_arch.py
@@ -0,0 +1,158 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.spectral_norm as SpectralNorm
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization
+from .vgg_arch import VGGFeatureExtractor
+
+
+class SFTUpBlock(nn.Module):
+    """Spatial feature transform (SFT) with upsampling block."""
+
+    def __init__(self, in_channel, out_channel, kernel_size=3, padding=1):
+        super(SFTUpBlock, self).__init__()
+        self.conv1 = nn.Sequential(
+            Blur(in_channel),
+            SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
+            nn.LeakyReLU(0.04, True),
+            # The official codes use two LeakyReLU here, so 0.04 for equivalent
+        )
+        self.convup = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+            SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        # for SFT scale and shift
+        self.scale_block = nn.Sequential(
+            SpectralNorm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+            SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)))
+        self.shift_block = nn.Sequential(
+            SpectralNorm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+            SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid())
+        # The official codes use sigmoid for shift block, do not know why
+
+    def forward(self, x, updated_feat):
+        out = self.conv1(x)
+        # SFT
+        scale = self.scale_block(updated_feat)
+        shift = self.shift_block(updated_feat)
+        out = out * scale + shift
+        # upsample
+        out = self.convup(out)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class DFDNet(nn.Module):
+    """DFDNet: Deep Face Dictionary Network.
+
+    It only processes faces with 512x512 size.
+    """
+
+    def __init__(self, num_feat, dict_path):
+        super().__init__()
+        self.parts = ['left_eye', 'right_eye', 'nose', 'mouth']
+        # part_sizes: [80, 80, 50, 110]
+        channel_sizes = [128, 256, 512, 512]
+        self.feature_sizes = np.array([256, 128, 64, 32])
+        self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4']
+        self.flag_dict_device = False
+
+        # dict
+        self.dict = torch.load(dict_path)
+
+        # vgg face extractor
+        self.vgg_extractor = VGGFeatureExtractor(
+            layer_name_list=self.vgg_layers,
+            vgg_type='vgg19',
+            use_input_norm=True,
+            range_norm=True,
+            requires_grad=False)
+
+        # attention block for fusing dictionary features and input features
+        self.attn_blocks = nn.ModuleDict()
+        for idx, feat_size in enumerate(self.feature_sizes):
+            for name in self.parts:
+                self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx])
+
+        # multi scale dilation block
+        self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1])
+
+        # upsampling and reconstruction
+        self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8)
+        self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4)
+        self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2)
+        self.upsample3 = SFTUpBlock(num_feat * 2, num_feat)
+        self.upsample4 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat),
+            UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh())
+
+    def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size):
+        """swap the features from the dictionary."""
+        # get the original vgg features
+        part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone()
+        # resize original vgg features
+        part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False)
+        # use adaptive instance normalization to adjust color and illuminations
+        dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat)
+        # get similarity scores
+        similarity_score = F.conv2d(part_resize_feat, dict_feat)
+        similarity_score = F.softmax(similarity_score.view(-1), dim=0)
+        # select the most similar features in the dict (after norm)
+        select_idx = torch.argmax(similarity_score)
+        swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4])
+        # attention
+        attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat)
+        attn_feat = attn * swap_feat
+        # update features
+        updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat
+        return updated_feat
+
+    def put_dict_to_device(self, x):
+        if self.flag_dict_device is False:
+            for k, v in self.dict.items():
+                for kk, vv in v.items():
+                    self.dict[k][kk] = vv.to(x)
+            self.flag_dict_device = True
+
+    def forward(self, x, part_locations):
+        """
+        Now only support testing with batch size = 0.
+
+        Args:
+            x (Tensor): Input faces with shape (b, c, 512, 512).
+            part_locations (list[Tensor]): Part locations.
+        """
+        self.put_dict_to_device(x)
+        # extract vggface features
+        vgg_features = self.vgg_extractor(x)
+        # update vggface features using the dictionary for each part
+        updated_vgg_features = []
+        batch = 0  # only supports testing with batch size = 0
+        for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes):
+            dict_features = self.dict[f'{f_size}']
+            vgg_feat = vgg_features[vgg_layer]
+            updated_feat = vgg_feat.clone()
+
+            # swap features from dictionary
+            for part_idx, part_name in enumerate(self.parts):
+                location = (part_locations[part_idx][batch] // (512 / f_size)).int()
+                updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name,
+                                              f_size)
+
+            updated_vgg_features.append(updated_feat)
+
+        vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4'])
+        # use updated vgg features to modulate the upsampled features with
+        # SFT (Spatial Feature Transform) scaling and shifting manner.
+        upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3])
+        upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2])
+        upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1])
+        upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0])
+        out = self.upsample4(upsampled_feat)
+
+        return out
diff --git a/basicsr/archs/dfdnet_util.py b/basicsr/archs/dfdnet_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f5991f369043e240f4af32e8aefbc97625567e5
--- /dev/null
+++ b/basicsr/archs/dfdnet_util.py
@@ -0,0 +1,162 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.spectral_norm as SpectralNorm
+from torch.autograd import Function
+
+
+class BlurFunctionBackward(Function):
+
+    @staticmethod
+    def forward(ctx, grad_output, kernel, kernel_flip):
+        ctx.save_for_backward(kernel, kernel_flip)
+        grad_input = F.conv2d(grad_output, kernel_flip, padding=1, groups=grad_output.shape[1])
+        return grad_input
+
+    @staticmethod
+    def backward(ctx, gradgrad_output):
+        kernel, kernel_flip = ctx.saved_tensors
+        grad_input = F.conv2d(gradgrad_output, kernel, padding=1, groups=gradgrad_output.shape[1])
+        return grad_input, None, None
+
+
+class BlurFunction(Function):
+
+    @staticmethod
+    def forward(ctx, x, kernel, kernel_flip):
+        ctx.save_for_backward(kernel, kernel_flip)
+        output = F.conv2d(x, kernel, padding=1, groups=x.shape[1])
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        kernel, kernel_flip = ctx.saved_tensors
+        grad_input = BlurFunctionBackward.apply(grad_output, kernel, kernel_flip)
+        return grad_input, None, None
+
+
+blur = BlurFunction.apply
+
+
+class Blur(nn.Module):
+
+    def __init__(self, channel):
+        super().__init__()
+        kernel = torch.tensor([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=torch.float32)
+        kernel = kernel.view(1, 1, 3, 3)
+        kernel = kernel / kernel.sum()
+        kernel_flip = torch.flip(kernel, [2, 3])
+
+        self.kernel = kernel.repeat(channel, 1, 1, 1)
+        self.kernel_flip = kernel_flip.repeat(channel, 1, 1, 1)
+
+    def forward(self, x):
+        return blur(x, self.kernel.type_as(x), self.kernel_flip.type_as(x))
+
+
+def calc_mean_std(feat, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    n, c = size[:2]
+    feat_var = feat.view(n, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(n, c, 1, 1)
+    feat_mean = feat.view(n, c, -1).mean(dim=2).view(n, c, 1, 1)
+    return feat_mean, feat_std
+
+
+def adaptive_instance_normalization(content_feat, style_feat):
+    """Adaptive instance normalization.
+
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+def AttentionBlock(in_channel):
+    return nn.Sequential(
+        SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+        SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)))
+
+
+def conv_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=True):
+    """Conv block used in MSDilationBlock."""
+
+    return nn.Sequential(
+        SpectralNorm(
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=((kernel_size - 1) // 2) * dilation,
+                bias=bias)),
+        nn.LeakyReLU(0.2),
+        SpectralNorm(
+            nn.Conv2d(
+                out_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=((kernel_size - 1) // 2) * dilation,
+                bias=bias)),
+    )
+
+
+class MSDilationBlock(nn.Module):
+    """Multi-scale dilation block."""
+
+    def __init__(self, in_channels, kernel_size=3, dilation=(1, 1, 1, 1), bias=True):
+        super(MSDilationBlock, self).__init__()
+
+        self.conv_blocks = nn.ModuleList()
+        for i in range(4):
+            self.conv_blocks.append(conv_block(in_channels, in_channels, kernel_size, dilation=dilation[i], bias=bias))
+        self.conv_fusion = SpectralNorm(
+            nn.Conv2d(
+                in_channels * 4,
+                in_channels,
+                kernel_size=kernel_size,
+                stride=1,
+                padding=(kernel_size - 1) // 2,
+                bias=bias))
+
+    def forward(self, x):
+        out = []
+        for i in range(4):
+            out.append(self.conv_blocks[i](x))
+        out = torch.cat(out, 1)
+        out = self.conv_fusion(out) + x
+        return out
+
+
+class UpResBlock(nn.Module):
+
+    def __init__(self, in_channel):
+        super(UpResBlock, self).__init__()
+        self.body = nn.Sequential(
+            nn.Conv2d(in_channel, in_channel, 3, 1, 1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(in_channel, in_channel, 3, 1, 1),
+        )
+
+    def forward(self, x):
+        out = x + self.body(x)
+        return out
diff --git a/basicsr/archs/discriminator_arch.py b/basicsr/archs/discriminator_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dfbcd5e36c9d9e9e530ee1556ab4ecbaa5f3c08
--- /dev/null
+++ b/basicsr/archs/discriminator_arch.py
@@ -0,0 +1,73 @@
+from torch import nn as nn
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+@ARCH_REGISTRY.register()
+class VGGStyleDiscriminator128(nn.Module):
+    """VGG style discriminator with input size 128 x 128.
+
+    It is used to train SRGAN and ESRGAN.
+
+    Args:
+        num_in_ch (int): Channel number of inputs. Default: 3.
+        num_feat (int): Channel number of base intermediate features.
+            Default: 64.
+    """
+
+    def __init__(self, num_in_ch, num_feat):
+        super(VGGStyleDiscriminator128, self).__init__()
+
+        self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True)
+        self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False)
+        self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True)
+
+        self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False)
+        self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True)
+        self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False)
+        self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True)
+
+        self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False)
+        self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True)
+        self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False)
+        self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True)
+
+        self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False)
+        self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
+        self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
+        self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
+
+        self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False)
+        self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
+        self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
+        self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
+
+        self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100)
+        self.linear2 = nn.Linear(100, 1)
+
+        # activation function
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        assert x.size(2) == 128 and x.size(3) == 128, (f'Input spatial size must be 128x128, '
+                                                       f'but received {x.size()}.')
+
+        feat = self.lrelu(self.conv0_0(x))
+        feat = self.lrelu(self.bn0_1(self.conv0_1(feat)))  # output spatial size: (64, 64)
+
+        feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))
+        feat = self.lrelu(self.bn1_1(self.conv1_1(feat)))  # output spatial size: (32, 32)
+
+        feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))
+        feat = self.lrelu(self.bn2_1(self.conv2_1(feat)))  # output spatial size: (16, 16)
+
+        feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))
+        feat = self.lrelu(self.bn3_1(self.conv3_1(feat)))  # output spatial size: (8, 8)
+
+        feat = self.lrelu(self.bn4_0(self.conv4_0(feat)))
+        feat = self.lrelu(self.bn4_1(self.conv4_1(feat)))  # output spatial size: (4, 4)
+
+        feat = feat.view(feat.size(0), -1)
+        feat = self.lrelu(self.linear1(feat))
+        out = self.linear2(feat)
+        return out
diff --git a/basicsr/archs/duf_arch.py b/basicsr/archs/duf_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eab5cb8e0eb008dbe7f90fca5fabc125022be04
--- /dev/null
+++ b/basicsr/archs/duf_arch.py
@@ -0,0 +1,283 @@
+import numpy as np
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+class DenseBlocksTemporalReduce(nn.Module):
+    """A concatenation of 3 dense blocks with reduction in temporal dimension.
+
+    Note that the output temporal dimension is 6 fewer the input temporal
+    dimension, since there are 3 blocks.
+
+    Args:
+        num_feat (int): Number of channels in the blocks. Default: 64.
+        num_grow_ch (int): Growing factor of the dense blocks. Default: 32
+        adapt_official_weights (bool): Whether to adapt the weights
+            translated from the official implementation. Set to false if you
+            want to train from scratch. Default: False.
+    """
+
+    def __init__(self, num_feat=64, num_grow_ch=32, adapt_official_weights=False):
+        super(DenseBlocksTemporalReduce, self).__init__()
+        if adapt_official_weights:
+            eps = 1e-3
+            momentum = 1e-3
+        else:  # pytorch default values
+            eps = 1e-05
+            momentum = 0.1
+
+        self.temporal_reduce1 = nn.Sequential(
+            nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(num_feat, num_feat, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True),
+            nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(num_feat, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
+
+        self.temporal_reduce2 = nn.Sequential(
+            nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(
+                num_feat + num_grow_ch,
+                num_feat + num_grow_ch, (1, 1, 1),
+                stride=(1, 1, 1),
+                padding=(0, 0, 0),
+                bias=True), nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(num_feat + num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
+
+        self.temporal_reduce3 = nn.Sequential(
+            nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(
+                num_feat + 2 * num_grow_ch,
+                num_feat + 2 * num_grow_ch, (1, 1, 1),
+                stride=(1, 1, 1),
+                padding=(0, 0, 0),
+                bias=True), nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum),
+            nn.ReLU(inplace=True),
+            nn.Conv3d(
+                num_feat + 2 * num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input tensor with shape (b, num_feat, t, h, w).
+
+        Returns:
+            Tensor: Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w).
+        """
+        x1 = self.temporal_reduce1(x)
+        x1 = torch.cat((x[:, :, 1:-1, :, :], x1), 1)
+
+        x2 = self.temporal_reduce2(x1)
+        x2 = torch.cat((x1[:, :, 1:-1, :, :], x2), 1)
+
+        x3 = self.temporal_reduce3(x2)
+        x3 = torch.cat((x2[:, :, 1:-1, :, :], x3), 1)
+
+        return x3
+
+
+class DenseBlocks(nn.Module):
+    """ A concatenation of N dense blocks.
+
+    Args:
+        num_feat (int): Number of channels in the blocks. Default: 64.
+        num_grow_ch (int): Growing factor of the dense blocks. Default: 32.
+        num_block (int): Number of dense blocks. The values are:
+            DUF-S (16 layers): 3
+            DUF-M (18 layers): 9
+            DUF-L (52 layers): 21
+        adapt_official_weights (bool): Whether to adapt the weights
+            translated from the official implementation. Set to false if you
+            want to train from scratch. Default: False.
+    """
+
+    def __init__(self, num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False):
+        super(DenseBlocks, self).__init__()
+        if adapt_official_weights:
+            eps = 1e-3
+            momentum = 1e-3
+        else:  # pytorch default values
+            eps = 1e-05
+            momentum = 0.1
+
+        self.dense_blocks = nn.ModuleList()
+        for i in range(0, num_block):
+            self.dense_blocks.append(
+                nn.Sequential(
+                    nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+                    nn.Conv3d(
+                        num_feat + i * num_grow_ch,
+                        num_feat + i * num_grow_ch, (1, 1, 1),
+                        stride=(1, 1, 1),
+                        padding=(0, 0, 0),
+                        bias=True), nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum),
+                    nn.ReLU(inplace=True),
+                    nn.Conv3d(
+                        num_feat + i * num_grow_ch,
+                        num_grow_ch, (3, 3, 3),
+                        stride=(1, 1, 1),
+                        padding=(1, 1, 1),
+                        bias=True)))
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input tensor with shape (b, num_feat, t, h, w).
+
+        Returns:
+            Tensor: Output with shape
+                (b, num_feat + num_block * num_grow_ch, t, h, w).
+        """
+        for i in range(0, len(self.dense_blocks)):
+            y = self.dense_blocks[i](x)
+            x = torch.cat((x, y), 1)
+        return x
+
+
+class DynamicUpsamplingFilter(nn.Module):
+    """Dynamic upsampling filter used in DUF.
+
+    Ref: https://github.com/yhjo09/VSR-DUF.
+    It only supports input with 3 channels. And it applies the same filters
+    to 3 channels.
+
+    Args:
+        filter_size (tuple): Filter size of generated filters.
+            The shape is (kh, kw). Default: (5, 5).
+    """
+
+    def __init__(self, filter_size=(5, 5)):
+        super(DynamicUpsamplingFilter, self).__init__()
+        if not isinstance(filter_size, tuple):
+            raise TypeError('The type of filter_size must be tuple, ' f'but got type{filter_size}')
+        if len(filter_size) != 2:
+            raise ValueError('The length of filter size must be 2, ' f'but got {len(filter_size)}.')
+        # generate a local expansion filter, similar to im2col
+        self.filter_size = filter_size
+        filter_prod = np.prod(filter_size)
+        expansion_filter = torch.eye(int(filter_prod)).view(filter_prod, 1, *filter_size)  # (kh*kw, 1, kh, kw)
+        self.expansion_filter = expansion_filter.repeat(3, 1, 1, 1)  # repeat for all the 3 channels
+
+    def forward(self, x, filters):
+        """Forward function for DynamicUpsamplingFilter.
+
+        Args:
+            x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w).
+            filters (Tensor): Generated dynamic filters.
+                The shape is (n, filter_prod, upsampling_square, h, w).
+                filter_prod: prod of filter kenrel size, e.g., 1*5*5=25.
+                upsampling_square: similar to pixel shuffle,
+                    upsampling_square = upsampling * upsampling
+                    e.g., for x 4 upsampling, upsampling_square= 4*4 = 16
+
+        Returns:
+            Tensor: Filtered image with shape (n, 3*upsampling_square, h, w)
+        """
+        n, filter_prod, upsampling_square, h, w = filters.size()
+        kh, kw = self.filter_size
+        expanded_input = F.conv2d(
+            x, self.expansion_filter.to(x), padding=(kh // 2, kw // 2), groups=3)  # (n, 3*filter_prod, h, w)
+        expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(0, 3, 4, 1,
+                                                                              2)  # (n, h, w, 3, filter_prod)
+        filters = filters.permute(0, 3, 4, 1, 2)  # (n, h, w, filter_prod, upsampling_square]
+        out = torch.matmul(expanded_input, filters)  # (n, h, w, 3, upsampling_square)
+        return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w)
+
+
+@ARCH_REGISTRY.register()
+class DUF(nn.Module):
+    """Network architecture for DUF
+
+    Paper: Jo et.al. Deep Video Super-Resolution Network Using Dynamic
+            Upsampling Filters Without Explicit Motion Compensation, CVPR, 2018
+    Code reference:
+        https://github.com/yhjo09/VSR-DUF
+    For all the models below, 'adapt_official_weights' is only necessary when
+    loading the weights converted from the official TensorFlow weights.
+    Please set it to False if you are training the model from scratch.
+
+    There are three models with different model size: DUF16Layers, DUF28Layers,
+    and DUF52Layers. This class is the base class for these models.
+
+    Args:
+        scale (int): The upsampling factor. Default: 4.
+        num_layer (int): The number of layers. Default: 52.
+        adapt_official_weights_weights (bool): Whether to adapt the weights
+            translated from the official implementation. Set to false if you
+            want to train from scratch. Default: False.
+    """
+
+    def __init__(self, scale=4, num_layer=52, adapt_official_weights=False):
+        super(DUF, self).__init__()
+        self.scale = scale
+        if adapt_official_weights:
+            eps = 1e-3
+            momentum = 1e-3
+        else:  # pytorch default values
+            eps = 1e-05
+            momentum = 0.1
+
+        self.conv3d1 = nn.Conv3d(3, 64, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)
+        self.dynamic_filter = DynamicUpsamplingFilter((5, 5))
+
+        if num_layer == 16:
+            num_block = 3
+            num_grow_ch = 32
+        elif num_layer == 28:
+            num_block = 9
+            num_grow_ch = 16
+        elif num_layer == 52:
+            num_block = 21
+            num_grow_ch = 16
+        else:
+            raise ValueError(f'Only supported (16, 28, 52) layers, but got {num_layer}.')
+
+        self.dense_block1 = DenseBlocks(
+            num_block=num_block, num_feat=64, num_grow_ch=num_grow_ch,
+            adapt_official_weights=adapt_official_weights)  # T = 7
+        self.dense_block2 = DenseBlocksTemporalReduce(
+            64 + num_grow_ch * num_block, num_grow_ch, adapt_official_weights=adapt_official_weights)  # T = 1
+        channels = 64 + num_grow_ch * num_block + num_grow_ch * 3
+        self.bn3d2 = nn.BatchNorm3d(channels, eps=eps, momentum=momentum)
+        self.conv3d2 = nn.Conv3d(channels, 256, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)
+
+        self.conv3d_r1 = nn.Conv3d(256, 256, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+        self.conv3d_r2 = nn.Conv3d(256, 3 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+
+        self.conv3d_f1 = nn.Conv3d(256, 512, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+        self.conv3d_f2 = nn.Conv3d(
+            512, 1 * 5 * 5 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input with shape (b, 7, c, h, w)
+
+        Returns:
+            Tensor: Output with shape (b, 1, h * scale, w * scale)
+        """
+        num_batches, num_imgs, _, h, w = x.size()
+
+        x = x.permute(0, 2, 1, 3, 4)  # (b, c, 7, h, w) for Conv3D
+        x_center = x[:, :, num_imgs // 2, :, :]
+
+        x = self.conv3d1(x)
+        x = self.dense_block1(x)
+        x = self.dense_block2(x)
+        x = F.relu(self.bn3d2(x), inplace=True)
+        x = F.relu(self.conv3d2(x), inplace=True)
+
+        # residual image
+        res = self.conv3d_r2(F.relu(self.conv3d_r1(x), inplace=True))
+
+        # filter
+        filter_ = self.conv3d_f2(F.relu(self.conv3d_f1(x), inplace=True))
+        filter_ = F.softmax(filter_.view(num_batches, 25, self.scale**2, h, w), dim=1)
+
+        # dynamic filter
+        out = self.dynamic_filter(x_center, filter_)
+        out += res.squeeze_(2)
+        out = F.pixel_shuffle(out, self.scale)
+
+        return out
diff --git a/basicsr/archs/edsr_arch.py b/basicsr/archs/edsr_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..b80566f11fbd4782d68eee8fbf7da686f89dc4e7
--- /dev/null
+++ b/basicsr/archs/edsr_arch.py
@@ -0,0 +1,61 @@
+import torch
+from torch import nn as nn
+
+from basicsr.archs.arch_util import ResidualBlockNoBN, Upsample, make_layer
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+@ARCH_REGISTRY.register()
+class EDSR(nn.Module):
+    """EDSR network structure.
+
+    Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution.
+    Ref git repo: https://github.com/thstkdgus35/EDSR-PyTorch
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64.
+        num_block (int): Block number in the trunk network. Default: 16.
+        upscale (int): Upsampling factor. Support 2^n and 3.
+            Default: 4.
+        res_scale (float): Used to scale the residual in residual block.
+            Default: 1.
+        img_range (float): Image range. Default: 255.
+        rgb_mean (tuple[float]): Image mean in RGB orders.
+            Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
+    """
+
+    def __init__(self,
+                 num_in_ch,
+                 num_out_ch,
+                 num_feat=64,
+                 num_block=16,
+                 upscale=4,
+                 res_scale=1,
+                 img_range=255.,
+                 rgb_mean=(0.4488, 0.4371, 0.4040)):
+        super(EDSR, self).__init__()
+
+        self.img_range = img_range
+        self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat, res_scale=res_scale, pytorch_init=True)
+        self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.upsample = Upsample(upscale, num_feat)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+    def forward(self, x):
+        self.mean = self.mean.type_as(x)
+
+        x = (x - self.mean) * self.img_range
+        x = self.conv_first(x)
+        res = self.conv_after_body(self.body(x))
+        res += x
+
+        x = self.conv_last(self.upsample(res))
+        x = x / self.img_range + self.mean
+
+        return x
diff --git a/basicsr/archs/edvr_arch.py b/basicsr/archs/edvr_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..11b87bab3889cedad9701a2adf0ecb572cfdec93
--- /dev/null
+++ b/basicsr/archs/edvr_arch.py
@@ -0,0 +1,383 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import DCNv2Pack, ResidualBlockNoBN, make_layer
+
+
+class PCDAlignment(nn.Module):
+    """Alignment module using Pyramid, Cascading and Deformable convolution
+    (PCD). It is used in EDVR.
+
+    Ref:
+        EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
+
+    Args:
+        num_feat (int): Channel number of middle features. Default: 64.
+        deformable_groups (int): Deformable groups. Defaults: 8.
+    """
+
+    def __init__(self, num_feat=64, deformable_groups=8):
+        super(PCDAlignment, self).__init__()
+
+        # Pyramid has three levels:
+        # L3: level 3, 1/4 spatial size
+        # L2: level 2, 1/2 spatial size
+        # L1: level 1, original spatial size
+        self.offset_conv1 = nn.ModuleDict()
+        self.offset_conv2 = nn.ModuleDict()
+        self.offset_conv3 = nn.ModuleDict()
+        self.dcn_pack = nn.ModuleDict()
+        self.feat_conv = nn.ModuleDict()
+
+        # Pyramids
+        for i in range(3, 0, -1):
+            level = f'l{i}'
+            self.offset_conv1[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
+            if i == 3:
+                self.offset_conv2[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            else:
+                self.offset_conv2[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
+                self.offset_conv3[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.dcn_pack[level] = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups)
+
+            if i < 3:
+                self.feat_conv[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
+
+        # Cascading dcn
+        self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
+        self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.cas_dcnpack = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups)
+
+        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+    def forward(self, nbr_feat_l, ref_feat_l):
+        """Align neighboring frame features to the reference frame features.
+
+        Args:
+            nbr_feat_l (list[Tensor]): Neighboring feature list. It
+                contains three pyramid levels (L1, L2, L3),
+                each with shape (b, c, h, w).
+            ref_feat_l (list[Tensor]): Reference feature list. It
+                contains three pyramid levels (L1, L2, L3),
+                each with shape (b, c, h, w).
+
+        Returns:
+            Tensor: Aligned features.
+        """
+        # Pyramids
+        upsampled_offset, upsampled_feat = None, None
+        for i in range(3, 0, -1):
+            level = f'l{i}'
+            offset = torch.cat([nbr_feat_l[i - 1], ref_feat_l[i - 1]], dim=1)
+            offset = self.lrelu(self.offset_conv1[level](offset))
+            if i == 3:
+                offset = self.lrelu(self.offset_conv2[level](offset))
+            else:
+                offset = self.lrelu(self.offset_conv2[level](torch.cat([offset, upsampled_offset], dim=1)))
+                offset = self.lrelu(self.offset_conv3[level](offset))
+
+            feat = self.dcn_pack[level](nbr_feat_l[i - 1], offset)
+            if i < 3:
+                feat = self.feat_conv[level](torch.cat([feat, upsampled_feat], dim=1))
+            if i > 1:
+                feat = self.lrelu(feat)
+
+            if i > 1:  # upsample offset and features
+                # x2: when we upsample the offset, we should also enlarge
+                # the magnitude.
+                upsampled_offset = self.upsample(offset) * 2
+                upsampled_feat = self.upsample(feat)
+
+        # Cascading
+        offset = torch.cat([feat, ref_feat_l[0]], dim=1)
+        offset = self.lrelu(self.cas_offset_conv2(self.lrelu(self.cas_offset_conv1(offset))))
+        feat = self.lrelu(self.cas_dcnpack(feat, offset))
+        return feat
+
+
+class TSAFusion(nn.Module):
+    """Temporal Spatial Attention (TSA) fusion module.
+
+    Temporal: Calculate the correlation between center frame and
+        neighboring frames;
+    Spatial: It has 3 pyramid levels, the attention is similar to SFT.
+        (SFT: Recovering realistic texture in image super-resolution by deep
+            spatial feature transform.)
+
+    Args:
+        num_feat (int): Channel number of middle features. Default: 64.
+        num_frame (int): Number of frames. Default: 5.
+        center_frame_idx (int): The index of center frame. Default: 2.
+    """
+
+    def __init__(self, num_feat=64, num_frame=5, center_frame_idx=2):
+        super(TSAFusion, self).__init__()
+        self.center_frame_idx = center_frame_idx
+        # temporal attention (before fusion conv)
+        self.temporal_attn1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.temporal_attn2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.feat_fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1)
+
+        # spatial attention (after fusion conv)
+        self.max_pool = nn.MaxPool2d(3, stride=2, padding=1)
+        self.avg_pool = nn.AvgPool2d(3, stride=2, padding=1)
+        self.spatial_attn1 = nn.Conv2d(num_frame * num_feat, num_feat, 1)
+        self.spatial_attn2 = nn.Conv2d(num_feat * 2, num_feat, 1)
+        self.spatial_attn3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.spatial_attn4 = nn.Conv2d(num_feat, num_feat, 1)
+        self.spatial_attn5 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.spatial_attn_l1 = nn.Conv2d(num_feat, num_feat, 1)
+        self.spatial_attn_l2 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
+        self.spatial_attn_l3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.spatial_attn_add1 = nn.Conv2d(num_feat, num_feat, 1)
+        self.spatial_attn_add2 = nn.Conv2d(num_feat, num_feat, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
+
+    def forward(self, aligned_feat):
+        """
+        Args:
+            aligned_feat (Tensor): Aligned features with shape (b, t, c, h, w).
+
+        Returns:
+            Tensor: Features after TSA with the shape (b, c, h, w).
+        """
+        b, t, c, h, w = aligned_feat.size()
+        # temporal attention
+        embedding_ref = self.temporal_attn1(aligned_feat[:, self.center_frame_idx, :, :, :].clone())
+        embedding = self.temporal_attn2(aligned_feat.view(-1, c, h, w))
+        embedding = embedding.view(b, t, -1, h, w)  # (b, t, c, h, w)
+
+        corr_l = []  # correlation list
+        for i in range(t):
+            emb_neighbor = embedding[:, i, :, :, :]
+            corr = torch.sum(emb_neighbor * embedding_ref, 1)  # (b, h, w)
+            corr_l.append(corr.unsqueeze(1))  # (b, 1, h, w)
+        corr_prob = torch.sigmoid(torch.cat(corr_l, dim=1))  # (b, t, h, w)
+        corr_prob = corr_prob.unsqueeze(2).expand(b, t, c, h, w)
+        corr_prob = corr_prob.contiguous().view(b, -1, h, w)  # (b, t*c, h, w)
+        aligned_feat = aligned_feat.view(b, -1, h, w) * corr_prob
+
+        # fusion
+        feat = self.lrelu(self.feat_fusion(aligned_feat))
+
+        # spatial attention
+        attn = self.lrelu(self.spatial_attn1(aligned_feat))
+        attn_max = self.max_pool(attn)
+        attn_avg = self.avg_pool(attn)
+        attn = self.lrelu(self.spatial_attn2(torch.cat([attn_max, attn_avg], dim=1)))
+        # pyramid levels
+        attn_level = self.lrelu(self.spatial_attn_l1(attn))
+        attn_max = self.max_pool(attn_level)
+        attn_avg = self.avg_pool(attn_level)
+        attn_level = self.lrelu(self.spatial_attn_l2(torch.cat([attn_max, attn_avg], dim=1)))
+        attn_level = self.lrelu(self.spatial_attn_l3(attn_level))
+        attn_level = self.upsample(attn_level)
+
+        attn = self.lrelu(self.spatial_attn3(attn)) + attn_level
+        attn = self.lrelu(self.spatial_attn4(attn))
+        attn = self.upsample(attn)
+        attn = self.spatial_attn5(attn)
+        attn_add = self.spatial_attn_add2(self.lrelu(self.spatial_attn_add1(attn)))
+        attn = torch.sigmoid(attn)
+
+        # after initialization, * 2 makes (attn * 2) to be close to 1.
+        feat = feat * attn * 2 + attn_add
+        return feat
+
+
+class PredeblurModule(nn.Module):
+    """Pre-dublur module.
+
+    Args:
+        num_in_ch (int): Channel number of input image. Default: 3.
+        num_feat (int): Channel number of intermediate features. Default: 64.
+        hr_in (bool): Whether the input has high resolution. Default: False.
+    """
+
+    def __init__(self, num_in_ch=3, num_feat=64, hr_in=False):
+        super(PredeblurModule, self).__init__()
+        self.hr_in = hr_in
+
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        if self.hr_in:
+            # downsample x4 by stride conv
+            self.stride_conv_hr1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+            self.stride_conv_hr2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+
+        # generate feature pyramid
+        self.stride_conv_l2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+        self.stride_conv_l3 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+
+        self.resblock_l3 = ResidualBlockNoBN(num_feat=num_feat)
+        self.resblock_l2_1 = ResidualBlockNoBN(num_feat=num_feat)
+        self.resblock_l2_2 = ResidualBlockNoBN(num_feat=num_feat)
+        self.resblock_l1 = nn.ModuleList([ResidualBlockNoBN(num_feat=num_feat) for i in range(5)])
+
+        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+    def forward(self, x):
+        feat_l1 = self.lrelu(self.conv_first(x))
+        if self.hr_in:
+            feat_l1 = self.lrelu(self.stride_conv_hr1(feat_l1))
+            feat_l1 = self.lrelu(self.stride_conv_hr2(feat_l1))
+
+        # generate feature pyramid
+        feat_l2 = self.lrelu(self.stride_conv_l2(feat_l1))
+        feat_l3 = self.lrelu(self.stride_conv_l3(feat_l2))
+
+        feat_l3 = self.upsample(self.resblock_l3(feat_l3))
+        feat_l2 = self.resblock_l2_1(feat_l2) + feat_l3
+        feat_l2 = self.upsample(self.resblock_l2_2(feat_l2))
+
+        for i in range(2):
+            feat_l1 = self.resblock_l1[i](feat_l1)
+        feat_l1 = feat_l1 + feat_l2
+        for i in range(2, 5):
+            feat_l1 = self.resblock_l1[i](feat_l1)
+        return feat_l1
+
+
+@ARCH_REGISTRY.register()
+class EDVR(nn.Module):
+    """EDVR network structure for video super-resolution.
+
+    Now only support X4 upsampling factor.
+    Paper:
+        EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
+
+    Args:
+        num_in_ch (int): Channel number of input image. Default: 3.
+        num_out_ch (int): Channel number of output image. Default: 3.
+        num_feat (int): Channel number of intermediate features. Default: 64.
+        num_frame (int): Number of input frames. Default: 5.
+        deformable_groups (int): Deformable groups. Defaults: 8.
+        num_extract_block (int): Number of blocks for feature extraction.
+            Default: 5.
+        num_reconstruct_block (int): Number of blocks for reconstruction.
+            Default: 10.
+        center_frame_idx (int): The index of center frame. Frame counting from
+            0. Default: Middle of input frames.
+        hr_in (bool): Whether the input has high resolution. Default: False.
+        with_predeblur (bool): Whether has predeblur module.
+            Default: False.
+        with_tsa (bool): Whether has TSA module. Default: True.
+    """
+
+    def __init__(self,
+                 num_in_ch=3,
+                 num_out_ch=3,
+                 num_feat=64,
+                 num_frame=5,
+                 deformable_groups=8,
+                 num_extract_block=5,
+                 num_reconstruct_block=10,
+                 center_frame_idx=None,
+                 hr_in=False,
+                 with_predeblur=False,
+                 with_tsa=True):
+        super(EDVR, self).__init__()
+        if center_frame_idx is None:
+            self.center_frame_idx = num_frame // 2
+        else:
+            self.center_frame_idx = center_frame_idx
+        self.hr_in = hr_in
+        self.with_predeblur = with_predeblur
+        self.with_tsa = with_tsa
+
+        # extract features for each frame
+        if self.with_predeblur:
+            self.predeblur = PredeblurModule(num_feat=num_feat, hr_in=self.hr_in)
+            self.conv_1x1 = nn.Conv2d(num_feat, num_feat, 1, 1)
+        else:
+            self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+
+        # extrat pyramid features
+        self.feature_extraction = make_layer(ResidualBlockNoBN, num_extract_block, num_feat=num_feat)
+        self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+        self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+        self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+
+        # pcd and tsa module
+        self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=deformable_groups)
+        if self.with_tsa:
+            self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_frame, center_frame_idx=self.center_frame_idx)
+        else:
+            self.fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1)
+
+        # reconstruction
+        self.reconstruction = make_layer(ResidualBlockNoBN, num_reconstruct_block, num_feat=num_feat)
+        # upsample
+        self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
+        self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1)
+        self.pixel_shuffle = nn.PixelShuffle(2)
+        self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
+        self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
+
+        # activation function
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+    def forward(self, x):
+        b, t, c, h, w = x.size()
+        if self.hr_in:
+            assert h % 16 == 0 and w % 16 == 0, ('The height and width must be multiple of 16.')
+        else:
+            assert h % 4 == 0 and w % 4 == 0, ('The height and width must be multiple of 4.')
+
+        x_center = x[:, self.center_frame_idx, :, :, :].contiguous()
+
+        # extract features for each frame
+        # L1
+        if self.with_predeblur:
+            feat_l1 = self.conv_1x1(self.predeblur(x.view(-1, c, h, w)))
+            if self.hr_in:
+                h, w = h // 4, w // 4
+        else:
+            feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w)))
+
+        feat_l1 = self.feature_extraction(feat_l1)
+        # L2
+        feat_l2 = self.lrelu(self.conv_l2_1(feat_l1))
+        feat_l2 = self.lrelu(self.conv_l2_2(feat_l2))
+        # L3
+        feat_l3 = self.lrelu(self.conv_l3_1(feat_l2))
+        feat_l3 = self.lrelu(self.conv_l3_2(feat_l3))
+
+        feat_l1 = feat_l1.view(b, t, -1, h, w)
+        feat_l2 = feat_l2.view(b, t, -1, h // 2, w // 2)
+        feat_l3 = feat_l3.view(b, t, -1, h // 4, w // 4)
+
+        # PCD alignment
+        ref_feat_l = [  # reference feature list
+            feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(),
+            feat_l3[:, self.center_frame_idx, :, :, :].clone()
+        ]
+        aligned_feat = []
+        for i in range(t):
+            nbr_feat_l = [  # neighboring feature list
+                feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone()
+            ]
+            aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l))
+        aligned_feat = torch.stack(aligned_feat, dim=1)  # (b, t, c, h, w)
+
+        if not self.with_tsa:
+            aligned_feat = aligned_feat.view(b, -1, h, w)
+        feat = self.fusion(aligned_feat)
+
+        out = self.reconstruction(feat)
+        out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
+        out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
+        out = self.lrelu(self.conv_hr(out))
+        out = self.conv_last(out)
+        if self.hr_in:
+            base = x_center
+        else:
+            base = F.interpolate(x_center, scale_factor=4, mode='bilinear', align_corners=False)
+        out += base
+        return out
diff --git a/basicsr/archs/inception.py b/basicsr/archs/inception.py
new file mode 100644
index 0000000000000000000000000000000000000000..de1abef67270dc1aba770943b53577029141f527
--- /dev/null
+++ b/basicsr/archs/inception.py
@@ -0,0 +1,307 @@
+# Modified from https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/inception.py # noqa: E501
+# For FID metric
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.model_zoo import load_url
+from torchvision import models
+
+# Inception weights ported to Pytorch from
+# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
+FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'  # noqa: E501
+LOCAL_FID_WEIGHTS = 'experiments/pretrained_models/pt_inception-2015-12-05-6726825d.pth'  # noqa: E501
+
+
+class InceptionV3(nn.Module):
+    """Pretrained InceptionV3 network returning feature maps"""
+
+    # Index of default block of inception to return,
+    # corresponds to output of final average pooling
+    DEFAULT_BLOCK_INDEX = 3
+
+    # Maps feature dimensionality to their output blocks indices
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,  # First max pooling features
+        192: 1,  # Second max pooling features
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self,
+                 output_blocks=(DEFAULT_BLOCK_INDEX),
+                 resize_input=True,
+                 normalize_input=True,
+                 requires_grad=False,
+                 use_fid_inception=True):
+        """Build pretrained InceptionV3.
+
+        Args:
+            output_blocks (list[int]): Indices of blocks to return features of.
+                Possible values are:
+                - 0: corresponds to output of first max pooling
+                - 1: corresponds to output of second max pooling
+                - 2: corresponds to output which is fed to aux classifier
+                - 3: corresponds to output of final average pooling
+            resize_input (bool): If true, bilinearly resizes input to width and
+                height 299 before feeding input to model. As the network
+                without fully connected layers is fully convolutional, it
+                should be able to handle inputs of arbitrary size, so resizing
+                might not be strictly needed. Default: True.
+            normalize_input (bool): If true, scales the input from range (0, 1)
+                to the range the pretrained Inception network expects,
+                namely (-1, 1). Default: True.
+            requires_grad (bool): If true, parameters of the model require
+                gradients. Possibly useful for finetuning the network.
+                Default: False.
+            use_fid_inception (bool): If true, uses the pretrained Inception
+                model used in Tensorflow's FID implementation.
+                If false, uses the pretrained Inception model available in
+                torchvision. The FID Inception model has different weights
+                and a slightly different structure from torchvision's
+                Inception model. If you want to compute FID scores, you are
+                strongly advised to set this parameter to true to get
+                comparable results. Default: True.
+        """
+        super(InceptionV3, self).__init__()
+
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, ('Last possible output block index is 3')
+
+        self.blocks = nn.ModuleList()
+
+        if use_fid_inception:
+            inception = fid_inception_v3()
+        else:
+            try:
+                inception = models.inception_v3(pretrained=True, init_weights=False)
+            except TypeError:
+                # pytorch < 1.5 does not have init_weights for inception_v3
+                inception = models.inception_v3(pretrained=True)
+
+        # Block 0: input to maxpool1
+        block0 = [
+            inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3,
+            nn.MaxPool2d(kernel_size=3, stride=2)
+        ]
+        self.blocks.append(nn.Sequential(*block0))
+
+        # Block 1: maxpool1 to maxpool2
+        if self.last_needed_block >= 1:
+            block1 = [inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2)]
+            self.blocks.append(nn.Sequential(*block1))
+
+        # Block 2: maxpool2 to aux classifier
+        if self.last_needed_block >= 2:
+            block2 = [
+                inception.Mixed_5b,
+                inception.Mixed_5c,
+                inception.Mixed_5d,
+                inception.Mixed_6a,
+                inception.Mixed_6b,
+                inception.Mixed_6c,
+                inception.Mixed_6d,
+                inception.Mixed_6e,
+            ]
+            self.blocks.append(nn.Sequential(*block2))
+
+        # Block 3: aux classifier to final avgpool
+        if self.last_needed_block >= 3:
+            block3 = [
+                inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c,
+                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+            ]
+            self.blocks.append(nn.Sequential(*block3))
+
+        for param in self.parameters():
+            param.requires_grad = requires_grad
+
+    def forward(self, x):
+        """Get Inception feature maps.
+
+        Args:
+            x (Tensor): Input tensor of shape (b, 3, h, w).
+                Values are expected to be in range (-1, 1). You can also input
+                (0, 1) with setting normalize_input = True.
+
+        Returns:
+            list[Tensor]: Corresponding to the selected output block, sorted
+            ascending by index.
+        """
+        output = []
+
+        if self.resize_input:
+            x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False)
+
+        if self.normalize_input:
+            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if idx in self.output_blocks:
+                output.append(x)
+
+            if idx == self.last_needed_block:
+                break
+
+        return output
+
+
+def fid_inception_v3():
+    """Build pretrained Inception model for FID computation.
+
+    The Inception model for FID computation uses a different set of weights
+    and has a slightly different structure than torchvision's Inception.
+
+    This method first constructs torchvision's Inception and then patches the
+    necessary parts that are different in the FID Inception model.
+    """
+    try:
+        inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False, init_weights=False)
+    except TypeError:
+        # pytorch < 1.5 does not have init_weights for inception_v3
+        inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False)
+
+    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
+    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
+    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
+    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
+    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
+    inception.Mixed_7b = FIDInceptionE_1(1280)
+    inception.Mixed_7c = FIDInceptionE_2(2048)
+
+    if os.path.exists(LOCAL_FID_WEIGHTS):
+        state_dict = torch.load(LOCAL_FID_WEIGHTS, map_location=lambda storage, loc: storage)
+    else:
+        state_dict = load_url(FID_WEIGHTS_URL, progress=True)
+
+    inception.load_state_dict(state_dict)
+    return inception
+
+
+class FIDInceptionA(models.inception.InceptionA):
+    """InceptionA block patched for FID computation"""
+
+    def __init__(self, in_channels, pool_features):
+        super(FIDInceptionA, self).__init__(in_channels, pool_features)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionC(models.inception.InceptionC):
+    """InceptionC block patched for FID computation"""
+
+    def __init__(self, in_channels, channels_7x7):
+        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_1(models.inception.InceptionE):
+    """First InceptionE block patched for FID computation"""
+
+    def __init__(self, in_channels):
+        super(FIDInceptionE_1, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_2(models.inception.InceptionE):
+    """Second InceptionE block patched for FID computation"""
+
+    def __init__(self, in_channels):
+        super(FIDInceptionE_2, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: The FID Inception model uses max pooling instead of average
+        # pooling. This is likely an error in this specific Inception
+        # implementation, as other Inception models use average pooling here
+        # (which matches the description in the paper).
+        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
diff --git a/basicsr/archs/rcan_arch.py b/basicsr/archs/rcan_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..f15e9215e3ce402a1b71ab82061421658896b0e8
--- /dev/null
+++ b/basicsr/archs/rcan_arch.py
@@ -0,0 +1,135 @@
+import torch
+from torch import nn as nn
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import Upsample, make_layer
+
+
+class ChannelAttention(nn.Module):
+    """Channel attention used in RCAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        squeeze_factor (int): Channel squeeze factor. Default: 16.
+    """
+
+    def __init__(self, num_feat, squeeze_factor=16):
+        super(ChannelAttention, self).__init__()
+        self.attention = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),
+            nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid())
+
+    def forward(self, x):
+        y = self.attention(x)
+        return x * y
+
+
+class RCAB(nn.Module):
+    """Residual Channel Attention Block (RCAB) used in RCAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        squeeze_factor (int): Channel squeeze factor. Default: 16.
+        res_scale (float): Scale the residual. Default: 1.
+    """
+
+    def __init__(self, num_feat, squeeze_factor=16, res_scale=1):
+        super(RCAB, self).__init__()
+        self.res_scale = res_scale
+
+        self.rcab = nn.Sequential(
+            nn.Conv2d(num_feat, num_feat, 3, 1, 1), nn.ReLU(True), nn.Conv2d(num_feat, num_feat, 3, 1, 1),
+            ChannelAttention(num_feat, squeeze_factor))
+
+    def forward(self, x):
+        res = self.rcab(x) * self.res_scale
+        return res + x
+
+
+class ResidualGroup(nn.Module):
+    """Residual Group of RCAB.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_block (int): Block number in the body network.
+        squeeze_factor (int): Channel squeeze factor. Default: 16.
+        res_scale (float): Scale the residual. Default: 1.
+    """
+
+    def __init__(self, num_feat, num_block, squeeze_factor=16, res_scale=1):
+        super(ResidualGroup, self).__init__()
+
+        self.residual_group = make_layer(
+            RCAB, num_block, num_feat=num_feat, squeeze_factor=squeeze_factor, res_scale=res_scale)
+        self.conv = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+
+    def forward(self, x):
+        res = self.conv(self.residual_group(x))
+        return res + x
+
+
+@ARCH_REGISTRY.register()
+class RCAN(nn.Module):
+    """Residual Channel Attention Networks.
+
+    Paper: Image Super-Resolution Using Very Deep Residual Channel Attention
+        Networks
+    Ref git repo: https://github.com/yulunzhang/RCAN.
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64.
+        num_group (int): Number of ResidualGroup. Default: 10.
+        num_block (int): Number of RCAB in ResidualGroup. Default: 16.
+        squeeze_factor (int): Channel squeeze factor. Default: 16.
+        upscale (int): Upsampling factor. Support 2^n and 3.
+            Default: 4.
+        res_scale (float): Used to scale the residual in residual block.
+            Default: 1.
+        img_range (float): Image range. Default: 255.
+        rgb_mean (tuple[float]): Image mean in RGB orders.
+            Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
+    """
+
+    def __init__(self,
+                 num_in_ch,
+                 num_out_ch,
+                 num_feat=64,
+                 num_group=10,
+                 num_block=16,
+                 squeeze_factor=16,
+                 upscale=4,
+                 res_scale=1,
+                 img_range=255.,
+                 rgb_mean=(0.4488, 0.4371, 0.4040)):
+        super(RCAN, self).__init__()
+
+        self.img_range = img_range
+        self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(
+            ResidualGroup,
+            num_group,
+            num_feat=num_feat,
+            num_block=num_block,
+            squeeze_factor=squeeze_factor,
+            res_scale=res_scale)
+        self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.upsample = Upsample(upscale, num_feat)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+    def forward(self, x):
+        self.mean = self.mean.type_as(x)
+
+        x = (x - self.mean) * self.img_range
+        x = self.conv_first(x)
+        res = self.conv_after_body(self.body(x))
+        res += x
+
+        x = self.conv_last(self.upsample(res))
+        x = x / self.img_range + self.mean
+
+        return x
diff --git a/basicsr/archs/ridnet_arch.py b/basicsr/archs/ridnet_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..bddab3c0c5e4e04d74f8c9ef6ee30043892561d8
--- /dev/null
+++ b/basicsr/archs/ridnet_arch.py
@@ -0,0 +1,184 @@
+import torch
+import torch.nn as nn
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import ResidualBlockNoBN, make_layer
+
+
+class MeanShift(nn.Conv2d):
+    """ Data normalization with mean and std.
+
+    Args:
+        rgb_range (int): Maximum value of RGB.
+        rgb_mean (list[float]): Mean for RGB channels.
+        rgb_std (list[float]): Std for RGB channels.
+        sign (int): For substraction, sign is -1, for addition, sign is 1.
+            Default: -1.
+        requires_grad (bool): Whether to update the self.weight and self.bias.
+            Default: True.
+    """
+
+    def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1, requires_grad=True):
+        super(MeanShift, self).__init__(3, 3, kernel_size=1)
+        std = torch.Tensor(rgb_std)
+        self.weight.data = torch.eye(3).view(3, 3, 1, 1)
+        self.weight.data.div_(std.view(3, 1, 1, 1))
+        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean)
+        self.bias.data.div_(std)
+        self.requires_grad = requires_grad
+
+
+class EResidualBlockNoBN(nn.Module):
+    """Enhanced Residual block without BN.
+
+    There are three convolution layers in residual branch.
+
+    It has a style of:
+        ---Conv-ReLU-Conv-ReLU-Conv-+-ReLU-
+         |__________________________|
+    """
+
+    def __init__(self, in_channels, out_channels):
+        super(EResidualBlockNoBN, self).__init__()
+
+        self.body = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, 3, 1, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, 3, 1, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, 1, 1, 0),
+        )
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        out = self.body(x)
+        out = self.relu(out + x)
+        return out
+
+
+class MergeRun(nn.Module):
+    """ Merge-and-run unit.
+
+    This unit contains two branches with different dilated convolutions,
+    followed by a convolution to process the concatenated features.
+
+    Paper: Real Image Denoising with Feature Attention
+    Ref git repo: https://github.com/saeed-anwar/RIDNet
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
+        super(MergeRun, self).__init__()
+
+        self.dilation1 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size, stride, 2, 2), nn.ReLU(inplace=True))
+        self.dilation2 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size, stride, 3, 3), nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size, stride, 4, 4), nn.ReLU(inplace=True))
+
+        self.aggregation = nn.Sequential(
+            nn.Conv2d(out_channels * 2, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True))
+
+    def forward(self, x):
+        dilation1 = self.dilation1(x)
+        dilation2 = self.dilation2(x)
+        out = torch.cat([dilation1, dilation2], dim=1)
+        out = self.aggregation(out)
+        out = out + x
+        return out
+
+
+class ChannelAttention(nn.Module):
+    """Channel attention.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        squeeze_factor (int): Channel squeeze factor. Default:
+    """
+
+    def __init__(self, mid_channels, squeeze_factor=16):
+        super(ChannelAttention, self).__init__()
+        self.attention = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1), nn.Conv2d(mid_channels, mid_channels // squeeze_factor, 1, padding=0),
+            nn.ReLU(inplace=True), nn.Conv2d(mid_channels // squeeze_factor, mid_channels, 1, padding=0), nn.Sigmoid())
+
+    def forward(self, x):
+        y = self.attention(x)
+        return x * y
+
+
+class EAM(nn.Module):
+    """Enhancement attention modules (EAM) in RIDNet.
+
+    This module contains a merge-and-run unit, a residual block,
+    an enhanced residual block and a feature attention unit.
+
+    Attributes:
+        merge: The merge-and-run unit.
+        block1: The residual block.
+        block2: The enhanced residual block.
+        ca: The feature/channel attention unit.
+    """
+
+    def __init__(self, in_channels, mid_channels, out_channels):
+        super(EAM, self).__init__()
+
+        self.merge = MergeRun(in_channels, mid_channels)
+        self.block1 = ResidualBlockNoBN(mid_channels)
+        self.block2 = EResidualBlockNoBN(mid_channels, out_channels)
+        self.ca = ChannelAttention(out_channels)
+        # The residual block in the paper contains a relu after addition.
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        out = self.merge(x)
+        out = self.relu(self.block1(out))
+        out = self.block2(out)
+        out = self.ca(out)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class RIDNet(nn.Module):
+    """RIDNet: Real Image Denoising with Feature Attention.
+
+    Ref git repo: https://github.com/saeed-anwar/RIDNet
+
+    Args:
+        in_channels (int): Channel number of inputs.
+        mid_channels (int): Channel number of EAM modules.
+            Default: 64.
+        out_channels (int): Channel number of outputs.
+        num_block (int): Number of EAM. Default: 4.
+        img_range (float): Image range. Default: 255.
+        rgb_mean (tuple[float]): Image mean in RGB orders.
+            Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 mid_channels,
+                 out_channels,
+                 num_block=4,
+                 img_range=255.,
+                 rgb_mean=(0.4488, 0.4371, 0.4040),
+                 rgb_std=(1.0, 1.0, 1.0)):
+        super(RIDNet, self).__init__()
+
+        self.sub_mean = MeanShift(img_range, rgb_mean, rgb_std)
+        self.add_mean = MeanShift(img_range, rgb_mean, rgb_std, 1)
+
+        self.head = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
+        self.body = make_layer(
+            EAM, num_block, in_channels=mid_channels, mid_channels=mid_channels, out_channels=mid_channels)
+        self.tail = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        res = self.sub_mean(x)
+        res = self.tail(self.body(self.relu(self.head(res))))
+        res = self.add_mean(res)
+
+        out = x + res
+        return out
diff --git a/basicsr/archs/rrdbnet_arch.py b/basicsr/archs/rrdbnet_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..8362a9c5b02a06ff07aa35d2d31b71ecd25fbfc2
--- /dev/null
+++ b/basicsr/archs/rrdbnet_arch.py
@@ -0,0 +1,104 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import default_init_weights, make_layer
+
+
+class ResidualDenseBlock(nn.Module):
+    """Residual Dense Block.
+
+    Used in RRDB block in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat=64, num_grow_ch=32):
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        # Emperically, we use 0.2 to scale the residual for better performance
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    """Residual in Residual Dense Block.
+
+    Used in RRDB-Net in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat, num_grow_ch=32):
+        super(RRDB, self).__init__()
+        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
+
+    def forward(self, x):
+        out = self.rdb1(x)
+        out = self.rdb2(out)
+        out = self.rdb3(out)
+        # Emperically, we use 0.2 to scale the residual for better performance
+        return out * 0.2 + x
+
+
+@ARCH_REGISTRY.register()
+class RRDBNet(nn.Module):
+    """Networks consisting of Residual in Residual Dense Block, which is used
+    in ESRGAN.
+
+    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
+    Currently, it supports x4 upsampling scale factor.
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64
+        num_block (int): Block number in the trunk network. Defaults: 23
+        num_grow_ch (int): Channels for each growth. Default: 32.
+    """
+
+    def __init__(self, num_in_ch, num_out_ch, num_feat=64, num_block=23, num_grow_ch=32):
+        super(RRDBNet, self).__init__()
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
+        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        # upsample
+        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        feat = self.conv_first(x)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        # upsample
+        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out
diff --git a/basicsr/archs/spynet_arch.py b/basicsr/archs/spynet_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c7af133daef0496b79a57517e1942d06f2d0061
--- /dev/null
+++ b/basicsr/archs/spynet_arch.py
@@ -0,0 +1,96 @@
+import math
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import flow_warp
+
+
+class BasicModule(nn.Module):
+    """Basic Module for SpyNet.
+    """
+
+    def __init__(self):
+        super(BasicModule, self).__init__()
+
+        self.basic_module = nn.Sequential(
+            nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
+            nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
+            nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
+            nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3))
+
+    def forward(self, tensor_input):
+        return self.basic_module(tensor_input)
+
+
+@ARCH_REGISTRY.register()
+class SpyNet(nn.Module):
+    """SpyNet architecture.
+
+    Args:
+        load_path (str): path for pretrained SpyNet. Default: None.
+    """
+
+    def __init__(self, load_path=None):
+        super(SpyNet, self).__init__()
+        self.basic_module = nn.ModuleList([BasicModule() for _ in range(6)])
+        if load_path:
+            self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params'])
+
+        self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+        self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+    def preprocess(self, tensor_input):
+        tensor_output = (tensor_input - self.mean) / self.std
+        return tensor_output
+
+    def process(self, ref, supp):
+        flow = []
+
+        ref = [self.preprocess(ref)]
+        supp = [self.preprocess(supp)]
+
+        for level in range(5):
+            ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False))
+            supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False))
+
+        flow = ref[0].new_zeros(
+            [ref[0].size(0), 2,
+             int(math.floor(ref[0].size(2) / 2.0)),
+             int(math.floor(ref[0].size(3) / 2.0))])
+
+        for level in range(len(ref)):
+            upsampled_flow = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+
+            if upsampled_flow.size(2) != ref[level].size(2):
+                upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 0, 0, 1], mode='replicate')
+            if upsampled_flow.size(3) != ref[level].size(3):
+                upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 1, 0, 0], mode='replicate')
+
+            flow = self.basic_module[level](torch.cat([
+                ref[level],
+                flow_warp(
+                    supp[level], upsampled_flow.permute(0, 2, 3, 1), interp_mode='bilinear', padding_mode='border'),
+                upsampled_flow
+            ], 1)) + upsampled_flow
+
+        return flow
+
+    def forward(self, ref, supp):
+        assert ref.size() == supp.size()
+
+        h, w = ref.size(2), ref.size(3)
+        w_floor = math.floor(math.ceil(w / 32.0) * 32.0)
+        h_floor = math.floor(math.ceil(h / 32.0) * 32.0)
+
+        ref = F.interpolate(input=ref, size=(h_floor, w_floor), mode='bilinear', align_corners=False)
+        supp = F.interpolate(input=supp, size=(h_floor, w_floor), mode='bilinear', align_corners=False)
+
+        flow = F.interpolate(input=self.process(ref, supp), size=(h, w), mode='bilinear', align_corners=False)
+
+        flow[:, 0, :, :] *= float(w) / float(w_floor)
+        flow[:, 1, :, :] *= float(h) / float(h_floor)
+
+        return flow
diff --git a/basicsr/archs/srresnet_arch.py b/basicsr/archs/srresnet_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..e83c03007cd2e397fb3cca5f2b1670caf3fee178
--- /dev/null
+++ b/basicsr/archs/srresnet_arch.py
@@ -0,0 +1,68 @@
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import ResidualBlockNoBN, default_init_weights, make_layer
+
+
+@ARCH_REGISTRY.register()
+class MSRResNet(nn.Module):
+    """Modified SRResNet.
+
+    A compacted version modified from SRResNet in
+    "Photo-Realistic Single Image Super-Resolution Using a Generative
+    Adversarial Network"
+    It uses residual blocks without BN, similar to EDSR.
+    Currently, it supports x2, x3 and x4 upsampling scale factor.
+
+    Args:
+        num_in_ch (int): Channel number of inputs. Default: 3.
+        num_out_ch (int): Channel number of outputs. Default: 3.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64.
+        num_block (int): Block number in the body network. Default: 16.
+        upscale (int): Upsampling factor. Support x2, x3 and x4.
+            Default: 4.
+    """
+
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4):
+        super(MSRResNet, self).__init__()
+        self.upscale = upscale
+
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat)
+
+        # upsampling
+        if self.upscale in [2, 3]:
+            self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1)
+            self.pixel_shuffle = nn.PixelShuffle(self.upscale)
+        elif self.upscale == 4:
+            self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
+            self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
+            self.pixel_shuffle = nn.PixelShuffle(2)
+
+        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+        # activation function
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+        # initialization
+        default_init_weights([self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1)
+        if self.upscale == 4:
+            default_init_weights(self.upconv2, 0.1)
+
+    def forward(self, x):
+        feat = self.lrelu(self.conv_first(x))
+        out = self.body(feat)
+
+        if self.upscale == 4:
+            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
+            out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
+        elif self.upscale in [2, 3]:
+            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
+
+        out = self.conv_last(self.lrelu(self.conv_hr(out)))
+        base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False)
+        out += base
+        return out
diff --git a/basicsr/archs/stylegan2_arch.py b/basicsr/archs/stylegan2_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..beda6a1951c60b5af16c8d6c4f6a331ee4934ce2
--- /dev/null
+++ b/basicsr/archs/stylegan2_arch.py
@@ -0,0 +1,799 @@
+import math
+import random
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu
+from basicsr.ops.upfirdn2d import upfirdn2d
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+class NormStyleCode(nn.Module):
+
+    def forward(self, x):
+        """Normalize the style codes.
+
+        Args:
+            x (Tensor): Style codes with shape (b, c).
+
+        Returns:
+            Tensor: Normalized tensor.
+        """
+        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
+
+
+def make_resample_kernel(k):
+    """Make resampling kernel for UpFirDn.
+
+    Args:
+        k (list[int]): A list indicating the 1D resample kernel magnitude.
+
+    Returns:
+        Tensor: 2D resampled kernel.
+    """
+    k = torch.tensor(k, dtype=torch.float32)
+    if k.ndim == 1:
+        k = k[None, :] * k[:, None]  # to 2D kernel, outer product
+    # normalize
+    k /= k.sum()
+    return k
+
+
+class UpFirDnUpsample(nn.Module):
+    """Upsample, FIR filter, and downsample (upsampole version).
+
+    References:
+    1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html  # noqa: E501
+    2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html  # noqa: E501
+
+    Args:
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude.
+        factor (int): Upsampling scale factor. Default: 2.
+    """
+
+    def __init__(self, resample_kernel, factor=2):
+        super(UpFirDnUpsample, self).__init__()
+        self.kernel = make_resample_kernel(resample_kernel) * (factor**2)
+        self.factor = factor
+
+        pad = self.kernel.shape[0] - factor
+        self.pad = ((pad + 1) // 2 + factor - 1, pad // 2)
+
+    def forward(self, x):
+        out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad)
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(factor={self.factor})')
+
+
+class UpFirDnDownsample(nn.Module):
+    """Upsample, FIR filter, and downsample (downsampole version).
+
+    Args:
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude.
+        factor (int): Downsampling scale factor. Default: 2.
+    """
+
+    def __init__(self, resample_kernel, factor=2):
+        super(UpFirDnDownsample, self).__init__()
+        self.kernel = make_resample_kernel(resample_kernel)
+        self.factor = factor
+
+        pad = self.kernel.shape[0] - factor
+        self.pad = ((pad + 1) // 2, pad // 2)
+
+    def forward(self, x):
+        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad)
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(factor={self.factor})')
+
+
+class UpFirDnSmooth(nn.Module):
+    """Upsample, FIR filter, and downsample (smooth version).
+
+    Args:
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude.
+        upsample_factor (int): Upsampling scale factor. Default: 1.
+        downsample_factor (int): Downsampling scale factor. Default: 1.
+        kernel_size (int): Kernel size: Deafult: 1.
+    """
+
+    def __init__(self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1):
+        super(UpFirDnSmooth, self).__init__()
+        self.upsample_factor = upsample_factor
+        self.downsample_factor = downsample_factor
+        self.kernel = make_resample_kernel(resample_kernel)
+        if upsample_factor > 1:
+            self.kernel = self.kernel * (upsample_factor**2)
+
+        if upsample_factor > 1:
+            pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1)
+            self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1)
+        elif downsample_factor > 1:
+            pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1)
+            self.pad = ((pad + 1) // 2, pad // 2)
+        else:
+            raise NotImplementedError
+
+    def forward(self, x):
+        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad)
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(upsample_factor={self.upsample_factor}'
+                f', downsample_factor={self.downsample_factor})')
+
+
+class EqualLinear(nn.Module):
+    """Equalized Linear as StyleGAN2.
+
+    Args:
+        in_channels (int): Size of each sample.
+        out_channels (int): Size of each output sample.
+        bias (bool): If set to ``False``, the layer will not learn an additive
+            bias. Default: ``True``.
+        bias_init_val (float): Bias initialized value. Default: 0.
+        lr_mul (float): Learning rate multiplier. Default: 1.
+        activation (None | str): The activation after ``linear`` operation.
+            Supported: 'fused_lrelu', None. Default: None.
+    """
+
+    def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None):
+        super(EqualLinear, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.lr_mul = lr_mul
+        self.activation = activation
+        if self.activation not in ['fused_lrelu', None]:
+            raise ValueError(f'Wrong activation value in EqualLinear: {activation}'
+                             "Supported ones are: ['fused_lrelu', None].")
+        self.scale = (1 / math.sqrt(in_channels)) * lr_mul
+
+        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
+        else:
+            self.register_parameter('bias', None)
+
+    def forward(self, x):
+        if self.bias is None:
+            bias = None
+        else:
+            bias = self.bias * self.lr_mul
+        if self.activation == 'fused_lrelu':
+            out = F.linear(x, self.weight * self.scale)
+            out = fused_leaky_relu(out, bias)
+        else:
+            out = F.linear(x, self.weight * self.scale, bias=bias)
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
+                f'out_channels={self.out_channels}, bias={self.bias is not None})')
+
+
+class ModulatedConv2d(nn.Module):
+    """Modulated Conv2d used in StyleGAN2.
+
+    There is no bias in ModulatedConv2d.
+
+    Args:
+        in_channels (int): Channel number of the input.
+        out_channels (int): Channel number of the output.
+        kernel_size (int): Size of the convolving kernel.
+        num_style_feat (int): Channel number of style features.
+        demodulate (bool): Whether to demodulate in the conv layer.
+            Default: True.
+        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
+            Default: None.
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude. Default: (1, 3, 3, 1).
+        eps (float): A value added to the denominator for numerical stability.
+            Default: 1e-8.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 num_style_feat,
+                 demodulate=True,
+                 sample_mode=None,
+                 resample_kernel=(1, 3, 3, 1),
+                 eps=1e-8):
+        super(ModulatedConv2d, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.demodulate = demodulate
+        self.sample_mode = sample_mode
+        self.eps = eps
+
+        if self.sample_mode == 'upsample':
+            self.smooth = UpFirDnSmooth(
+                resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size)
+        elif self.sample_mode == 'downsample':
+            self.smooth = UpFirDnSmooth(
+                resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size)
+        elif self.sample_mode is None:
+            pass
+        else:
+            raise ValueError(f'Wrong sample mode {self.sample_mode}, '
+                             "supported ones are ['upsample', 'downsample', None].")
+
+        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
+        # modulation inside each modulated conv
+        self.modulation = EqualLinear(
+            num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None)
+
+        self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size))
+        self.padding = kernel_size // 2
+
+    def forward(self, x, style):
+        """Forward function.
+
+        Args:
+            x (Tensor): Tensor with shape (b, c, h, w).
+            style (Tensor): Tensor with shape (b, num_style_feat).
+
+        Returns:
+            Tensor: Modulated tensor after convolution.
+        """
+        b, c, h, w = x.shape  # c = c_in
+        # weight modulation
+        style = self.modulation(style).view(b, 1, c, 1, 1)
+        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
+        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)
+
+        if self.demodulate:
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
+            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
+
+        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
+
+        if self.sample_mode == 'upsample':
+            x = x.view(1, b * c, h, w)
+            weight = weight.view(b, self.out_channels, c, self.kernel_size, self.kernel_size)
+            weight = weight.transpose(1, 2).reshape(b * c, self.out_channels, self.kernel_size, self.kernel_size)
+            out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b)
+            out = out.view(b, self.out_channels, *out.shape[2:4])
+            out = self.smooth(out)
+        elif self.sample_mode == 'downsample':
+            x = self.smooth(x)
+            x = x.view(1, b * c, *x.shape[2:4])
+            out = F.conv2d(x, weight, padding=0, stride=2, groups=b)
+            out = out.view(b, self.out_channels, *out.shape[2:4])
+        else:
+            x = x.view(1, b * c, h, w)
+            # weight: (b*c_out, c_in, k, k), groups=b
+            out = F.conv2d(x, weight, padding=self.padding, groups=b)
+            out = out.view(b, self.out_channels, *out.shape[2:4])
+
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
+                f'out_channels={self.out_channels}, '
+                f'kernel_size={self.kernel_size}, '
+                f'demodulate={self.demodulate}, sample_mode={self.sample_mode})')
+
+
+class StyleConv(nn.Module):
+    """Style conv.
+
+    Args:
+        in_channels (int): Channel number of the input.
+        out_channels (int): Channel number of the output.
+        kernel_size (int): Size of the convolving kernel.
+        num_style_feat (int): Channel number of style features.
+        demodulate (bool): Whether demodulate in the conv layer. Default: True.
+        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
+            Default: None.
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude. Default: (1, 3, 3, 1).
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 num_style_feat,
+                 demodulate=True,
+                 sample_mode=None,
+                 resample_kernel=(1, 3, 3, 1)):
+        super(StyleConv, self).__init__()
+        self.modulated_conv = ModulatedConv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            num_style_feat,
+            demodulate=demodulate,
+            sample_mode=sample_mode,
+            resample_kernel=resample_kernel)
+        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
+        self.activate = FusedLeakyReLU(out_channels)
+
+    def forward(self, x, style, noise=None):
+        # modulate
+        out = self.modulated_conv(x, style)
+        # noise injection
+        if noise is None:
+            b, _, h, w = out.shape
+            noise = out.new_empty(b, 1, h, w).normal_()
+        out = out + self.weight * noise
+        # activation (with bias)
+        out = self.activate(out)
+        return out
+
+
+class ToRGB(nn.Module):
+    """To RGB from features.
+
+    Args:
+        in_channels (int): Channel number of input.
+        num_style_feat (int): Channel number of style features.
+        upsample (bool): Whether to upsample. Default: True.
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude. Default: (1, 3, 3, 1).
+    """
+
+    def __init__(self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)):
+        super(ToRGB, self).__init__()
+        if upsample:
+            self.upsample = UpFirDnUpsample(resample_kernel, factor=2)
+        else:
+            self.upsample = None
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, x, style, skip=None):
+        """Forward function.
+
+        Args:
+            x (Tensor): Feature tensor with shape (b, c, h, w).
+            style (Tensor): Tensor with shape (b, num_style_feat).
+            skip (Tensor): Base/skip tensor. Default: None.
+
+        Returns:
+            Tensor: RGB images.
+        """
+        out = self.modulated_conv(x, style)
+        out = out + self.bias
+        if skip is not None:
+            if self.upsample:
+                skip = self.upsample(skip)
+            out = out + skip
+        return out
+
+
+class ConstantInput(nn.Module):
+    """Constant input.
+
+    Args:
+        num_channel (int): Channel number of constant input.
+        size (int): Spatial size of constant input.
+    """
+
+    def __init__(self, num_channel, size):
+        super(ConstantInput, self).__init__()
+        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
+
+    def forward(self, batch):
+        out = self.weight.repeat(batch, 1, 1, 1)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class StyleGAN2Generator(nn.Module):
+    """StyleGAN2 Generator.
+
+    Args:
+        out_size (int): The spatial size of outputs.
+        num_style_feat (int): Channel number of style features. Default: 512.
+        num_mlp (int): Layer number of MLP style layers. Default: 8.
+        channel_multiplier (int): Channel multiplier for large networks of
+            StyleGAN2. Default: 2.
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude. A cross production will be applied to extent 1D resample
+            kenrel to 2D resample kernel. Default: (1, 3, 3, 1).
+        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
+        narrow (float): Narrow ratio for channels. Default: 1.0.
+    """
+
+    def __init__(self,
+                 out_size,
+                 num_style_feat=512,
+                 num_mlp=8,
+                 channel_multiplier=2,
+                 resample_kernel=(1, 3, 3, 1),
+                 lr_mlp=0.01,
+                 narrow=1):
+        super(StyleGAN2Generator, self).__init__()
+        # Style MLP layers
+        self.num_style_feat = num_style_feat
+        style_mlp_layers = [NormStyleCode()]
+        for i in range(num_mlp):
+            style_mlp_layers.append(
+                EqualLinear(
+                    num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp,
+                    activation='fused_lrelu'))
+        self.style_mlp = nn.Sequential(*style_mlp_layers)
+
+        channels = {
+            '4': int(512 * narrow),
+            '8': int(512 * narrow),
+            '16': int(512 * narrow),
+            '32': int(512 * narrow),
+            '64': int(256 * channel_multiplier * narrow),
+            '128': int(128 * channel_multiplier * narrow),
+            '256': int(64 * channel_multiplier * narrow),
+            '512': int(32 * channel_multiplier * narrow),
+            '1024': int(16 * channel_multiplier * narrow)
+        }
+        self.channels = channels
+
+        self.constant_input = ConstantInput(channels['4'], size=4)
+        self.style_conv1 = StyleConv(
+            channels['4'],
+            channels['4'],
+            kernel_size=3,
+            num_style_feat=num_style_feat,
+            demodulate=True,
+            sample_mode=None,
+            resample_kernel=resample_kernel)
+        self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, resample_kernel=resample_kernel)
+
+        self.log_size = int(math.log(out_size, 2))
+        self.num_layers = (self.log_size - 2) * 2 + 1
+        self.num_latent = self.log_size * 2 - 2
+
+        self.style_convs = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+        self.noises = nn.Module()
+
+        in_channels = channels['4']
+        # noise
+        for layer_idx in range(self.num_layers):
+            resolution = 2**((layer_idx + 5) // 2)
+            shape = [1, 1, resolution, resolution]
+            self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
+        # style convs and to_rgbs
+        for i in range(3, self.log_size + 1):
+            out_channels = channels[f'{2**i}']
+            self.style_convs.append(
+                StyleConv(
+                    in_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode='upsample',
+                    resample_kernel=resample_kernel,
+                ))
+            self.style_convs.append(
+                StyleConv(
+                    out_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode=None,
+                    resample_kernel=resample_kernel))
+            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel))
+            in_channels = out_channels
+
+    def make_noise(self):
+        """Make noise for noise injection."""
+        device = self.constant_input.weight.device
+        noises = [torch.randn(1, 1, 4, 4, device=device)]
+
+        for i in range(3, self.log_size + 1):
+            for _ in range(2):
+                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
+
+        return noises
+
+    def get_latent(self, x):
+        return self.style_mlp(x)
+
+    def mean_latent(self, num_latent):
+        latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
+        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
+        return latent
+
+    def forward(self,
+                styles,
+                input_is_latent=False,
+                noise=None,
+                randomize_noise=True,
+                truncation=1,
+                truncation_latent=None,
+                inject_index=None,
+                return_latents=False):
+        """Forward function for StyleGAN2Generator.
+
+        Args:
+            styles (list[Tensor]): Sample codes of styles.
+            input_is_latent (bool): Whether input is latent style.
+                Default: False.
+            noise (Tensor | None): Input noise or None. Default: None.
+            randomize_noise (bool): Randomize noise, used when 'noise' is
+                False. Default: True.
+            truncation (float): TODO. Default: 1.
+            truncation_latent (Tensor | None): TODO. Default: None.
+            inject_index (int | None): The injection index for mixing noise.
+                Default: None.
+            return_latents (bool): Whether to return style latents.
+                Default: False.
+        """
+        # style codes -> latents with Style MLP layer
+        if not input_is_latent:
+            styles = [self.style_mlp(s) for s in styles]
+        # noises
+        if noise is None:
+            if randomize_noise:
+                noise = [None] * self.num_layers  # for each style conv layer
+            else:  # use the stored noise
+                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
+        # style truncation
+        if truncation < 1:
+            style_truncation = []
+            for style in styles:
+                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
+            styles = style_truncation
+        # get style latent with injection
+        if len(styles) == 1:
+            inject_index = self.num_latent
+
+            if styles[0].ndim < 3:
+                # repeat latent code for all the layers
+                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+            else:  # used for encoder with different latent code for each layer
+                latent = styles[0]
+        elif len(styles) == 2:  # mixing noises
+            if inject_index is None:
+                inject_index = random.randint(1, self.num_latent - 1)
+            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
+            latent = torch.cat([latent1, latent2], 1)
+
+        # main generation
+        out = self.constant_input(latent.shape[0])
+        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
+        skip = self.to_rgb1(out, latent[:, 1])
+
+        i = 1
+        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
+                                                        noise[2::2], self.to_rgbs):
+            out = conv1(out, latent[:, i], noise=noise1)
+            out = conv2(out, latent[:, i + 1], noise=noise2)
+            skip = to_rgb(out, latent[:, i + 2], skip)
+            i += 2
+
+        image = skip
+
+        if return_latents:
+            return image, latent
+        else:
+            return image, None
+
+
+class ScaledLeakyReLU(nn.Module):
+    """Scaled LeakyReLU.
+
+    Args:
+        negative_slope (float): Negative slope. Default: 0.2.
+    """
+
+    def __init__(self, negative_slope=0.2):
+        super(ScaledLeakyReLU, self).__init__()
+        self.negative_slope = negative_slope
+
+    def forward(self, x):
+        out = F.leaky_relu(x, negative_slope=self.negative_slope)
+        return out * math.sqrt(2)
+
+
+class EqualConv2d(nn.Module):
+    """Equalized Linear as StyleGAN2.
+
+    Args:
+        in_channels (int): Channel number of the input.
+        out_channels (int): Channel number of the output.
+        kernel_size (int): Size of the convolving kernel.
+        stride (int): Stride of the convolution. Default: 1
+        padding (int): Zero-padding added to both sides of the input.
+            Default: 0.
+        bias (bool): If ``True``, adds a learnable bias to the output.
+            Default: ``True``.
+        bias_init_val (float): Bias initialized value. Default: 0.
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0):
+        super(EqualConv2d, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
+
+        self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
+        else:
+            self.register_parameter('bias', None)
+
+    def forward(self, x):
+        out = F.conv2d(
+            x,
+            self.weight * self.scale,
+            bias=self.bias,
+            stride=self.stride,
+            padding=self.padding,
+        )
+
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
+                f'out_channels={self.out_channels}, '
+                f'kernel_size={self.kernel_size},'
+                f' stride={self.stride}, padding={self.padding}, '
+                f'bias={self.bias is not None})')
+
+
+class ConvLayer(nn.Sequential):
+    """Conv Layer used in StyleGAN2 Discriminator.
+
+    Args:
+        in_channels (int): Channel number of the input.
+        out_channels (int): Channel number of the output.
+        kernel_size (int): Kernel size.
+        downsample (bool): Whether downsample by a factor of 2.
+            Default: False.
+        resample_kernel (list[int]): A list indicating the 1D resample
+            kernel magnitude. A cross production will be applied to
+            extent 1D resample kenrel to 2D resample kernel.
+            Default: (1, 3, 3, 1).
+        bias (bool): Whether with bias. Default: True.
+        activate (bool): Whether use activateion. Default: True.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 downsample=False,
+                 resample_kernel=(1, 3, 3, 1),
+                 bias=True,
+                 activate=True):
+        layers = []
+        # downsample
+        if downsample:
+            layers.append(
+                UpFirDnSmooth(resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size))
+            stride = 2
+            self.padding = 0
+        else:
+            stride = 1
+            self.padding = kernel_size // 2
+        # conv
+        layers.append(
+            EqualConv2d(
+                in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias
+                and not activate))
+        # activation
+        if activate:
+            if bias:
+                layers.append(FusedLeakyReLU(out_channels))
+            else:
+                layers.append(ScaledLeakyReLU(0.2))
+
+        super(ConvLayer, self).__init__(*layers)
+
+
+class ResBlock(nn.Module):
+    """Residual block used in StyleGAN2 Discriminator.
+
+    Args:
+        in_channels (int): Channel number of the input.
+        out_channels (int): Channel number of the output.
+        resample_kernel (list[int]): A list indicating the 1D resample
+            kernel magnitude. A cross production will be applied to
+            extent 1D resample kenrel to 2D resample kernel.
+            Default: (1, 3, 3, 1).
+    """
+
+    def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)):
+        super(ResBlock, self).__init__()
+
+        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
+        self.conv2 = ConvLayer(
+            in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True)
+        self.skip = ConvLayer(
+            in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.conv2(out)
+        skip = self.skip(x)
+        out = (out + skip) / math.sqrt(2)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class StyleGAN2Discriminator(nn.Module):
+    """StyleGAN2 Discriminator.
+
+    Args:
+        out_size (int): The spatial size of outputs.
+        channel_multiplier (int): Channel multiplier for large networks of
+            StyleGAN2. Default: 2.
+        resample_kernel (list[int]): A list indicating the 1D resample kernel
+            magnitude. A cross production will be applied to extent 1D resample
+            kenrel to 2D resample kernel. Default: (1, 3, 3, 1).
+        stddev_group (int): For group stddev statistics. Default: 4.
+        narrow (float): Narrow ratio for channels. Default: 1.0.
+    """
+
+    def __init__(self, out_size, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), stddev_group=4, narrow=1):
+        super(StyleGAN2Discriminator, self).__init__()
+
+        channels = {
+            '4': int(512 * narrow),
+            '8': int(512 * narrow),
+            '16': int(512 * narrow),
+            '32': int(512 * narrow),
+            '64': int(256 * channel_multiplier * narrow),
+            '128': int(128 * channel_multiplier * narrow),
+            '256': int(64 * channel_multiplier * narrow),
+            '512': int(32 * channel_multiplier * narrow),
+            '1024': int(16 * channel_multiplier * narrow)
+        }
+
+        log_size = int(math.log(out_size, 2))
+
+        conv_body = [ConvLayer(3, channels[f'{out_size}'], 1, bias=True, activate=True)]
+
+        in_channels = channels[f'{out_size}']
+        for i in range(log_size, 2, -1):
+            out_channels = channels[f'{2**(i - 1)}']
+            conv_body.append(ResBlock(in_channels, out_channels, resample_kernel))
+            in_channels = out_channels
+        self.conv_body = nn.Sequential(*conv_body)
+
+        self.final_conv = ConvLayer(in_channels + 1, channels['4'], 3, bias=True, activate=True)
+        self.final_linear = nn.Sequential(
+            EqualLinear(
+                channels['4'] * 4 * 4, channels['4'], bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'),
+            EqualLinear(channels['4'], 1, bias=True, bias_init_val=0, lr_mul=1, activation=None),
+        )
+        self.stddev_group = stddev_group
+        self.stddev_feat = 1
+
+    def forward(self, x):
+        out = self.conv_body(x)
+
+        b, c, h, w = out.shape
+        # concatenate a group stddev statistics to out
+        group = min(b, self.stddev_group)  # Minibatch must be divisible by (or smaller than) group_size
+        stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w)
+        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+        stddev = stddev.repeat(group, 1, h, w)
+        out = torch.cat([out, stddev], 1)
+
+        out = self.final_conv(out)
+        out = out.view(b, -1)
+        out = self.final_linear(out)
+
+        return out
diff --git a/basicsr/archs/tof_arch.py b/basicsr/archs/tof_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..09cf7069157604b8b925219d8c8be2a14c9df26f
--- /dev/null
+++ b/basicsr/archs/tof_arch.py
@@ -0,0 +1,172 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import flow_warp
+
+
+class BasicModule(nn.Module):
+    """Basic module of SPyNet.
+
+    Note that unlike the architecture in spynet_arch.py, the basic module
+    here contains batch normalization.
+    """
+
+    def __init__(self):
+        super(BasicModule, self).__init__()
+        self.basic_module = nn.Sequential(
+            nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False),
+            nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3, bias=False),
+            nn.BatchNorm2d(64), nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False),
+            nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3, bias=False),
+            nn.BatchNorm2d(16), nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3))
+
+    def forward(self, tensor_input):
+        """
+        Args:
+            tensor_input (Tensor): Input tensor with shape (b, 8, h, w).
+                8 channels contain:
+                [reference image (3), neighbor image (3), initial flow (2)].
+
+        Returns:
+            Tensor: Estimated flow with shape (b, 2, h, w)
+        """
+        return self.basic_module(tensor_input)
+
+
+class SPyNetTOF(nn.Module):
+    """SPyNet architecture for TOF.
+
+    Note that this implementation is specifically for TOFlow. Please use
+    spynet_arch.py for general use. They differ in the following aspects:
+        1. The basic modules here contain BatchNorm.
+        2. Normalization and denormalization are not done here, as
+            they are done in TOFlow.
+    Paper:
+        Optical Flow Estimation using a Spatial Pyramid Network
+    Code reference:
+        https://github.com/Coldog2333/pytoflow
+
+    Args:
+        load_path (str): Path for pretrained SPyNet. Default: None.
+    """
+
+    def __init__(self, load_path=None):
+        super(SPyNetTOF, self).__init__()
+
+        self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)])
+        if load_path:
+            self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params'])
+
+    def forward(self, ref, supp):
+        """
+        Args:
+            ref (Tensor): Reference image with shape of (b, 3, h, w).
+            supp: The supporting image to be warped: (b, 3, h, w).
+
+        Returns:
+            Tensor: Estimated optical flow: (b, 2, h, w).
+        """
+        num_batches, _, h, w = ref.size()
+        ref = [ref]
+        supp = [supp]
+
+        # generate downsampled frames
+        for _ in range(3):
+            ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False))
+            supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False))
+
+        # flow computation
+        flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16)
+        for i in range(4):
+            flow_up = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+            flow = flow_up + self.basic_module[i](
+                torch.cat([ref[i], flow_warp(supp[i], flow_up.permute(0, 2, 3, 1)), flow_up], 1))
+        return flow
+
+
+@ARCH_REGISTRY.register()
+class TOFlow(nn.Module):
+    """PyTorch implementation of TOFlow.
+
+    In TOFlow, the LR frames are pre-upsampled and have the same size with
+    the GT frames.
+    Paper:
+        Xue et al., Video Enhancement with Task-Oriented Flow, IJCV 2018
+    Code reference:
+        1. https://github.com/anchen1011/toflow
+        2. https://github.com/Coldog2333/pytoflow
+
+    Args:
+        adapt_official_weights (bool): Whether to adapt the weights translated
+            from the official implementation. Set to false if you want to
+            train from scratch. Default: False
+    """
+
+    def __init__(self, adapt_official_weights=False):
+        super(TOFlow, self).__init__()
+        self.adapt_official_weights = adapt_official_weights
+        self.ref_idx = 0 if adapt_official_weights else 3
+
+        self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+        self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+        # flow estimation module
+        self.spynet = SPyNetTOF()
+
+        # reconstruction module
+        self.conv_1 = nn.Conv2d(3 * 7, 64, 9, 1, 4)
+        self.conv_2 = nn.Conv2d(64, 64, 9, 1, 4)
+        self.conv_3 = nn.Conv2d(64, 64, 1)
+        self.conv_4 = nn.Conv2d(64, 3, 1)
+
+        # activation function
+        self.relu = nn.ReLU(inplace=True)
+
+    def normalize(self, img):
+        return (img - self.mean) / self.std
+
+    def denormalize(self, img):
+        return img * self.std + self.mean
+
+    def forward(self, lrs):
+        """
+        Args:
+            lrs: Input lr frames: (b, 7, 3, h, w).
+
+        Returns:
+            Tensor: SR frame: (b, 3, h, w).
+        """
+        # In the official implementation, the 0-th frame is the reference frame
+        if self.adapt_official_weights:
+            lrs = lrs[:, [3, 0, 1, 2, 4, 5, 6], :, :, :]
+
+        num_batches, num_lrs, _, h, w = lrs.size()
+
+        lrs = self.normalize(lrs.view(-1, 3, h, w))
+        lrs = lrs.view(num_batches, num_lrs, 3, h, w)
+
+        lr_ref = lrs[:, self.ref_idx, :, :, :]
+        lr_aligned = []
+        for i in range(7):  # 7 frames
+            if i == self.ref_idx:
+                lr_aligned.append(lr_ref)
+            else:
+                lr_supp = lrs[:, i, :, :, :]
+                flow = self.spynet(lr_ref, lr_supp)
+                lr_aligned.append(flow_warp(lr_supp, flow.permute(0, 2, 3, 1)))
+
+        # reconstruction
+        hr = torch.stack(lr_aligned, dim=1)
+        hr = hr.view(num_batches, -1, h, w)
+        hr = self.relu(self.conv_1(hr))
+        hr = self.relu(self.conv_2(hr))
+        hr = self.relu(self.conv_3(hr))
+        hr = self.conv_4(hr) + lr_ref
+
+        return self.denormalize(hr)
diff --git a/basicsr/archs/vgg_arch.py b/basicsr/archs/vgg_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..05200334e477e59feefd1e4a0b5e94204e4eb2fa
--- /dev/null
+++ b/basicsr/archs/vgg_arch.py
@@ -0,0 +1,161 @@
+import os
+import torch
+from collections import OrderedDict
+from torch import nn as nn
+from torchvision.models import vgg as vgg
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth'
+NAMES = {
+    'vgg11': [
+        'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2',
+        'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2',
+        'pool5'
+    ],
+    'vgg13': [
+        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
+        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4',
+        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5'
+    ],
+    'vgg16': [
+        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
+        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
+        'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
+        'pool5'
+    ],
+    'vgg19': [
+        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
+        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1',
+        'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1',
+        'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5'
+    ]
+}
+
+
+def insert_bn(names):
+    """Insert bn layer after each conv.
+
+    Args:
+        names (list): The list of layer names.
+
+    Returns:
+        list: The list of layer names with bn layers.
+    """
+    names_bn = []
+    for name in names:
+        names_bn.append(name)
+        if 'conv' in name:
+            position = name.replace('conv', '')
+            names_bn.append('bn' + position)
+    return names_bn
+
+
+@ARCH_REGISTRY.register()
+class VGGFeatureExtractor(nn.Module):
+    """VGG network for feature extraction.
+
+    In this implementation, we allow users to choose whether use normalization
+    in the input feature and the type of vgg network. Note that the pretrained
+    path must fit the vgg type.
+
+    Args:
+        layer_name_list (list[str]): Forward function returns the corresponding
+            features according to the layer_name_list.
+            Example: {'relu1_1', 'relu2_1', 'relu3_1'}.
+        vgg_type (str): Set the type of vgg network. Default: 'vgg19'.
+        use_input_norm (bool): If True, normalize the input image. Importantly,
+            the input feature must in the range [0, 1]. Default: True.
+        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
+            Default: False.
+        requires_grad (bool): If true, the parameters of VGG network will be
+            optimized. Default: False.
+        remove_pooling (bool): If true, the max pooling operations in VGG net
+            will be removed. Default: False.
+        pooling_stride (int): The stride of max pooling operation. Default: 2.
+    """
+
+    def __init__(self,
+                 layer_name_list,
+                 vgg_type='vgg19',
+                 use_input_norm=True,
+                 range_norm=False,
+                 requires_grad=False,
+                 remove_pooling=False,
+                 pooling_stride=2):
+        super(VGGFeatureExtractor, self).__init__()
+
+        self.layer_name_list = layer_name_list
+        self.use_input_norm = use_input_norm
+        self.range_norm = range_norm
+
+        self.names = NAMES[vgg_type.replace('_bn', '')]
+        if 'bn' in vgg_type:
+            self.names = insert_bn(self.names)
+
+        # only borrow layers that will be used to avoid unused params
+        max_idx = 0
+        for v in layer_name_list:
+            idx = self.names.index(v)
+            if idx > max_idx:
+                max_idx = idx
+
+        if os.path.exists(VGG_PRETRAIN_PATH):
+            vgg_net = getattr(vgg, vgg_type)(pretrained=False)
+            state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage)
+            vgg_net.load_state_dict(state_dict)
+        else:
+            vgg_net = getattr(vgg, vgg_type)(pretrained=True)
+
+        features = vgg_net.features[:max_idx + 1]
+
+        modified_net = OrderedDict()
+        for k, v in zip(self.names, features):
+            if 'pool' in k:
+                # if remove_pooling is true, pooling operation will be removed
+                if remove_pooling:
+                    continue
+                else:
+                    # in some cases, we may want to change the default stride
+                    modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride)
+            else:
+                modified_net[k] = v
+
+        self.vgg_net = nn.Sequential(modified_net)
+
+        if not requires_grad:
+            self.vgg_net.eval()
+            for param in self.parameters():
+                param.requires_grad = False
+        else:
+            self.vgg_net.train()
+            for param in self.parameters():
+                param.requires_grad = True
+
+        if self.use_input_norm:
+            # the mean is for image with range [0, 1]
+            self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+            # the std is for image with range [0, 1]
+            self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+    def forward(self, x):
+        """Forward function.
+
+        Args:
+            x (Tensor): Input tensor with shape (n, c, h, w).
+
+        Returns:
+            Tensor: Forward results.
+        """
+        if self.range_norm:
+            x = (x + 1) / 2
+        if self.use_input_norm:
+            x = (x - self.mean) / self.std
+
+        output = {}
+        for key, layer in self.vgg_net._modules.items():
+            x = layer(x)
+            if key in self.layer_name_list:
+                output[key] = x.clone()
+
+        return output
diff --git a/basicsr/data/__init__.py b/basicsr/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6adb4bb6a926af7a46aaec4794eee95fda02a33
--- /dev/null
+++ b/basicsr/data/__init__.py
@@ -0,0 +1,100 @@
+import importlib
+import numpy as np
+import random
+import torch
+import torch.utils.data
+from copy import deepcopy
+from functools import partial
+from os import path as osp
+
+from basicsr.data.prefetch_dataloader import PrefetchDataLoader
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.dist_util import get_dist_info
+from basicsr.utils.registry import DATASET_REGISTRY
+
+__all__ = ['build_dataset', 'build_dataloader']
+
+# automatically scan and import dataset modules for registry
+# scan all the files under the data folder with '_dataset' in file names
+data_folder = osp.dirname(osp.abspath(__file__))
+dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
+# import all the dataset modules
+_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames]
+
+
+def build_dataset(dataset_opt):
+    """Build dataset from options.
+
+    Args:
+        dataset_opt (dict): Configuration for dataset. It must constain:
+            name (str): Dataset name.
+            type (str): Dataset type.
+    """
+    dataset_opt = deepcopy(dataset_opt)
+    dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt)
+    logger = get_root_logger()
+    logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} ' 'is built.')
+    return dataset
+
+
+def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None):
+    """Build dataloader.
+
+    Args:
+        dataset (torch.utils.data.Dataset): Dataset.
+        dataset_opt (dict): Dataset options. It contains the following keys:
+            phase (str): 'train' or 'val'.
+            num_worker_per_gpu (int): Number of workers for each GPU.
+            batch_size_per_gpu (int): Training batch size for each GPU.
+        num_gpu (int): Number of GPUs. Used only in the train phase.
+            Default: 1.
+        dist (bool): Whether in distributed training. Used only in the train
+            phase. Default: False.
+        sampler (torch.utils.data.sampler): Data sampler. Default: None.
+        seed (int | None): Seed. Default: None
+    """
+    phase = dataset_opt['phase']
+    rank, _ = get_dist_info()
+    if phase == 'train':
+        if dist:  # distributed training
+            batch_size = dataset_opt['batch_size_per_gpu']
+            num_workers = dataset_opt['num_worker_per_gpu']
+        else:  # non-distributed training
+            multiplier = 1 if num_gpu == 0 else num_gpu
+            batch_size = dataset_opt['batch_size_per_gpu'] * multiplier
+            num_workers = dataset_opt['num_worker_per_gpu'] * multiplier
+        dataloader_args = dict(
+            dataset=dataset,
+            batch_size=batch_size,
+            shuffle=False,
+            num_workers=num_workers,
+            sampler=sampler,
+            drop_last=True)
+        if sampler is None:
+            dataloader_args['shuffle'] = True
+        dataloader_args['worker_init_fn'] = partial(
+            worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None
+    elif phase in ['val', 'test']:  # validation
+        dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
+    else:
+        raise ValueError(f'Wrong dataset phase: {phase}. ' "Supported ones are 'train', 'val' and 'test'.")
+
+    dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False)
+
+    prefetch_mode = dataset_opt.get('prefetch_mode')
+    if prefetch_mode == 'cpu':  # CPUPrefetcher
+        num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1)
+        logger = get_root_logger()
+        logger.info(f'Use {prefetch_mode} prefetch dataloader: ' f'num_prefetch_queue = {num_prefetch_queue}')
+        return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args)
+    else:
+        # prefetch_mode=None: Normal dataloader
+        # prefetch_mode='cuda': dataloader for CUDAPrefetcher
+        return torch.utils.data.DataLoader(**dataloader_args)
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+    # Set the worker seed to num_workers * rank + worker_id + seed
+    worker_seed = num_workers * rank + worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
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diff --git a/basicsr/data/data_sampler.py b/basicsr/data/data_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..575452d9f844a928f7f42296c81635cfbadec7c2
--- /dev/null
+++ b/basicsr/data/data_sampler.py
@@ -0,0 +1,48 @@
+import math
+import torch
+from torch.utils.data.sampler import Sampler
+
+
+class EnlargedSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset.
+
+    Modified from torch.utils.data.distributed.DistributedSampler
+    Support enlarging the dataset for iteration-based training, for saving
+    time when restart the dataloader after each epoch
+
+    Args:
+        dataset (torch.utils.data.Dataset): Dataset used for sampling.
+        num_replicas (int | None): Number of processes participating in
+            the training. It is usually the world_size.
+        rank (int | None): Rank of the current process within num_replicas.
+        ratio (int): Enlarging ratio. Default: 1.
+    """
+
+    def __init__(self, dataset, num_replicas, rank, ratio=1):
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas)
+        self.total_size = self.num_samples * self.num_replicas
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        indices = torch.randperm(self.total_size, generator=g).tolist()
+
+        dataset_size = len(self.dataset)
+        indices = [v % dataset_size for v in indices]
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
diff --git a/basicsr/data/data_util.py b/basicsr/data/data_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..63b1bce8e089485182c962e830a163d6d0059da8
--- /dev/null
+++ b/basicsr/data/data_util.py
@@ -0,0 +1,305 @@
+import cv2
+import numpy as np
+import torch
+from os import path as osp
+from torch.nn import functional as F
+
+from basicsr.data.transforms import mod_crop
+from basicsr.utils import img2tensor, scandir
+
+
+def read_img_seq(path, require_mod_crop=False, scale=1):
+    """Read a sequence of images from a given folder path.
+
+    Args:
+        path (list[str] | str): List of image paths or image folder path.
+        require_mod_crop (bool): Require mod crop for each image.
+            Default: False.
+        scale (int): Scale factor for mod_crop. Default: 1.
+
+    Returns:
+        Tensor: size (t, c, h, w), RGB, [0, 1].
+    """
+    if isinstance(path, list):
+        img_paths = path
+    else:
+        img_paths = sorted(list(scandir(path, full_path=True)))
+    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
+    if require_mod_crop:
+        imgs = [mod_crop(img, scale) for img in imgs]
+    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
+    imgs = torch.stack(imgs, dim=0)
+    return imgs
+
+
+def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'):
+    """Generate an index list for reading `num_frames` frames from a sequence
+    of images.
+
+    Args:
+        crt_idx (int): Current center index.
+        max_frame_num (int): Max number of the sequence of images (from 1).
+        num_frames (int): Reading num_frames frames.
+        padding (str): Padding mode, one of
+            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
+            Examples: current_idx = 0, num_frames = 5
+            The generated frame indices under different padding mode:
+            replicate: [0, 0, 0, 1, 2]
+            reflection: [2, 1, 0, 1, 2]
+            reflection_circle: [4, 3, 0, 1, 2]
+            circle: [3, 4, 0, 1, 2]
+
+    Returns:
+        list[int]: A list of indices.
+    """
+    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
+    assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.'
+
+    max_frame_num = max_frame_num - 1  # start from 0
+    num_pad = num_frames // 2
+
+    indices = []
+    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
+        if i < 0:
+            if padding == 'replicate':
+                pad_idx = 0
+            elif padding == 'reflection':
+                pad_idx = -i
+            elif padding == 'reflection_circle':
+                pad_idx = crt_idx + num_pad - i
+            else:
+                pad_idx = num_frames + i
+        elif i > max_frame_num:
+            if padding == 'replicate':
+                pad_idx = max_frame_num
+            elif padding == 'reflection':
+                pad_idx = max_frame_num * 2 - i
+            elif padding == 'reflection_circle':
+                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
+            else:
+                pad_idx = i - num_frames
+        else:
+            pad_idx = i
+        indices.append(pad_idx)
+    return indices
+
+
+def paired_paths_from_lmdb(folders, keys):
+    """Generate paired paths from lmdb files.
+
+    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:
+
+    lq.lmdb
+    ├── data.mdb
+    ├── lock.mdb
+    ├── meta_info.txt
+
+    The data.mdb and lock.mdb are standard lmdb files and you can refer to
+    https://lmdb.readthedocs.io/en/release/ for more details.
+
+    The meta_info.txt is a specified txt file to record the meta information
+    of our datasets. It will be automatically created when preparing
+    datasets by our provided dataset tools.
+    Each line in the txt file records
+    1)image name (with extension),
+    2)image shape,
+    3)compression level, separated by a white space.
+    Example: `baboon.png (120,125,3) 1`
+
+    We use the image name without extension as the lmdb key.
+    Note that we use the same key for the corresponding lq and gt images.
+
+    Args:
+        folders (list[str]): A list of folder path. The order of list should
+            be [input_folder, gt_folder].
+        keys (list[str]): A list of keys identifying folders. The order should
+            be in consistent with folders, e.g., ['lq', 'gt'].
+            Note that this key is different from lmdb keys.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
+                               f'But got {len(folders)}')
+    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
+    input_folder, gt_folder = folders
+    input_key, gt_key = keys
+
+    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
+        raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb '
+                         f'formats. But received {input_key}: {input_folder}; '
+                         f'{gt_key}: {gt_folder}')
+    # ensure that the two meta_info files are the same
+    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
+        input_lmdb_keys = [line.split('.')[0] for line in fin]
+    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
+        gt_lmdb_keys = [line.split('.')[0] for line in fin]
+    if set(input_lmdb_keys) != set(gt_lmdb_keys):
+        raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.')
+    else:
+        paths = []
+        for lmdb_key in sorted(input_lmdb_keys):
+            paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)]))
+        return paths
+
+
+def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl):
+    """Generate paired paths from an meta information file.
+
+    Each line in the meta information file contains the image names and
+    image shape (usually for gt), separated by a white space.
+
+    Example of an meta information file:
+    ```
+    0001_s001.png (480,480,3)
+    0001_s002.png (480,480,3)
+    ```
+
+    Args:
+        folders (list[str]): A list of folder path. The order of list should
+            be [input_folder, gt_folder].
+        keys (list[str]): A list of keys identifying folders. The order should
+            be in consistent with folders, e.g., ['lq', 'gt'].
+        meta_info_file (str): Path to the meta information file.
+        filename_tmpl (str): Template for each filename. Note that the
+            template excludes the file extension. Usually the filename_tmpl is
+            for files in the input folder.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
+                               f'But got {len(folders)}')
+    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
+    input_folder, gt_folder = folders
+    input_key, gt_key = keys
+
+    with open(meta_info_file, 'r') as fin:
+        gt_names = [line.split(' ')[0] for line in fin]
+
+    paths = []
+    for gt_name in gt_names:
+        basename, ext = osp.splitext(osp.basename(gt_name))
+        input_name = f'{filename_tmpl.format(basename)}{ext}'
+        input_path = osp.join(input_folder, input_name)
+        gt_path = osp.join(gt_folder, gt_name)
+        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
+    return paths
+
+
+def paired_paths_from_folder(folders, keys, filename_tmpl):
+    """Generate paired paths from folders.
+
+    Args:
+        folders (list[str]): A list of folder path. The order of list should
+            be [input_folder, gt_folder].
+        keys (list[str]): A list of keys identifying folders. The order should
+            be in consistent with folders, e.g., ['lq', 'gt'].
+        filename_tmpl (str): Template for each filename. Note that the
+            template excludes the file extension. Usually the filename_tmpl is
+            for files in the input folder.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
+                               f'But got {len(folders)}')
+    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
+    input_folder, gt_folder = folders
+    input_key, gt_key = keys
+
+    input_paths = list(scandir(input_folder))
+    gt_paths = list(scandir(gt_folder))
+    assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: '
+                                               f'{len(input_paths)}, {len(gt_paths)}.')
+    paths = []
+    for gt_path in gt_paths:
+        basename, ext = osp.splitext(osp.basename(gt_path))
+        input_name = f'{filename_tmpl.format(basename)}{ext}'
+        input_path = osp.join(input_folder, input_name)
+        assert input_name in input_paths, (f'{input_name} is not in ' f'{input_key}_paths.')
+        gt_path = osp.join(gt_folder, gt_path)
+        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
+    return paths
+
+
+def paths_from_folder(folder):
+    """Generate paths from folder.
+
+    Args:
+        folder (str): Folder path.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+
+    paths = list(scandir(folder))
+    paths = [osp.join(folder, path) for path in paths]
+    return paths
+
+
+def paths_from_lmdb(folder):
+    """Generate paths from lmdb.
+
+    Args:
+        folder (str): Folder path.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    if not folder.endswith('.lmdb'):
+        raise ValueError(f'Folder {folder}folder should in lmdb format.')
+    with open(osp.join(folder, 'meta_info.txt')) as fin:
+        paths = [line.split('.')[0] for line in fin]
+    return paths
+
+
+def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
+    """Generate Gaussian kernel used in `duf_downsample`.
+
+    Args:
+        kernel_size (int): Kernel size. Default: 13.
+        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.
+
+    Returns:
+        np.array: The Gaussian kernel.
+    """
+    from scipy.ndimage import filters as filters
+    kernel = np.zeros((kernel_size, kernel_size))
+    # set element at the middle to one, a dirac delta
+    kernel[kernel_size // 2, kernel_size // 2] = 1
+    # gaussian-smooth the dirac, resulting in a gaussian filter
+    return filters.gaussian_filter(kernel, sigma)
+
+
+def duf_downsample(x, kernel_size=13, scale=4):
+    """Downsamping with Gaussian kernel used in the DUF official code.
+
+    Args:
+        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
+        kernel_size (int): Kernel size. Default: 13.
+        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
+            Default: 4.
+
+    Returns:
+        Tensor: DUF downsampled frames.
+    """
+    assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.'
+
+    squeeze_flag = False
+    if x.ndim == 4:
+        squeeze_flag = True
+        x = x.unsqueeze(0)
+    b, t, c, h, w = x.size()
+    x = x.view(-1, 1, h, w)
+    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
+    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')
+
+    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
+    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0)
+    x = F.conv2d(x, gaussian_filter, stride=scale)
+    x = x[:, :, 2:-2, 2:-2]
+    x = x.view(b, t, c, x.size(2), x.size(3))
+    if squeeze_flag:
+        x = x.squeeze(0)
+    return x
diff --git a/basicsr/data/degradations.py b/basicsr/data/degradations.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fe408d25c641aaef82030bb16b178380be71899
--- /dev/null
+++ b/basicsr/data/degradations.py
@@ -0,0 +1,347 @@
+import cv2
+import math
+import numpy as np
+import random
+
+# -------------------------------------------------------------------- #
+# --------------------------- blur kernels --------------------------- #
+# -------------------------------------------------------------------- #
+
+
+def sigma_matrix2(sig_x, sig_y, theta):
+    """Calculate the rotated sigma matrix (two dimensional matrix).
+
+    Args:
+        sig_x (float):
+        sig_y (float):
+        theta (float): Radian measurement.
+
+    Returns:
+        ndarray: Rotated sigma matrix.
+    """
+    D = np.array([[sig_x**2, 0], [0, sig_y**2]])
+    U = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
+    return np.dot(U, np.dot(D, U.T))
+
+
+def mesh_grid(kernel_size):
+    """Generate the mesh grid, centering at zero.
+
+    Args:
+        kernel_size (int):
+
+    Returns:
+        xy (ndarray): with the shape (kernel_size, kernel_size, 2)
+        xx (ndarray): with the shape (kernel_size, kernel_size)
+        yy (ndarray): with the shape (kernel_size, kernel_size)
+    """
+    ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.)
+    xx, yy = np.meshgrid(ax, ax)
+    xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size,
+                                                                           1))).reshape(kernel_size, kernel_size, 2)
+    return xy, xx, yy
+
+
+def pdf2(sigma_matrix, grid):
+    """Calculate PDF of the bivariate Gaussian distribution.
+
+    Args:
+        sigma_matrix (ndarray): with the shape (2, 2)
+        grid (ndarray): generated by :func:`mesh_grid`,
+            with the shape (K, K, 2), K is the kernel size.
+
+    Returns:
+        kernel (ndarrray): un-normalized kernel.
+    """
+    inverse_sigma = np.linalg.inv(sigma_matrix)
+    kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2))
+    return kernel
+
+
+def mass_center_shift(kernel_size, kernel):
+    """Calculate the shift of the mass center of a kenrel.
+
+    Args:
+        kernel_size (int):
+        kernel (ndarray): normalized kernel.
+
+    Returns:
+        delta_h (float):
+        delta_w (float):
+    """
+    ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.)
+    col_sum, row_sum = np.sum(kernel, axis=0), np.sum(kernel, axis=1)
+    delta_h = np.dot(row_sum, ax)
+    delta_w = np.dot(col_sum, ax)
+    return delta_h, delta_w
+
+
+def bivariate_anisotropic_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None):
+    """Generate a bivariate anisotropic Gaussian kernel.
+
+    Args:
+        kernel_size (int):
+        sig_x (float):
+        sig_y (float):
+        theta (float): Radian measurement.
+        grid (ndarray, optional): generated by :func:`mesh_grid`,
+            with the shape (K, K, 2), K is the kernel size. Default: None
+
+    Returns:
+        kernel (ndarray): normalized kernel.
+    """
+    if grid is None:
+        grid, _, _ = mesh_grid(kernel_size)
+    sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
+    kernel = pdf2(sigma_matrix, grid)
+    kernel = kernel / np.sum(kernel)
+    return kernel
+
+
+def bivariate_isotropic_Gaussian(kernel_size, sig, grid=None):
+    """Generate a bivariate isotropic Gaussian kernel.
+
+    Args:
+        kernel_size (int):
+        sig (float):
+        grid (ndarray, optional): generated by :func:`mesh_grid`,
+            with the shape (K, K, 2), K is the kernel size. Default: None
+
+    Returns:
+        kernel (ndarray): normalized kernel.
+    """
+    if grid is None:
+        grid, _, _ = mesh_grid(kernel_size)
+    sigma_matrix = np.array([[sig**2, 0], [0, sig**2]])
+    kernel = pdf2(sigma_matrix, grid)
+    kernel = kernel / np.sum(kernel)
+    return kernel
+
+
+def random_bivariate_anisotropic_Gaussian(kernel_size,
+                                          sigma_x_range,
+                                          sigma_y_range,
+                                          rotation_range,
+                                          noise_range=None,
+                                          strict=False):
+    """Randomly generate bivariate anisotropic Gaussian kernels.
+
+    Args:
+        kernel_size (int):
+        sigma_x_range (tuple): [0.6, 5]
+        sigma_y_range (tuple): [0.6, 5]
+        rotation range (tuple): [-math.pi, math.pi]
+        noise_range(tuple, optional): multiplicative kernel noise,
+            [0.75, 1.25]. Default: None
+
+    Returns:
+        kernel (ndarray):
+    """
+    assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'
+    assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.'
+    assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.'
+    assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.'
+    sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])
+    sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])
+    if strict:
+        sigma_max = np.max([sigma_x, sigma_y])
+        sigma_min = np.min([sigma_x, sigma_y])
+        sigma_x, sigma_y = sigma_max, sigma_min
+    rotation = np.random.uniform(rotation_range[0], rotation_range[1])
+
+    kernel = bivariate_anisotropic_Gaussian(kernel_size, sigma_x, sigma_y, rotation)
+
+    # add multiplicative noise
+    if noise_range is not None:
+        assert noise_range[0] < noise_range[1], 'Wrong noise range.'
+        noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape)
+        kernel = kernel * noise
+    kernel = kernel / np.sum(kernel)
+    if strict:
+        return kernel, sigma_x, sigma_y, rotation
+    else:
+        return kernel
+
+
+def random_bivariate_isotropic_Gaussian(kernel_size, sigma_range, noise_range=None, strict=False):
+    """Randomly generate bivariate isotropic Gaussian kernels.
+
+    Args:
+        kernel_size (int):
+        sigma_range (tuple): [0.6, 5]
+        noise_range(tuple, optional): multiplicative kernel noise,
+            [0.75, 1.25]. Default: None
+
+    Returns:
+        kernel (ndarray):
+    """
+    assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'
+    assert sigma_range[0] < sigma_range[1], 'Wrong sigma_x_range.'
+    sigma = np.random.uniform(sigma_range[0], sigma_range[1])
+
+    kernel = bivariate_isotropic_Gaussian(kernel_size, sigma)
+
+    # add multiplicative noise
+    if noise_range is not None:
+        assert noise_range[0] < noise_range[1], 'Wrong noise range.'
+        noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape)
+        kernel = kernel * noise
+    kernel = kernel / np.sum(kernel)
+    if strict:
+        return kernel, sigma
+    else:
+        return kernel
+
+
+def random_mixed_kernels(kernel_list,
+                         kernel_prob,
+                         kernel_size=21,
+                         sigma_x_range=[0.6, 5],
+                         sigma_y_range=[0.6, 5],
+                         rotation_range=[-math.pi, math.pi],
+                         beta_range=[0.5, 8],
+                         noise_range=None):
+    """Randomly generate mixed kernels.
+
+    Args:
+        kernel_list (tuple): a list name of kenrel types,
+            support ['iso', 'aniso']
+        kernel_prob (tuple): corresponding kernel probability for each
+            kernel type
+        kernel_size (int):
+        sigma_x_range (tuple): [0.6, 5]
+        sigma_y_range (tuple): [0.6, 5]
+        rotation range (tuple): [-math.pi, math.pi]
+        beta_range (tuple): [0.5, 8]
+        noise_range(tuple, optional): multiplicative kernel noise,
+            [0.75, 1.25]. Default: None
+
+    Returns:
+        kernel (ndarray):
+    """
+    kernel_type = random.choices(kernel_list, kernel_prob)[0]
+    if kernel_type == 'iso':
+        kernel = random_bivariate_isotropic_Gaussian(kernel_size, sigma_x_range, noise_range=noise_range)
+    elif kernel_type == 'aniso':
+        kernel = random_bivariate_anisotropic_Gaussian(
+            kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range)
+
+    # add multiplicative noise
+    if noise_range is not None:
+        assert noise_range[0] < noise_range[1], 'Wrong noise range.'
+        noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape)
+        kernel = kernel * noise
+    kernel = kernel / np.sum(kernel)
+    return kernel
+
+
+# ------------------------------------------------------------- #
+# --------------------------- noise --------------------------- #
+# ------------------------------------------------------------- #
+
+# ----------------------- Gaussian Noise ----------------------- #
+
+
+def generate_gaussian_noise(img, sigma=10, gray_noise=False):
+    """Generate Gaussian noise.
+
+    Args:
+        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
+        sigma (float): Noise scale (measured in range 255). Default: 10.
+
+    Returns:
+        (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1],
+            float32.
+    """
+    noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255.
+    if gray_noise:
+        noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255.
+        noise = np.expand_dims(noise, axis=2).repeat(3, axis=2)
+    else:
+        noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255.
+    return noise
+
+
+def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False):
+    """Add Gaussian noise.
+
+    Args:
+        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
+        sigma (float): Noise scale (measured in range 255). Default: 10.
+
+    Returns:
+        (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1],
+            float32.
+    """
+    noise = generate_gaussian_noise(img, sigma, gray_noise)
+    out = img + noise
+    if clip and rounds:
+        out = np.clip((out * 255.0).round(), 0, 255) / 255.
+    elif clip:
+        out = np.clip(out, 0, 1)
+    elif rounds:
+        out = (out * 255.0).round() / 255.
+    return out
+
+
+# ----------------------- Random Gaussian Noise ----------------------- #
+def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0):
+    sigma = np.random.uniform(sigma_range[0], sigma_range[1])
+    if np.random.uniform() < gray_prob:
+        gray_noise = True
+    else:
+        gray_noise = False
+    return generate_gaussian_noise(img, sigma, gray_noise)
+
+
+def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False):
+    noise = random_generate_gaussian_noise(img, sigma_range, gray_prob)
+    out = img + noise
+    if clip and rounds:
+        out = np.clip((out * 255.0).round(), 0, 255) / 255.
+    elif clip:
+        out = np.clip(out, 0, 1)
+    elif rounds:
+        out = (out * 255.0).round() / 255.
+    return out
+
+
+# ------------------------------------------------------------------------ #
+# --------------------------- JPEG compression --------------------------- #
+# ------------------------------------------------------------------------ #
+
+
+def add_jpg_compression(img, quality=90):
+    """Add JPG compression artifacts.
+
+    Args:
+        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
+        quality (float): JPG compression quality. 0 for lowest quality, 100 for
+            best quality. Default: 90.
+
+    Returns:
+        (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1],
+            float32.
+    """
+    img = np.clip(img, 0, 1)
+    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+    _, encimg = cv2.imencode('.jpg', img * 255., encode_param)
+    img = np.float32(cv2.imdecode(encimg, 1)) / 255.
+    return img
+
+
+def random_add_jpg_compression(img, quality_range=(90, 100)):
+    """Randomly add JPG compression artifacts.
+
+    Args:
+        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
+        quality_range (tuple[float] | list[float]): JPG compression quality
+            range. 0 for lowest quality, 100 for best quality.
+            Default: (90, 100).
+
+    Returns:
+        (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1],
+            float32.
+    """
+    quality = np.random.uniform(quality_range[0], quality_range[1])
+    return add_jpg_compression(img, quality)
diff --git a/basicsr/data/ffhq_dataset.py b/basicsr/data/ffhq_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..159142c89ba2923d5cc1d66ebe369794071f645d
--- /dev/null
+++ b/basicsr/data/ffhq_dataset.py
@@ -0,0 +1,63 @@
+from os import path as osp
+from torch.utils import data as data
+from torchvision.transforms.functional import normalize
+
+from basicsr.data.transforms import augment
+from basicsr.utils import FileClient, imfrombytes, img2tensor
+from basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class FFHQDataset(data.Dataset):
+    """FFHQ dataset for StyleGAN.
+
+    Args:
+        opt (dict): Config for train datasets. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            io_backend (dict): IO backend type and other kwarg.
+            mean (list | tuple): Image mean.
+            std (list | tuple): Image std.
+            use_hflip (bool): Whether to horizontally flip.
+
+    """
+
+    def __init__(self, opt):
+        super(FFHQDataset, self).__init__()
+        self.opt = opt
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+
+        self.gt_folder = opt['dataroot_gt']
+        self.mean = opt['mean']
+        self.std = opt['std']
+
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.io_backend_opt['db_paths'] = self.gt_folder
+            if not self.gt_folder.endswith('.lmdb'):
+                raise ValueError("'dataroot_gt' should end with '.lmdb', " f'but received {self.gt_folder}')
+            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
+                self.paths = [line.split('.')[0] for line in fin]
+        else:
+            # FFHQ has 70000 images in total
+            self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)]
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        # load gt image
+        gt_path = self.paths[index]
+        img_bytes = self.file_client.get(gt_path)
+        img_gt = imfrombytes(img_bytes, float32=True)
+
+        # random horizontal flip
+        img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False)
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True)
+        # normalize
+        normalize(img_gt, self.mean, self.std, inplace=True)
+        return {'gt': img_gt, 'gt_path': gt_path}
+
+    def __len__(self):
+        return len(self.paths)
diff --git a/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt b/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..081969018371c1891961a9f7bf5f00f80c9dee9f
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
@@ -0,0 +1,32592 @@
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diff --git a/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt b/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..45badd0e5d4448958b2693ca69ac807da255d2b0
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
@@ -0,0 +1,4 @@
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diff --git a/basicsr/data/meta_info/meta_info_REDS_GT.txt b/basicsr/data/meta_info/meta_info_REDS_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..34fb398a02f05074e0a25cc5d038f409f295bed4
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_REDS_GT.txt
@@ -0,0 +1,270 @@
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diff --git a/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b407561e6baca502c386a66b4e52b06729f88284
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt
@@ -0,0 +1,4 @@
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diff --git a/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..11121311d80a5c1a0c373fbbf40d322288751a7c
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt
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diff --git a/basicsr/data/meta_info/meta_info_Vimeo90K_test_GT.txt b/basicsr/data/meta_info/meta_info_Vimeo90K_test_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d61bb71e3f290f8409a2b234de7f7e402af02996
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_Vimeo90K_test_GT.txt
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diff --git a/basicsr/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt b/basicsr/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..79e7915d4d31b06258e02e3fb812da735de3855d
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt
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diff --git a/basicsr/data/meta_info/meta_info_Vimeo90K_test_medium_GT.txt b/basicsr/data/meta_info/meta_info_Vimeo90K_test_medium_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e7e6dff35d8831eeb25e2236c533ce46f655660
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_Vimeo90K_test_medium_GT.txt
@@ -0,0 +1,4977 @@
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diff --git a/basicsr/data/meta_info/meta_info_Vimeo90K_test_slow_GT.txt b/basicsr/data/meta_info/meta_info_Vimeo90K_test_slow_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..694ebb55c62437d1f62e66297b99182eb338fe8e
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_Vimeo90K_test_slow_GT.txt
@@ -0,0 +1,1613 @@
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diff --git a/basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt b/basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0f15a07572e8bb134e76229113a8db1fe7c849d
--- /dev/null
+++ b/basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
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diff --git a/basicsr/data/paired_image_dataset.py b/basicsr/data/paired_image_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6a6c07b1af76412bf862d57453a768432634f09
--- /dev/null
+++ b/basicsr/data/paired_image_dataset.py
@@ -0,0 +1,101 @@
+from torch.utils import data as data
+from torchvision.transforms.functional import normalize
+
+from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file
+from basicsr.data.transforms import augment, paired_random_crop
+from basicsr.utils import FileClient, imfrombytes, img2tensor
+from basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class PairedImageDataset(data.Dataset):
+    """Paired image dataset for image restoration.
+
+    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
+    GT image pairs.
+
+    There are three modes:
+    1. 'lmdb': Use lmdb files.
+        If opt['io_backend'] == lmdb.
+    2. 'meta_info_file': Use meta information file to generate paths.
+        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
+    3. 'folder': Scan folders to generate paths.
+        The rest.
+
+    Args:
+        opt (dict): Config for train datasets. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            dataroot_lq (str): Data root path for lq.
+            meta_info_file (str): Path for meta information file.
+            io_backend (dict): IO backend type and other kwarg.
+            filename_tmpl (str): Template for each filename. Note that the
+                template excludes the file extension. Default: '{}'.
+            gt_size (int): Cropped patched size for gt patches.
+            use_flip (bool): Use horizontal flips.
+            use_rot (bool): Use rotation (use vertical flip and transposing h
+                and w for implementation).
+
+            scale (bool): Scale, which will be added automatically.
+            phase (str): 'train' or 'val'.
+    """
+
+    def __init__(self, opt):
+        super(PairedImageDataset, self).__init__()
+        self.opt = opt
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        self.mean = opt['mean'] if 'mean' in opt else None
+        self.std = opt['std'] if 'std' in opt else None
+
+        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
+        if 'filename_tmpl' in opt:
+            self.filename_tmpl = opt['filename_tmpl']
+        else:
+            self.filename_tmpl = '{}'
+
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
+            self.io_backend_opt['client_keys'] = ['lq', 'gt']
+            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
+        elif 'meta_info_file' in self.opt and self.opt['meta_info_file'] is not None:
+            self.paths = paired_paths_from_meta_info_file([self.lq_folder, self.gt_folder], ['lq', 'gt'],
+                                                          self.opt['meta_info_file'], self.filename_tmpl)
+        else:
+            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        scale = self.opt['scale']
+
+        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
+        # image range: [0, 1], float32.
+        gt_path = self.paths[index]['gt_path']
+        img_bytes = self.file_client.get(gt_path, 'gt')
+        img_gt = imfrombytes(img_bytes, float32=True)
+        lq_path = self.paths[index]['lq_path']
+        img_bytes = self.file_client.get(lq_path, 'lq')
+        img_lq = imfrombytes(img_bytes, float32=True)
+
+        # augmentation for training
+        if self.opt['phase'] == 'train':
+            gt_size = self.opt['gt_size']
+            # random crop
+            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
+            # flip, rotation
+            img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'], self.opt['use_rot'])
+
+        # TODO: color space transform
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
+        # normalize
+        if self.mean is not None or self.std is not None:
+            normalize(img_lq, self.mean, self.std, inplace=True)
+            normalize(img_gt, self.mean, self.std, inplace=True)
+
+        return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
+
+    def __len__(self):
+        return len(self.paths)
diff --git a/basicsr/data/prefetch_dataloader.py b/basicsr/data/prefetch_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..5088425050d4cc98114a9b93eb50ea60273f35a0
--- /dev/null
+++ b/basicsr/data/prefetch_dataloader.py
@@ -0,0 +1,125 @@
+import queue as Queue
+import threading
+import torch
+from torch.utils.data import DataLoader
+
+
+class PrefetchGenerator(threading.Thread):
+    """A general prefetch generator.
+
+    Ref:
+    https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
+
+    Args:
+        generator: Python generator.
+        num_prefetch_queue (int): Number of prefetch queue.
+    """
+
+    def __init__(self, generator, num_prefetch_queue):
+        threading.Thread.__init__(self)
+        self.queue = Queue.Queue(num_prefetch_queue)
+        self.generator = generator
+        self.daemon = True
+        self.start()
+
+    def run(self):
+        for item in self.generator:
+            self.queue.put(item)
+        self.queue.put(None)
+
+    def __next__(self):
+        next_item = self.queue.get()
+        if next_item is None:
+            raise StopIteration
+        return next_item
+
+    def __iter__(self):
+        return self
+
+
+class PrefetchDataLoader(DataLoader):
+    """Prefetch version of dataloader.
+
+    Ref:
+    https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#
+
+    TODO:
+    Need to test on single gpu and ddp (multi-gpu). There is a known issue in
+    ddp.
+
+    Args:
+        num_prefetch_queue (int): Number of prefetch queue.
+        kwargs (dict): Other arguments for dataloader.
+    """
+
+    def __init__(self, num_prefetch_queue, **kwargs):
+        self.num_prefetch_queue = num_prefetch_queue
+        super(PrefetchDataLoader, self).__init__(**kwargs)
+
+    def __iter__(self):
+        return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)
+
+
+class CPUPrefetcher():
+    """CPU prefetcher.
+
+    Args:
+        loader: Dataloader.
+    """
+
+    def __init__(self, loader):
+        self.ori_loader = loader
+        self.loader = iter(loader)
+
+    def next(self):
+        try:
+            return next(self.loader)
+        except StopIteration:
+            return None
+
+    def reset(self):
+        self.loader = iter(self.ori_loader)
+
+
+class CUDAPrefetcher():
+    """CUDA prefetcher.
+
+    Ref:
+    https://github.com/NVIDIA/apex/issues/304#
+
+    It may consums more GPU memory.
+
+    Args:
+        loader: Dataloader.
+        opt (dict): Options.
+    """
+
+    def __init__(self, loader, opt):
+        self.ori_loader = loader
+        self.loader = iter(loader)
+        self.opt = opt
+        self.stream = torch.cuda.Stream()
+        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
+        self.preload()
+
+    def preload(self):
+        try:
+            self.batch = next(self.loader)  # self.batch is a dict
+        except StopIteration:
+            self.batch = None
+            return None
+        # put tensors to gpu
+        with torch.cuda.stream(self.stream):
+            for k, v in self.batch.items():
+                if torch.is_tensor(v):
+                    self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True)
+
+    def next(self):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        batch = self.batch
+        self.preload()
+        return batch
+
+    def reset(self):
+        self.loader = iter(self.ori_loader)
+        self.preload()
diff --git a/basicsr/data/reds_dataset.py b/basicsr/data/reds_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b55593a8805fc2bf7d34ce31cf886f21f2263dd
--- /dev/null
+++ b/basicsr/data/reds_dataset.py
@@ -0,0 +1,209 @@
+import numpy as np
+import random
+import torch
+from pathlib import Path
+from torch.utils import data as data
+
+from basicsr.data.transforms import augment, paired_random_crop
+from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
+from basicsr.utils.flow_util import dequantize_flow
+from basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class REDSDataset(data.Dataset):
+    """REDS dataset for training.
+
+    The keys are generated from a meta info txt file.
+    basicsr/data/meta_info/meta_info_REDS_GT.txt
+
+    Each line contains:
+    1. subfolder (clip) name; 2. frame number; 3. image shape, seperated by
+    a white space.
+    Examples:
+    000 100 (720,1280,3)
+    001 100 (720,1280,3)
+    ...
+
+    Key examples: "000/00000000"
+    GT (gt): Ground-Truth;
+    LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
+
+    Args:
+        opt (dict): Config for train dataset. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            dataroot_lq (str): Data root path for lq.
+            dataroot_flow (str, optional): Data root path for flow.
+            meta_info_file (str): Path for meta information file.
+            val_partition (str): Validation partition types. 'REDS4' or
+                'official'.
+            io_backend (dict): IO backend type and other kwarg.
+
+            num_frame (int): Window size for input frames.
+            gt_size (int): Cropped patched size for gt patches.
+            interval_list (list): Interval list for temporal augmentation.
+            random_reverse (bool): Random reverse input frames.
+            use_flip (bool): Use horizontal flips.
+            use_rot (bool): Use rotation (use vertical flip and transposing h
+                and w for implementation).
+
+            scale (bool): Scale, which will be added automatically.
+    """
+
+    def __init__(self, opt):
+        super(REDSDataset, self).__init__()
+        self.opt = opt
+        self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq'])
+        self.flow_root = Path(opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None
+        assert opt['num_frame'] % 2 == 1, (f'num_frame should be odd number, but got {opt["num_frame"]}')
+        self.num_frame = opt['num_frame']
+        self.num_half_frames = opt['num_frame'] // 2
+
+        self.keys = []
+        with open(opt['meta_info_file'], 'r') as fin:
+            for line in fin:
+                folder, frame_num, _ = line.split(' ')
+                self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])
+
+        # remove the video clips used in validation
+        if opt['val_partition'] == 'REDS4':
+            val_partition = ['000', '011', '015', '020']
+        elif opt['val_partition'] == 'official':
+            val_partition = [f'{v:03d}' for v in range(240, 270)]
+        else:
+            raise ValueError(f'Wrong validation partition {opt["val_partition"]}.'
+                             f"Supported ones are ['official', 'REDS4'].")
+        self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]
+
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        self.is_lmdb = False
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.is_lmdb = True
+            if self.flow_root is not None:
+                self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root]
+                self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow']
+            else:
+                self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
+                self.io_backend_opt['client_keys'] = ['lq', 'gt']
+
+        # temporal augmentation configs
+        self.interval_list = opt['interval_list']
+        self.random_reverse = opt['random_reverse']
+        interval_str = ','.join(str(x) for x in opt['interval_list'])
+        logger = get_root_logger()
+        logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
+                    f'random reverse is {self.random_reverse}.')
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        scale = self.opt['scale']
+        gt_size = self.opt['gt_size']
+        key = self.keys[index]
+        clip_name, frame_name = key.split('/')  # key example: 000/00000000
+        center_frame_idx = int(frame_name)
+
+        # determine the neighboring frames
+        interval = random.choice(self.interval_list)
+
+        # ensure not exceeding the borders
+        start_frame_idx = center_frame_idx - self.num_half_frames * interval
+        end_frame_idx = center_frame_idx + self.num_half_frames * interval
+        # each clip has 100 frames starting from 0 to 99
+        while (start_frame_idx < 0) or (end_frame_idx > 99):
+            center_frame_idx = random.randint(0, 99)
+            start_frame_idx = (center_frame_idx - self.num_half_frames * interval)
+            end_frame_idx = center_frame_idx + self.num_half_frames * interval
+        frame_name = f'{center_frame_idx:08d}'
+        neighbor_list = list(range(start_frame_idx, end_frame_idx + 1, interval))
+        # random reverse
+        if self.random_reverse and random.random() < 0.5:
+            neighbor_list.reverse()
+
+        assert len(neighbor_list) == self.num_frame, (f'Wrong length of neighbor list: {len(neighbor_list)}')
+
+        # get the GT frame (as the center frame)
+        if self.is_lmdb:
+            img_gt_path = f'{clip_name}/{frame_name}'
+        else:
+            img_gt_path = self.gt_root / clip_name / f'{frame_name}.png'
+        img_bytes = self.file_client.get(img_gt_path, 'gt')
+        img_gt = imfrombytes(img_bytes, float32=True)
+
+        # get the neighboring LQ frames
+        img_lqs = []
+        for neighbor in neighbor_list:
+            if self.is_lmdb:
+                img_lq_path = f'{clip_name}/{neighbor:08d}'
+            else:
+                img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png'
+            img_bytes = self.file_client.get(img_lq_path, 'lq')
+            img_lq = imfrombytes(img_bytes, float32=True)
+            img_lqs.append(img_lq)
+
+        # get flows
+        if self.flow_root is not None:
+            img_flows = []
+            # read previous flows
+            for i in range(self.num_half_frames, 0, -1):
+                if self.is_lmdb:
+                    flow_path = f'{clip_name}/{frame_name}_p{i}'
+                else:
+                    flow_path = (self.flow_root / clip_name / f'{frame_name}_p{i}.png')
+                img_bytes = self.file_client.get(flow_path, 'flow')
+                cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False)  # uint8, [0, 255]
+                dx, dy = np.split(cat_flow, 2, axis=0)
+                flow = dequantize_flow(dx, dy, max_val=20, denorm=False)  # we use max_val 20 here.
+                img_flows.append(flow)
+            # read next flows
+            for i in range(1, self.num_half_frames + 1):
+                if self.is_lmdb:
+                    flow_path = f'{clip_name}/{frame_name}_n{i}'
+                else:
+                    flow_path = (self.flow_root / clip_name / f'{frame_name}_n{i}.png')
+                img_bytes = self.file_client.get(flow_path, 'flow')
+                cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False)  # uint8, [0, 255]
+                dx, dy = np.split(cat_flow, 2, axis=0)
+                flow = dequantize_flow(dx, dy, max_val=20, denorm=False)  # we use max_val 20 here.
+                img_flows.append(flow)
+
+            # for random crop, here, img_flows and img_lqs have the same
+            # spatial size
+            img_lqs.extend(img_flows)
+
+        # randomly crop
+        img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path)
+        if self.flow_root is not None:
+            img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.num_frame:]
+
+        # augmentation - flip, rotate
+        img_lqs.append(img_gt)
+        if self.flow_root is not None:
+            img_results, img_flows = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot'], img_flows)
+        else:
+            img_results = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot'])
+
+        img_results = img2tensor(img_results)
+        img_lqs = torch.stack(img_results[0:-1], dim=0)
+        img_gt = img_results[-1]
+
+        if self.flow_root is not None:
+            img_flows = img2tensor(img_flows)
+            # add the zero center flow
+            img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0]))
+            img_flows = torch.stack(img_flows, dim=0)
+
+        # img_lqs: (t, c, h, w)
+        # img_flows: (t, 2, h, w)
+        # img_gt: (c, h, w)
+        # key: str
+        if self.flow_root is not None:
+            return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key}
+        else:
+            return {'lq': img_lqs, 'gt': img_gt, 'key': key}
+
+    def __len__(self):
+        return len(self.keys)
diff --git a/basicsr/data/single_image_dataset.py b/basicsr/data/single_image_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..9470df93395fa9b38fd8eaffcff97584f85bc78c
--- /dev/null
+++ b/basicsr/data/single_image_dataset.py
@@ -0,0 +1,65 @@
+from os import path as osp
+from torch.utils import data as data
+from torchvision.transforms.functional import normalize
+
+from basicsr.data.data_util import paths_from_lmdb
+from basicsr.utils import FileClient, imfrombytes, img2tensor, scandir
+from basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class SingleImageDataset(data.Dataset):
+    """Read only lq images in the test phase.
+
+    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc).
+
+    There are two modes:
+    1. 'meta_info_file': Use meta information file to generate paths.
+    2. 'folder': Scan folders to generate paths.
+
+    Args:
+        opt (dict): Config for train datasets. It contains the following keys:
+            dataroot_lq (str): Data root path for lq.
+            meta_info_file (str): Path for meta information file.
+            io_backend (dict): IO backend type and other kwarg.
+    """
+
+    def __init__(self, opt):
+        super(SingleImageDataset, self).__init__()
+        self.opt = opt
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        self.mean = opt['mean'] if 'mean' in opt else None
+        self.std = opt['std'] if 'std' in opt else None
+        self.lq_folder = opt['dataroot_lq']
+
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.io_backend_opt['db_paths'] = [self.lq_folder]
+            self.io_backend_opt['client_keys'] = ['lq']
+            self.paths = paths_from_lmdb(self.lq_folder)
+        elif 'meta_info_file' in self.opt:
+            with open(self.opt['meta_info_file'], 'r') as fin:
+                self.paths = [osp.join(self.lq_folder, line.split(' ')[0]) for line in fin]
+        else:
+            self.paths = sorted(list(scandir(self.lq_folder, full_path=True)))
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        # load lq image
+        lq_path = self.paths[index]
+        img_bytes = self.file_client.get(lq_path, 'lq')
+        img_lq = imfrombytes(img_bytes, float32=True)
+
+        # TODO: color space transform
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True)
+        # normalize
+        if self.mean is not None or self.std is not None:
+            normalize(img_lq, self.mean, self.std, inplace=True)
+        return {'lq': img_lq, 'lq_path': lq_path}
+
+    def __len__(self):
+        return len(self.paths)
diff --git a/basicsr/data/transforms.py b/basicsr/data/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..aead9dc73ed063e1c5865040eaa2652b26aa3ad3
--- /dev/null
+++ b/basicsr/data/transforms.py
@@ -0,0 +1,165 @@
+import cv2
+import random
+
+
+def mod_crop(img, scale):
+    """Mod crop images, used during testing.
+
+    Args:
+        img (ndarray): Input image.
+        scale (int): Scale factor.
+
+    Returns:
+        ndarray: Result image.
+    """
+    img = img.copy()
+    if img.ndim in (2, 3):
+        h, w = img.shape[0], img.shape[1]
+        h_remainder, w_remainder = h % scale, w % scale
+        img = img[:h - h_remainder, :w - w_remainder, ...]
+    else:
+        raise ValueError(f'Wrong img ndim: {img.ndim}.')
+    return img
+
+
+def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path):
+    """Paired random crop.
+
+    It crops lists of lq and gt images with corresponding locations.
+
+    Args:
+        img_gts (list[ndarray] | ndarray): GT images. Note that all images
+            should have the same shape. If the input is an ndarray, it will
+            be transformed to a list containing itself.
+        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
+            should have the same shape. If the input is an ndarray, it will
+            be transformed to a list containing itself.
+        gt_patch_size (int): GT patch size.
+        scale (int): Scale factor.
+        gt_path (str): Path to ground-truth.
+
+    Returns:
+        list[ndarray] | ndarray: GT images and LQ images. If returned results
+            only have one element, just return ndarray.
+    """
+
+    if not isinstance(img_gts, list):
+        img_gts = [img_gts]
+    if not isinstance(img_lqs, list):
+        img_lqs = [img_lqs]
+
+    h_lq, w_lq, _ = img_lqs[0].shape
+    h_gt, w_gt, _ = img_gts[0].shape
+    lq_patch_size = gt_patch_size // scale
+
+    if h_gt != h_lq * scale or w_gt != w_lq * scale:
+        raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
+                         f'multiplication of LQ ({h_lq}, {w_lq}).')
+    if h_lq < lq_patch_size or w_lq < lq_patch_size:
+        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
+                         f'({lq_patch_size}, {lq_patch_size}). '
+                         f'Please remove {gt_path}.')
+
+    # randomly choose top and left coordinates for lq patch
+    top = random.randint(0, h_lq - lq_patch_size)
+    left = random.randint(0, w_lq - lq_patch_size)
+
+    # crop lq patch
+    img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
+
+    # crop corresponding gt patch
+    top_gt, left_gt = int(top * scale), int(left * scale)
+    img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
+    if len(img_gts) == 1:
+        img_gts = img_gts[0]
+    if len(img_lqs) == 1:
+        img_lqs = img_lqs[0]
+    return img_gts, img_lqs
+
+
+def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
+    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
+
+    We use vertical flip and transpose for rotation implementation.
+    All the images in the list use the same augmentation.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
+            is an ndarray, it will be transformed to a list.
+        hflip (bool): Horizontal flip. Default: True.
+        rotation (bool): Ratotation. Default: True.
+        flows (list[ndarray]: Flows to be augmented. If the input is an
+            ndarray, it will be transformed to a list.
+            Dimension is (h, w, 2). Default: None.
+        return_status (bool): Return the status of flip and rotation.
+            Default: False.
+
+    Returns:
+        list[ndarray] | ndarray: Augmented images and flows. If returned
+            results only have one element, just return ndarray.
+
+    """
+    hflip = hflip and random.random() < 0.5
+    vflip = rotation and random.random() < 0.5
+    rot90 = rotation and random.random() < 0.5
+
+    def _augment(img):
+        if hflip:  # horizontal
+            cv2.flip(img, 1, img)
+        if vflip:  # vertical
+            cv2.flip(img, 0, img)
+        if rot90:
+            img = img.transpose(1, 0, 2)
+        return img
+
+    def _augment_flow(flow):
+        if hflip:  # horizontal
+            cv2.flip(flow, 1, flow)
+            flow[:, :, 0] *= -1
+        if vflip:  # vertical
+            cv2.flip(flow, 0, flow)
+            flow[:, :, 1] *= -1
+        if rot90:
+            flow = flow.transpose(1, 0, 2)
+            flow = flow[:, :, [1, 0]]
+        return flow
+
+    if not isinstance(imgs, list):
+        imgs = [imgs]
+    imgs = [_augment(img) for img in imgs]
+    if len(imgs) == 1:
+        imgs = imgs[0]
+
+    if flows is not None:
+        if not isinstance(flows, list):
+            flows = [flows]
+        flows = [_augment_flow(flow) for flow in flows]
+        if len(flows) == 1:
+            flows = flows[0]
+        return imgs, flows
+    else:
+        if return_status:
+            return imgs, (hflip, vflip, rot90)
+        else:
+            return imgs
+
+
+def img_rotate(img, angle, center=None, scale=1.0):
+    """Rotate image.
+
+    Args:
+        img (ndarray): Image to be rotated.
+        angle (float): Rotation angle in degrees. Positive values mean
+            counter-clockwise rotation.
+        center (tuple[int]): Rotation center. If the center is None,
+            initialize it as the center of the image. Default: None.
+        scale (float): Isotropic scale factor. Default: 1.0.
+    """
+    (h, w) = img.shape[:2]
+
+    if center is None:
+        center = (w // 2, h // 2)
+
+    matrix = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated_img = cv2.warpAffine(img, matrix, (w, h))
+    return rotated_img
diff --git a/basicsr/data/video_test_dataset.py b/basicsr/data/video_test_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9610d8567bf07ee0180a373f7c494b0f557389f
--- /dev/null
+++ b/basicsr/data/video_test_dataset.py
@@ -0,0 +1,287 @@
+import glob
+import torch
+from os import path as osp
+from torch.utils import data as data
+
+from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class VideoTestDataset(data.Dataset):
+    """Video test dataset.
+
+    Supported datasets: Vid4, REDS4, REDSofficial.
+    More generally, it supports testing dataset with following structures:
+
+    dataroot
+    ├── subfolder1
+        ├── frame000
+        ├── frame001
+        ├── ...
+    ├── subfolder1
+        ├── frame000
+        ├── frame001
+        ├── ...
+    ├── ...
+
+    For testing datasets, there is no need to prepare LMDB files.
+
+    Args:
+        opt (dict): Config for train dataset. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            dataroot_lq (str): Data root path for lq.
+            io_backend (dict): IO backend type and other kwarg.
+            cache_data (bool): Whether to cache testing datasets.
+            name (str): Dataset name.
+            meta_info_file (str): The path to the file storing the list of test
+                folders. If not provided, all the folders in the dataroot will
+                be used.
+            num_frame (int): Window size for input frames.
+            padding (str): Padding mode.
+    """
+
+    def __init__(self, opt):
+        super(VideoTestDataset, self).__init__()
+        self.opt = opt
+        self.cache_data = opt['cache_data']
+        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
+        self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []}
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.'
+
+        logger = get_root_logger()
+        logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
+        self.imgs_lq, self.imgs_gt = {}, {}
+        if 'meta_info_file' in opt:
+            with open(opt['meta_info_file'], 'r') as fin:
+                subfolders = [line.split(' ')[0] for line in fin]
+                subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders]
+                subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders]
+        else:
+            subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
+            subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*')))
+
+        if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']:
+            for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt):
+                # get frame list for lq and gt
+                subfolder_name = osp.basename(subfolder_lq)
+                img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True)))
+                img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True)))
+
+                max_idx = len(img_paths_lq)
+                assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})'
+                                                      f' and gt folders ({len(img_paths_gt)})')
+
+                self.data_info['lq_path'].extend(img_paths_lq)
+                self.data_info['gt_path'].extend(img_paths_gt)
+                self.data_info['folder'].extend([subfolder_name] * max_idx)
+                for i in range(max_idx):
+                    self.data_info['idx'].append(f'{i}/{max_idx}')
+                border_l = [0] * max_idx
+                for i in range(self.opt['num_frame'] // 2):
+                    border_l[i] = 1
+                    border_l[max_idx - i - 1] = 1
+                self.data_info['border'].extend(border_l)
+
+                # cache data or save the frame list
+                if self.cache_data:
+                    logger.info(f'Cache {subfolder_name} for VideoTestDataset...')
+                    self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq)
+                    self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt)
+                else:
+                    self.imgs_lq[subfolder_name] = img_paths_lq
+                    self.imgs_gt[subfolder_name] = img_paths_gt
+        else:
+            raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}')
+
+    def __getitem__(self, index):
+        folder = self.data_info['folder'][index]
+        idx, max_idx = self.data_info['idx'][index].split('/')
+        idx, max_idx = int(idx), int(max_idx)
+        border = self.data_info['border'][index]
+        lq_path = self.data_info['lq_path'][index]
+
+        select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])
+
+        if self.cache_data:
+            imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx))
+            img_gt = self.imgs_gt[folder][idx]
+        else:
+            img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
+            imgs_lq = read_img_seq(img_paths_lq)
+            img_gt = read_img_seq([self.imgs_gt[folder][idx]])
+            img_gt.squeeze_(0)
+
+        return {
+            'lq': imgs_lq,  # (t, c, h, w)
+            'gt': img_gt,  # (c, h, w)
+            'folder': folder,  # folder name
+            'idx': self.data_info['idx'][index],  # e.g., 0/99
+            'border': border,  # 1 for border, 0 for non-border
+            'lq_path': lq_path  # center frame
+        }
+
+    def __len__(self):
+        return len(self.data_info['gt_path'])
+
+
+@DATASET_REGISTRY.register()
+class VideoTestVimeo90KDataset(data.Dataset):
+    """Video test dataset for Vimeo90k-Test dataset.
+
+    It only keeps the center frame for testing.
+    For testing datasets, there is no need to prepare LMDB files.
+
+    Args:
+        opt (dict): Config for train dataset. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            dataroot_lq (str): Data root path for lq.
+            io_backend (dict): IO backend type and other kwarg.
+            cache_data (bool): Whether to cache testing datasets.
+            name (str): Dataset name.
+            meta_info_file (str): The path to the file storing the list of test
+                folders. If not provided, all the folders in the dataroot will
+                be used.
+            num_frame (int): Window size for input frames.
+            padding (str): Padding mode.
+    """
+
+    def __init__(self, opt):
+        super(VideoTestVimeo90KDataset, self).__init__()
+        self.opt = opt
+        self.cache_data = opt['cache_data']
+        if self.cache_data:
+            raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.')
+        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
+        self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []}
+        neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])]
+
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.'
+
+        logger = get_root_logger()
+        logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
+        with open(opt['meta_info_file'], 'r') as fin:
+            subfolders = [line.split(' ')[0] for line in fin]
+        for idx, subfolder in enumerate(subfolders):
+            gt_path = osp.join(self.gt_root, subfolder, 'im4.png')
+            self.data_info['gt_path'].append(gt_path)
+            lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list]
+            self.data_info['lq_path'].append(lq_paths)
+            self.data_info['folder'].append('vimeo90k')
+            self.data_info['idx'].append(f'{idx}/{len(subfolders)}')
+            self.data_info['border'].append(0)
+
+    def __getitem__(self, index):
+        lq_path = self.data_info['lq_path'][index]
+        gt_path = self.data_info['gt_path'][index]
+        imgs_lq = read_img_seq(lq_path)
+        img_gt = read_img_seq([gt_path])
+        img_gt.squeeze_(0)
+
+        return {
+            'lq': imgs_lq,  # (t, c, h, w)
+            'gt': img_gt,  # (c, h, w)
+            'folder': self.data_info['folder'][index],  # folder name
+            'idx': self.data_info['idx'][index],  # e.g., 0/843
+            'border': self.data_info['border'][index],  # 0 for non-border
+            'lq_path': lq_path[self.opt['num_frame'] // 2]  # center frame
+        }
+
+    def __len__(self):
+        return len(self.data_info['gt_path'])
+
+
+@DATASET_REGISTRY.register()
+class VideoTestDUFDataset(VideoTestDataset):
+    """ Video test dataset for DUF dataset.
+
+    Args:
+        opt (dict): Config for train dataset.
+            Most of keys are the same as VideoTestDataset.
+            It has the follwing extra keys:
+
+            use_duf_downsampling (bool): Whether to use duf downsampling to
+                generate low-resolution frames.
+            scale (bool): Scale, which will be added automatically.
+    """
+
+    def __getitem__(self, index):
+        folder = self.data_info['folder'][index]
+        idx, max_idx = self.data_info['idx'][index].split('/')
+        idx, max_idx = int(idx), int(max_idx)
+        border = self.data_info['border'][index]
+        lq_path = self.data_info['lq_path'][index]
+
+        select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])
+
+        if self.cache_data:
+            if self.opt['use_duf_downsampling']:
+                # read imgs_gt to generate low-resolution frames
+                imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx))
+                imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale'])
+            else:
+                imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx))
+            img_gt = self.imgs_gt[folder][idx]
+        else:
+            if self.opt['use_duf_downsampling']:
+                img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx]
+                # read imgs_gt to generate low-resolution frames
+                imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale'])
+                imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale'])
+            else:
+                img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
+                imgs_lq = read_img_seq(img_paths_lq)
+            img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale'])
+            img_gt.squeeze_(0)
+
+        return {
+            'lq': imgs_lq,  # (t, c, h, w)
+            'gt': img_gt,  # (c, h, w)
+            'folder': folder,  # folder name
+            'idx': self.data_info['idx'][index],  # e.g., 0/99
+            'border': border,  # 1 for border, 0 for non-border
+            'lq_path': lq_path  # center frame
+        }
+
+
+@DATASET_REGISTRY.register()
+class VideoRecurrentTestDataset(VideoTestDataset):
+    """Video test dataset for recurrent architectures, which takes LR video
+    frames as input and output corresponding HR video frames.
+
+    Args:
+        Same as VideoTestDataset.
+        Unused opt:
+            padding (str): Padding mode.
+
+    """
+
+    def __init__(self, opt):
+        super(VideoRecurrentTestDataset, self).__init__(opt)
+        # Find unique folder strings
+        self.folders = sorted(list(set(self.data_info['folder'])))
+
+    def __getitem__(self, index):
+        folder = self.folders[index]
+
+        if self.cache_data:
+            imgs_lq = self.imgs_lq[folder]
+            imgs_gt = self.imgs_gt[folder]
+        else:
+            raise NotImplementedError('Without cache_data is not implemented.')
+
+        return {
+            'lq': imgs_lq,
+            'gt': imgs_gt,
+            'folder': folder,
+        }
+
+    def __len__(self):
+        return len(self.folders)
diff --git a/basicsr/data/vimeo90k_dataset.py b/basicsr/data/vimeo90k_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..14714a571b53a4eefff350435ec0fb0af176a58a
--- /dev/null
+++ b/basicsr/data/vimeo90k_dataset.py
@@ -0,0 +1,126 @@
+import random
+import torch
+from pathlib import Path
+from torch.utils import data as data
+
+from basicsr.data.transforms import augment, paired_random_crop
+from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
+from basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class Vimeo90KDataset(data.Dataset):
+    """Vimeo90K dataset for training.
+
+    The keys are generated from a meta info txt file.
+    basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
+
+    Each line contains:
+    1. clip name; 2. frame number; 3. image shape, seperated by a white space.
+    Examples:
+        00001/0001 7 (256,448,3)
+        00001/0002 7 (256,448,3)
+
+    Key examples: "00001/0001"
+    GT (gt): Ground-Truth;
+    LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
+
+    The neighboring frame list for different num_frame:
+    num_frame | frame list
+             1 | 4
+             3 | 3,4,5
+             5 | 2,3,4,5,6
+             7 | 1,2,3,4,5,6,7
+
+    Args:
+        opt (dict): Config for train dataset. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            dataroot_lq (str): Data root path for lq.
+            meta_info_file (str): Path for meta information file.
+            io_backend (dict): IO backend type and other kwarg.
+
+            num_frame (int): Window size for input frames.
+            gt_size (int): Cropped patched size for gt patches.
+            random_reverse (bool): Random reverse input frames.
+            use_flip (bool): Use horizontal flips.
+            use_rot (bool): Use rotation (use vertical flip and transposing h
+                and w for implementation).
+
+            scale (bool): Scale, which will be added automatically.
+    """
+
+    def __init__(self, opt):
+        super(Vimeo90KDataset, self).__init__()
+        self.opt = opt
+        self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq'])
+
+        with open(opt['meta_info_file'], 'r') as fin:
+            self.keys = [line.split(' ')[0] for line in fin]
+
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        self.is_lmdb = False
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.is_lmdb = True
+            self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
+            self.io_backend_opt['client_keys'] = ['lq', 'gt']
+
+        # indices of input images
+        self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])]
+
+        # temporal augmentation configs
+        self.random_reverse = opt['random_reverse']
+        logger = get_root_logger()
+        logger.info(f'Random reverse is {self.random_reverse}.')
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        # random reverse
+        if self.random_reverse and random.random() < 0.5:
+            self.neighbor_list.reverse()
+
+        scale = self.opt['scale']
+        gt_size = self.opt['gt_size']
+        key = self.keys[index]
+        clip, seq = key.split('/')  # key example: 00001/0001
+
+        # get the GT frame (im4.png)
+        if self.is_lmdb:
+            img_gt_path = f'{key}/im4'
+        else:
+            img_gt_path = self.gt_root / clip / seq / 'im4.png'
+        img_bytes = self.file_client.get(img_gt_path, 'gt')
+        img_gt = imfrombytes(img_bytes, float32=True)
+
+        # get the neighboring LQ frames
+        img_lqs = []
+        for neighbor in self.neighbor_list:
+            if self.is_lmdb:
+                img_lq_path = f'{clip}/{seq}/im{neighbor}'
+            else:
+                img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
+            img_bytes = self.file_client.get(img_lq_path, 'lq')
+            img_lq = imfrombytes(img_bytes, float32=True)
+            img_lqs.append(img_lq)
+
+        # randomly crop
+        img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path)
+
+        # augmentation - flip, rotate
+        img_lqs.append(img_gt)
+        img_results = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot'])
+
+        img_results = img2tensor(img_results)
+        img_lqs = torch.stack(img_results[0:-1], dim=0)
+        img_gt = img_results[-1]
+
+        # img_lqs: (t, c, h, w)
+        # img_gt: (c, h, w)
+        # key: str
+        return {'lq': img_lqs, 'gt': img_gt, 'key': key}
+
+    def __len__(self):
+        return len(self.keys)
diff --git a/basicsr/losses/__init__.py b/basicsr/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b184e74c861e6fca0c548692a9a949a6100b0aa
--- /dev/null
+++ b/basicsr/losses/__init__.py
@@ -0,0 +1,26 @@
+from copy import deepcopy
+
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import LOSS_REGISTRY
+from .losses import (CharbonnierLoss, GANLoss, L1Loss, MSELoss, PerceptualLoss, WeightedTVLoss, g_path_regularize,
+                     gradient_penalty_loss, r1_penalty)
+
+__all__ = [
+    'L1Loss', 'MSELoss', 'CharbonnierLoss', 'WeightedTVLoss', 'PerceptualLoss', 'GANLoss', 'gradient_penalty_loss',
+    'r1_penalty', 'g_path_regularize'
+]
+
+
+def build_loss(opt):
+    """Build loss from options.
+
+    Args:
+        opt (dict): Configuration. It must constain:
+            type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    loss_type = opt.pop('type')
+    loss = LOSS_REGISTRY.get(loss_type)(**opt)
+    logger = get_root_logger()
+    logger.info(f'Loss [{loss.__class__.__name__}] is created.')
+    return loss
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diff --git a/basicsr/losses/loss_util.py b/basicsr/losses/loss_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..744eeb46d1f3b5a7b4553ca23237ddd9c899a698
--- /dev/null
+++ b/basicsr/losses/loss_util.py
@@ -0,0 +1,95 @@
+import functools
+from torch.nn import functional as F
+
+
+def reduce_loss(loss, reduction):
+    """Reduce loss as specified.
+
+    Args:
+        loss (Tensor): Elementwise loss tensor.
+        reduction (str): Options are 'none', 'mean' and 'sum'.
+
+    Returns:
+        Tensor: Reduced loss tensor.
+    """
+    reduction_enum = F._Reduction.get_enum(reduction)
+    # none: 0, elementwise_mean:1, sum: 2
+    if reduction_enum == 0:
+        return loss
+    elif reduction_enum == 1:
+        return loss.mean()
+    else:
+        return loss.sum()
+
+
+def weight_reduce_loss(loss, weight=None, reduction='mean'):
+    """Apply element-wise weight and reduce loss.
+
+    Args:
+        loss (Tensor): Element-wise loss.
+        weight (Tensor): Element-wise weights. Default: None.
+        reduction (str): Same as built-in losses of PyTorch. Options are
+            'none', 'mean' and 'sum'. Default: 'mean'.
+
+    Returns:
+        Tensor: Loss values.
+    """
+    # if weight is specified, apply element-wise weight
+    if weight is not None:
+        assert weight.dim() == loss.dim()
+        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
+        loss = loss * weight
+
+    # if weight is not specified or reduction is sum, just reduce the loss
+    if weight is None or reduction == 'sum':
+        loss = reduce_loss(loss, reduction)
+    # if reduction is mean, then compute mean over weight region
+    elif reduction == 'mean':
+        if weight.size(1) > 1:
+            weight = weight.sum()
+        else:
+            weight = weight.sum() * loss.size(1)
+        loss = loss.sum() / weight
+
+    return loss
+
+
+def weighted_loss(loss_func):
+    """Create a weighted version of a given loss function.
+
+    To use this decorator, the loss function must have the signature like
+    `loss_func(pred, target, **kwargs)`. The function only needs to compute
+    element-wise loss without any reduction. This decorator will add weight
+    and reduction arguments to the function. The decorated function will have
+    the signature like `loss_func(pred, target, weight=None, reduction='mean',
+    **kwargs)`.
+
+    :Example:
+
+    >>> import torch
+    >>> @weighted_loss
+    >>> def l1_loss(pred, target):
+    >>>     return (pred - target).abs()
+
+    >>> pred = torch.Tensor([0, 2, 3])
+    >>> target = torch.Tensor([1, 1, 1])
+    >>> weight = torch.Tensor([1, 0, 1])
+
+    >>> l1_loss(pred, target)
+    tensor(1.3333)
+    >>> l1_loss(pred, target, weight)
+    tensor(1.5000)
+    >>> l1_loss(pred, target, reduction='none')
+    tensor([1., 1., 2.])
+    >>> l1_loss(pred, target, weight, reduction='sum')
+    tensor(3.)
+    """
+
+    @functools.wraps(loss_func)
+    def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
+        # get element-wise loss
+        loss = loss_func(pred, target, **kwargs)
+        loss = weight_reduce_loss(loss, weight, reduction)
+        return loss
+
+    return wrapper
diff --git a/basicsr/losses/losses.py b/basicsr/losses/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbd513dde6c65389672378c07e1e470e6464603d
--- /dev/null
+++ b/basicsr/losses/losses.py
@@ -0,0 +1,423 @@
+import math
+import torch
+from torch import autograd as autograd
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.archs.vgg_arch import VGGFeatureExtractor
+from basicsr.utils.registry import LOSS_REGISTRY
+from .loss_util import weighted_loss
+
+_reduction_modes = ['none', 'mean', 'sum']
+
+
+@weighted_loss
+def l1_loss(pred, target):
+    return F.l1_loss(pred, target, reduction='none')
+
+
+@weighted_loss
+def mse_loss(pred, target):
+    return F.mse_loss(pred, target, reduction='none')
+
+
+@weighted_loss
+def charbonnier_loss(pred, target, eps=1e-12):
+    return torch.sqrt((pred - target)**2 + eps)
+
+
+@LOSS_REGISTRY.register()
+class L1Loss(nn.Module):
+    """L1 (mean absolute error, MAE) loss.
+
+    Args:
+        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        super(L1Loss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
+                weights. Default: None.
+        """
+        return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class MSELoss(nn.Module):
+    """MSE (L2) loss.
+
+    Args:
+        loss_weight (float): Loss weight for MSE loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        super(MSELoss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
+                weights. Default: None.
+        """
+        return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class CharbonnierLoss(nn.Module):
+    """Charbonnier loss (one variant of Robust L1Loss, a differentiable
+    variant of L1Loss).
+
+    Described in "Deep Laplacian Pyramid Networks for Fast and Accurate
+        Super-Resolution".
+
+    Args:
+        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+        eps (float): A value used to control the curvature near zero.
+            Default: 1e-12.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12):
+        super(CharbonnierLoss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+        self.eps = eps
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
+                weights. Default: None.
+        """
+        return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class WeightedTVLoss(L1Loss):
+    """Weighted TV loss.
+
+        Args:
+            loss_weight (float): Loss weight. Default: 1.0.
+    """
+
+    def __init__(self, loss_weight=1.0):
+        super(WeightedTVLoss, self).__init__(loss_weight=loss_weight)
+
+    def forward(self, pred, weight=None):
+        y_diff = super(WeightedTVLoss, self).forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=weight[:, :, :-1, :])
+        x_diff = super(WeightedTVLoss, self).forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=weight[:, :, :, :-1])
+
+        loss = x_diff + y_diff
+
+        return loss
+
+
+@LOSS_REGISTRY.register()
+class PerceptualLoss(nn.Module):
+    """Perceptual loss with commonly used style loss.
+
+    Args:
+        layer_weights (dict): The weight for each layer of vgg feature.
+            Here is an example: {'conv5_4': 1.}, which means the conv5_4
+            feature layer (before relu5_4) will be extracted with weight
+            1.0 in calculting losses.
+        vgg_type (str): The type of vgg network used as feature extractor.
+            Default: 'vgg19'.
+        use_input_norm (bool):  If True, normalize the input image in vgg.
+            Default: True.
+        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
+            Default: False.
+        perceptual_weight (float): If `perceptual_weight > 0`, the perceptual
+            loss will be calculated and the loss will multiplied by the
+            weight. Default: 1.0.
+        style_weight (float): If `style_weight > 0`, the style loss will be
+            calculated and the loss will multiplied by the weight.
+            Default: 0.
+        criterion (str): Criterion used for perceptual loss. Default: 'l1'.
+    """
+
+    def __init__(self,
+                 layer_weights,
+                 vgg_type='vgg19',
+                 use_input_norm=True,
+                 range_norm=False,
+                 perceptual_weight=1.0,
+                 style_weight=0.,
+                 criterion='l1'):
+        super(PerceptualLoss, self).__init__()
+        self.perceptual_weight = perceptual_weight
+        self.style_weight = style_weight
+        self.layer_weights = layer_weights
+        self.vgg = VGGFeatureExtractor(
+            layer_name_list=list(layer_weights.keys()),
+            vgg_type=vgg_type,
+            use_input_norm=use_input_norm,
+            range_norm=range_norm)
+
+        self.criterion_type = criterion
+        if self.criterion_type == 'l1':
+            self.criterion = torch.nn.L1Loss()
+        elif self.criterion_type == 'l2':
+            self.criterion = torch.nn.L2loss()
+        elif self.criterion_type == 'fro':
+            self.criterion = None
+        else:
+            raise NotImplementedError(f'{criterion} criterion has not been supported.')
+
+    def forward(self, x, gt):
+        """Forward function.
+
+        Args:
+            x (Tensor): Input tensor with shape (n, c, h, w).
+            gt (Tensor): Ground-truth tensor with shape (n, c, h, w).
+
+        Returns:
+            Tensor: Forward results.
+        """
+        # extract vgg features
+        x_features = self.vgg(x)
+        gt_features = self.vgg(gt.detach())
+
+        # calculate perceptual loss
+        if self.perceptual_weight > 0:
+            percep_loss = 0
+            for k in x_features.keys():
+                if self.criterion_type == 'fro':
+                    percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k]
+                else:
+                    percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k]
+            percep_loss *= self.perceptual_weight
+        else:
+            percep_loss = None
+
+        # calculate style loss
+        if self.style_weight > 0:
+            style_loss = 0
+            for k in x_features.keys():
+                if self.criterion_type == 'fro':
+                    style_loss += torch.norm(
+                        self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k]
+                else:
+                    style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat(
+                        gt_features[k])) * self.layer_weights[k]
+            style_loss *= self.style_weight
+        else:
+            style_loss = None
+
+        return percep_loss, style_loss
+
+    def _gram_mat(self, x):
+        """Calculate Gram matrix.
+
+        Args:
+            x (torch.Tensor): Tensor with shape of (n, c, h, w).
+
+        Returns:
+            torch.Tensor: Gram matrix.
+        """
+        n, c, h, w = x.size()
+        features = x.view(n, c, w * h)
+        features_t = features.transpose(1, 2)
+        gram = features.bmm(features_t) / (c * h * w)
+        return gram
+
+
+@LOSS_REGISTRY.register()
+class GANLoss(nn.Module):
+    """Define GAN loss.
+
+    Args:
+        gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'.
+        real_label_val (float): The value for real label. Default: 1.0.
+        fake_label_val (float): The value for fake label. Default: 0.0.
+        loss_weight (float): Loss weight. Default: 1.0.
+            Note that loss_weight is only for generators; and it is always 1.0
+            for discriminators.
+    """
+
+    def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0):
+        super(GANLoss, self).__init__()
+        self.gan_type = gan_type
+        self.loss_weight = loss_weight
+        self.real_label_val = real_label_val
+        self.fake_label_val = fake_label_val
+
+        if self.gan_type == 'vanilla':
+            self.loss = nn.BCEWithLogitsLoss()
+        elif self.gan_type == 'lsgan':
+            self.loss = nn.MSELoss()
+        elif self.gan_type == 'wgan':
+            self.loss = self._wgan_loss
+        elif self.gan_type == 'wgan_softplus':
+            self.loss = self._wgan_softplus_loss
+        elif self.gan_type == 'hinge':
+            self.loss = nn.ReLU()
+        else:
+            raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.')
+
+    def _wgan_loss(self, input, target):
+        """wgan loss.
+
+        Args:
+            input (Tensor): Input tensor.
+            target (bool): Target label.
+
+        Returns:
+            Tensor: wgan loss.
+        """
+        return -input.mean() if target else input.mean()
+
+    def _wgan_softplus_loss(self, input, target):
+        """wgan loss with soft plus. softplus is a smooth approximation to the
+        ReLU function.
+
+        In StyleGAN2, it is called:
+            Logistic loss for discriminator;
+            Non-saturating loss for generator.
+
+        Args:
+            input (Tensor): Input tensor.
+            target (bool): Target label.
+
+        Returns:
+            Tensor: wgan loss.
+        """
+        return F.softplus(-input).mean() if target else F.softplus(input).mean()
+
+    def get_target_label(self, input, target_is_real):
+        """Get target label.
+
+        Args:
+            input (Tensor): Input tensor.
+            target_is_real (bool): Whether the target is real or fake.
+
+        Returns:
+            (bool | Tensor): Target tensor. Return bool for wgan, otherwise,
+                return Tensor.
+        """
+
+        if self.gan_type in ['wgan', 'wgan_softplus']:
+            return target_is_real
+        target_val = (self.real_label_val if target_is_real else self.fake_label_val)
+        return input.new_ones(input.size()) * target_val
+
+    def forward(self, input, target_is_real, is_disc=False):
+        """
+        Args:
+            input (Tensor): The input for the loss module, i.e., the network
+                prediction.
+            target_is_real (bool): Whether the targe is real or fake.
+            is_disc (bool): Whether the loss for discriminators or not.
+                Default: False.
+
+        Returns:
+            Tensor: GAN loss value.
+        """
+        target_label = self.get_target_label(input, target_is_real)
+        if self.gan_type == 'hinge':
+            if is_disc:  # for discriminators in hinge-gan
+                input = -input if target_is_real else input
+                loss = self.loss(1 + input).mean()
+            else:  # for generators in hinge-gan
+                loss = -input.mean()
+        else:  # other gan types
+            loss = self.loss(input, target_label)
+
+        # loss_weight is always 1.0 for discriminators
+        return loss if is_disc else loss * self.loss_weight
+
+
+def r1_penalty(real_pred, real_img):
+    """R1 regularization for discriminator. The core idea is to
+        penalize the gradient on real data alone: when the
+        generator distribution produces the true data distribution
+        and the discriminator is equal to 0 on the data manifold, the
+        gradient penalty ensures that the discriminator cannot create
+        a non-zero gradient orthogonal to the data manifold without
+        suffering a loss in the GAN game.
+
+        Ref:
+        Eq. 9 in Which training methods for GANs do actually converge.
+        """
+    grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0]
+    grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean()
+    return grad_penalty
+
+
+def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01):
+    noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3])
+    grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0]
+    path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))
+
+    path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length)
+
+    path_penalty = (path_lengths - path_mean).pow(2).mean()
+
+    return path_penalty, path_lengths.detach().mean(), path_mean.detach()
+
+
+def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None):
+    """Calculate gradient penalty for wgan-gp.
+
+    Args:
+        discriminator (nn.Module): Network for the discriminator.
+        real_data (Tensor): Real input data.
+        fake_data (Tensor): Fake input data.
+        weight (Tensor): Weight tensor. Default: None.
+
+    Returns:
+        Tensor: A tensor for gradient penalty.
+    """
+
+    batch_size = real_data.size(0)
+    alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1))
+
+    # interpolate between real_data and fake_data
+    interpolates = alpha * real_data + (1. - alpha) * fake_data
+    interpolates = autograd.Variable(interpolates, requires_grad=True)
+
+    disc_interpolates = discriminator(interpolates)
+    gradients = autograd.grad(
+        outputs=disc_interpolates,
+        inputs=interpolates,
+        grad_outputs=torch.ones_like(disc_interpolates),
+        create_graph=True,
+        retain_graph=True,
+        only_inputs=True)[0]
+
+    if weight is not None:
+        gradients = gradients * weight
+
+    gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()
+    if weight is not None:
+        gradients_penalty /= torch.mean(weight)
+
+    return gradients_penalty
diff --git a/basicsr/metrics/__init__.py b/basicsr/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..045b23f0862c6c54af6c4cb80baa4430528e670b
--- /dev/null
+++ b/basicsr/metrics/__init__.py
@@ -0,0 +1,20 @@
+from copy import deepcopy
+
+from basicsr.utils.registry import METRIC_REGISTRY
+from .niqe import calculate_niqe
+from .psnr_ssim import calculate_psnr, calculate_ssim
+
+__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe']
+
+
+def calculate_metric(data, opt):
+    """Calculate metric from data and options.
+
+    Args:
+        opt (dict): Configuration. It must constain:
+            type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    metric_type = opt.pop('type')
+    metric = METRIC_REGISTRY.get(metric_type)(**data, **opt)
+    return metric
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diff --git a/basicsr/metrics/fid.py b/basicsr/metrics/fid.py
new file mode 100644
index 0000000000000000000000000000000000000000..a54411f565c52d98b8f3046872fe107689c4fdf0
--- /dev/null
+++ b/basicsr/metrics/fid.py
@@ -0,0 +1,93 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from scipy import linalg
+from tqdm import tqdm
+
+from basicsr.archs.inception import InceptionV3
+
+
+def load_patched_inception_v3(device='cuda', resize_input=True, normalize_input=False):
+    # we may not resize the input, but in [rosinality/stylegan2-pytorch] it
+    # does resize the input.
+    inception = InceptionV3([3], resize_input=resize_input, normalize_input=normalize_input)
+    inception = nn.DataParallel(inception).eval().to(device)
+    return inception
+
+
+@torch.no_grad()
+def extract_inception_features(data_generator, inception, len_generator=None, device='cuda'):
+    """Extract inception features.
+
+    Args:
+        data_generator (generator): A data generator.
+        inception (nn.Module): Inception model.
+        len_generator (int): Length of the data_generator to show the
+            progressbar. Default: None.
+        device (str): Device. Default: cuda.
+
+    Returns:
+        Tensor: Extracted features.
+    """
+    if len_generator is not None:
+        pbar = tqdm(total=len_generator, unit='batch', desc='Extract')
+    else:
+        pbar = None
+    features = []
+
+    for data in data_generator:
+        if pbar:
+            pbar.update(1)
+        data = data.to(device)
+        feature = inception(data)[0].view(data.shape[0], -1)
+        features.append(feature.to('cpu'))
+    if pbar:
+        pbar.close()
+    features = torch.cat(features, 0)
+    return features
+
+
+def calculate_fid(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+        d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+    Stable version by Dougal J. Sutherland.
+
+    Args:
+        mu1 (np.array): The sample mean over activations.
+        sigma1 (np.array): The covariance matrix over activations for
+            generated samples.
+        mu2 (np.array): The sample mean over activations, precalculated on an
+               representative data set.
+        sigma2 (np.array): The covariance matrix over activations,
+            precalculated on an representative data set.
+
+    Returns:
+        float: The Frechet Distance.
+    """
+    assert mu1.shape == mu2.shape, 'Two mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, ('Two covariances have different dimensions')
+
+    cov_sqrt, _ = linalg.sqrtm(sigma1 @ sigma2, disp=False)
+
+    # Product might be almost singular
+    if not np.isfinite(cov_sqrt).all():
+        print('Product of cov matrices is singular. Adding {eps} to diagonal ' 'of cov estimates')
+        offset = np.eye(sigma1.shape[0]) * eps
+        cov_sqrt = linalg.sqrtm((sigma1 + offset) @ (sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(cov_sqrt):
+        if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3):
+            m = np.max(np.abs(cov_sqrt.imag))
+            raise ValueError(f'Imaginary component {m}')
+        cov_sqrt = cov_sqrt.real
+
+    mean_diff = mu1 - mu2
+    mean_norm = mean_diff @ mean_diff
+    trace = np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(cov_sqrt)
+    fid = mean_norm + trace
+
+    return fid
diff --git a/basicsr/metrics/metric_util.py b/basicsr/metrics/metric_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d18f0f7816431bed6af9d58319c6435bdf5c971
--- /dev/null
+++ b/basicsr/metrics/metric_util.py
@@ -0,0 +1,45 @@
+import numpy as np
+
+from basicsr.utils.matlab_functions import bgr2ycbcr
+
+
+def reorder_image(img, input_order='HWC'):
+    """Reorder images to 'HWC' order.
+
+    If the input_order is (h, w), return (h, w, 1);
+    If the input_order is (c, h, w), return (h, w, c);
+    If the input_order is (h, w, c), return as it is.
+
+    Args:
+        img (ndarray): Input image.
+        input_order (str): Whether the input order is 'HWC' or 'CHW'.
+            If the input image shape is (h, w), input_order will not have
+            effects. Default: 'HWC'.
+
+    Returns:
+        ndarray: reordered image.
+    """
+
+    if input_order not in ['HWC', 'CHW']:
+        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' "'HWC' and 'CHW'")
+    if len(img.shape) == 2:
+        img = img[..., None]
+    if input_order == 'CHW':
+        img = img.transpose(1, 2, 0)
+    return img
+
+
+def to_y_channel(img):
+    """Change to Y channel of YCbCr.
+
+    Args:
+        img (ndarray): Images with range [0, 255].
+
+    Returns:
+        (ndarray): Images with range [0, 255] (float type) without round.
+    """
+    img = img.astype(np.float32) / 255.
+    if img.ndim == 3 and img.shape[2] == 3:
+        img = bgr2ycbcr(img, y_only=True)
+        img = img[..., None]
+    return img * 255.
diff --git a/basicsr/metrics/niqe.py b/basicsr/metrics/niqe.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ef7c42094b734061d8a10ac160a5b699b8e9153
--- /dev/null
+++ b/basicsr/metrics/niqe.py
@@ -0,0 +1,189 @@
+import cv2
+import math
+import numpy as np
+from scipy.ndimage.filters import convolve
+from scipy.special import gamma
+
+from basicsr.metrics.metric_util import reorder_image, to_y_channel
+
+
+def estimate_aggd_param(block):
+    """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) paramters.
+
+    Args:
+        block (ndarray): 2D Image block.
+
+    Returns:
+        tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD
+            distribution (Estimating the parames in Equation 7 in the paper).
+    """
+    block = block.flatten()
+    gam = np.arange(0.2, 10.001, 0.001)  # len = 9801
+    gam_reciprocal = np.reciprocal(gam)
+    r_gam = np.square(gamma(gam_reciprocal * 2)) / (gamma(gam_reciprocal) * gamma(gam_reciprocal * 3))
+
+    left_std = np.sqrt(np.mean(block[block < 0]**2))
+    right_std = np.sqrt(np.mean(block[block > 0]**2))
+    gammahat = left_std / right_std
+    rhat = (np.mean(np.abs(block)))**2 / np.mean(block**2)
+    rhatnorm = (rhat * (gammahat**3 + 1) * (gammahat + 1)) / ((gammahat**2 + 1)**2)
+    array_position = np.argmin((r_gam - rhatnorm)**2)
+
+    alpha = gam[array_position]
+    beta_l = left_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))
+    beta_r = right_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))
+    return (alpha, beta_l, beta_r)
+
+
+def compute_feature(block):
+    """Compute features.
+
+    Args:
+        block (ndarray): 2D Image block.
+
+    Returns:
+        list: Features with length of 18.
+    """
+    feat = []
+    alpha, beta_l, beta_r = estimate_aggd_param(block)
+    feat.extend([alpha, (beta_l + beta_r) / 2])
+
+    # distortions disturb the fairly regular structure of natural images.
+    # This deviation can be captured by analyzing the sample distribution of
+    # the products of pairs of adjacent coefficients computed along
+    # horizontal, vertical and diagonal orientations.
+    shifts = [[0, 1], [1, 0], [1, 1], [1, -1]]
+    for i in range(len(shifts)):
+        shifted_block = np.roll(block, shifts[i], axis=(0, 1))
+        alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block)
+        # Eq. 8
+        mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha))
+        feat.extend([alpha, mean, beta_l, beta_r])
+    return feat
+
+
+def niqe(img, mu_pris_param, cov_pris_param, gaussian_window, block_size_h=96, block_size_w=96):
+    """Calculate NIQE (Natural Image Quality Evaluator) metric.
+
+    Ref: Making a "Completely Blind" Image Quality Analyzer.
+    This implementation could produce almost the same results as the official
+    MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip
+
+    Note that we do not include block overlap height and width, since they are
+    always 0 in the official implementation.
+
+    For good performance, it is advisable by the official implemtation to
+    divide the distorted image in to the same size patched as used for the
+    construction of multivariate Gaussian model.
+
+    Args:
+        img (ndarray): Input image whose quality needs to be computed. The
+            image must be a gray or Y (of YCbCr) image with shape (h, w).
+            Range [0, 255] with float type.
+        mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian
+            model calculated on the pristine dataset.
+        cov_pris_param (ndarray): Covariance of a pre-defined multivariate
+            Gaussian model calculated on the pristine dataset.
+        gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the
+            image.
+        block_size_h (int): Height of the blocks in to which image is divided.
+            Default: 96 (the official recommended value).
+        block_size_w (int): Width of the blocks in to which image is divided.
+            Default: 96 (the official recommended value).
+    """
+    assert img.ndim == 2, ('Input image must be a gray or Y (of YCbCr) image with shape (h, w).')
+    # crop image
+    h, w = img.shape
+    num_block_h = math.floor(h / block_size_h)
+    num_block_w = math.floor(w / block_size_w)
+    img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w]
+
+    distparam = []  # dist param is actually the multiscale features
+    for scale in (1, 2):  # perform on two scales (1, 2)
+        mu = convolve(img, gaussian_window, mode='nearest')
+        sigma = np.sqrt(np.abs(convolve(np.square(img), gaussian_window, mode='nearest') - np.square(mu)))
+        # normalize, as in Eq. 1 in the paper
+        img_nomalized = (img - mu) / (sigma + 1)
+
+        feat = []
+        for idx_w in range(num_block_w):
+            for idx_h in range(num_block_h):
+                # process ecah block
+                block = img_nomalized[idx_h * block_size_h // scale:(idx_h + 1) * block_size_h // scale,
+                                      idx_w * block_size_w // scale:(idx_w + 1) * block_size_w // scale]
+                feat.append(compute_feature(block))
+
+        distparam.append(np.array(feat))
+        # TODO: matlab bicubic downsample with anti-aliasing
+        # for simplicity, now we use opencv instead, which will result in
+        # a slight difference.
+        if scale == 1:
+            h, w = img.shape
+            img = cv2.resize(img / 255., (w // 2, h // 2), interpolation=cv2.INTER_LINEAR)
+            img = img * 255.
+
+    distparam = np.concatenate(distparam, axis=1)
+
+    # fit a MVG (multivariate Gaussian) model to distorted patch features
+    mu_distparam = np.nanmean(distparam, axis=0)
+    # use nancov. ref: https://ww2.mathworks.cn/help/stats/nancov.html
+    distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)]
+    cov_distparam = np.cov(distparam_no_nan, rowvar=False)
+
+    # compute niqe quality, Eq. 10 in the paper
+    invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2)
+    quality = np.matmul(
+        np.matmul((mu_pris_param - mu_distparam), invcov_param), np.transpose((mu_pris_param - mu_distparam)))
+    quality = np.sqrt(quality)
+
+    return quality
+
+
+def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y'):
+    """Calculate NIQE (Natural Image Quality Evaluator) metric.
+
+    Ref: Making a "Completely Blind" Image Quality Analyzer.
+    This implementation could produce almost the same results as the official
+    MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip
+
+    We use the official params estimated from the pristine dataset.
+    We use the recommended block size (96, 96) without overlaps.
+
+    Args:
+        img (ndarray): Input image whose quality needs to be computed.
+            The input image must be in range [0, 255] with float/int type.
+            The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order)
+            If the input order is 'HWC' or 'CHW', it will be converted to gray
+            or Y (of YCbCr) image according to the ``convert_to`` argument.
+        crop_border (int): Cropped pixels in each edge of an image. These
+            pixels are not involved in the metric calculation.
+        input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'.
+            Default: 'HWC'.
+        convert_to (str): Whether coverted to 'y' (of MATLAB YCbCr) or 'gray'.
+            Default: 'y'.
+
+    Returns:
+        float: NIQE result.
+    """
+
+    # we use the official params estimated from the pristine dataset.
+    niqe_pris_params = np.load('basicsr/metrics/niqe_pris_params.npz')
+    mu_pris_param = niqe_pris_params['mu_pris_param']
+    cov_pris_param = niqe_pris_params['cov_pris_param']
+    gaussian_window = niqe_pris_params['gaussian_window']
+
+    img = img.astype(np.float32)
+    if input_order != 'HW':
+        img = reorder_image(img, input_order=input_order)
+        if convert_to == 'y':
+            img = to_y_channel(img)
+        elif convert_to == 'gray':
+            img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255.
+        img = np.squeeze(img)
+
+    if crop_border != 0:
+        img = img[crop_border:-crop_border, crop_border:-crop_border]
+
+    niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window)
+
+    return niqe_result
diff --git a/basicsr/metrics/niqe_pris_params.npz b/basicsr/metrics/niqe_pris_params.npz
new file mode 100644
index 0000000000000000000000000000000000000000..42f06a9a18e6ed8bbf7933bec1477b189ef798de
--- /dev/null
+++ b/basicsr/metrics/niqe_pris_params.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a7c182a68c9e7f1b2e2e5ec723279d6f65d912b6fcaf37eb2bf03d7367c4296
+size 11850
diff --git a/basicsr/metrics/psnr_ssim.py b/basicsr/metrics/psnr_ssim.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbd950699c2495880236883861d9e199f900eae8
--- /dev/null
+++ b/basicsr/metrics/psnr_ssim.py
@@ -0,0 +1,128 @@
+import cv2
+import numpy as np
+
+from basicsr.metrics.metric_util import reorder_image, to_y_channel
+from basicsr.utils.registry import METRIC_REGISTRY
+
+
+@METRIC_REGISTRY.register()
+def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
+    """Calculate PSNR (Peak Signal-to-Noise Ratio).
+
+    Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
+
+    Args:
+        img1 (ndarray): Images with range [0, 255].
+        img2 (ndarray): Images with range [0, 255].
+        crop_border (int): Cropped pixels in each edge of an image. These
+            pixels are not involved in the PSNR calculation.
+        input_order (str): Whether the input order is 'HWC' or 'CHW'.
+            Default: 'HWC'.
+        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
+
+    Returns:
+        float: psnr result.
+    """
+
+    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
+    if input_order not in ['HWC', 'CHW']:
+        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
+    img1 = reorder_image(img1, input_order=input_order)
+    img2 = reorder_image(img2, input_order=input_order)
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+
+    if crop_border != 0:
+        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
+        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
+
+    if test_y_channel:
+        img1 = to_y_channel(img1)
+        img2 = to_y_channel(img2)
+
+    mse = np.mean((img1 - img2)**2)
+    if mse == 0:
+        return float('inf')
+    return 20. * np.log10(255. / np.sqrt(mse))
+
+
+def _ssim(img1, img2):
+    """Calculate SSIM (structural similarity) for one channel images.
+
+    It is called by func:`calculate_ssim`.
+
+    Args:
+        img1 (ndarray): Images with range [0, 255] with order 'HWC'.
+        img2 (ndarray): Images with range [0, 255] with order 'HWC'.
+
+    Returns:
+        float: ssim result.
+    """
+
+    C1 = (0.01 * 255)**2
+    C2 = (0.03 * 255)**2
+
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+    kernel = cv2.getGaussianKernel(11, 1.5)
+    window = np.outer(kernel, kernel.transpose())
+
+    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
+    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+    mu1_sq = mu1**2
+    mu2_sq = mu2**2
+    mu1_mu2 = mu1 * mu2
+    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
+    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
+    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+    return ssim_map.mean()
+
+
+@METRIC_REGISTRY.register()
+def calculate_ssim(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
+    """Calculate SSIM (structural similarity).
+
+    Ref:
+    Image quality assessment: From error visibility to structural similarity
+
+    The results are the same as that of the official released MATLAB code in
+    https://ece.uwaterloo.ca/~z70wang/research/ssim/.
+
+    For three-channel images, SSIM is calculated for each channel and then
+    averaged.
+
+    Args:
+        img1 (ndarray): Images with range [0, 255].
+        img2 (ndarray): Images with range [0, 255].
+        crop_border (int): Cropped pixels in each edge of an image. These
+            pixels are not involved in the SSIM calculation.
+        input_order (str): Whether the input order is 'HWC' or 'CHW'.
+            Default: 'HWC'.
+        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
+
+    Returns:
+        float: ssim result.
+    """
+
+    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
+    if input_order not in ['HWC', 'CHW']:
+        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
+    img1 = reorder_image(img1, input_order=input_order)
+    img2 = reorder_image(img2, input_order=input_order)
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+
+    if crop_border != 0:
+        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
+        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
+
+    if test_y_channel:
+        img1 = to_y_channel(img1)
+        img2 = to_y_channel(img2)
+
+    ssims = []
+    for i in range(img1.shape[2]):
+        ssims.append(_ssim(img1[..., i], img2[..., i]))
+    return np.array(ssims).mean()
diff --git a/basicsr/models/__init__.py b/basicsr/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..00bde45f003698a5b15d3517ae47b59ef1d86e0c
--- /dev/null
+++ b/basicsr/models/__init__.py
@@ -0,0 +1,30 @@
+import importlib
+from copy import deepcopy
+from os import path as osp
+
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.registry import MODEL_REGISTRY
+
+__all__ = ['build_model']
+
+# automatically scan and import model modules for registry
+# scan all the files under the 'models' folder and collect files ending with
+# '_model.py'
+model_folder = osp.dirname(osp.abspath(__file__))
+model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
+# import all the model modules
+_model_modules = [importlib.import_module(f'basicsr.models.{file_name}') for file_name in model_filenames]
+
+
+def build_model(opt):
+    """Build model from options.
+
+    Args:
+        opt (dict): Configuration. It must constain:
+            model_type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    model = MODEL_REGISTRY.get(opt['model_type'])(opt)
+    logger = get_root_logger()
+    logger.info(f'Model [{model.__class__.__name__}] is created.')
+    return model
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diff --git a/basicsr/models/base_model.py b/basicsr/models/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf1f90acf89484ffc67301cdff188f06fe8963f1
--- /dev/null
+++ b/basicsr/models/base_model.py
@@ -0,0 +1,322 @@
+import logging
+import os
+import torch
+from collections import OrderedDict
+from copy import deepcopy
+from torch.nn.parallel import DataParallel, DistributedDataParallel
+
+from basicsr.models import lr_scheduler as lr_scheduler
+from basicsr.utils.dist_util import master_only
+
+logger = logging.getLogger('basicsr')
+
+
+class BaseModel():
+    """Base model."""
+
+    def __init__(self, opt):
+        self.opt = opt
+        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
+        self.is_train = opt['is_train']
+        self.schedulers = []
+        self.optimizers = []
+
+    def feed_data(self, data):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        pass
+
+    def save(self, epoch, current_iter):
+        """Save networks and training state."""
+        pass
+
+    def validation(self, dataloader, current_iter, tb_logger, save_img=False):
+        """Validation function.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader): Validation dataloader.
+            current_iter (int): Current iteration.
+            tb_logger (tensorboard logger): Tensorboard logger.
+            save_img (bool): Whether to save images. Default: False.
+        """
+        if self.opt['dist']:
+            self.dist_validation(dataloader, current_iter, tb_logger, save_img)
+        else:
+            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
+
+    def model_ema(self, decay=0.999):
+        net_g = self.get_bare_model(self.net_g)
+
+        net_g_params = dict(net_g.named_parameters())
+        net_g_ema_params = dict(self.net_g_ema.named_parameters())
+
+        for k in net_g_ema_params.keys():
+            net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay)
+
+    def get_current_log(self):
+        return self.log_dict
+
+    def model_to_device(self, net):
+        """Model to device. It also warps models with DistributedDataParallel
+        or DataParallel.
+
+        Args:
+            net (nn.Module)
+        """
+        net = net.to(self.device)
+        if self.opt['dist']:
+            find_unused_parameters = self.opt.get('find_unused_parameters', False)
+            net = DistributedDataParallel(
+                net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters)
+        elif self.opt['num_gpu'] > 1:
+            net = DataParallel(net)
+        return net
+
+    def get_optimizer(self, optim_type, params, lr, **kwargs):
+        if optim_type == 'Adam':
+            optimizer = torch.optim.Adam(params, lr, **kwargs)
+        else:
+            raise NotImplementedError(f'optimizer {optim_type} is not supperted yet.')
+        return optimizer
+
+    def setup_schedulers(self):
+        """Set up schedulers."""
+        train_opt = self.opt['train']
+        scheduler_type = train_opt['scheduler'].pop('type')
+        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
+            for optimizer in self.optimizers:
+                self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler']))
+        elif scheduler_type == 'CosineAnnealingRestartLR':
+            for optimizer in self.optimizers:
+                self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler']))
+        else:
+            raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.')
+
+    def get_bare_model(self, net):
+        """Get bare model, especially under wrapping with
+        DistributedDataParallel or DataParallel.
+        """
+        if isinstance(net, (DataParallel, DistributedDataParallel)):
+            net = net.module
+        return net
+
+    @master_only
+    def print_network(self, net):
+        """Print the str and parameter number of a network.
+
+        Args:
+            net (nn.Module)
+        """
+        if isinstance(net, (DataParallel, DistributedDataParallel)):
+            net_cls_str = (f'{net.__class__.__name__} - ' f'{net.module.__class__.__name__}')
+        else:
+            net_cls_str = f'{net.__class__.__name__}'
+
+        net = self.get_bare_model(net)
+        net_str = str(net)
+        net_params = sum(map(lambda x: x.numel(), net.parameters()))
+
+        logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}')
+        logger.info(net_str)
+
+    def _set_lr(self, lr_groups_l):
+        """Set learning rate for warmup.
+
+        Args:
+            lr_groups_l (list): List for lr_groups, each for an optimizer.
+        """
+        for optimizer, lr_groups in zip(self.optimizers, lr_groups_l):
+            for param_group, lr in zip(optimizer.param_groups, lr_groups):
+                param_group['lr'] = lr
+
+    def _get_init_lr(self):
+        """Get the initial lr, which is set by the scheduler.
+        """
+        init_lr_groups_l = []
+        for optimizer in self.optimizers:
+            init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups])
+        return init_lr_groups_l
+
+    def update_learning_rate(self, current_iter, warmup_iter=-1):
+        """Update learning rate.
+
+        Args:
+            current_iter (int): Current iteration.
+            warmup_iter (int)： Warmup iter numbers. -1 for no warmup.
+                Default： -1.
+        """
+        if current_iter > 1:
+            for scheduler in self.schedulers:
+                scheduler.step()
+        # set up warm-up learning rate
+        if current_iter < warmup_iter:
+            # get initial lr for each group
+            init_lr_g_l = self._get_init_lr()
+            # modify warming-up learning rates
+            # currently only support linearly warm up
+            warm_up_lr_l = []
+            for init_lr_g in init_lr_g_l:
+                warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g])
+            # set learning rate
+            self._set_lr(warm_up_lr_l)
+
+    def get_current_learning_rate(self):
+        return [param_group['lr'] for param_group in self.optimizers[0].param_groups]
+
+    @master_only
+    def save_network(self, net, net_label, current_iter, param_key='params'):
+        """Save networks.
+
+        Args:
+            net (nn.Module | list[nn.Module]): Network(s) to be saved.
+            net_label (str): Network label.
+            current_iter (int): Current iter number.
+            param_key (str | list[str]): The parameter key(s) to save network.
+                Default: 'params'.
+        """
+        if current_iter == -1:
+            current_iter = 'latest'
+        save_filename = f'{net_label}_{current_iter}.pth'
+        save_path = os.path.join(self.opt['path']['models'], save_filename)
+
+        net = net if isinstance(net, list) else [net]
+        param_key = param_key if isinstance(param_key, list) else [param_key]
+        assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.'
+
+        save_dict = {}
+        for net_, param_key_ in zip(net, param_key):
+            net_ = self.get_bare_model(net_)
+            state_dict = net_.state_dict()
+            for key, param in state_dict.items():
+                if key.startswith('module.'):  # remove unnecessary 'module.'
+                    key = key[7:]
+                state_dict[key] = param.cpu()
+            save_dict[param_key_] = state_dict
+
+        torch.save(save_dict, save_path)
+
+    def _print_different_keys_loading(self, crt_net, load_net, strict=True):
+        """Print keys with differnet name or different size when loading models.
+
+        1. Print keys with differnet names.
+        2. If strict=False, print the same key but with different tensor size.
+            It also ignore these keys with different sizes (not load).
+
+        Args:
+            crt_net (torch model): Current network.
+            load_net (dict): Loaded network.
+            strict (bool): Whether strictly loaded. Default: True.
+        """
+        crt_net = self.get_bare_model(crt_net)
+        crt_net = crt_net.state_dict()
+        crt_net_keys = set(crt_net.keys())
+        load_net_keys = set(load_net.keys())
+
+        if crt_net_keys != load_net_keys:
+            logger.warning('Current net - loaded net:')
+            for v in sorted(list(crt_net_keys - load_net_keys)):
+                logger.warning(f'  {v}')
+            logger.warning('Loaded net - current net:')
+            for v in sorted(list(load_net_keys - crt_net_keys)):
+                logger.warning(f'  {v}')
+
+        # check the size for the same keys
+        if not strict:
+            common_keys = crt_net_keys & load_net_keys
+            for k in common_keys:
+                if crt_net[k].size() != load_net[k].size():
+                    logger.warning(f'Size different, ignore [{k}]: crt_net: '
+                                   f'{crt_net[k].shape}; load_net: {load_net[k].shape}')
+                    load_net[k + '.ignore'] = load_net.pop(k)
+
+    def load_network(self, net, load_path, strict=True, param_key='params'):
+        """Load network.
+
+        Args:
+            load_path (str): The path of networks to be loaded.
+            net (nn.Module): Network.
+            strict (bool): Whether strictly loaded.
+            param_key (str): The parameter key of loaded network. If set to
+                None, use the root 'path'.
+                Default: 'params'.
+        """
+        net = self.get_bare_model(net)
+        logger.info(f'Loading {net.__class__.__name__} model from {load_path}.')
+        load_net = torch.load(load_path, map_location=lambda storage, loc: storage)
+        if param_key is not None:
+            if param_key not in load_net and 'params' in load_net:
+                param_key = 'params'
+                logger.info('Loading: params_ema does not exist, use params.')
+            load_net = load_net[param_key]
+        # remove unnecessary 'module.'
+        for k, v in deepcopy(load_net).items():
+            if k.startswith('module.'):
+                load_net[k[7:]] = v
+                load_net.pop(k)
+        self._print_different_keys_loading(net, load_net, strict)
+        net.load_state_dict(load_net, strict=strict)
+
+    @master_only
+    def save_training_state(self, epoch, current_iter):
+        """Save training states during training, which will be used for
+        resuming.
+
+        Args:
+            epoch (int): Current epoch.
+            current_iter (int): Current iteration.
+        """
+        if current_iter != -1:
+            state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []}
+            for o in self.optimizers:
+                state['optimizers'].append(o.state_dict())
+            for s in self.schedulers:
+                state['schedulers'].append(s.state_dict())
+            save_filename = f'{current_iter}.state'
+            save_path = os.path.join(self.opt['path']['training_states'], save_filename)
+            torch.save(state, save_path)
+
+    def resume_training(self, resume_state):
+        """Reload the optimizers and schedulers for resumed training.
+
+        Args:
+            resume_state (dict): Resume state.
+        """
+        resume_optimizers = resume_state['optimizers']
+        resume_schedulers = resume_state['schedulers']
+        assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers'
+        assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers'
+        for i, o in enumerate(resume_optimizers):
+            self.optimizers[i].load_state_dict(o)
+        for i, s in enumerate(resume_schedulers):
+            self.schedulers[i].load_state_dict(s)
+
+    def reduce_loss_dict(self, loss_dict):
+        """reduce loss dict.
+
+        In distributed training, it averages the losses among different GPUs .
+
+        Args:
+            loss_dict (OrderedDict): Loss dict.
+        """
+        with torch.no_grad():
+            if self.opt['dist']:
+                keys = []
+                losses = []
+                for name, value in loss_dict.items():
+                    keys.append(name)
+                    losses.append(value)
+                losses = torch.stack(losses, 0)
+                torch.distributed.reduce(losses, dst=0)
+                if self.opt['rank'] == 0:
+                    losses /= self.opt['world_size']
+                loss_dict = {key: loss for key, loss in zip(keys, losses)}
+
+            log_dict = OrderedDict()
+            for name, value in loss_dict.items():
+                log_dict[name] = value.mean().item()
+
+            return log_dict
diff --git a/basicsr/models/edvr_model.py b/basicsr/models/edvr_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a04bcfb42d43a5b4ed01ff503a831787970a468
--- /dev/null
+++ b/basicsr/models/edvr_model.py
@@ -0,0 +1,66 @@
+import logging
+from torch.nn.parallel import DistributedDataParallel
+
+from basicsr.utils.registry import MODEL_REGISTRY
+from .video_base_model import VideoBaseModel
+
+logger = logging.getLogger('basicsr')
+
+
+@MODEL_REGISTRY.register()
+class EDVRModel(VideoBaseModel):
+    """EDVR Model.
+
+    Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks.  # noqa: E501
+    """
+
+    def __init__(self, opt):
+        super(EDVRModel, self).__init__(opt)
+        if self.is_train:
+            self.train_tsa_iter = opt['train'].get('tsa_iter')
+
+    def setup_optimizers(self):
+        train_opt = self.opt['train']
+        dcn_lr_mul = train_opt.get('dcn_lr_mul', 1)
+        logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.')
+        if dcn_lr_mul == 1:
+            optim_params = self.net_g.parameters()
+        else:  # separate dcn params and normal params for differnet lr
+            normal_params = []
+            dcn_params = []
+            for name, param in self.net_g.named_parameters():
+                if 'dcn' in name:
+                    dcn_params.append(param)
+                else:
+                    normal_params.append(param)
+            optim_params = [
+                {  # add normal params first
+                    'params': normal_params,
+                    'lr': train_opt['optim_g']['lr']
+                },
+                {
+                    'params': dcn_params,
+                    'lr': train_opt['optim_g']['lr'] * dcn_lr_mul
+                },
+            ]
+
+        optim_type = train_opt['optim_g'].pop('type')
+        self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g'])
+        self.optimizers.append(self.optimizer_g)
+
+    def optimize_parameters(self, current_iter):
+        if self.train_tsa_iter:
+            if current_iter == 1:
+                logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.')
+                for name, param in self.net_g.named_parameters():
+                    if 'fusion' not in name:
+                        param.requires_grad = False
+            elif current_iter == self.train_tsa_iter:
+                logger.warning('Train all the parameters.')
+                for param in self.net_g.parameters():
+                    param.requires_grad = True
+                if isinstance(self.net_g, DistributedDataParallel):
+                    logger.warning('Set net_g.find_unused_parameters = False.')
+                    self.net_g.find_unused_parameters = False
+
+        super(VideoBaseModel, self).optimize_parameters(current_iter)
diff --git a/basicsr/models/esrgan_model.py b/basicsr/models/esrgan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d746d0e29418d9e8f35fa9c1e3a315d694075be
--- /dev/null
+++ b/basicsr/models/esrgan_model.py
@@ -0,0 +1,83 @@
+import torch
+from collections import OrderedDict
+
+from basicsr.utils.registry import MODEL_REGISTRY
+from .srgan_model import SRGANModel
+
+
+@MODEL_REGISTRY.register()
+class ESRGANModel(SRGANModel):
+    """ESRGAN model for single image super-resolution."""
+
+    def optimize_parameters(self, current_iter):
+        # optimize net_g
+        for p in self.net_d.parameters():
+            p.requires_grad = False
+
+        self.optimizer_g.zero_grad()
+        self.output = self.net_g(self.lq)
+
+        l_g_total = 0
+        loss_dict = OrderedDict()
+        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
+            # pixel loss
+            if self.cri_pix:
+                l_g_pix = self.cri_pix(self.output, self.gt)
+                l_g_total += l_g_pix
+                loss_dict['l_g_pix'] = l_g_pix
+            # perceptual loss
+            if self.cri_perceptual:
+                l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
+                if l_g_percep is not None:
+                    l_g_total += l_g_percep
+                    loss_dict['l_g_percep'] = l_g_percep
+                if l_g_style is not None:
+                    l_g_total += l_g_style
+                    loss_dict['l_g_style'] = l_g_style
+            # gan loss (relativistic gan)
+            real_d_pred = self.net_d(self.gt).detach()
+            fake_g_pred = self.net_d(self.output)
+            l_g_real = self.cri_gan(real_d_pred - torch.mean(fake_g_pred), False, is_disc=False)
+            l_g_fake = self.cri_gan(fake_g_pred - torch.mean(real_d_pred), True, is_disc=False)
+            l_g_gan = (l_g_real + l_g_fake) / 2
+
+            l_g_total += l_g_gan
+            loss_dict['l_g_gan'] = l_g_gan
+
+            l_g_total.backward()
+            self.optimizer_g.step()
+
+        # optimize net_d
+        for p in self.net_d.parameters():
+            p.requires_grad = True
+
+        self.optimizer_d.zero_grad()
+        # gan loss (relativistic gan)
+
+        # In order to avoid the error in distributed training:
+        # "Error detected in CudnnBatchNormBackward: RuntimeError: one of
+        # the variables needed for gradient computation has been modified by
+        # an inplace operation",
+        # we separate the backwards for real and fake, and also detach the
+        # tensor for calculating mean.
+
+        # real
+        fake_d_pred = self.net_d(self.output).detach()
+        real_d_pred = self.net_d(self.gt)
+        l_d_real = self.cri_gan(real_d_pred - torch.mean(fake_d_pred), True, is_disc=True) * 0.5
+        l_d_real.backward()
+        # fake
+        fake_d_pred = self.net_d(self.output.detach())
+        l_d_fake = self.cri_gan(fake_d_pred - torch.mean(real_d_pred.detach()), False, is_disc=True) * 0.5
+        l_d_fake.backward()
+        self.optimizer_d.step()
+
+        loss_dict['l_d_real'] = l_d_real
+        loss_dict['l_d_fake'] = l_d_fake
+        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
+        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
+
+        self.log_dict = self.reduce_loss_dict(loss_dict)
+
+        if self.ema_decay > 0:
+            self.model_ema(decay=self.ema_decay)
diff --git a/basicsr/models/lr_scheduler.py b/basicsr/models/lr_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..a423ce656c044ed5861a0056020074a517f04156
--- /dev/null
+++ b/basicsr/models/lr_scheduler.py
@@ -0,0 +1,96 @@
+import math
+from collections import Counter
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class MultiStepRestartLR(_LRScheduler):
+    """ MultiStep with restarts learning rate scheme.
+
+    Args:
+        optimizer (torch.nn.optimizer): Torch optimizer.
+        milestones (list): Iterations that will decrease learning rate.
+        gamma (float): Decrease ratio. Default: 0.1.
+        restarts (list): Restart iterations. Default: [0].
+        restart_weights (list): Restart weights at each restart iteration.
+            Default: [1].
+        last_epoch (int): Used in _LRScheduler. Default: -1.
+    """
+
+    def __init__(self, optimizer, milestones, gamma=0.1, restarts=(0, ), restart_weights=(1, ), last_epoch=-1):
+        self.milestones = Counter(milestones)
+        self.gamma = gamma
+        self.restarts = restarts
+        self.restart_weights = restart_weights
+        assert len(self.restarts) == len(self.restart_weights), 'restarts and their weights do not match.'
+        super(MultiStepRestartLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if self.last_epoch in self.restarts:
+            weight = self.restart_weights[self.restarts.index(self.last_epoch)]
+            return [group['initial_lr'] * weight for group in self.optimizer.param_groups]
+        if self.last_epoch not in self.milestones:
+            return [group['lr'] for group in self.optimizer.param_groups]
+        return [group['lr'] * self.gamma**self.milestones[self.last_epoch] for group in self.optimizer.param_groups]
+
+
+def get_position_from_periods(iteration, cumulative_period):
+    """Get the position from a period list.
+
+    It will return the index of the right-closest number in the period list.
+    For example, the cumulative_period = [100, 200, 300, 400],
+    if iteration == 50, return 0;
+    if iteration == 210, return 2;
+    if iteration == 300, return 2.
+
+    Args:
+        iteration (int): Current iteration.
+        cumulative_period (list[int]): Cumulative period list.
+
+    Returns:
+        int: The position of the right-closest number in the period list.
+    """
+    for i, period in enumerate(cumulative_period):
+        if iteration <= period:
+            return i
+
+
+class CosineAnnealingRestartLR(_LRScheduler):
+    """ Cosine annealing with restarts learning rate scheme.
+
+    An example of config:
+    periods = [10, 10, 10, 10]
+    restart_weights = [1, 0.5, 0.5, 0.5]
+    eta_min=1e-7
+
+    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
+    scheduler will restart with the weights in restart_weights.
+
+    Args:
+        optimizer (torch.nn.optimizer): Torch optimizer.
+        periods (list): Period for each cosine anneling cycle.
+        restart_weights (list): Restart weights at each restart iteration.
+            Default: [1].
+        eta_min (float): The mimimum lr. Default: 0.
+        last_epoch (int): Used in _LRScheduler. Default: -1.
+    """
+
+    def __init__(self, optimizer, periods, restart_weights=(1, ), eta_min=0, last_epoch=-1):
+        self.periods = periods
+        self.restart_weights = restart_weights
+        self.eta_min = eta_min
+        assert (len(self.periods) == len(
+            self.restart_weights)), 'periods and restart_weights should have the same length.'
+        self.cumulative_period = [sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))]
+        super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        idx = get_position_from_periods(self.last_epoch, self.cumulative_period)
+        current_weight = self.restart_weights[idx]
+        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
+        current_period = self.periods[idx]
+
+        return [
+            self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) *
+            (1 + math.cos(math.pi * ((self.last_epoch - nearest_restart) / current_period)))
+            for base_lr in self.base_lrs
+        ]
diff --git a/basicsr/models/sr_model.py b/basicsr/models/sr_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b50c6d6bb0489fa212ce09f5a940c78f0d014d4
--- /dev/null
+++ b/basicsr/models/sr_model.py
@@ -0,0 +1,209 @@
+import torch
+from collections import OrderedDict
+from os import path as osp
+from tqdm import tqdm
+
+from basicsr.archs import build_network
+from basicsr.losses import build_loss
+from basicsr.metrics import calculate_metric
+from basicsr.utils import get_root_logger, imwrite, tensor2img
+from basicsr.utils.registry import MODEL_REGISTRY
+from .base_model import BaseModel
+
+
+@MODEL_REGISTRY.register()
+class SRModel(BaseModel):
+    """Base SR model for single image super-resolution."""
+
+    def __init__(self, opt):
+        super(SRModel, self).__init__(opt)
+
+        # define network
+        self.net_g = build_network(opt['network_g'])
+        self.net_g = self.model_to_device(self.net_g)
+        self.print_network(self.net_g)
+
+        # load pretrained models
+        load_path = self.opt['path'].get('pretrain_network_g', None)
+        if load_path is not None:
+            self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True))
+
+        if self.is_train:
+            self.init_training_settings()
+
+    def init_training_settings(self):
+        self.net_g.train()
+        train_opt = self.opt['train']
+
+        self.ema_decay = train_opt.get('ema_decay', 0)
+        if self.ema_decay > 0:
+            logger = get_root_logger()
+            logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}')
+            # define network net_g with Exponential Moving Average (EMA)
+            # net_g_ema is used only for testing on one GPU and saving
+            # There is no need to wrap with DistributedDataParallel
+            self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
+            # load pretrained model
+            load_path = self.opt['path'].get('pretrain_network_g', None)
+            if load_path is not None:
+                self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
+            else:
+                self.model_ema(0)  # copy net_g weight
+            self.net_g_ema.eval()
+
+        # define losses
+        if train_opt.get('pixel_opt'):
+            self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
+        else:
+            self.cri_pix = None
+
+        if train_opt.get('perceptual_opt'):
+            self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
+        else:
+            self.cri_perceptual = None
+
+        if self.cri_pix is None and self.cri_perceptual is None:
+            raise ValueError('Both pixel and perceptual losses are None.')
+
+        # set up optimizers and schedulers
+        self.setup_optimizers()
+        self.setup_schedulers()
+
+    def setup_optimizers(self):
+        train_opt = self.opt['train']
+        optim_params = []
+        for k, v in self.net_g.named_parameters():
+            if v.requires_grad:
+                optim_params.append(v)
+            else:
+                logger = get_root_logger()
+                logger.warning(f'Params {k} will not be optimized.')
+
+        optim_type = train_opt['optim_g'].pop('type')
+        self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g'])
+        self.optimizers.append(self.optimizer_g)
+
+    def feed_data(self, data):
+        self.lq = data['lq'].to(self.device)
+        if 'gt' in data:
+            self.gt = data['gt'].to(self.device)
+
+    def optimize_parameters(self, current_iter):
+        self.optimizer_g.zero_grad()
+        self.output = self.net_g(self.lq)
+
+        l_total = 0
+        loss_dict = OrderedDict()
+        # pixel loss
+        if self.cri_pix:
+            l_pix = self.cri_pix(self.output, self.gt)
+            l_total += l_pix
+            loss_dict['l_pix'] = l_pix
+        # perceptual loss
+        if self.cri_perceptual:
+            l_percep, l_style = self.cri_perceptual(self.output, self.gt)
+            if l_percep is not None:
+                l_total += l_percep
+                loss_dict['l_percep'] = l_percep
+            if l_style is not None:
+                l_total += l_style
+                loss_dict['l_style'] = l_style
+
+        l_total.backward()
+        self.optimizer_g.step()
+
+        self.log_dict = self.reduce_loss_dict(loss_dict)
+
+        if self.ema_decay > 0:
+            self.model_ema(decay=self.ema_decay)
+
+    def test(self):
+        if hasattr(self, 'net_g_ema'):
+            self.net_g_ema.eval()
+            with torch.no_grad():
+                self.output = self.net_g_ema(self.lq)
+        else:
+            self.net_g.eval()
+            with torch.no_grad():
+                self.output = self.net_g(self.lq)
+            self.net_g.train()
+
+    def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        if self.opt['rank'] == 0:
+            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
+
+    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        dataset_name = dataloader.dataset.opt['name']
+        with_metrics = self.opt['val'].get('metrics') is not None
+        if with_metrics:
+            self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
+        pbar = tqdm(total=len(dataloader), unit='image')
+
+        for idx, val_data in enumerate(dataloader):
+            img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
+            self.feed_data(val_data)
+            self.test()
+
+            visuals = self.get_current_visuals()
+            sr_img = tensor2img([visuals['result']])
+            if 'gt' in visuals:
+                gt_img = tensor2img([visuals['gt']])
+                del self.gt
+
+            # tentative for out of GPU memory
+            del self.lq
+            del self.output
+            torch.cuda.empty_cache()
+
+            if save_img:
+                if self.opt['is_train']:
+                    save_img_path = osp.join(self.opt['path']['visualization'], img_name,
+                                             f'{img_name}_{current_iter}.png')
+                else:
+                    if self.opt['val']['suffix']:
+                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
+                                                 f'{img_name}_{self.opt["val"]["suffix"]}.png')
+                    else:
+                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
+                                                 f'{img_name}_{self.opt["name"]}.png')
+                imwrite(sr_img, save_img_path)
+
+            if with_metrics:
+                # calculate metrics
+                for name, opt_ in self.opt['val']['metrics'].items():
+                    metric_data = dict(img1=sr_img, img2=gt_img)
+                    self.metric_results[name] += calculate_metric(metric_data, opt_)
+            pbar.update(1)
+            pbar.set_description(f'Test {img_name}')
+        pbar.close()
+
+        if with_metrics:
+            for metric in self.metric_results.keys():
+                self.metric_results[metric] /= (idx + 1)
+
+            self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
+
+    def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
+        log_str = f'Validation {dataset_name}\n'
+        for metric, value in self.metric_results.items():
+            log_str += f'\t # {metric}: {value:.4f}\n'
+        logger = get_root_logger()
+        logger.info(log_str)
+        if tb_logger:
+            for metric, value in self.metric_results.items():
+                tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
+
+    def get_current_visuals(self):
+        out_dict = OrderedDict()
+        out_dict['lq'] = self.lq.detach().cpu()
+        out_dict['result'] = self.output.detach().cpu()
+        if hasattr(self, 'gt'):
+            out_dict['gt'] = self.gt.detach().cpu()
+        return out_dict
+
+    def save(self, epoch, current_iter):
+        if hasattr(self, 'net_g_ema'):
+            self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
+        else:
+            self.save_network(self.net_g, 'net_g', current_iter)
+        self.save_training_state(epoch, current_iter)
diff --git a/basicsr/models/srgan_model.py b/basicsr/models/srgan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9d154c61c5df9b03f8832e89a9430bed5bac0a5
--- /dev/null
+++ b/basicsr/models/srgan_model.py
@@ -0,0 +1,143 @@
+import torch
+from collections import OrderedDict
+
+from basicsr.archs import build_network
+from basicsr.losses import build_loss
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import MODEL_REGISTRY
+from .sr_model import SRModel
+
+
+@MODEL_REGISTRY.register()
+class SRGANModel(SRModel):
+    """SRGAN model for single image super-resolution."""
+
+    def init_training_settings(self):
+        train_opt = self.opt['train']
+
+        self.ema_decay = train_opt.get('ema_decay', 0)
+        if self.ema_decay > 0:
+            logger = get_root_logger()
+            logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}')
+            # define network net_g with Exponential Moving Average (EMA)
+            # net_g_ema is used only for testing on one GPU and saving
+            # There is no need to wrap with DistributedDataParallel
+            self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
+            # load pretrained model
+            load_path = self.opt['path'].get('pretrain_network_g', None)
+            if load_path is not None:
+                self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
+            else:
+                self.model_ema(0)  # copy net_g weight
+            self.net_g_ema.eval()
+
+        # define network net_d
+        self.net_d = build_network(self.opt['network_d'])
+        self.net_d = self.model_to_device(self.net_d)
+        self.print_network(self.net_d)
+
+        # load pretrained models
+        load_path = self.opt['path'].get('pretrain_network_d', None)
+        if load_path is not None:
+            self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
+
+        self.net_g.train()
+        self.net_d.train()
+
+        # define losses
+        if train_opt.get('pixel_opt'):
+            self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
+        else:
+            self.cri_pix = None
+
+        if train_opt.get('perceptual_opt'):
+            self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
+        else:
+            self.cri_perceptual = None
+
+        if train_opt.get('gan_opt'):
+            self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
+
+        self.net_d_iters = train_opt.get('net_d_iters', 1)
+        self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
+
+        # set up optimizers and schedulers
+        self.setup_optimizers()
+        self.setup_schedulers()
+
+    def setup_optimizers(self):
+        train_opt = self.opt['train']
+        # optimizer g
+        optim_type = train_opt['optim_g'].pop('type')
+        self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g'])
+        self.optimizers.append(self.optimizer_g)
+        # optimizer d
+        optim_type = train_opt['optim_d'].pop('type')
+        self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d'])
+        self.optimizers.append(self.optimizer_d)
+
+    def optimize_parameters(self, current_iter):
+        # optimize net_g
+        for p in self.net_d.parameters():
+            p.requires_grad = False
+
+        self.optimizer_g.zero_grad()
+        self.output = self.net_g(self.lq)
+
+        l_g_total = 0
+        loss_dict = OrderedDict()
+        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
+            # pixel loss
+            if self.cri_pix:
+                l_g_pix = self.cri_pix(self.output, self.gt)
+                l_g_total += l_g_pix
+                loss_dict['l_g_pix'] = l_g_pix
+            # perceptual loss
+            if self.cri_perceptual:
+                l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
+                if l_g_percep is not None:
+                    l_g_total += l_g_percep
+                    loss_dict['l_g_percep'] = l_g_percep
+                if l_g_style is not None:
+                    l_g_total += l_g_style
+                    loss_dict['l_g_style'] = l_g_style
+            # gan loss
+            fake_g_pred = self.net_d(self.output)
+            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
+            l_g_total += l_g_gan
+            loss_dict['l_g_gan'] = l_g_gan
+
+            l_g_total.backward()
+            self.optimizer_g.step()
+
+        # optimize net_d
+        for p in self.net_d.parameters():
+            p.requires_grad = True
+
+        self.optimizer_d.zero_grad()
+        # real
+        real_d_pred = self.net_d(self.gt)
+        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
+        loss_dict['l_d_real'] = l_d_real
+        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
+        l_d_real.backward()
+        # fake
+        fake_d_pred = self.net_d(self.output.detach())
+        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
+        loss_dict['l_d_fake'] = l_d_fake
+        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
+        l_d_fake.backward()
+        self.optimizer_d.step()
+
+        self.log_dict = self.reduce_loss_dict(loss_dict)
+
+        if self.ema_decay > 0:
+            self.model_ema(decay=self.ema_decay)
+
+    def save(self, epoch, current_iter):
+        if hasattr(self, 'net_g_ema'):
+            self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
+        else:
+            self.save_network(self.net_g, 'net_g', current_iter)
+        self.save_network(self.net_d, 'net_d', current_iter)
+        self.save_training_state(epoch, current_iter)
diff --git a/basicsr/models/stylegan2_model.py b/basicsr/models/stylegan2_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8acac34f3b523138b1a255f538d88d2e304528a
--- /dev/null
+++ b/basicsr/models/stylegan2_model.py
@@ -0,0 +1,282 @@
+import cv2
+import math
+import numpy as np
+import random
+import torch
+from collections import OrderedDict
+from os import path as osp
+
+from basicsr.archs import build_network
+from basicsr.losses import build_loss
+from basicsr.losses.losses import g_path_regularize, r1_penalty
+from basicsr.utils import imwrite, tensor2img
+from basicsr.utils.registry import MODEL_REGISTRY
+from .base_model import BaseModel
+
+
+@MODEL_REGISTRY.register()
+class StyleGAN2Model(BaseModel):
+    """StyleGAN2 model."""
+
+    def __init__(self, opt):
+        super(StyleGAN2Model, self).__init__(opt)
+
+        # define network net_g
+        self.net_g = build_network(opt['network_g'])
+        self.net_g = self.model_to_device(self.net_g)
+        self.print_network(self.net_g)
+        # load pretrained model
+        load_path = self.opt['path'].get('pretrain_network_g', None)
+        if load_path is not None:
+            param_key = self.opt['path'].get('param_key_g', 'params')
+            self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
+
+        # latent dimension: self.num_style_feat
+        self.num_style_feat = opt['network_g']['num_style_feat']
+        num_val_samples = self.opt['val'].get('num_val_samples', 16)
+        self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device)
+
+        if self.is_train:
+            self.init_training_settings()
+
+    def init_training_settings(self):
+        train_opt = self.opt['train']
+
+        # define network net_d
+        self.net_d = build_network(self.opt['network_d'])
+        self.net_d = self.model_to_device(self.net_d)
+        self.print_network(self.net_d)
+
+        # load pretrained model
+        load_path = self.opt['path'].get('pretrain_network_d', None)
+        if load_path is not None:
+            self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
+
+        # define network net_g with Exponential Moving Average (EMA)
+        # net_g_ema only used for testing on one GPU and saving, do not need to
+        # wrap with DistributedDataParallel
+        self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
+        # load pretrained model
+        load_path = self.opt['path'].get('pretrain_network_g', None)
+        if load_path is not None:
+            self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
+        else:
+            self.model_ema(0)  # copy net_g weight
+
+        self.net_g.train()
+        self.net_d.train()
+        self.net_g_ema.eval()
+
+        # define losses
+        # gan loss (wgan)
+        self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
+        # regularization weights
+        self.r1_reg_weight = train_opt['r1_reg_weight']  # for discriminator
+        self.path_reg_weight = train_opt['path_reg_weight']  # for generator
+
+        self.net_g_reg_every = train_opt['net_g_reg_every']
+        self.net_d_reg_every = train_opt['net_d_reg_every']
+        self.mixing_prob = train_opt['mixing_prob']
+
+        self.mean_path_length = 0
+
+        # set up optimizers and schedulers
+        self.setup_optimizers()
+        self.setup_schedulers()
+
+    def setup_optimizers(self):
+        train_opt = self.opt['train']
+        # optimizer g
+        net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1)
+        if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC':
+            normal_params = []
+            style_mlp_params = []
+            modulation_conv_params = []
+            for name, param in self.net_g.named_parameters():
+                if 'modulation' in name:
+                    normal_params.append(param)
+                elif 'style_mlp' in name:
+                    style_mlp_params.append(param)
+                elif 'modulated_conv' in name:
+                    modulation_conv_params.append(param)
+                else:
+                    normal_params.append(param)
+            optim_params_g = [
+                {  # add normal params first
+                    'params': normal_params,
+                    'lr': train_opt['optim_g']['lr']
+                },
+                {
+                    'params': style_mlp_params,
+                    'lr': train_opt['optim_g']['lr'] * 0.01
+                },
+                {
+                    'params': modulation_conv_params,
+                    'lr': train_opt['optim_g']['lr'] / 3
+                }
+            ]
+        else:
+            normal_params = []
+            for name, param in self.net_g.named_parameters():
+                normal_params.append(param)
+            optim_params_g = [{  # add normal params first
+                'params': normal_params,
+                'lr': train_opt['optim_g']['lr']
+            }]
+
+        optim_type = train_opt['optim_g'].pop('type')
+        lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
+        betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)
+        self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
+        self.optimizers.append(self.optimizer_g)
+
+        # optimizer d
+        net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
+        if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC':
+            normal_params = []
+            linear_params = []
+            for name, param in self.net_d.named_parameters():
+                if 'final_linear' in name:
+                    linear_params.append(param)
+                else:
+                    normal_params.append(param)
+            optim_params_d = [
+                {  # add normal params first
+                    'params': normal_params,
+                    'lr': train_opt['optim_d']['lr']
+                },
+                {
+                    'params': linear_params,
+                    'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512))
+                }
+            ]
+        else:
+            normal_params = []
+            for name, param in self.net_d.named_parameters():
+                normal_params.append(param)
+            optim_params_d = [{  # add normal params first
+                'params': normal_params,
+                'lr': train_opt['optim_d']['lr']
+            }]
+
+        optim_type = train_opt['optim_d'].pop('type')
+        lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
+        betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio)
+        self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
+        self.optimizers.append(self.optimizer_d)
+
+    def feed_data(self, data):
+        self.real_img = data['gt'].to(self.device)
+
+    def make_noise(self, batch, num_noise):
+        if num_noise == 1:
+            noises = torch.randn(batch, self.num_style_feat, device=self.device)
+        else:
+            noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0)
+        return noises
+
+    def mixing_noise(self, batch, prob):
+        if random.random() < prob:
+            return self.make_noise(batch, 2)
+        else:
+            return [self.make_noise(batch, 1)]
+
+    def optimize_parameters(self, current_iter):
+        loss_dict = OrderedDict()
+
+        # optimize net_d
+        for p in self.net_d.parameters():
+            p.requires_grad = True
+        self.optimizer_d.zero_grad()
+
+        batch = self.real_img.size(0)
+        noise = self.mixing_noise(batch, self.mixing_prob)
+        fake_img, _ = self.net_g(noise)
+        fake_pred = self.net_d(fake_img.detach())
+
+        real_pred = self.net_d(self.real_img)
+        # wgan loss with softplus (logistic loss) for discriminator
+        l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True)
+        loss_dict['l_d'] = l_d
+        # In wgan, real_score should be positive and fake_score should be
+        # negative
+        loss_dict['real_score'] = real_pred.detach().mean()
+        loss_dict['fake_score'] = fake_pred.detach().mean()
+        l_d.backward()
+
+        if current_iter % self.net_d_reg_every == 0:
+            self.real_img.requires_grad = True
+            real_pred = self.net_d(self.real_img)
+            l_d_r1 = r1_penalty(real_pred, self.real_img)
+            l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
+            # TODO: why do we need to add 0 * real_pred, otherwise, a runtime
+            # error will arise: RuntimeError: Expected to have finished
+            # reduction in the prior iteration before starting a new one.
+            # This error indicates that your module has parameters that were
+            # not used in producing loss.
+            loss_dict['l_d_r1'] = l_d_r1.detach().mean()
+            l_d_r1.backward()
+
+        self.optimizer_d.step()
+
+        # optimize net_g
+        for p in self.net_d.parameters():
+            p.requires_grad = False
+        self.optimizer_g.zero_grad()
+
+        noise = self.mixing_noise(batch, self.mixing_prob)
+        fake_img, _ = self.net_g(noise)
+        fake_pred = self.net_d(fake_img)
+
+        # wgan loss with softplus (non-saturating loss) for generator
+        l_g = self.cri_gan(fake_pred, True, is_disc=False)
+        loss_dict['l_g'] = l_g
+        l_g.backward()
+
+        if current_iter % self.net_g_reg_every == 0:
+            path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink'])
+            noise = self.mixing_noise(path_batch_size, self.mixing_prob)
+            fake_img, latents = self.net_g(noise, return_latents=True)
+            l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length)
+
+            l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0])
+            # TODO:  why do we need to add 0 * fake_img[0, 0, 0, 0]
+            l_g_path.backward()
+            loss_dict['l_g_path'] = l_g_path.detach().mean()
+            loss_dict['path_length'] = path_lengths
+
+        self.optimizer_g.step()
+
+        self.log_dict = self.reduce_loss_dict(loss_dict)
+
+        # EMA
+        self.model_ema(decay=0.5**(32 / (10 * 1000)))
+
+    def test(self):
+        with torch.no_grad():
+            self.net_g_ema.eval()
+            self.output, _ = self.net_g_ema([self.fixed_sample])
+
+    def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        if self.opt['rank'] == 0:
+            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
+
+    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        assert dataloader is None, 'Validation dataloader should be None.'
+        self.test()
+        result = tensor2img(self.output, min_max=(-1, 1))
+        if self.opt['is_train']:
+            save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png')
+        else:
+            save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png')
+        imwrite(result, save_img_path)
+        # add sample images to tb_logger
+        result = (result / 255.).astype(np.float32)
+        result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
+        if tb_logger is not None:
+            tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC')
+
+    def save(self, epoch, current_iter):
+        self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
+        self.save_network(self.net_d, 'net_d', current_iter)
+        self.save_training_state(epoch, current_iter)
diff --git a/basicsr/models/video_base_model.py b/basicsr/models/video_base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..60f8a8f961091ee5f5598057105f5cbcd812003e
--- /dev/null
+++ b/basicsr/models/video_base_model.py
@@ -0,0 +1,146 @@
+import torch
+from collections import Counter
+from os import path as osp
+from torch import distributed as dist
+from tqdm import tqdm
+
+from basicsr.metrics import calculate_metric
+from basicsr.utils import get_root_logger, imwrite, tensor2img
+from basicsr.utils.dist_util import get_dist_info
+from basicsr.utils.registry import MODEL_REGISTRY
+from .sr_model import SRModel
+
+
+@MODEL_REGISTRY.register()
+class VideoBaseModel(SRModel):
+    """Base video SR model."""
+
+    def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        dataset = dataloader.dataset
+        dataset_name = dataset.opt['name']
+        with_metrics = self.opt['val']['metrics'] is not None
+        # initialize self.metric_results
+        # It is a dict: {
+        #    'folder1': tensor (num_frame x len(metrics)),
+        #    'folder2': tensor (num_frame x len(metrics))
+        # }
+        if with_metrics and not hasattr(self, 'metric_results'):
+            self.metric_results = {}
+            num_frame_each_folder = Counter(dataset.data_info['folder'])
+            for folder, num_frame in num_frame_each_folder.items():
+                self.metric_results[folder] = torch.zeros(
+                    num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda')
+        rank, world_size = get_dist_info()
+        if with_metrics:
+            for _, tensor in self.metric_results.items():
+                tensor.zero_()
+        # record all frames (border and center frames)
+        if rank == 0:
+            pbar = tqdm(total=len(dataset), unit='frame')
+        for idx in range(rank, len(dataset), world_size):
+            val_data = dataset[idx]
+            val_data['lq'].unsqueeze_(0)
+            val_data['gt'].unsqueeze_(0)
+            folder = val_data['folder']
+            frame_idx, max_idx = val_data['idx'].split('/')
+            lq_path = val_data['lq_path']
+
+            self.feed_data(val_data)
+            self.test()
+            visuals = self.get_current_visuals()
+            result_img = tensor2img([visuals['result']])
+            if 'gt' in visuals:
+                gt_img = tensor2img([visuals['gt']])
+                del self.gt
+
+            # tentative for out of GPU memory
+            del self.lq
+            del self.output
+            torch.cuda.empty_cache()
+
+            if save_img:
+                if self.opt['is_train']:
+                    raise NotImplementedError('saving image is not supported during training.')
+                else:
+                    if 'vimeo' in dataset_name.lower():  # vimeo90k dataset
+                        split_result = lq_path.split('/')
+                        img_name = (f'{split_result[-3]}_{split_result[-2]}_' f'{split_result[-1].split(".")[0]}')
+                    else:  # other datasets, e.g., REDS, Vid4
+                        img_name = osp.splitext(osp.basename(lq_path))[0]
+
+                    if self.opt['val']['suffix']:
+                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder,
+                                                 f'{img_name}_{self.opt["val"]["suffix"]}.png')
+                    else:
+                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder,
+                                                 f'{img_name}_{self.opt["name"]}.png')
+                imwrite(result_img, save_img_path)
+
+            if with_metrics:
+                # calculate metrics
+                for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()):
+                    metric_data = dict(img1=result_img, img2=gt_img)
+                    result = calculate_metric(metric_data, opt_)
+                    self.metric_results[folder][int(frame_idx), metric_idx] += result
+
+            # progress bar
+            if rank == 0:
+                for _ in range(world_size):
+                    pbar.update(1)
+                    pbar.set_description(f'Test {folder}:' f'{int(frame_idx) + world_size}/{max_idx}')
+        if rank == 0:
+            pbar.close()
+
+        if with_metrics:
+            if self.opt['dist']:
+                # collect data among GPUs
+                for _, tensor in self.metric_results.items():
+                    dist.reduce(tensor, 0)
+                dist.barrier()
+            else:
+                pass  # assume use one gpu in non-dist testing
+
+            if rank == 0:
+                self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
+
+    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
+        logger = get_root_logger()
+        logger.warning('nondist_validation is not implemented. Run dist_validation.')
+        self.dist_validation(dataloader, current_iter, tb_logger, save_img)
+
+    def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
+        # average all frames for each sub-folder
+        # metric_results_avg is a dict:{
+        #    'folder1': tensor (len(metrics)),
+        #    'folder2': tensor (len(metrics))
+        # }
+        metric_results_avg = {
+            folder: torch.mean(tensor, dim=0).cpu()
+            for (folder, tensor) in self.metric_results.items()
+        }
+        # total_avg_results is a dict: {
+        #    'metric1': float,
+        #    'metric2': float
+        # }
+        total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
+        for folder, tensor in metric_results_avg.items():
+            for idx, metric in enumerate(total_avg_results.keys()):
+                total_avg_results[metric] += metric_results_avg[folder][idx].item()
+        # average among folders
+        for metric in total_avg_results.keys():
+            total_avg_results[metric] /= len(metric_results_avg)
+
+        log_str = f'Validation {dataset_name}\n'
+        for metric_idx, (metric, value) in enumerate(total_avg_results.items()):
+            log_str += f'\t # {metric}: {value:.4f}'
+            for folder, tensor in metric_results_avg.items():
+                log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}'
+            log_str += '\n'
+
+        logger = get_root_logger()
+        logger.info(log_str)
+        if tb_logger:
+            for metric_idx, (metric, value) in enumerate(total_avg_results.items()):
+                tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
+                for folder, tensor in metric_results_avg.items():
+                    tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter)
diff --git a/basicsr/models/video_gan_model.py b/basicsr/models/video_gan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..7500cfd27da2195d69d10a5cbd21a611c2c9dcd6
--- /dev/null
+++ b/basicsr/models/video_gan_model.py
@@ -0,0 +1,17 @@
+from basicsr.utils.registry import MODEL_REGISTRY
+from .srgan_model import SRGANModel
+from .video_base_model import VideoBaseModel
+
+
+@MODEL_REGISTRY.register()
+class VideoGANModel(SRGANModel, VideoBaseModel):
+    """Video GAN model.
+
+    Use multiple inheritance.
+    It will first use the functions of SRGANModel:
+        init_training_settings
+        setup_optimizers
+        optimize_parameters
+        save
+    Then find functions in VideoBaseModel.
+    """
diff --git a/basicsr/ops/__init__.py b/basicsr/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/basicsr/ops/__pycache__/__init__.cpython-36.pyc b/basicsr/ops/__pycache__/__init__.cpython-36.pyc
new file mode 100644
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diff --git a/basicsr/ops/__pycache__/__init__.cpython-37.pyc b/basicsr/ops/__pycache__/__init__.cpython-37.pyc
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diff --git a/basicsr/ops/dcn/__init__.py b/basicsr/ops/dcn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..32e3592f896d61b4127e09d0476381b9d55e32ff
--- /dev/null
+++ b/basicsr/ops/dcn/__init__.py
@@ -0,0 +1,7 @@
+from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv,
+                          modulated_deform_conv)
+
+__all__ = [
+    'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv',
+    'modulated_deform_conv'
+]
diff --git a/basicsr/ops/dcn/__pycache__/__init__.cpython-36.pyc b/basicsr/ops/dcn/__pycache__/__init__.cpython-36.pyc
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diff --git a/basicsr/ops/dcn/__pycache__/deform_conv.cpython-37.pyc b/basicsr/ops/dcn/__pycache__/deform_conv.cpython-37.pyc
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diff --git a/basicsr/ops/dcn/deform_conv.py b/basicsr/ops/dcn/deform_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..734154f9ed9447d585eae7df6886acb136f8a3cf
--- /dev/null
+++ b/basicsr/ops/dcn/deform_conv.py
@@ -0,0 +1,377 @@
+import math
+import torch
+from torch import nn as nn
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn import functional as F
+from torch.nn.modules.utils import _pair, _single
+
+try:
+    from . import deform_conv_ext
+except ImportError:
+    import os
+    BASICSR_JIT = os.getenv('BASICSR_JIT')
+    if BASICSR_JIT == 'True':
+        from torch.utils.cpp_extension import load
+        module_path = os.path.dirname(__file__)
+        deform_conv_ext = load(
+            'deform_conv',
+            sources=[
+                os.path.join(module_path, 'src', 'deform_conv_ext.cpp'),
+                os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'),
+                os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'),
+            ],
+        )
+
+
+class DeformConvFunction(Function):
+
+    @staticmethod
+    def forward(ctx,
+                input,
+                offset,
+                weight,
+                stride=1,
+                padding=0,
+                dilation=1,
+                groups=1,
+                deformable_groups=1,
+                im2col_step=64):
+        if input is not None and input.dim() != 4:
+            raise ValueError(f'Expected 4D tensor as input, got {input.dim()}' 'D tensor instead.')
+        ctx.stride = _pair(stride)
+        ctx.padding = _pair(padding)
+        ctx.dilation = _pair(dilation)
+        ctx.groups = groups
+        ctx.deformable_groups = deformable_groups
+        ctx.im2col_step = im2col_step
+
+        ctx.save_for_backward(input, offset, weight)
+
+        output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride))
+
+        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones
+
+        if not input.is_cuda:
+            raise NotImplementedError
+        else:
+            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
+            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
+            deform_conv_ext.deform_conv_forward(input, weight,
+                                                offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
+                                                weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
+                                                ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
+                                                ctx.deformable_groups, cur_im2col_step)
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        input, offset, weight = ctx.saved_tensors
+
+        grad_input = grad_offset = grad_weight = None
+
+        if not grad_output.is_cuda:
+            raise NotImplementedError
+        else:
+            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
+            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
+                grad_input = torch.zeros_like(input)
+                grad_offset = torch.zeros_like(offset)
+                deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input,
+                                                           grad_offset, weight, ctx.bufs_[0], weight.size(3),
+                                                           weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
+                                                           ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
+                                                           ctx.deformable_groups, cur_im2col_step)
+
+            if ctx.needs_input_grad[2]:
+                grad_weight = torch.zeros_like(weight)
+                deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight,
+                                                                ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
+                                                                weight.size(2), ctx.stride[1], ctx.stride[0],
+                                                                ctx.padding[1], ctx.padding[0], ctx.dilation[1],
+                                                                ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1,
+                                                                cur_im2col_step)
+
+        return (grad_input, grad_offset, grad_weight, None, None, None, None, None)
+
+    @staticmethod
+    def _output_size(input, weight, padding, dilation, stride):
+        channels = weight.size(0)
+        output_size = (input.size(0), channels)
+        for d in range(input.dim() - 2):
+            in_size = input.size(d + 2)
+            pad = padding[d]
+            kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
+            stride_ = stride[d]
+            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
+        if not all(map(lambda s: s > 0, output_size)):
+            raise ValueError('convolution input is too small (output would be ' f'{"x".join(map(str, output_size))})')
+        return output_size
+
+
+class ModulatedDeformConvFunction(Function):
+
+    @staticmethod
+    def forward(ctx,
+                input,
+                offset,
+                mask,
+                weight,
+                bias=None,
+                stride=1,
+                padding=0,
+                dilation=1,
+                groups=1,
+                deformable_groups=1):
+        ctx.stride = stride
+        ctx.padding = padding
+        ctx.dilation = dilation
+        ctx.groups = groups
+        ctx.deformable_groups = deformable_groups
+        ctx.with_bias = bias is not None
+        if not ctx.with_bias:
+            bias = input.new_empty(1)  # fake tensor
+        if not input.is_cuda:
+            raise NotImplementedError
+        if weight.requires_grad or mask.requires_grad or offset.requires_grad \
+                or input.requires_grad:
+            ctx.save_for_backward(input, offset, mask, weight, bias)
+        output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight))
+        ctx._bufs = [input.new_empty(0), input.new_empty(0)]
+        deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output,
+                                                      ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride,
+                                                      ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
+                                                      ctx.groups, ctx.deformable_groups, ctx.with_bias)
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        if not grad_output.is_cuda:
+            raise NotImplementedError
+        input, offset, mask, weight, bias = ctx.saved_tensors
+        grad_input = torch.zeros_like(input)
+        grad_offset = torch.zeros_like(offset)
+        grad_mask = torch.zeros_like(mask)
+        grad_weight = torch.zeros_like(weight)
+        grad_bias = torch.zeros_like(bias)
+        deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1],
+                                                       grad_input, grad_weight, grad_bias, grad_offset, grad_mask,
+                                                       grad_output, weight.shape[2], weight.shape[3], ctx.stride,
+                                                       ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
+                                                       ctx.groups, ctx.deformable_groups, ctx.with_bias)
+        if not ctx.with_bias:
+            grad_bias = None
+
+        return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None)
+
+    @staticmethod
+    def _infer_shape(ctx, input, weight):
+        n = input.size(0)
+        channels_out = weight.size(0)
+        height, width = input.shape[2:4]
+        kernel_h, kernel_w = weight.shape[2:4]
+        height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1
+        width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1
+        return n, channels_out, height_out, width_out
+
+
+deform_conv = DeformConvFunction.apply
+modulated_deform_conv = ModulatedDeformConvFunction.apply
+
+
+class DeformConv(nn.Module):
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 deformable_groups=1,
+                 bias=False):
+        super(DeformConv, self).__init__()
+
+        assert not bias
+        assert in_channels % groups == 0, \
+            f'in_channels {in_channels} is not divisible by groups {groups}'
+        assert out_channels % groups == 0, \
+            f'out_channels {out_channels} is not divisible ' \
+            f'by groups {groups}'
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.stride = _pair(stride)
+        self.padding = _pair(padding)
+        self.dilation = _pair(dilation)
+        self.groups = groups
+        self.deformable_groups = deformable_groups
+        # enable compatibility with nn.Conv2d
+        self.transposed = False
+        self.output_padding = _single(0)
+
+        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        n = self.in_channels
+        for k in self.kernel_size:
+            n *= k
+        stdv = 1. / math.sqrt(n)
+        self.weight.data.uniform_(-stdv, stdv)
+
+    def forward(self, x, offset):
+        # To fix an assert error in deform_conv_cuda.cpp:128
+        # input image is smaller than kernel
+        input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1])
+        if input_pad:
+            pad_h = max(self.kernel_size[0] - x.size(2), 0)
+            pad_w = max(self.kernel_size[1] - x.size(3), 0)
+            x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
+            offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
+        out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups,
+                          self.deformable_groups)
+        if input_pad:
+            out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous()
+        return out
+
+
+class DeformConvPack(DeformConv):
+    """A Deformable Conv Encapsulation that acts as normal Conv layers.
+
+    Args:
+        in_channels (int): Same as nn.Conv2d.
+        out_channels (int): Same as nn.Conv2d.
+        kernel_size (int or tuple[int]): Same as nn.Conv2d.
+        stride (int or tuple[int]): Same as nn.Conv2d.
+        padding (int or tuple[int]): Same as nn.Conv2d.
+        dilation (int or tuple[int]): Same as nn.Conv2d.
+        groups (int): Same as nn.Conv2d.
+        bias (bool or str): If specified as `auto`, it will be decided by the
+            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
+            False.
+    """
+
+    _version = 2
+
+    def __init__(self, *args, **kwargs):
+        super(DeformConvPack, self).__init__(*args, **kwargs)
+
+        self.conv_offset = nn.Conv2d(
+            self.in_channels,
+            self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
+            kernel_size=self.kernel_size,
+            stride=_pair(self.stride),
+            padding=_pair(self.padding),
+            dilation=_pair(self.dilation),
+            bias=True)
+        self.init_offset()
+
+    def init_offset(self):
+        self.conv_offset.weight.data.zero_()
+        self.conv_offset.bias.data.zero_()
+
+    def forward(self, x):
+        offset = self.conv_offset(x)
+        return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups,
+                           self.deformable_groups)
+
+
+class ModulatedDeformConv(nn.Module):
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 deformable_groups=1,
+                 bias=True):
+        super(ModulatedDeformConv, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.groups = groups
+        self.deformable_groups = deformable_groups
+        self.with_bias = bias
+        # enable compatibility with nn.Conv2d
+        self.transposed = False
+        self.output_padding = _single(0)
+
+        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size))
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.init_weights()
+
+    def init_weights(self):
+        n = self.in_channels
+        for k in self.kernel_size:
+            n *= k
+        stdv = 1. / math.sqrt(n)
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.zero_()
+
+    def forward(self, x, offset, mask):
+        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
+                                     self.groups, self.deformable_groups)
+
+
+class ModulatedDeformConvPack(ModulatedDeformConv):
+    """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers.
+
+    Args:
+        in_channels (int): Same as nn.Conv2d.
+        out_channels (int): Same as nn.Conv2d.
+        kernel_size (int or tuple[int]): Same as nn.Conv2d.
+        stride (int or tuple[int]): Same as nn.Conv2d.
+        padding (int or tuple[int]): Same as nn.Conv2d.
+        dilation (int or tuple[int]): Same as nn.Conv2d.
+        groups (int): Same as nn.Conv2d.
+        bias (bool or str): If specified as `auto`, it will be decided by the
+            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
+            False.
+    """
+
+    _version = 2
+
+    def __init__(self, *args, **kwargs):
+        super(ModulatedDeformConvPack, self).__init__(*args, **kwargs)
+
+        self.conv_offset = nn.Conv2d(
+            self.in_channels,
+            self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
+            kernel_size=self.kernel_size,
+            stride=_pair(self.stride),
+            padding=_pair(self.padding),
+            dilation=_pair(self.dilation),
+            bias=True)
+        self.init_weights()
+
+    def init_weights(self):
+        super(ModulatedDeformConvPack, self).init_weights()
+        if hasattr(self, 'conv_offset'):
+            self.conv_offset.weight.data.zero_()
+            self.conv_offset.bias.data.zero_()
+
+    def forward(self, x):
+        out = self.conv_offset(x)
+        o1, o2, mask = torch.chunk(out, 3, dim=1)
+        offset = torch.cat((o1, o2), dim=1)
+        mask = torch.sigmoid(mask)
+        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
+                                     self.groups, self.deformable_groups)
diff --git a/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/basicsr/ops/dcn/src/deform_conv_cuda.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..5d9424908ed2dbd4ac3cdb98d13e09287a4d2f2d
--- /dev/null
+++ b/basicsr/ops/dcn/src/deform_conv_cuda.cpp
@@ -0,0 +1,685 @@
+// modify from
+// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
+
+#include <torch/extension.h>
+#include <ATen/DeviceGuard.h>
+
+#include <cmath>
+#include <vector>
+
+void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset,
+                       const int channels, const int height, const int width,
+                       const int ksize_h, const int ksize_w, const int pad_h,
+                       const int pad_w, const int stride_h, const int stride_w,
+                       const int dilation_h, const int dilation_w,
+                       const int parallel_imgs, const int deformable_group,
+                       at::Tensor data_col);
+
+void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset,
+                       const int channels, const int height, const int width,
+                       const int ksize_h, const int ksize_w, const int pad_h,
+                       const int pad_w, const int stride_h, const int stride_w,
+                       const int dilation_h, const int dilation_w,
+                       const int parallel_imgs, const int deformable_group,
+                       at::Tensor grad_im);
+
+void deformable_col2im_coord(
+    const at::Tensor data_col, const at::Tensor data_im,
+    const at::Tensor data_offset, const int channels, const int height,
+    const int width, const int ksize_h, const int ksize_w, const int pad_h,
+    const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w, const int parallel_imgs,
+    const int deformable_group, at::Tensor grad_offset);
+
+void modulated_deformable_im2col_cuda(
+    const at::Tensor data_im, const at::Tensor data_offset,
+    const at::Tensor data_mask, const int batch_size, const int channels,
+    const int height_im, const int width_im, const int height_col,
+    const int width_col, const int kernel_h, const int kenerl_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w, const int deformable_group,
+    at::Tensor data_col);
+
+void modulated_deformable_col2im_cuda(
+    const at::Tensor data_col, const at::Tensor data_offset,
+    const at::Tensor data_mask, const int batch_size, const int channels,
+    const int height_im, const int width_im, const int height_col,
+    const int width_col, const int kernel_h, const int kenerl_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w, const int deformable_group,
+    at::Tensor grad_im);
+
+void modulated_deformable_col2im_coord_cuda(
+    const at::Tensor data_col, const at::Tensor data_im,
+    const at::Tensor data_offset, const at::Tensor data_mask,
+    const int batch_size, const int channels, const int height_im,
+    const int width_im, const int height_col, const int width_col,
+    const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w, const int dilation_h,
+    const int dilation_w, const int deformable_group, at::Tensor grad_offset,
+    at::Tensor grad_mask);
+
+void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput,
+                 at::Tensor weight, int kH, int kW, int dH, int dW, int padH,
+                 int padW, int dilationH, int dilationW, int group,
+                 int deformable_group) {
+  TORCH_CHECK(weight.ndimension() == 4,
+           "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, "
+           "but got: %s",
+           weight.ndimension());
+
+  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
+
+  TORCH_CHECK(kW > 0 && kH > 0,
+           "kernel size should be greater than zero, but got kH: %d kW: %d", kH,
+           kW);
+
+  TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW),
+           "kernel size should be consistent with weight, ",
+           "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH,
+           kW, weight.size(2), weight.size(3));
+
+  TORCH_CHECK(dW > 0 && dH > 0,
+           "stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
+
+  TORCH_CHECK(
+      dilationW > 0 && dilationH > 0,
+      "dilation should be greater than 0, but got dilationH: %d dilationW: %d",
+      dilationH, dilationW);
+
+  int ndim = input.ndimension();
+  int dimf = 0;
+  int dimh = 1;
+  int dimw = 2;
+
+  if (ndim == 4) {
+    dimf++;
+    dimh++;
+    dimw++;
+  }
+
+  TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
+           ndim);
+
+  long nInputPlane = weight.size(1) * group;
+  long inputHeight = input.size(dimh);
+  long inputWidth = input.size(dimw);
+  long nOutputPlane = weight.size(0);
+  long outputHeight =
+      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
+  long outputWidth =
+      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
+
+  TORCH_CHECK(nInputPlane % deformable_group == 0,
+           "input channels must divide deformable group size");
+
+  if (outputWidth < 1 || outputHeight < 1)
+    AT_ERROR(
+        "Given input size: (%ld x %ld x %ld). "
+        "Calculated output size: (%ld x %ld x %ld). Output size is too small",
+        nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
+        outputWidth);
+
+  TORCH_CHECK(input.size(1) == nInputPlane,
+           "invalid number of input planes, expected: %d, but got: %d",
+           nInputPlane, input.size(1));
+
+  TORCH_CHECK((inputHeight >= kH && inputWidth >= kW),
+           "input image is smaller than kernel");
+
+  TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
+           "invalid spatial size of offset, expected height: %d width: %d, but "
+           "got height: %d width: %d",
+           outputHeight, outputWidth, offset.size(2), offset.size(3));
+
+  TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
+           "invalid number of channels of offset");
+
+  if (gradOutput != NULL) {
+    TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane,
+             "invalid number of gradOutput planes, expected: %d, but got: %d",
+             nOutputPlane, gradOutput->size(dimf));
+
+    TORCH_CHECK((gradOutput->size(dimh) == outputHeight &&
+              gradOutput->size(dimw) == outputWidth),
+             "invalid size of gradOutput, expected height: %d width: %d , but "
+             "got height: %d width: %d",
+             outputHeight, outputWidth, gradOutput->size(dimh),
+             gradOutput->size(dimw));
+  }
+}
+
+int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
+                             at::Tensor offset, at::Tensor output,
+                             at::Tensor columns, at::Tensor ones, int kW,
+                             int kH, int dW, int dH, int padW, int padH,
+                             int dilationW, int dilationH, int group,
+                             int deformable_group, int im2col_step) {
+  // todo: resize columns to include im2col: done
+  // todo: add im2col_step as input
+  // todo: add new output buffer and transpose it to output (or directly
+  // transpose output) todo: possibly change data indexing because of
+  // parallel_imgs
+
+  shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW,
+              dilationH, dilationW, group, deformable_group);
+  at::DeviceGuard guard(input.device());
+
+  input = input.contiguous();
+  offset = offset.contiguous();
+  weight = weight.contiguous();
+
+  int batch = 1;
+  if (input.ndimension() == 3) {
+    // Force batch
+    batch = 0;
+    input.unsqueeze_(0);
+    offset.unsqueeze_(0);
+  }
+
+  // todo: assert batchsize dividable by im2col_step
+
+  long batchSize = input.size(0);
+  long nInputPlane = input.size(1);
+  long inputHeight = input.size(2);
+  long inputWidth = input.size(3);
+
+  long nOutputPlane = weight.size(0);
+
+  long outputWidth =
+      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
+  long outputHeight =
+      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
+
+  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
+
+  output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane,
+                        outputHeight, outputWidth});
+  columns = at::zeros(
+      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
+      input.options());
+
+  if (ones.ndimension() != 2 ||
+      ones.size(0) * ones.size(1) < outputHeight * outputWidth) {
+    ones = at::ones({outputHeight, outputWidth}, input.options());
+  }
+
+  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
+                      inputHeight, inputWidth});
+  offset =
+      offset.view({batchSize / im2col_step, im2col_step,
+                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+
+  at::Tensor output_buffer =
+      at::zeros({batchSize / im2col_step, nOutputPlane,
+                 im2col_step * outputHeight, outputWidth},
+                output.options());
+
+  output_buffer = output_buffer.view(
+      {output_buffer.size(0), group, output_buffer.size(1) / group,
+       output_buffer.size(2), output_buffer.size(3)});
+
+  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
+    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
+                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
+                      dilationW, im2col_step, deformable_group, columns);
+
+    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
+    weight = weight.view({group, weight.size(0) / group, weight.size(1),
+                          weight.size(2), weight.size(3)});
+
+    for (int g = 0; g < group; g++) {
+      output_buffer[elt][g] = output_buffer[elt][g]
+                                  .flatten(1)
+                                  .addmm_(weight[g].flatten(1), columns[g])
+                                  .view_as(output_buffer[elt][g]);
+    }
+  }
+
+  output_buffer = output_buffer.view(
+      {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2),
+       output_buffer.size(3), output_buffer.size(4)});
+
+  output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane,
+                                      im2col_step, outputHeight, outputWidth});
+  output_buffer.transpose_(1, 2);
+  output.copy_(output_buffer);
+  output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth});
+
+  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
+  offset = offset.view(
+      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+
+  if (batch == 0) {
+    output = output.view({nOutputPlane, outputHeight, outputWidth});
+    input = input.view({nInputPlane, inputHeight, inputWidth});
+    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
+  }
+
+  return 1;
+}
+
+int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
+                                    at::Tensor gradOutput, at::Tensor gradInput,
+                                    at::Tensor gradOffset, at::Tensor weight,
+                                    at::Tensor columns, int kW, int kH, int dW,
+                                    int dH, int padW, int padH, int dilationW,
+                                    int dilationH, int group,
+                                    int deformable_group, int im2col_step) {
+  shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW,
+              dilationH, dilationW, group, deformable_group);
+  at::DeviceGuard guard(input.device());
+
+  input = input.contiguous();
+  offset = offset.contiguous();
+  gradOutput = gradOutput.contiguous();
+  weight = weight.contiguous();
+
+  int batch = 1;
+
+  if (input.ndimension() == 3) {
+    // Force batch
+    batch = 0;
+    input = input.view({1, input.size(0), input.size(1), input.size(2)});
+    offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)});
+    gradOutput = gradOutput.view(
+        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
+  }
+
+  long batchSize = input.size(0);
+  long nInputPlane = input.size(1);
+  long inputHeight = input.size(2);
+  long inputWidth = input.size(3);
+
+  long nOutputPlane = weight.size(0);
+
+  long outputWidth =
+      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
+  long outputHeight =
+      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
+
+  TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
+  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
+  columns = at::zeros(
+      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
+      input.options());
+
+  // change order of grad output
+  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
+                                nOutputPlane, outputHeight, outputWidth});
+  gradOutput.transpose_(1, 2);
+
+  gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane,
+                              inputHeight, inputWidth});
+  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
+                      inputHeight, inputWidth});
+  gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step,
+                                deformable_group * 2 * kH * kW, outputHeight,
+                                outputWidth});
+  offset =
+      offset.view({batchSize / im2col_step, im2col_step,
+                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+
+  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
+    // divide into groups
+    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
+    weight = weight.view({group, weight.size(0) / group, weight.size(1),
+                          weight.size(2), weight.size(3)});
+    gradOutput = gradOutput.view(
+        {gradOutput.size(0), group, gradOutput.size(1) / group,
+         gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)});
+
+    for (int g = 0; g < group; g++) {
+      columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
+                                     gradOutput[elt][g].flatten(1), 0.0f, 1.0f);
+    }
+
+    columns =
+        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
+    gradOutput = gradOutput.view(
+        {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2),
+         gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)});
+
+    deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane,
+                            inputHeight, inputWidth, kH, kW, padH, padW, dH, dW,
+                            dilationH, dilationW, im2col_step, deformable_group,
+                            gradOffset[elt]);
+
+    deformable_col2im(columns, offset[elt], nInputPlane, inputHeight,
+                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
+                      dilationW, im2col_step, deformable_group, gradInput[elt]);
+  }
+
+  gradOutput.transpose_(1, 2);
+  gradOutput =
+      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
+
+  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
+  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
+  gradOffset = gradOffset.view(
+      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+  offset = offset.view(
+      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+
+  if (batch == 0) {
+    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
+    input = input.view({nInputPlane, inputHeight, inputWidth});
+    gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth});
+    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
+    gradOffset =
+        gradOffset.view({offset.size(1), offset.size(2), offset.size(3)});
+  }
+
+  return 1;
+}
+
+int deform_conv_backward_parameters_cuda(
+    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
+    at::Tensor gradWeight,  // at::Tensor gradBias,
+    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
+    int padW, int padH, int dilationW, int dilationH, int group,
+    int deformable_group, float scale, int im2col_step) {
+  // todo: transpose and reshape outGrad
+  // todo: reshape columns
+  // todo: add im2col_step as input
+
+  shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH,
+              padW, dilationH, dilationW, group, deformable_group);
+  at::DeviceGuard guard(input.device());
+
+  input = input.contiguous();
+  offset = offset.contiguous();
+  gradOutput = gradOutput.contiguous();
+
+  int batch = 1;
+
+  if (input.ndimension() == 3) {
+    // Force batch
+    batch = 0;
+    input = input.view(
+        at::IntList({1, input.size(0), input.size(1), input.size(2)}));
+    gradOutput = gradOutput.view(
+        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
+  }
+
+  long batchSize = input.size(0);
+  long nInputPlane = input.size(1);
+  long inputHeight = input.size(2);
+  long inputWidth = input.size(3);
+
+  long nOutputPlane = gradWeight.size(0);
+
+  long outputWidth =
+      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
+  long outputHeight =
+      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
+
+  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
+
+  columns = at::zeros(
+      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
+      input.options());
+
+  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
+                                nOutputPlane, outputHeight, outputWidth});
+  gradOutput.transpose_(1, 2);
+
+  at::Tensor gradOutputBuffer = at::zeros_like(gradOutput);
+  gradOutputBuffer =
+      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step,
+                             outputHeight, outputWidth});
+  gradOutputBuffer.copy_(gradOutput);
+  gradOutputBuffer =
+      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane,
+                             im2col_step * outputHeight, outputWidth});
+
+  gradOutput.transpose_(1, 2);
+  gradOutput =
+      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
+
+  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
+                      inputHeight, inputWidth});
+  offset =
+      offset.view({batchSize / im2col_step, im2col_step,
+                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+
+  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
+    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
+                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
+                      dilationW, im2col_step, deformable_group, columns);
+
+    // divide into group
+    gradOutputBuffer = gradOutputBuffer.view(
+        {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group,
+         gradOutputBuffer.size(2), gradOutputBuffer.size(3)});
+    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
+    gradWeight =
+        gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1),
+                         gradWeight.size(2), gradWeight.size(3)});
+
+    for (int g = 0; g < group; g++) {
+      gradWeight[g] = gradWeight[g]
+                          .flatten(1)
+                          .addmm_(gradOutputBuffer[elt][g].flatten(1),
+                                  columns[g].transpose(1, 0), 1.0, scale)
+                          .view_as(gradWeight[g]);
+    }
+    gradOutputBuffer = gradOutputBuffer.view(
+        {gradOutputBuffer.size(0),
+         gradOutputBuffer.size(1) * gradOutputBuffer.size(2),
+         gradOutputBuffer.size(3), gradOutputBuffer.size(4)});
+    columns =
+        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
+    gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1),
+                                  gradWeight.size(2), gradWeight.size(3),
+                                  gradWeight.size(4)});
+  }
+
+  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
+  offset = offset.view(
+      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
+
+  if (batch == 0) {
+    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
+    input = input.view({nInputPlane, inputHeight, inputWidth});
+  }
+
+  return 1;
+}
+
+void modulated_deform_conv_cuda_forward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
+    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
+    const int pad_h, const int pad_w, const int dilation_h,
+    const int dilation_w, const int group, const int deformable_group,
+    const bool with_bias) {
+  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
+  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
+  at::DeviceGuard guard(input.device());
+
+  const int batch = input.size(0);
+  const int channels = input.size(1);
+  const int height = input.size(2);
+  const int width = input.size(3);
+
+  const int channels_out = weight.size(0);
+  const int channels_kernel = weight.size(1);
+  const int kernel_h_ = weight.size(2);
+  const int kernel_w_ = weight.size(3);
+
+  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
+    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
+             kernel_h_, kernel_w, kernel_h_, kernel_w_);
+  if (channels != channels_kernel * group)
+    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
+             channels, channels_kernel * group);
+
+  const int height_out =
+      (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int width_out =
+      (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+
+  if (ones.ndimension() != 2 ||
+      ones.size(0) * ones.size(1) < height_out * width_out) {
+    // Resize plane and fill with ones...
+    ones = at::ones({height_out, width_out}, input.options());
+  }
+
+  // resize output
+  output = output.view({batch, channels_out, height_out, width_out}).zero_();
+  // resize temporary columns
+  columns =
+      at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out},
+                input.options());
+
+  output = output.view({output.size(0), group, output.size(1) / group,
+                        output.size(2), output.size(3)});
+
+  for (int b = 0; b < batch; b++) {
+    modulated_deformable_im2col_cuda(
+        input[b], offset[b], mask[b], 1, channels, height, width, height_out,
+        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
+        dilation_h, dilation_w, deformable_group, columns);
+
+    // divide into group
+    weight = weight.view({group, weight.size(0) / group, weight.size(1),
+                          weight.size(2), weight.size(3)});
+    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
+
+    for (int g = 0; g < group; g++) {
+      output[b][g] = output[b][g]
+                         .flatten(1)
+                         .addmm_(weight[g].flatten(1), columns[g])
+                         .view_as(output[b][g]);
+    }
+
+    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
+                          weight.size(3), weight.size(4)});
+    columns =
+        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
+  }
+
+  output = output.view({output.size(0), output.size(1) * output.size(2),
+                        output.size(3), output.size(4)});
+
+  if (with_bias) {
+    output += bias.view({1, bias.size(0), 1, 1});
+  }
+}
+
+void modulated_deform_conv_cuda_backward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor columns,
+    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
+    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
+    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
+    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
+    const bool with_bias) {
+  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
+  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
+  at::DeviceGuard guard(input.device());
+
+  const int batch = input.size(0);
+  const int channels = input.size(1);
+  const int height = input.size(2);
+  const int width = input.size(3);
+
+  const int channels_kernel = weight.size(1);
+  const int kernel_h_ = weight.size(2);
+  const int kernel_w_ = weight.size(3);
+  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
+    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
+             kernel_h_, kernel_w, kernel_h_, kernel_w_);
+  if (channels != channels_kernel * group)
+    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
+             channels, channels_kernel * group);
+
+  const int height_out =
+      (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int width_out =
+      (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+
+  if (ones.ndimension() != 2 ||
+      ones.size(0) * ones.size(1) < height_out * width_out) {
+    // Resize plane and fill with ones...
+    ones = at::ones({height_out, width_out}, input.options());
+  }
+
+  grad_input = grad_input.view({batch, channels, height, width});
+  columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out},
+                      input.options());
+
+  grad_output =
+      grad_output.view({grad_output.size(0), group, grad_output.size(1) / group,
+                        grad_output.size(2), grad_output.size(3)});
+
+  for (int b = 0; b < batch; b++) {
+    // divide int group
+    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
+    weight = weight.view({group, weight.size(0) / group, weight.size(1),
+                          weight.size(2), weight.size(3)});
+
+    for (int g = 0; g < group; g++) {
+      columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
+                        grad_output[b][g].flatten(1), 0.0f, 1.0f);
+    }
+
+    columns =
+        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
+    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
+                          weight.size(3), weight.size(4)});
+
+    // gradient w.r.t. input coordinate data
+    modulated_deformable_col2im_coord_cuda(
+        columns, input[b], offset[b], mask[b], 1, channels, height, width,
+        height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h,
+        stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b],
+        grad_mask[b]);
+    // gradient w.r.t. input data
+    modulated_deformable_col2im_cuda(
+        columns, offset[b], mask[b], 1, channels, height, width, height_out,
+        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
+        dilation_h, dilation_w, deformable_group, grad_input[b]);
+
+    // gradient w.r.t. weight, dWeight should accumulate across the batch and
+    // group
+    modulated_deformable_im2col_cuda(
+        input[b], offset[b], mask[b], 1, channels, height, width, height_out,
+        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
+        dilation_h, dilation_w, deformable_group, columns);
+
+    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
+    grad_weight = grad_weight.view({group, grad_weight.size(0) / group,
+                                    grad_weight.size(1), grad_weight.size(2),
+                                    grad_weight.size(3)});
+    if (with_bias)
+      grad_bias = grad_bias.view({group, grad_bias.size(0) / group});
+
+    for (int g = 0; g < group; g++) {
+      grad_weight[g] =
+          grad_weight[g]
+              .flatten(1)
+              .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1))
+              .view_as(grad_weight[g]);
+      if (with_bias) {
+        grad_bias[g] =
+            grad_bias[g]
+                .view({-1, 1})
+                .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1}))
+                .view(-1);
+      }
+    }
+
+    columns =
+        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
+    grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
+                                    grad_weight.size(2), grad_weight.size(3),
+                                    grad_weight.size(4)});
+    if (with_bias)
+      grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
+  }
+  grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1),
+                                  grad_output.size(2), grad_output.size(3),
+                                  grad_output.size(4)});
+}
diff --git a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..98752dccf8c58817ca1a952554dd3f33188a2d34
--- /dev/null
+++ b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu
@@ -0,0 +1,867 @@
+/*!
+ ******************* BEGIN Caffe Copyright Notice and Disclaimer ****************
+ *
+ * COPYRIGHT
+ *
+ * All contributions by the University of California:
+ * Copyright (c) 2014-2017 The Regents of the University of California (Regents)
+ * All rights reserved.
+ *
+ * All other contributions:
+ * Copyright (c) 2014-2017, the respective contributors
+ * All rights reserved.
+ *
+ * Caffe uses a shared copyright model: each contributor holds copyright over
+ * their contributions to Caffe. The project versioning records all such
+ * contribution and copyright details. If a contributor wants to further mark
+ * their specific copyright on a particular contribution, they should indicate
+ * their copyright solely in the commit message of the change when it is
+ * committed.
+ *
+ * LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * CONTRIBUTION AGREEMENT
+ *
+ * By contributing to the BVLC/caffe repository through pull-request, comment,
+ * or otherwise, the contributor releases their content to the
+ * license and copyright terms herein.
+ *
+ ***************** END Caffe Copyright Notice and Disclaimer ********************
+ *
+ * Copyright (c) 2018 Microsoft
+ * Licensed under The MIT License [see LICENSE for details]
+ * \file modulated_deformable_im2col.cuh
+ * \brief Function definitions of converting an image to
+ * column matrix based on kernel, padding, dilation, and offset.
+ * These functions are mainly used in deformable convolution operators.
+ * \ref: https://arxiv.org/abs/1703.06211
+ * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng
+ */
+
+// modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <THC/THCAtomics.cuh>
+#include <stdio.h>
+#include <math.h>
+#include <float.h>
+
+using namespace at;
+
+#define CUDA_KERNEL_LOOP(i, n)                                 \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+const int CUDA_NUM_THREADS = 1024;
+const int kMaxGridNum = 65535;
+
+inline int GET_BLOCKS(const int N)
+{
+  return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS);
+}
+
+template <typename scalar_t>
+__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width,
+                                               const int height, const int width, scalar_t h, scalar_t w)
+{
+
+  int h_low = floor(h);
+  int w_low = floor(w);
+  int h_high = h_low + 1;
+  int w_high = w_low + 1;
+
+  scalar_t lh = h - h_low;
+  scalar_t lw = w - w_low;
+  scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+    v1 = bottom_data[h_low * data_width + w_low];
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+    v2 = bottom_data[h_low * data_width + w_high];
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+    v3 = bottom_data[h_high * data_width + w_low];
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+    v4 = bottom_data[h_high * data_width + w_high];
+
+  scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+  scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  return val;
+}
+
+template <typename scalar_t>
+__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w,
+                                        const int h, const int w, const int height, const int width)
+{
+
+  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
+  {
+    //empty
+    return 0;
+  }
+
+  int argmax_h_low = floor(argmax_h);
+  int argmax_w_low = floor(argmax_w);
+  int argmax_h_high = argmax_h_low + 1;
+  int argmax_w_high = argmax_w_low + 1;
+
+  scalar_t weight = 0;
+  if (h == argmax_h_low && w == argmax_w_low)
+    weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
+  if (h == argmax_h_low && w == argmax_w_high)
+    weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
+  if (h == argmax_h_high && w == argmax_w_low)
+    weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
+  if (h == argmax_h_high && w == argmax_w_high)
+    weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
+  return weight;
+}
+
+template <typename scalar_t>
+__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w,
+                                          const int height, const int width, const scalar_t *im_data,
+                                          const int data_width, const int bp_dir)
+{
+
+  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
+  {
+    //empty
+    return 0;
+  }
+
+  int argmax_h_low = floor(argmax_h);
+  int argmax_w_low = floor(argmax_w);
+  int argmax_h_high = argmax_h_low + 1;
+  int argmax_w_high = argmax_w_low + 1;
+
+  scalar_t weight = 0;
+
+  if (bp_dir == 0)
+  {
+    if (argmax_h_low >= 0 && argmax_w_low >= 0)
+      weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low];
+    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
+      weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high];
+    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
+      weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low];
+    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
+      weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high];
+  }
+  else if (bp_dir == 1)
+  {
+    if (argmax_h_low >= 0 && argmax_w_low >= 0)
+      weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low];
+    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
+      weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high];
+    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
+      weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low];
+    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
+      weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high];
+  }
+
+  return weight;
+}
+
+template <typename scalar_t>
+__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset,
+                                             const int height, const int width, const int kernel_h, const int kernel_w,
+                                             const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+                                             const int dilation_h, const int dilation_w, const int channel_per_deformable_group,
+                                             const int batch_size, const int num_channels, const int deformable_group,
+                                             const int height_col, const int width_col,
+                                             scalar_t *data_col)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    // index index of output matrix
+    const int w_col = index % width_col;
+    const int h_col = (index / width_col) % height_col;
+    const int b_col = (index / width_col / height_col) % batch_size;
+    const int c_im = (index / width_col / height_col) / batch_size;
+    const int c_col = c_im * kernel_h * kernel_w;
+
+    // compute deformable group index
+    const int deformable_group_index = c_im / channel_per_deformable_group;
+
+    const int h_in = h_col * stride_h - pad_h;
+    const int w_in = w_col * stride_w - pad_w;
+    scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
+    //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
+    const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
+    const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+
+    for (int i = 0; i < kernel_h; ++i)
+    {
+      for (int j = 0; j < kernel_w; ++j)
+      {
+        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
+        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
+        const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
+        const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
+        scalar_t val = static_cast<scalar_t>(0);
+        const scalar_t h_im = h_in + i * dilation_h + offset_h;
+        const scalar_t w_im = w_in + j * dilation_w + offset_w;
+        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
+        {
+          //const scalar_t map_h = i * dilation_h + offset_h;
+          //const scalar_t map_w = j * dilation_w + offset_w;
+          //const int cur_height = height - h_in;
+          //const int cur_width = width - w_in;
+          //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
+          val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
+        }
+        *data_col_ptr = val;
+        data_col_ptr += batch_size * height_col * width_col;
+      }
+    }
+  }
+}
+
+void deformable_im2col(
+    const at::Tensor data_im, const at::Tensor data_offset, const int channels,
+    const int height, const int width, const int ksize_h, const int ksize_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w, const int parallel_imgs,
+    const int deformable_group, at::Tensor data_col)
+{
+  // num_axes should be smaller than block size
+  // todo: check parallel_imgs is correctly passed in
+  int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
+  int num_kernels = channels * height_col * width_col * parallel_imgs;
+  int channel_per_deformable_group = channels / deformable_group;
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      data_im.scalar_type(), "deformable_im2col_gpu", ([&] {
+        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
+        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
+        scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
+
+        deformable_im2col_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
+            num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w,
+            pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
+            channel_per_deformable_group, parallel_imgs, channels, deformable_group,
+            height_col, width_col, data_col_);
+      }));
+
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in deformable_im2col: %s\n", cudaGetErrorString(err));
+  }
+}
+
+template <typename scalar_t>
+__global__ void deformable_col2im_gpu_kernel(
+    const int n, const scalar_t *data_col, const scalar_t *data_offset,
+    const int channels, const int height, const int width,
+    const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
+    const int channel_per_deformable_group,
+    const int batch_size, const int deformable_group,
+    const int height_col, const int width_col,
+    scalar_t *grad_im)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    const int j = (index / width_col / height_col / batch_size) % kernel_w;
+    const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h;
+    const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h;
+    // compute the start and end of the output
+
+    const int deformable_group_index = c / channel_per_deformable_group;
+
+    int w_out = index % width_col;
+    int h_out = (index / width_col) % height_col;
+    int b = (index / width_col / height_col) % batch_size;
+    int w_in = w_out * stride_w - pad_w;
+    int h_in = h_out * stride_h - pad_h;
+
+    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) *
+                                                        2 * kernel_h * kernel_w * height_col * width_col;
+    const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
+    const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
+    const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
+    const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
+    const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h;
+    const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w;
+
+    const scalar_t cur_top_grad = data_col[index];
+    const int cur_h = (int)cur_inv_h_data;
+    const int cur_w = (int)cur_inv_w_data;
+    for (int dy = -2; dy <= 2; dy++)
+    {
+      for (int dx = -2; dx <= 2; dx++)
+      {
+        if (cur_h + dy >= 0 && cur_h + dy < height &&
+            cur_w + dx >= 0 && cur_w + dx < width &&
+            abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
+            abs(cur_inv_w_data - (cur_w + dx)) < 1)
+        {
+          int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
+          scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width);
+          atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad);
+        }
+      }
+    }
+  }
+}
+
+void deformable_col2im(
+    const at::Tensor data_col, const at::Tensor data_offset, const int channels,
+    const int height, const int width, const int ksize_h,
+    const int ksize_w, const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
+    const int parallel_imgs, const int deformable_group,
+    at::Tensor grad_im)
+{
+
+  // todo: make sure parallel_imgs is passed in correctly
+  int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
+  int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs;
+  int channel_per_deformable_group = channels / deformable_group;
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      data_col.scalar_type(), "deformable_col2im_gpu", ([&] {
+        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
+        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
+        scalar_t *grad_im_ = grad_im.data_ptr<scalar_t>();
+
+        deformable_col2im_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
+            num_kernels, data_col_, data_offset_, channels, height, width, ksize_h,
+            ksize_w, pad_h, pad_w, stride_h, stride_w,
+            dilation_h, dilation_w, channel_per_deformable_group,
+            parallel_imgs, deformable_group, height_col, width_col, grad_im_);
+      }));
+
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in deformable_col2im: %s\n", cudaGetErrorString(err));
+  }
+}
+
+template <typename scalar_t>
+__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col,
+                                                   const scalar_t *data_im, const scalar_t *data_offset,
+                                                   const int channels, const int height, const int width,
+                                                   const int kernel_h, const int kernel_w,
+                                                   const int pad_h, const int pad_w,
+                                                   const int stride_h, const int stride_w,
+                                                   const int dilation_h, const int dilation_w,
+                                                   const int channel_per_deformable_group,
+                                                   const int batch_size, const int offset_channels, const int deformable_group,
+                                                   const int height_col, const int width_col, scalar_t *grad_offset)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    scalar_t val = 0;
+    int w = index % width_col;
+    int h = (index / width_col) % height_col;
+    int c = (index / width_col / height_col) % offset_channels;
+    int b = (index / width_col / height_col) / offset_channels;
+    // compute the start and end of the output
+
+    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
+    const int col_step = kernel_h * kernel_w;
+    int cnt = 0;
+    const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group *
+                                                  batch_size * width_col * height_col;
+    const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) *
+                                                channel_per_deformable_group / kernel_h / kernel_w * height * width;
+    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 *
+                                                        kernel_h * kernel_w * height_col * width_col;
+
+    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
+
+    for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step)
+    {
+      const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w;
+      const int bp_dir = offset_c % 2;
+
+      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
+      int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
+      int w_out = col_pos % width_col;
+      int h_out = (col_pos / width_col) % height_col;
+      int w_in = w_out * stride_w - pad_w;
+      int h_in = h_out * stride_h - pad_h;
+      const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
+      const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out);
+      const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
+      const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
+      scalar_t inv_h = h_in + i * dilation_h + offset_h;
+      scalar_t inv_w = w_in + j * dilation_w + offset_w;
+      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width)
+      {
+        inv_h = inv_w = -2;
+      }
+      const scalar_t weight = get_coordinate_weight(
+          inv_h, inv_w,
+          height, width, data_im_ptr + cnt * height * width, width, bp_dir);
+      val += weight * data_col_ptr[col_pos];
+      cnt += 1;
+    }
+
+    grad_offset[index] = val;
+  }
+}
+
+void deformable_col2im_coord(
+    const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset,
+    const int channels, const int height, const int width, const int ksize_h,
+    const int ksize_w, const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, const int dilation_h, const int dilation_w,
+    const int parallel_imgs, const int deformable_group, at::Tensor grad_offset)
+{
+
+  int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
+  int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs;
+  int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group;
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] {
+        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
+        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
+        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
+        scalar_t *grad_offset_ = grad_offset.data_ptr<scalar_t>();
+
+        deformable_col2im_coord_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
+            num_kernels, data_col_, data_im_, data_offset_, channels, height, width,
+            ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w,
+            dilation_h, dilation_w, channel_per_deformable_group,
+            parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group,
+            height_col, width_col, grad_offset_);
+      }));
+}
+
+template <typename scalar_t>
+__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width,
+                                         const int height, const int width, scalar_t h, scalar_t w)
+{
+  int h_low = floor(h);
+  int w_low = floor(w);
+  int h_high = h_low + 1;
+  int w_high = w_low + 1;
+
+  scalar_t lh = h - h_low;
+  scalar_t lw = w - w_low;
+  scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+    v1 = bottom_data[h_low * data_width + w_low];
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+    v2 = bottom_data[h_low * data_width + w_high];
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+    v3 = bottom_data[h_high * data_width + w_low];
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+    v4 = bottom_data[h_high * data_width + w_high];
+
+  scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+  scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  return val;
+}
+
+template <typename scalar_t>
+__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w,
+                                             const int h, const int w, const int height, const int width)
+{
+  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
+  {
+    //empty
+    return 0;
+  }
+
+  int argmax_h_low = floor(argmax_h);
+  int argmax_w_low = floor(argmax_w);
+  int argmax_h_high = argmax_h_low + 1;
+  int argmax_w_high = argmax_w_low + 1;
+
+  scalar_t weight = 0;
+  if (h == argmax_h_low && w == argmax_w_low)
+    weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
+  if (h == argmax_h_low && w == argmax_w_high)
+    weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
+  if (h == argmax_h_high && w == argmax_w_low)
+    weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
+  if (h == argmax_h_high && w == argmax_w_high)
+    weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
+  return weight;
+}
+
+template <typename scalar_t>
+__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w,
+                                               const int height, const int width, const scalar_t *im_data,
+                                               const int data_width, const int bp_dir)
+{
+  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
+  {
+    //empty
+    return 0;
+  }
+
+  int argmax_h_low = floor(argmax_h);
+  int argmax_w_low = floor(argmax_w);
+  int argmax_h_high = argmax_h_low + 1;
+  int argmax_w_high = argmax_w_low + 1;
+
+  scalar_t weight = 0;
+
+  if (bp_dir == 0)
+  {
+    if (argmax_h_low >= 0 && argmax_w_low >= 0)
+      weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low];
+    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
+      weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high];
+    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
+      weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low];
+    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
+      weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high];
+  }
+  else if (bp_dir == 1)
+  {
+    if (argmax_h_low >= 0 && argmax_w_low >= 0)
+      weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low];
+    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
+      weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high];
+    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
+      weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low];
+    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
+      weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high];
+  }
+
+  return weight;
+}
+
+template <typename scalar_t>
+__global__ void modulated_deformable_im2col_gpu_kernel(const int n,
+                                                       const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask,
+                                                       const int height, const int width, const int kernel_h, const int kernel_w,
+                                                       const int pad_h, const int pad_w,
+                                                       const int stride_h, const int stride_w,
+                                                       const int dilation_h, const int dilation_w,
+                                                       const int channel_per_deformable_group,
+                                                       const int batch_size, const int num_channels, const int deformable_group,
+                                                       const int height_col, const int width_col,
+                                                       scalar_t *data_col)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    // index index of output matrix
+    const int w_col = index % width_col;
+    const int h_col = (index / width_col) % height_col;
+    const int b_col = (index / width_col / height_col) % batch_size;
+    const int c_im = (index / width_col / height_col) / batch_size;
+    const int c_col = c_im * kernel_h * kernel_w;
+
+    // compute deformable group index
+    const int deformable_group_index = c_im / channel_per_deformable_group;
+
+    const int h_in = h_col * stride_h - pad_h;
+    const int w_in = w_col * stride_w - pad_w;
+
+    scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
+    //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
+    const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
+    const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+
+    const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
+
+    for (int i = 0; i < kernel_h; ++i)
+    {
+      for (int j = 0; j < kernel_w; ++j)
+      {
+        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
+        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
+        const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
+        const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
+        const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
+        const scalar_t mask = data_mask_ptr[data_mask_hw_ptr];
+        scalar_t val = static_cast<scalar_t>(0);
+        const scalar_t h_im = h_in + i * dilation_h + offset_h;
+        const scalar_t w_im = w_in + j * dilation_w + offset_w;
+        //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
+        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
+        {
+          //const float map_h = i * dilation_h + offset_h;
+          //const float map_w = j * dilation_w + offset_w;
+          //const int cur_height = height - h_in;
+          //const int cur_width = width - w_in;
+          //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
+          val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
+        }
+        *data_col_ptr = val * mask;
+        data_col_ptr += batch_size * height_col * width_col;
+        //data_col_ptr += height_col * width_col;
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+__global__ void modulated_deformable_col2im_gpu_kernel(const int n,
+                                                       const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask,
+                                                       const int channels, const int height, const int width,
+                                                       const int kernel_h, const int kernel_w,
+                                                       const int pad_h, const int pad_w,
+                                                       const int stride_h, const int stride_w,
+                                                       const int dilation_h, const int dilation_w,
+                                                       const int channel_per_deformable_group,
+                                                       const int batch_size, const int deformable_group,
+                                                       const int height_col, const int width_col,
+                                                       scalar_t *grad_im)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    const int j = (index / width_col / height_col / batch_size) % kernel_w;
+    const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h;
+    const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h;
+    // compute the start and end of the output
+
+    const int deformable_group_index = c / channel_per_deformable_group;
+
+    int w_out = index % width_col;
+    int h_out = (index / width_col) % height_col;
+    int b = (index / width_col / height_col) % batch_size;
+    int w_in = w_out * stride_w - pad_w;
+    int h_in = h_out * stride_h - pad_h;
+
+    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+    const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
+    const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
+    const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
+    const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
+    const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
+    const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
+    const scalar_t mask = data_mask_ptr[data_mask_hw_ptr];
+    const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h;
+    const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w;
+
+    const scalar_t cur_top_grad = data_col[index] * mask;
+    const int cur_h = (int)cur_inv_h_data;
+    const int cur_w = (int)cur_inv_w_data;
+    for (int dy = -2; dy <= 2; dy++)
+    {
+      for (int dx = -2; dx <= 2; dx++)
+      {
+        if (cur_h + dy >= 0 && cur_h + dy < height &&
+            cur_w + dx >= 0 && cur_w + dx < width &&
+            abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
+            abs(cur_inv_w_data - (cur_w + dx)) < 1)
+        {
+          int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
+          scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width);
+          atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad);
+        }
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n,
+                                                             const scalar_t *data_col, const scalar_t *data_im,
+                                                             const scalar_t *data_offset, const scalar_t *data_mask,
+                                                             const int channels, const int height, const int width,
+                                                             const int kernel_h, const int kernel_w,
+                                                             const int pad_h, const int pad_w,
+                                                             const int stride_h, const int stride_w,
+                                                             const int dilation_h, const int dilation_w,
+                                                             const int channel_per_deformable_group,
+                                                             const int batch_size, const int offset_channels, const int deformable_group,
+                                                             const int height_col, const int width_col,
+                                                             scalar_t *grad_offset, scalar_t *grad_mask)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    scalar_t val = 0, mval = 0;
+    int w = index % width_col;
+    int h = (index / width_col) % height_col;
+    int c = (index / width_col / height_col) % offset_channels;
+    int b = (index / width_col / height_col) / offset_channels;
+    // compute the start and end of the output
+
+    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
+    const int col_step = kernel_h * kernel_w;
+    int cnt = 0;
+    const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col;
+    const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width;
+    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
+    const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
+
+    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
+
+    for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step)
+    {
+      const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w;
+      const int bp_dir = offset_c % 2;
+
+      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
+      int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
+      int w_out = col_pos % width_col;
+      int h_out = (col_pos / width_col) % height_col;
+      int w_in = w_out * stride_w - pad_w;
+      int h_in = h_out * stride_h - pad_h;
+      const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
+      const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out);
+      const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
+      const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
+      const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
+      const scalar_t mask = data_mask_ptr[data_mask_hw_ptr];
+      scalar_t inv_h = h_in + i * dilation_h + offset_h;
+      scalar_t inv_w = w_in + j * dilation_w + offset_w;
+      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width)
+      {
+        inv_h = inv_w = -2;
+      }
+      else
+      {
+        mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w);
+      }
+      const scalar_t weight = dmcn_get_coordinate_weight(
+          inv_h, inv_w,
+          height, width, data_im_ptr + cnt * height * width, width, bp_dir);
+      val += weight * data_col_ptr[col_pos] * mask;
+      cnt += 1;
+    }
+    // KERNEL_ASSIGN(grad_offset[index], offset_req, val);
+    grad_offset[index] = val;
+    if (offset_c % 2 == 0)
+      // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval);
+      grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval;
+  }
+}
+
+void modulated_deformable_im2col_cuda(
+    const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask,
+    const int batch_size, const int channels, const int height_im, const int width_im,
+    const int height_col, const int width_col, const int kernel_h, const int kenerl_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
+    const int deformable_group, at::Tensor data_col)
+{
+  // num_axes should be smaller than block size
+  const int channel_per_deformable_group = channels / deformable_group;
+  const int num_kernels = channels * batch_size * height_col * width_col;
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] {
+        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
+        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
+        const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
+        scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
+
+        modulated_deformable_im2col_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
+            num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w,
+            pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group,
+            batch_size, channels, deformable_group, height_col, width_col, data_col_);
+      }));
+
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
+  }
+}
+
+void modulated_deformable_col2im_cuda(
+    const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask,
+    const int batch_size, const int channels, const int height_im, const int width_im,
+    const int height_col, const int width_col, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
+    const int deformable_group, at::Tensor grad_im)
+{
+
+  const int channel_per_deformable_group = channels / deformable_group;
+  const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col;
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] {
+        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
+        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
+        const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
+        scalar_t *grad_im_ = grad_im.data_ptr<scalar_t>();
+
+        modulated_deformable_col2im_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
+            num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im,
+            kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
+            dilation_h, dilation_w, channel_per_deformable_group,
+            batch_size, deformable_group, height_col, width_col, grad_im_);
+      }));
+
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
+  }
+}
+
+void modulated_deformable_col2im_coord_cuda(
+    const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask,
+    const int batch_size, const int channels, const int height_im, const int width_im,
+    const int height_col, const int width_col, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
+    const int deformable_group,
+    at::Tensor grad_offset, at::Tensor grad_mask)
+{
+  const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group;
+  const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group;
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
+      data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] {
+        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
+        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
+        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
+        const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
+        scalar_t *grad_offset_ = grad_offset.data_ptr<scalar_t>();
+        scalar_t *grad_mask_ = grad_mask.data_ptr<scalar_t>();
+
+        modulated_deformable_col2im_coord_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
+            num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im,
+            kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
+            dilation_h, dilation_w, channel_per_deformable_group,
+            batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col,
+            grad_offset_, grad_mask_);
+      }));
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err));
+  }
+}
diff --git a/basicsr/ops/dcn/src/deform_conv_ext.cpp b/basicsr/ops/dcn/src/deform_conv_ext.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..41c6df6f721bd95a525fd6a03dd9882e863de042
--- /dev/null
+++ b/basicsr/ops/dcn/src/deform_conv_ext.cpp
@@ -0,0 +1,164 @@
+// modify from
+// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
+
+#include <torch/extension.h>
+#include <ATen/DeviceGuard.h>
+
+#include <cmath>
+#include <vector>
+
+#define WITH_CUDA  // always use cuda
+#ifdef WITH_CUDA
+int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
+                             at::Tensor offset, at::Tensor output,
+                             at::Tensor columns, at::Tensor ones, int kW,
+                             int kH, int dW, int dH, int padW, int padH,
+                             int dilationW, int dilationH, int group,
+                             int deformable_group, int im2col_step);
+
+int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
+                                    at::Tensor gradOutput, at::Tensor gradInput,
+                                    at::Tensor gradOffset, at::Tensor weight,
+                                    at::Tensor columns, int kW, int kH, int dW,
+                                    int dH, int padW, int padH, int dilationW,
+                                    int dilationH, int group,
+                                    int deformable_group, int im2col_step);
+
+int deform_conv_backward_parameters_cuda(
+    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
+    at::Tensor gradWeight,  // at::Tensor gradBias,
+    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
+    int padW, int padH, int dilationW, int dilationH, int group,
+    int deformable_group, float scale, int im2col_step);
+
+void modulated_deform_conv_cuda_forward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
+    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
+    const int pad_h, const int pad_w, const int dilation_h,
+    const int dilation_w, const int group, const int deformable_group,
+    const bool with_bias);
+
+void modulated_deform_conv_cuda_backward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor columns,
+    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
+    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
+    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
+    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
+    const bool with_bias);
+#endif
+
+int deform_conv_forward(at::Tensor input, at::Tensor weight,
+                             at::Tensor offset, at::Tensor output,
+                             at::Tensor columns, at::Tensor ones, int kW,
+                             int kH, int dW, int dH, int padW, int padH,
+                             int dilationW, int dilationH, int group,
+                             int deformable_group, int im2col_step) {
+  if (input.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_conv_forward_cuda(input, weight, offset, output, columns,
+        ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group,
+        deformable_group, im2col_step);
+#else
+    AT_ERROR("deform conv is not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("deform conv is not implemented on CPU");
+}
+
+int deform_conv_backward_input(at::Tensor input, at::Tensor offset,
+                                    at::Tensor gradOutput, at::Tensor gradInput,
+                                    at::Tensor gradOffset, at::Tensor weight,
+                                    at::Tensor columns, int kW, int kH, int dW,
+                                    int dH, int padW, int padH, int dilationW,
+                                    int dilationH, int group,
+                                    int deformable_group, int im2col_step) {
+  if (input.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_conv_backward_input_cuda(input, offset, gradOutput,
+        gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH,
+        dilationW, dilationH, group, deformable_group, im2col_step);
+#else
+    AT_ERROR("deform conv is not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("deform conv is not implemented on CPU");
+}
+
+int deform_conv_backward_parameters(
+    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
+    at::Tensor gradWeight,  // at::Tensor gradBias,
+    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
+    int padW, int padH, int dilationW, int dilationH, int group,
+    int deformable_group, float scale, int im2col_step) {
+  if (input.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_conv_backward_parameters_cuda(input, offset, gradOutput,
+        gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW,
+        dilationH, group, deformable_group, scale, im2col_step);
+#else
+    AT_ERROR("deform conv is not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("deform conv is not implemented on CPU");
+}
+
+void modulated_deform_conv_forward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
+    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
+    const int pad_h, const int pad_w, const int dilation_h,
+    const int dilation_w, const int group, const int deformable_group,
+    const bool with_bias) {
+  if (input.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return modulated_deform_conv_cuda_forward(input, weight, bias, ones,
+        offset, mask, output, columns, kernel_h, kernel_w, stride_h,
+        stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
+        deformable_group, with_bias);
+#else
+    AT_ERROR("modulated deform conv is not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("modulated deform conv is not implemented on CPU");
+}
+
+void modulated_deform_conv_backward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor columns,
+    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
+    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
+    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
+    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
+    const bool with_bias) {
+  if (input.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return modulated_deform_conv_cuda_backward(input, weight, bias, ones,
+        offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset,
+        grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w,
+        pad_h, pad_w, dilation_h, dilation_w, group, deformable_group,
+        with_bias);
+#else
+    AT_ERROR("modulated deform conv is not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("modulated deform conv is not implemented on CPU");
+}
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("deform_conv_forward", &deform_conv_forward,
+        "deform forward");
+  m.def("deform_conv_backward_input", &deform_conv_backward_input,
+        "deform_conv_backward_input");
+  m.def("deform_conv_backward_parameters",
+        &deform_conv_backward_parameters,
+        "deform_conv_backward_parameters");
+  m.def("modulated_deform_conv_forward",
+        &modulated_deform_conv_forward,
+        "modulated deform conv forward");
+  m.def("modulated_deform_conv_backward",
+        &modulated_deform_conv_backward,
+        "modulated deform conv backward");
+}
diff --git a/basicsr/ops/fused_act/__init__.py b/basicsr/ops/fused_act/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..241dc0754fae7d88dbbd9a02e665ca30a73c7422
--- /dev/null
+++ b/basicsr/ops/fused_act/__init__.py
@@ -0,0 +1,3 @@
+from .fused_act import FusedLeakyReLU, fused_leaky_relu
+
+__all__ = ['FusedLeakyReLU', 'fused_leaky_relu']
diff --git a/basicsr/ops/fused_act/__pycache__/__init__.cpython-36.pyc b/basicsr/ops/fused_act/__pycache__/__init__.cpython-36.pyc
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diff --git a/basicsr/ops/fused_act/__pycache__/fused_act.cpython-37.pyc b/basicsr/ops/fused_act/__pycache__/fused_act.cpython-37.pyc
new file mode 100644
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diff --git a/basicsr/ops/fused_act/fused_act.py b/basicsr/ops/fused_act/fused_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..588f815e596ab0fc83ab0f9d21426c22ec5ed7c3
--- /dev/null
+++ b/basicsr/ops/fused_act/fused_act.py
@@ -0,0 +1,89 @@
+# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
+
+import torch
+from torch import nn
+from torch.autograd import Function
+
+try:
+    from . import fused_act_ext
+except ImportError:
+    import os
+    BASICSR_JIT = os.getenv('BASICSR_JIT')
+    if BASICSR_JIT == 'True':
+        from torch.utils.cpp_extension import load
+        module_path = os.path.dirname(__file__)
+        fused_act_ext = load(
+            'fused',
+            sources=[
+                os.path.join(module_path, 'src', 'fused_bias_act.cpp'),
+                os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'),
+            ],
+        )
+
+
+class FusedLeakyReLUFunctionBackward(Function):
+
+    @staticmethod
+    def forward(ctx, grad_output, out, negative_slope, scale):
+        ctx.save_for_backward(out)
+        ctx.negative_slope = negative_slope
+        ctx.scale = scale
+
+        empty = grad_output.new_empty(0)
+
+        grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale)
+
+        dim = [0]
+
+        if grad_input.ndim > 2:
+            dim += list(range(2, grad_input.ndim))
+
+        grad_bias = grad_input.sum(dim).detach()
+
+        return grad_input, grad_bias
+
+    @staticmethod
+    def backward(ctx, gradgrad_input, gradgrad_bias):
+        out, = ctx.saved_tensors
+        gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope,
+                                                    ctx.scale)
+
+        return gradgrad_out, None, None, None
+
+
+class FusedLeakyReLUFunction(Function):
+
+    @staticmethod
+    def forward(ctx, input, bias, negative_slope, scale):
+        empty = input.new_empty(0)
+        out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
+        ctx.save_for_backward(out)
+        ctx.negative_slope = negative_slope
+        ctx.scale = scale
+
+        return out
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        out, = ctx.saved_tensors
+
+        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale)
+
+        return grad_input, grad_bias, None, None
+
+
+class FusedLeakyReLU(nn.Module):
+
+    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
+        super().__init__()
+
+        self.bias = nn.Parameter(torch.zeros(channel))
+        self.negative_slope = negative_slope
+        self.scale = scale
+
+    def forward(self, input):
+        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
+
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
+    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/basicsr/ops/fused_act/src/fused_bias_act.cpp b/basicsr/ops/fused_act/src/fused_bias_act.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..85ed0a79fb9c75f83470ac834090f03608d998ee
--- /dev/null
+++ b/basicsr/ops/fused_act/src/fused_bias_act.cpp
@@ -0,0 +1,26 @@
+// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp
+#include <torch/extension.h>
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input,
+                                const torch::Tensor& bias,
+                                const torch::Tensor& refer,
+                                int act, int grad, float alpha, float scale);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor fused_bias_act(const torch::Tensor& input,
+                             const torch::Tensor& bias,
+                             const torch::Tensor& refer,
+                             int act, int grad, float alpha, float scale) {
+    CHECK_CUDA(input);
+    CHECK_CUDA(bias);
+
+    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
+}
diff --git a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..54c7ff53ce8306db2b3c582ec7fa6696a38b4df0
--- /dev/null
+++ b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu
@@ -0,0 +1,100 @@
+// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include <torch/types.h>
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+
+template <typename scalar_t>
+static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref,
+    int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) {
+    int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x;
+
+    scalar_t zero = 0.0;
+
+    for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) {
+        scalar_t x = p_x[xi];
+
+        if (use_bias) {
+            x += p_b[(xi / step_b) % size_b];
+        }
+
+        scalar_t ref = use_ref ? p_ref[xi] : zero;
+
+        scalar_t y;
+
+        switch (act * 10 + grad) {
+            default:
+            case 10: y = x; break;
+            case 11: y = x; break;
+            case 12: y = 0.0; break;
+
+            case 30: y = (x > 0.0) ? x : x * alpha; break;
+            case 31: y = (ref > 0.0) ? x : x * alpha; break;
+            case 32: y = 0.0; break;
+        }
+
+        out[xi] = y * scale;
+    }
+}
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+    int act, int grad, float alpha, float scale) {
+    int curDevice = -1;
+    cudaGetDevice(&curDevice);
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+    auto x = input.contiguous();
+    auto b = bias.contiguous();
+    auto ref = refer.contiguous();
+
+    int use_bias = b.numel() ? 1 : 0;
+    int use_ref = ref.numel() ? 1 : 0;
+
+    int size_x = x.numel();
+    int size_b = b.numel();
+    int step_b = 1;
+
+    for (int i = 1 + 1; i < x.dim(); i++) {
+        step_b *= x.size(i);
+    }
+
+    int loop_x = 4;
+    int block_size = 4 * 32;
+    int grid_size = (size_x - 1) / (loop_x * block_size) + 1;
+
+    auto y = torch::empty_like(x);
+
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] {
+        fused_bias_act_kernel<scalar_t><<<grid_size, block_size, 0, stream>>>(
+            y.data_ptr<scalar_t>(),
+            x.data_ptr<scalar_t>(),
+            b.data_ptr<scalar_t>(),
+            ref.data_ptr<scalar_t>(),
+            act,
+            grad,
+            alpha,
+            scale,
+            loop_x,
+            size_x,
+            step_b,
+            size_b,
+            use_bias,
+            use_ref
+        );
+    });
+
+    return y;
+}
diff --git a/basicsr/ops/upfirdn2d/__init__.py b/basicsr/ops/upfirdn2d/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..397e85bea063e97fc4c12ad4d3e15669b69290bd
--- /dev/null
+++ b/basicsr/ops/upfirdn2d/__init__.py
@@ -0,0 +1,3 @@
+from .upfirdn2d import upfirdn2d
+
+__all__ = ['upfirdn2d']
diff --git a/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-36.pyc b/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-36.pyc
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diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp b/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..43d0b6783a5b512b55815a291fcac2bebeea31e0
--- /dev/null
+++ b/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp
@@ -0,0 +1,24 @@
+// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp
+#include <torch/extension.h>
+
+
+torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
+                            int up_x, int up_y, int down_x, int down_y,
+                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
+                        int up_x, int up_y, int down_x, int down_y,
+                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
+    CHECK_CUDA(input);
+    CHECK_CUDA(kernel);
+
+    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
+}
diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu
new file mode 100644
index 0000000000000000000000000000000000000000..8870063bae4468deab2e721f0978fe9facfb01b1
--- /dev/null
+++ b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu
@@ -0,0 +1,370 @@
+// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include <torch/types.h>
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
+  int c = a / b;
+
+  if (c * b > a) {
+    c--;
+  }
+
+  return c;
+}
+
+struct UpFirDn2DKernelParams {
+  int up_x;
+  int up_y;
+  int down_x;
+  int down_y;
+  int pad_x0;
+  int pad_x1;
+  int pad_y0;
+  int pad_y1;
+
+  int major_dim;
+  int in_h;
+  int in_w;
+  int minor_dim;
+  int kernel_h;
+  int kernel_w;
+  int out_h;
+  int out_w;
+  int loop_major;
+  int loop_x;
+};
+
+template <typename scalar_t>
+__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input,
+                                       const scalar_t *kernel,
+                                       const UpFirDn2DKernelParams p) {
+  int minor_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  int out_y = minor_idx / p.minor_dim;
+  minor_idx -= out_y * p.minor_dim;
+  int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y;
+  int major_idx_base = blockIdx.z * p.loop_major;
+
+  if (out_x_base >= p.out_w || out_y >= p.out_h ||
+      major_idx_base >= p.major_dim) {
+    return;
+  }
+
+  int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0;
+  int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h);
+  int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y;
+  int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y;
+
+  for (int loop_major = 0, major_idx = major_idx_base;
+       loop_major < p.loop_major && major_idx < p.major_dim;
+       loop_major++, major_idx++) {
+    for (int loop_x = 0, out_x = out_x_base;
+         loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) {
+      int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0;
+      int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w);
+      int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x;
+      int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x;
+
+      const scalar_t *x_p =
+          &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim +
+                 minor_idx];
+      const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x];
+      int x_px = p.minor_dim;
+      int k_px = -p.up_x;
+      int x_py = p.in_w * p.minor_dim;
+      int k_py = -p.up_y * p.kernel_w;
+
+      scalar_t v = 0.0f;
+
+      for (int y = 0; y < h; y++) {
+        for (int x = 0; x < w; x++) {
+          v += static_cast<scalar_t>(*x_p) * static_cast<scalar_t>(*k_p);
+          x_p += x_px;
+          k_p += k_px;
+        }
+
+        x_p += x_py - w * x_px;
+        k_p += k_py - w * k_px;
+      }
+
+      out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
+          minor_idx] = v;
+    }
+  }
+}
+
+template <typename scalar_t, int up_x, int up_y, int down_x, int down_y,
+          int kernel_h, int kernel_w, int tile_out_h, int tile_out_w>
+__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input,
+                                 const scalar_t *kernel,
+                                 const UpFirDn2DKernelParams p) {
+  const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
+  const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;
+
+  __shared__ volatile float sk[kernel_h][kernel_w];
+  __shared__ volatile float sx[tile_in_h][tile_in_w];
+
+  int minor_idx = blockIdx.x;
+  int tile_out_y = minor_idx / p.minor_dim;
+  minor_idx -= tile_out_y * p.minor_dim;
+  tile_out_y *= tile_out_h;
+  int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
+  int major_idx_base = blockIdx.z * p.loop_major;
+
+  if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h |
+      major_idx_base >= p.major_dim) {
+    return;
+  }
+
+  for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w;
+       tap_idx += blockDim.x) {
+    int ky = tap_idx / kernel_w;
+    int kx = tap_idx - ky * kernel_w;
+    scalar_t v = 0.0;
+
+    if (kx < p.kernel_w & ky < p.kernel_h) {
+      v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
+    }
+
+    sk[ky][kx] = v;
+  }
+
+  for (int loop_major = 0, major_idx = major_idx_base;
+       loop_major < p.loop_major & major_idx < p.major_dim;
+       loop_major++, major_idx++) {
+    for (int loop_x = 0, tile_out_x = tile_out_x_base;
+         loop_x < p.loop_x & tile_out_x < p.out_w;
+         loop_x++, tile_out_x += tile_out_w) {
+      int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
+      int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
+      int tile_in_x = floor_div(tile_mid_x, up_x);
+      int tile_in_y = floor_div(tile_mid_y, up_y);
+
+      __syncthreads();
+
+      for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w;
+           in_idx += blockDim.x) {
+        int rel_in_y = in_idx / tile_in_w;
+        int rel_in_x = in_idx - rel_in_y * tile_in_w;
+        int in_x = rel_in_x + tile_in_x;
+        int in_y = rel_in_y + tile_in_y;
+
+        scalar_t v = 0.0;
+
+        if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
+          v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) *
+                        p.minor_dim +
+                    minor_idx];
+        }
+
+        sx[rel_in_y][rel_in_x] = v;
+      }
+
+      __syncthreads();
+      for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w;
+           out_idx += blockDim.x) {
+        int rel_out_y = out_idx / tile_out_w;
+        int rel_out_x = out_idx - rel_out_y * tile_out_w;
+        int out_x = rel_out_x + tile_out_x;
+        int out_y = rel_out_y + tile_out_y;
+
+        int mid_x = tile_mid_x + rel_out_x * down_x;
+        int mid_y = tile_mid_y + rel_out_y * down_y;
+        int in_x = floor_div(mid_x, up_x);
+        int in_y = floor_div(mid_y, up_y);
+        int rel_in_x = in_x - tile_in_x;
+        int rel_in_y = in_y - tile_in_y;
+        int kernel_x = (in_x + 1) * up_x - mid_x - 1;
+        int kernel_y = (in_y + 1) * up_y - mid_y - 1;
+
+        scalar_t v = 0.0;
+
+#pragma unroll
+        for (int y = 0; y < kernel_h / up_y; y++)
+#pragma unroll
+          for (int x = 0; x < kernel_w / up_x; x++)
+            v += sx[rel_in_y + y][rel_in_x + x] *
+                 sk[kernel_y + y * up_y][kernel_x + x * up_x];
+
+        if (out_x < p.out_w & out_y < p.out_h) {
+          out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
+              minor_idx] = v;
+        }
+      }
+    }
+  }
+}
+
+torch::Tensor upfirdn2d_op(const torch::Tensor &input,
+                           const torch::Tensor &kernel, int up_x, int up_y,
+                           int down_x, int down_y, int pad_x0, int pad_x1,
+                           int pad_y0, int pad_y1) {
+  int curDevice = -1;
+  cudaGetDevice(&curDevice);
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+  UpFirDn2DKernelParams p;
+
+  auto x = input.contiguous();
+  auto k = kernel.contiguous();
+
+  p.major_dim = x.size(0);
+  p.in_h = x.size(1);
+  p.in_w = x.size(2);
+  p.minor_dim = x.size(3);
+  p.kernel_h = k.size(0);
+  p.kernel_w = k.size(1);
+  p.up_x = up_x;
+  p.up_y = up_y;
+  p.down_x = down_x;
+  p.down_y = down_y;
+  p.pad_x0 = pad_x0;
+  p.pad_x1 = pad_x1;
+  p.pad_y0 = pad_y0;
+  p.pad_y1 = pad_y1;
+
+  p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) /
+            p.down_y;
+  p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) /
+            p.down_x;
+
+  auto out =
+      at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());
+
+  int mode = -1;
+
+  int tile_out_h = -1;
+  int tile_out_w = -1;
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 4 && p.kernel_w <= 4) {
+    mode = 1;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 3 && p.kernel_w <= 3) {
+    mode = 2;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 4 && p.kernel_w <= 4) {
+    mode = 3;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 2 && p.kernel_w <= 2) {
+    mode = 4;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
+      p.kernel_h <= 4 && p.kernel_w <= 4) {
+    mode = 5;
+    tile_out_h = 8;
+    tile_out_w = 32;
+  }
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
+      p.kernel_h <= 2 && p.kernel_w <= 2) {
+    mode = 6;
+    tile_out_h = 8;
+    tile_out_w = 32;
+  }
+
+  dim3 block_size;
+  dim3 grid_size;
+
+  if (tile_out_h > 0 && tile_out_w > 0) {
+    p.loop_major = (p.major_dim - 1) / 16384 + 1;
+    p.loop_x = 1;
+    block_size = dim3(32 * 8, 1, 1);
+    grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
+                     (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
+                     (p.major_dim - 1) / p.loop_major + 1);
+  } else {
+    p.loop_major = (p.major_dim - 1) / 16384 + 1;
+    p.loop_x = 4;
+    block_size = dim3(4, 32, 1);
+    grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1,
+                     (p.out_w - 1) / (p.loop_x * block_size.y) + 1,
+                     (p.major_dim - 1) / p.loop_major + 1);
+  }
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
+    switch (mode) {
+    case 1:
+      upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 4, 4, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 2:
+      upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 3, 3, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 3:
+      upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 4, 4, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 4:
+      upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 2, 2, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 5:
+      upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 6:
+      upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    default:
+      upfirdn2d_kernel_large<scalar_t><<<grid_size, block_size, 0, stream>>>(
+          out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(),
+          k.data_ptr<scalar_t>(), p);
+    }
+  });
+
+  return out;
+}
diff --git a/basicsr/ops/upfirdn2d/upfirdn2d.py b/basicsr/ops/upfirdn2d/upfirdn2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..667f96e1ded35d48f163f37e21d1ed8ff191aac3
--- /dev/null
+++ b/basicsr/ops/upfirdn2d/upfirdn2d.py
@@ -0,0 +1,186 @@
+# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
+
+import torch
+from torch.autograd import Function
+from torch.nn import functional as F
+
+try:
+    from . import upfirdn2d_ext
+except ImportError:
+    import os
+    BASICSR_JIT = os.getenv('BASICSR_JIT')
+    if BASICSR_JIT == 'True':
+        from torch.utils.cpp_extension import load
+        module_path = os.path.dirname(__file__)
+        upfirdn2d_ext = load(
+            'upfirdn2d',
+            sources=[
+                os.path.join(module_path, 'src', 'upfirdn2d.cpp'),
+                os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'),
+            ],
+        )
+
+
+class UpFirDn2dBackward(Function):
+
+    @staticmethod
+    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size):
+
+        up_x, up_y = up
+        down_x, down_y = down
+        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
+
+        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
+
+        grad_input = upfirdn2d_ext.upfirdn2d(
+            grad_output,
+            grad_kernel,
+            down_x,
+            down_y,
+            up_x,
+            up_y,
+            g_pad_x0,
+            g_pad_x1,
+            g_pad_y0,
+            g_pad_y1,
+        )
+        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
+
+        ctx.save_for_backward(kernel)
+
+        pad_x0, pad_x1, pad_y0, pad_y1 = pad
+
+        ctx.up_x = up_x
+        ctx.up_y = up_y
+        ctx.down_x = down_x
+        ctx.down_y = down_y
+        ctx.pad_x0 = pad_x0
+        ctx.pad_x1 = pad_x1
+        ctx.pad_y0 = pad_y0
+        ctx.pad_y1 = pad_y1
+        ctx.in_size = in_size
+        ctx.out_size = out_size
+
+        return grad_input
+
+    @staticmethod
+    def backward(ctx, gradgrad_input):
+        kernel, = ctx.saved_tensors
+
+        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
+
+        gradgrad_out = upfirdn2d_ext.upfirdn2d(
+            gradgrad_input,
+            kernel,
+            ctx.up_x,
+            ctx.up_y,
+            ctx.down_x,
+            ctx.down_y,
+            ctx.pad_x0,
+            ctx.pad_x1,
+            ctx.pad_y0,
+            ctx.pad_y1,
+        )
+        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
+        #                                  ctx.out_size[1], ctx.in_size[3])
+        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1])
+
+        return gradgrad_out, None, None, None, None, None, None, None, None
+
+
+class UpFirDn2d(Function):
+
+    @staticmethod
+    def forward(ctx, input, kernel, up, down, pad):
+        up_x, up_y = up
+        down_x, down_y = down
+        pad_x0, pad_x1, pad_y0, pad_y1 = pad
+
+        kernel_h, kernel_w = kernel.shape
+        batch, channel, in_h, in_w = input.shape
+        ctx.in_size = input.shape
+
+        input = input.reshape(-1, in_h, in_w, 1)
+
+        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
+
+        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+        ctx.out_size = (out_h, out_w)
+
+        ctx.up = (up_x, up_y)
+        ctx.down = (down_x, down_y)
+        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
+
+        g_pad_x0 = kernel_w - pad_x0 - 1
+        g_pad_y0 = kernel_h - pad_y0 - 1
+        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
+        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
+
+        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
+
+        out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1)
+        # out = out.view(major, out_h, out_w, minor)
+        out = out.view(-1, channel, out_h, out_w)
+
+        return out
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        kernel, grad_kernel = ctx.saved_tensors
+
+        grad_input = UpFirDn2dBackward.apply(
+            grad_output,
+            kernel,
+            grad_kernel,
+            ctx.up,
+            ctx.down,
+            ctx.pad,
+            ctx.g_pad,
+            ctx.in_size,
+            ctx.out_size,
+        )
+
+        return grad_input, None, None, None, None
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+    if input.device.type == 'cpu':
+        out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
+    else:
+        out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]))
+
+    return out
+
+
+def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
+    _, channel, in_h, in_w = input.shape
+    input = input.reshape(-1, in_h, in_w, 1)
+
+    _, in_h, in_w, minor = input.shape
+    kernel_h, kernel_w = kernel.shape
+
+    out = input.view(-1, in_h, 1, in_w, 1, minor)
+    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
+    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
+
+    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
+    out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]
+
+    out = out.permute(0, 3, 1, 2)
+    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
+    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+    out = F.conv2d(out, w)
+    out = out.reshape(
+        -1,
+        minor,
+        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
+    )
+    out = out.permute(0, 2, 3, 1)
+    out = out[:, ::down_y, ::down_x, :]
+
+    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+
+    return out.view(-1, channel, out_h, out_w)
diff --git a/basicsr/test.py b/basicsr/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f240b68c11c7070b14595798aa074aed2c19e859
--- /dev/null
+++ b/basicsr/test.py
@@ -0,0 +1,46 @@
+import logging
+import torch
+from os import path as osp
+
+from basicsr.data import build_dataloader, build_dataset
+from basicsr.models import build_model
+from basicsr.train import parse_options
+from basicsr.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs
+from basicsr.utils.options import dict2str
+
+
+def test_pipeline(root_path):
+    # parse options, set distributed setting, set ramdom seed
+    opt = parse_options(root_path, is_train=False)
+
+    torch.backends.cudnn.benchmark = True
+    # torch.backends.cudnn.deterministic = True
+
+    # mkdir and initialize loggers
+    make_exp_dirs(opt)
+    log_file = osp.join(opt['path']['log'], f"test_{opt['name']}_{get_time_str()}.log")
+    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
+    logger.info(get_env_info())
+    logger.info(dict2str(opt))
+
+    # create test dataset and dataloader
+    test_loaders = []
+    for phase, dataset_opt in sorted(opt['datasets'].items()):
+        test_set = build_dataset(dataset_opt)
+        test_loader = build_dataloader(
+            test_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
+        logger.info(f"Number of test images in {dataset_opt['name']}: {len(test_set)}")
+        test_loaders.append(test_loader)
+
+    # create model
+    model = build_model(opt)
+
+    for test_loader in test_loaders:
+        test_set_name = test_loader.dataset.opt['name']
+        logger.info(f'Testing {test_set_name}...')
+        model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img'])
+
+
+if __name__ == '__main__':
+    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
+    test_pipeline(root_path)
diff --git a/basicsr/train.py b/basicsr/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ff7d6efc5ed92db0021c7b2c14091b2bd20437a
--- /dev/null
+++ b/basicsr/train.py
@@ -0,0 +1,221 @@
+import argparse
+import datetime
+import logging
+import math
+import random
+import time
+import torch
+from os import path as osp
+
+from basicsr.data import build_dataloader, build_dataset
+from basicsr.data.data_sampler import EnlargedSampler
+from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
+from basicsr.models import build_model
+from basicsr.utils import (MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, init_tb_logger,
+                           init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed)
+from basicsr.utils.dist_util import get_dist_info, init_dist
+from basicsr.utils.options import dict2str, parse
+
+
+def parse_options(root_path, is_train=True):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.')
+    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    opt = parse(args.opt, root_path, is_train=is_train)
+
+    # distributed settings
+    if args.launcher == 'none':
+        opt['dist'] = False
+        print('Disable distributed.', flush=True)
+    else:
+        opt['dist'] = True
+        if args.launcher == 'slurm' and 'dist_params' in opt:
+            init_dist(args.launcher, **opt['dist_params'])
+        else:
+            init_dist(args.launcher)
+
+    opt['rank'], opt['world_size'] = get_dist_info()
+
+    # random seed
+    seed = opt.get('manual_seed')
+    if seed is None:
+        seed = random.randint(1, 10000)
+        opt['manual_seed'] = seed
+    set_random_seed(seed + opt['rank'])
+
+    return opt
+
+
+def init_loggers(opt):
+    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log")
+    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
+    logger.info(get_env_info())
+    logger.info(dict2str(opt))
+
+    # initialize wandb logger before tensorboard logger to allow proper sync:
+    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project')
+                                                     is not None) and ('debug' not in opt['name']):
+        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
+        init_wandb_logger(opt)
+    tb_logger = None
+    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
+        tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name']))
+    return logger, tb_logger
+
+
+def create_train_val_dataloader(opt, logger):
+    # create train and val dataloaders
+    train_loader, val_loader = None, None
+    for phase, dataset_opt in opt['datasets'].items():
+        if phase == 'train':
+            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
+            train_set = build_dataset(dataset_opt)
+            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
+            train_loader = build_dataloader(
+                train_set,
+                dataset_opt,
+                num_gpu=opt['num_gpu'],
+                dist=opt['dist'],
+                sampler=train_sampler,
+                seed=opt['manual_seed'])
+
+            num_iter_per_epoch = math.ceil(
+                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
+            total_iters = int(opt['train']['total_iter'])
+            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
+            logger.info('Training statistics:'
+                        f'\n\tNumber of train images: {len(train_set)}'
+                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
+                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
+                        f'\n\tWorld size (gpu number): {opt["world_size"]}'
+                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
+                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
+
+        elif phase == 'val':
+            val_set = build_dataset(dataset_opt)
+            val_loader = build_dataloader(
+                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
+            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: ' f'{len(val_set)}')
+        else:
+            raise ValueError(f'Dataset phase {phase} is not recognized.')
+
+    return train_loader, train_sampler, val_loader, total_epochs, total_iters
+
+
+def train_pipeline(root_path):
+    # parse options, set distributed setting, set ramdom seed
+    opt = parse_options(root_path, is_train=True)
+
+    torch.backends.cudnn.benchmark = True
+    # torch.backends.cudnn.deterministic = True
+
+    # load resume states if necessary
+    if opt['path'].get('resume_state'):
+        device_id = torch.cuda.current_device()
+        resume_state = torch.load(
+            opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id))
+    else:
+        resume_state = None
+
+    # mkdir for experiments and logger
+    if resume_state is None:
+        make_exp_dirs(opt)
+        if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0:
+            mkdir_and_rename(osp.join('tb_logger', opt['name']))
+
+    # initialize loggers
+    logger, tb_logger = init_loggers(opt)
+
+    # create train and validation dataloaders
+    result = create_train_val_dataloader(opt, logger)
+    train_loader, train_sampler, val_loader, total_epochs, total_iters = result
+
+    # create model
+    if resume_state:  # resume training
+        check_resume(opt, resume_state['iter'])
+        model = build_model(opt)
+        model.resume_training(resume_state)  # handle optimizers and schedulers
+        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
+        start_epoch = resume_state['epoch']
+        current_iter = resume_state['iter']
+    else:
+        model = build_model(opt)
+        start_epoch = 0
+        current_iter = 0
+
+    # create message logger (formatted outputs)
+    msg_logger = MessageLogger(opt, current_iter, tb_logger)
+
+    # dataloader prefetcher
+    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
+    if prefetch_mode is None or prefetch_mode == 'cpu':
+        prefetcher = CPUPrefetcher(train_loader)
+    elif prefetch_mode == 'cuda':
+        prefetcher = CUDAPrefetcher(train_loader, opt)
+        logger.info(f'Use {prefetch_mode} prefetch dataloader')
+        if opt['datasets']['train'].get('pin_memory') is not True:
+            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
+    else:
+        raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.")
+
+    # training
+    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')
+    data_time, iter_time = time.time(), time.time()
+    start_time = time.time()
+
+    for epoch in range(start_epoch, total_epochs + 1):
+        train_sampler.set_epoch(epoch)
+        prefetcher.reset()
+        train_data = prefetcher.next()
+
+        while train_data is not None:
+            data_time = time.time() - data_time
+
+            current_iter += 1
+            if current_iter > total_iters:
+                break
+            # update learning rate
+            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
+            # training
+            model.feed_data(train_data)
+            model.optimize_parameters(current_iter)
+            iter_time = time.time() - iter_time
+            # log
+            if current_iter % opt['logger']['print_freq'] == 0:
+                log_vars = {'epoch': epoch, 'iter': current_iter}
+                log_vars.update({'lrs': model.get_current_learning_rate()})
+                log_vars.update({'time': iter_time, 'data_time': data_time})
+                log_vars.update(model.get_current_log())
+                msg_logger(log_vars)
+
+            # save models and training states
+            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
+                logger.info('Saving models and training states.')
+                model.save(epoch, current_iter)
+
+            # validation
+            if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0):
+                model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
+
+            data_time = time.time()
+            iter_time = time.time()
+            train_data = prefetcher.next()
+        # end of iter
+
+    # end of epoch
+
+    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
+    logger.info(f'End of training. Time consumed: {consumed_time}')
+    logger.info('Save the latest model.')
+    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
+    if opt.get('val') is not None:
+        model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
+    if tb_logger:
+        tb_logger.close()
+
+
+if __name__ == '__main__':
+    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
+    train_pipeline(root_path)
diff --git a/basicsr/utils/__init__.py b/basicsr/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fcc1d540462712387523d1e326d1dfc2bcfbf32
--- /dev/null
+++ b/basicsr/utils/__init__.py
@@ -0,0 +1,29 @@
+from .file_client import FileClient
+from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img
+from .logger import MessageLogger, get_env_info, get_root_logger, init_tb_logger, init_wandb_logger
+from .misc import check_resume, get_time_str, make_exp_dirs, mkdir_and_rename, scandir, set_random_seed, sizeof_fmt
+
+__all__ = [
+    # file_client.py
+    'FileClient',
+    # img_util.py
+    'img2tensor',
+    'tensor2img',
+    'imfrombytes',
+    'imwrite',
+    'crop_border',
+    # logger.py
+    'MessageLogger',
+    'init_tb_logger',
+    'init_wandb_logger',
+    'get_root_logger',
+    'get_env_info',
+    # misc.py
+    'set_random_seed',
+    'get_time_str',
+    'mkdir_and_rename',
+    'make_exp_dirs',
+    'scandir',
+    'check_resume',
+    'sizeof_fmt'
+]
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diff --git a/basicsr/utils/dist_util.py b/basicsr/utils/dist_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fab887b2cb1ce8533d2e8fdee72ae0c24f68fd0
--- /dev/null
+++ b/basicsr/utils/dist_util.py
@@ -0,0 +1,82 @@
+# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py  # noqa: E501
+import functools
+import os
+import subprocess
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+
+def init_dist(launcher, backend='nccl', **kwargs):
+    if mp.get_start_method(allow_none=True) is None:
+        mp.set_start_method('spawn')
+    if launcher == 'pytorch':
+        _init_dist_pytorch(backend, **kwargs)
+    elif launcher == 'slurm':
+        _init_dist_slurm(backend, **kwargs)
+    else:
+        raise ValueError(f'Invalid launcher type: {launcher}')
+
+
+def _init_dist_pytorch(backend, **kwargs):
+    rank = int(os.environ['RANK'])
+    num_gpus = torch.cuda.device_count()
+    torch.cuda.set_device(rank % num_gpus)
+    dist.init_process_group(backend=backend, **kwargs)
+
+
+def _init_dist_slurm(backend, port=None):
+    """Initialize slurm distributed training environment.
+
+    If argument ``port`` is not specified, then the master port will be system
+    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
+    environment variable, then a default port ``29500`` will be used.
+
+    Args:
+        backend (str): Backend of torch.distributed.
+        port (int, optional): Master port. Defaults to None.
+    """
+    proc_id = int(os.environ['SLURM_PROCID'])
+    ntasks = int(os.environ['SLURM_NTASKS'])
+    node_list = os.environ['SLURM_NODELIST']
+    num_gpus = torch.cuda.device_count()
+    torch.cuda.set_device(proc_id % num_gpus)
+    addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1')
+    # specify master port
+    if port is not None:
+        os.environ['MASTER_PORT'] = str(port)
+    elif 'MASTER_PORT' in os.environ:
+        pass  # use MASTER_PORT in the environment variable
+    else:
+        # 29500 is torch.distributed default port
+        os.environ['MASTER_PORT'] = '29500'
+    os.environ['MASTER_ADDR'] = addr
+    os.environ['WORLD_SIZE'] = str(ntasks)
+    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
+    os.environ['RANK'] = str(proc_id)
+    dist.init_process_group(backend=backend)
+
+
+def get_dist_info():
+    if dist.is_available():
+        initialized = dist.is_initialized()
+    else:
+        initialized = False
+    if initialized:
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+    else:
+        rank = 0
+        world_size = 1
+    return rank, world_size
+
+
+def master_only(func):
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        rank, _ = get_dist_info()
+        if rank == 0:
+            return func(*args, **kwargs)
+
+    return wrapper
diff --git a/basicsr/utils/download_util.py b/basicsr/utils/download_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ef93a8228b4d09151b3e1c1e1e0c29fb8272759
--- /dev/null
+++ b/basicsr/utils/download_util.py
@@ -0,0 +1,64 @@
+import math
+import requests
+from tqdm import tqdm
+
+from .misc import sizeof_fmt
+
+
+def download_file_from_google_drive(file_id, save_path):
+    """Download files from google drive.
+
+    Ref:
+    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501
+
+    Args:
+        file_id (str): File id.
+        save_path (str): Save path.
+    """
+
+    session = requests.Session()
+    URL = 'https://docs.google.com/uc?export=download'
+    params = {'id': file_id}
+
+    response = session.get(URL, params=params, stream=True)
+    token = get_confirm_token(response)
+    if token:
+        params['confirm'] = token
+        response = session.get(URL, params=params, stream=True)
+
+    # get file size
+    response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
+    if 'Content-Range' in response_file_size.headers:
+        file_size = int(response_file_size.headers['Content-Range'].split('/')[1])
+    else:
+        file_size = None
+
+    save_response_content(response, save_path, file_size)
+
+
+def get_confirm_token(response):
+    for key, value in response.cookies.items():
+        if key.startswith('download_warning'):
+            return value
+    return None
+
+
+def save_response_content(response, destination, file_size=None, chunk_size=32768):
+    if file_size is not None:
+        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')
+
+        readable_file_size = sizeof_fmt(file_size)
+    else:
+        pbar = None
+
+    with open(destination, 'wb') as f:
+        downloaded_size = 0
+        for chunk in response.iter_content(chunk_size):
+            downloaded_size += chunk_size
+            if pbar is not None:
+                pbar.update(1)
+                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} ' f'/ {readable_file_size}')
+            if chunk:  # filter out keep-alive new chunks
+                f.write(chunk)
+        if pbar is not None:
+            pbar.close()
diff --git a/basicsr/utils/face_util.py b/basicsr/utils/face_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6f2552ed459e7c15929c30b503347d7d8b9cbdb
--- /dev/null
+++ b/basicsr/utils/face_util.py
@@ -0,0 +1,192 @@
+import cv2
+import numpy as np
+import os
+import torch
+from skimage import transform as trans
+
+from basicsr.utils import imwrite
+
+try:
+    import dlib
+except ImportError:
+    print('Please install dlib before testing face restoration.' 'Reference:　https://github.com/davisking/dlib')
+
+
+class FaceRestorationHelper(object):
+    """Helper for the face restoration pipeline."""
+
+    def __init__(self, upscale_factor, face_size=512):
+        self.upscale_factor = upscale_factor
+        self.face_size = (face_size, face_size)
+
+        # standard 5 landmarks for FFHQ faces with 1024 x 1024
+        self.face_template = np.array([[686.77227723, 488.62376238], [586.77227723, 493.59405941],
+                                       [337.91089109, 488.38613861], [437.95049505, 493.51485149],
+                                       [513.58415842, 678.5049505]])
+        self.face_template = self.face_template / (1024 // face_size)
+        # for estimation the 2D similarity transformation
+        self.similarity_trans = trans.SimilarityTransform()
+
+        self.all_landmarks_5 = []
+        self.all_landmarks_68 = []
+        self.affine_matrices = []
+        self.inverse_affine_matrices = []
+        self.cropped_faces = []
+        self.restored_faces = []
+        self.save_png = True
+
+    def init_dlib(self, detection_path, landmark5_path, landmark68_path):
+        """Initialize the dlib detectors and predictors."""
+        self.face_detector = dlib.cnn_face_detection_model_v1(detection_path)
+        self.shape_predictor_5 = dlib.shape_predictor(landmark5_path)
+        self.shape_predictor_68 = dlib.shape_predictor(landmark68_path)
+
+    def free_dlib_gpu_memory(self):
+        del self.face_detector
+        del self.shape_predictor_5
+        del self.shape_predictor_68
+
+    def read_input_image(self, img_path):
+        # self.input_img is Numpy array, (h, w, c) with RGB order
+        self.input_img = dlib.load_rgb_image(img_path)
+
+    def detect_faces(self, img_path, upsample_num_times=1, only_keep_largest=False):
+        """
+        Args:
+            img_path (str): Image path.
+            upsample_num_times (int): Upsamples the image before running the
+                face detector
+
+        Returns:
+            int: Number of detected faces.
+        """
+        self.read_input_image(img_path)
+        det_faces = self.face_detector(self.input_img, upsample_num_times)
+        if len(det_faces) == 0:
+            print('No face detected. Try to increase upsample_num_times.')
+        else:
+            if only_keep_largest:
+                print('Detect several faces and only keep the largest.')
+                face_areas = []
+                for i in range(len(det_faces)):
+                    face_area = (det_faces[i].rect.right() - det_faces[i].rect.left()) * (
+                        det_faces[i].rect.bottom() - det_faces[i].rect.top())
+                    face_areas.append(face_area)
+                largest_idx = face_areas.index(max(face_areas))
+                self.det_faces = [det_faces[largest_idx]]
+            else:
+                self.det_faces = det_faces
+        return len(self.det_faces)
+
+    def get_face_landmarks_5(self):
+        for face in self.det_faces:
+            shape = self.shape_predictor_5(self.input_img, face.rect)
+            landmark = np.array([[part.x, part.y] for part in shape.parts()])
+            self.all_landmarks_5.append(landmark)
+        return len(self.all_landmarks_5)
+
+    def get_face_landmarks_68(self):
+        """Get 68 densemarks for cropped images.
+
+        Should only have one face at most in the cropped image.
+        """
+        num_detected_face = 0
+        for idx, face in enumerate(self.cropped_faces):
+            # face detection
+            det_face = self.face_detector(face, 1)  # TODO: can we remove it?
+            if len(det_face) == 0:
+                print(f'Cannot find faces in cropped image with index {idx}.')
+                self.all_landmarks_68.append(None)
+            else:
+                if len(det_face) > 1:
+                    print('Detect several faces in the cropped face. Use the '
+                          ' largest one. Note that it will also cause overlap '
+                          'during paste_faces_to_input_image.')
+                    face_areas = []
+                    for i in range(len(det_face)):
+                        face_area = (det_face[i].rect.right() - det_face[i].rect.left()) * (
+                            det_face[i].rect.bottom() - det_face[i].rect.top())
+                        face_areas.append(face_area)
+                    largest_idx = face_areas.index(max(face_areas))
+                    face_rect = det_face[largest_idx].rect
+                else:
+                    face_rect = det_face[0].rect
+                shape = self.shape_predictor_68(face, face_rect)
+                landmark = np.array([[part.x, part.y] for part in shape.parts()])
+                self.all_landmarks_68.append(landmark)
+                num_detected_face += 1
+
+        return num_detected_face
+
+    def warp_crop_faces(self, save_cropped_path=None, save_inverse_affine_path=None):
+        """Get affine matrix, warp and cropped faces.
+
+        Also get inverse affine matrix for post-processing.
+        """
+        for idx, landmark in enumerate(self.all_landmarks_5):
+            # use 5 landmarks to get affine matrix
+            self.similarity_trans.estimate(landmark, self.face_template)
+            affine_matrix = self.similarity_trans.params[0:2, :]
+            self.affine_matrices.append(affine_matrix)
+            # warp and crop faces
+            cropped_face = cv2.warpAffine(self.input_img, affine_matrix, self.face_size)
+            self.cropped_faces.append(cropped_face)
+            # save the cropped face
+            if save_cropped_path is not None:
+                path, ext = os.path.splitext(save_cropped_path)
+                if self.save_png:
+                    save_path = f'{path}_{idx:02d}.png'
+                else:
+                    save_path = f'{path}_{idx:02d}{ext}'
+
+                imwrite(cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR), save_path)
+
+            # get inverse affine matrix
+            self.similarity_trans.estimate(self.face_template, landmark * self.upscale_factor)
+            inverse_affine = self.similarity_trans.params[0:2, :]
+            self.inverse_affine_matrices.append(inverse_affine)
+            # save inverse affine matrices
+            if save_inverse_affine_path is not None:
+                path, _ = os.path.splitext(save_inverse_affine_path)
+                save_path = f'{path}_{idx:02d}.pth'
+                torch.save(inverse_affine, save_path)
+
+    def add_restored_face(self, face):
+        self.restored_faces.append(face)
+
+    def paste_faces_to_input_image(self, save_path):
+        # operate in the BGR order
+        input_img = cv2.cvtColor(self.input_img, cv2.COLOR_RGB2BGR)
+        h, w, _ = input_img.shape
+        h_up, w_up = h * self.upscale_factor, w * self.upscale_factor
+        # simply resize the background
+        upsample_img = cv2.resize(input_img, (w_up, h_up))
+        assert len(self.restored_faces) == len(
+            self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
+        for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
+            inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
+            mask = np.ones((*self.face_size, 3), dtype=np.float32)
+            inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
+            # remove the black borders
+            inv_mask_erosion = cv2.erode(inv_mask, np.ones((2 * self.upscale_factor, 2 * self.upscale_factor),
+                                                           np.uint8))
+            inv_restored_remove_border = inv_mask_erosion * inv_restored
+            total_face_area = np.sum(inv_mask_erosion) // 3
+            # compute the fusion edge based on the area of face
+            w_edge = int(total_face_area**0.5) // 20
+            erosion_radius = w_edge * 2
+            inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+            blur_size = w_edge * 2
+            inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+            upsample_img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * upsample_img
+        if self.save_png:
+            save_path = save_path.replace('.jpg', '.png').replace('.jpeg', '.png')
+        imwrite(upsample_img.astype(np.uint8), save_path)
+
+    def clean_all(self):
+        self.all_landmarks_5 = []
+        self.all_landmarks_68 = []
+        self.restored_faces = []
+        self.affine_matrices = []
+        self.cropped_faces = []
+        self.inverse_affine_matrices = []
diff --git a/basicsr/utils/file_client.py b/basicsr/utils/file_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f38d9796da3899048924f2f803d1088927966b0
--- /dev/null
+++ b/basicsr/utils/file_client.py
@@ -0,0 +1,167 @@
+# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py  # noqa: E501
+from abc import ABCMeta, abstractmethod
+
+
+class BaseStorageBackend(metaclass=ABCMeta):
+    """Abstract class of storage backends.
+
+    All backends need to implement two apis: ``get()`` and ``get_text()``.
+    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
+    as texts.
+    """
+
+    @abstractmethod
+    def get(self, filepath):
+        pass
+
+    @abstractmethod
+    def get_text(self, filepath):
+        pass
+
+
+class MemcachedBackend(BaseStorageBackend):
+    """Memcached storage backend.
+
+    Attributes:
+        server_list_cfg (str): Config file for memcached server list.
+        client_cfg (str): Config file for memcached client.
+        sys_path (str | None): Additional path to be appended to `sys.path`.
+            Default: None.
+    """
+
+    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
+        if sys_path is not None:
+            import sys
+            sys.path.append(sys_path)
+        try:
+            import mc
+        except ImportError:
+            raise ImportError('Please install memcached to enable MemcachedBackend.')
+
+        self.server_list_cfg = server_list_cfg
+        self.client_cfg = client_cfg
+        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg)
+        # mc.pyvector servers as a point which points to a memory cache
+        self._mc_buffer = mc.pyvector()
+
+    def get(self, filepath):
+        filepath = str(filepath)
+        import mc
+        self._client.Get(filepath, self._mc_buffer)
+        value_buf = mc.ConvertBuffer(self._mc_buffer)
+        return value_buf
+
+    def get_text(self, filepath):
+        raise NotImplementedError
+
+
+class HardDiskBackend(BaseStorageBackend):
+    """Raw hard disks storage backend."""
+
+    def get(self, filepath):
+        filepath = str(filepath)
+        with open(filepath, 'rb') as f:
+            value_buf = f.read()
+        return value_buf
+
+    def get_text(self, filepath):
+        filepath = str(filepath)
+        with open(filepath, 'r') as f:
+            value_buf = f.read()
+        return value_buf
+
+
+class LmdbBackend(BaseStorageBackend):
+    """Lmdb storage backend.
+
+    Args:
+        db_paths (str | list[str]): Lmdb database paths.
+        client_keys (str | list[str]): Lmdb client keys. Default: 'default'.
+        readonly (bool, optional): Lmdb environment parameter. If True,
+            disallow any write operations. Default: True.
+        lock (bool, optional): Lmdb environment parameter. If False, when
+            concurrent access occurs, do not lock the database. Default: False.
+        readahead (bool, optional): Lmdb environment parameter. If False,
+            disable the OS filesystem readahead mechanism, which may improve
+            random read performance when a database is larger than RAM.
+            Default: False.
+
+    Attributes:
+        db_paths (list): Lmdb database path.
+        _client (list): A list of several lmdb envs.
+    """
+
+    def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs):
+        try:
+            import lmdb
+        except ImportError:
+            raise ImportError('Please install lmdb to enable LmdbBackend.')
+
+        if isinstance(client_keys, str):
+            client_keys = [client_keys]
+
+        if isinstance(db_paths, list):
+            self.db_paths = [str(v) for v in db_paths]
+        elif isinstance(db_paths, str):
+            self.db_paths = [str(db_paths)]
+        assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, '
+                                                        f'but received {len(client_keys)} and {len(self.db_paths)}.')
+
+        self._client = {}
+        for client, path in zip(client_keys, self.db_paths):
+            self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs)
+
+    def get(self, filepath, client_key):
+        """Get values according to the filepath from one lmdb named client_key.
+
+        Args:
+            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
+            client_key (str): Used for distinguishing differnet lmdb envs.
+        """
+        filepath = str(filepath)
+        assert client_key in self._client, (f'client_key {client_key} is not ' 'in lmdb clients.')
+        client = self._client[client_key]
+        with client.begin(write=False) as txn:
+            value_buf = txn.get(filepath.encode('ascii'))
+        return value_buf
+
+    def get_text(self, filepath):
+        raise NotImplementedError
+
+
+class FileClient(object):
+    """A general file client to access files in different backend.
+
+    The client loads a file or text in a specified backend from its path
+    and return it as a binary file. it can also register other backend
+    accessor with a given name and backend class.
+
+    Attributes:
+        backend (str): The storage backend type. Options are "disk",
+            "memcached" and "lmdb".
+        client (:obj:`BaseStorageBackend`): The backend object.
+    """
+
+    _backends = {
+        'disk': HardDiskBackend,
+        'memcached': MemcachedBackend,
+        'lmdb': LmdbBackend,
+    }
+
+    def __init__(self, backend='disk', **kwargs):
+        if backend not in self._backends:
+            raise ValueError(f'Backend {backend} is not supported. Currently supported ones'
+                             f' are {list(self._backends.keys())}')
+        self.backend = backend
+        self.client = self._backends[backend](**kwargs)
+
+    def get(self, filepath, client_key='default'):
+        # client_key is used only for lmdb, where different fileclients have
+        # different lmdb environments.
+        if self.backend == 'lmdb':
+            return self.client.get(filepath, client_key)
+        else:
+            return self.client.get(filepath)
+
+    def get_text(self, filepath):
+        return self.client.get_text(filepath)
diff --git a/basicsr/utils/flow_util.py b/basicsr/utils/flow_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..92cdcc0f3bf71a4dbe51e5d9d26aec8d720cf0be
--- /dev/null
+++ b/basicsr/utils/flow_util.py
@@ -0,0 +1,170 @@
+# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py  # noqa: E501
+import cv2
+import numpy as np
+import os
+
+
+def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
+    """Read an optical flow map.
+
+    Args:
+        flow_path (ndarray or str): Flow path.
+        quantize (bool): whether to read quantized pair, if set to True,
+            remaining args will be passed to :func:`dequantize_flow`.
+        concat_axis (int): The axis that dx and dy are concatenated,
+            can be either 0 or 1. Ignored if quantize is False.
+
+    Returns:
+        ndarray: Optical flow represented as a (h, w, 2) numpy array
+    """
+    if quantize:
+        assert concat_axis in [0, 1]
+        cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
+        if cat_flow.ndim != 2:
+            raise IOError(f'{flow_path} is not a valid quantized flow file, ' f'its dimension is {cat_flow.ndim}.')
+        assert cat_flow.shape[concat_axis] % 2 == 0
+        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
+        flow = dequantize_flow(dx, dy, *args, **kwargs)
+    else:
+        with open(flow_path, 'rb') as f:
+            try:
+                header = f.read(4).decode('utf-8')
+            except Exception:
+                raise IOError(f'Invalid flow file: {flow_path}')
+            else:
+                if header != 'PIEH':
+                    raise IOError(f'Invalid flow file: {flow_path}, ' 'header does not contain PIEH')
+
+            w = np.fromfile(f, np.int32, 1).squeeze()
+            h = np.fromfile(f, np.int32, 1).squeeze()
+            flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
+
+    return flow.astype(np.float32)
+
+
+def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
+    """Write optical flow to file.
+
+    If the flow is not quantized, it will be saved as a .flo file losslessly,
+    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
+    will be concatenated horizontally into a single image if quantize is True.)
+
+    Args:
+        flow (ndarray): (h, w, 2) array of optical flow.
+        filename (str): Output filepath.
+        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
+            images. If set to True, remaining args will be passed to
+            :func:`quantize_flow`.
+        concat_axis (int): The axis that dx and dy are concatenated,
+            can be either 0 or 1. Ignored if quantize is False.
+    """
+    if not quantize:
+        with open(filename, 'wb') as f:
+            f.write('PIEH'.encode('utf-8'))
+            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
+            flow = flow.astype(np.float32)
+            flow.tofile(f)
+            f.flush()
+    else:
+        assert concat_axis in [0, 1]
+        dx, dy = quantize_flow(flow, *args, **kwargs)
+        dxdy = np.concatenate((dx, dy), axis=concat_axis)
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        cv2.imwrite(filename, dxdy)
+
+
+def quantize_flow(flow, max_val=0.02, norm=True):
+    """Quantize flow to [0, 255].
+
+    After this step, the size of flow will be much smaller, and can be
+    dumped as jpeg images.
+
+    Args:
+        flow (ndarray): (h, w, 2) array of optical flow.
+        max_val (float): Maximum value of flow, values beyond
+                        [-max_val, max_val] will be truncated.
+        norm (bool): Whether to divide flow values by image width/height.
+
+    Returns:
+        tuple[ndarray]: Quantized dx and dy.
+    """
+    h, w, _ = flow.shape
+    dx = flow[..., 0]
+    dy = flow[..., 1]
+    if norm:
+        dx = dx / w  # avoid inplace operations
+        dy = dy / h
+    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
+    flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]]
+    return tuple(flow_comps)
+
+
+def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
+    """Recover from quantized flow.
+
+    Args:
+        dx (ndarray): Quantized dx.
+        dy (ndarray): Quantized dy.
+        max_val (float): Maximum value used when quantizing.
+        denorm (bool): Whether to multiply flow values with width/height.
+
+    Returns:
+        ndarray: Dequantized flow.
+    """
+    assert dx.shape == dy.shape
+    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
+
+    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
+
+    if denorm:
+        dx *= dx.shape[1]
+        dy *= dx.shape[0]
+    flow = np.dstack((dx, dy))
+    return flow
+
+
+def quantize(arr, min_val, max_val, levels, dtype=np.int64):
+    """Quantize an array of (-inf, inf) to [0, levels-1].
+
+    Args:
+        arr (ndarray): Input array.
+        min_val (scalar): Minimum value to be clipped.
+        max_val (scalar): Maximum value to be clipped.
+        levels (int): Quantization levels.
+        dtype (np.type): The type of the quantized array.
+
+    Returns:
+        tuple: Quantized array.
+    """
+    if not (isinstance(levels, int) and levels > 1):
+        raise ValueError(f'levels must be a positive integer, but got {levels}')
+    if min_val >= max_val:
+        raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')
+
+    arr = np.clip(arr, min_val, max_val) - min_val
+    quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)
+
+    return quantized_arr
+
+
+def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
+    """Dequantize an array.
+
+    Args:
+        arr (ndarray): Input array.
+        min_val (scalar): Minimum value to be clipped.
+        max_val (scalar): Maximum value to be clipped.
+        levels (int): Quantization levels.
+        dtype (np.type): The type of the dequantized array.
+
+    Returns:
+        tuple: Dequantized array.
+    """
+    if not (isinstance(levels, int) and levels > 1):
+        raise ValueError(f'levels must be a positive integer, but got {levels}')
+    if min_val >= max_val:
+        raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')
+
+    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val
+
+    return dequantized_arr
diff --git a/basicsr/utils/img_util.py b/basicsr/utils/img_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d409a132ff216e6943a276fb5d8cd5f410824883
--- /dev/null
+++ b/basicsr/utils/img_util.py
@@ -0,0 +1,170 @@
+import cv2
+import math
+import numpy as np
+import os
+import torch
+from torchvision.utils import make_grid
+
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+    """Numpy array to tensor.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Input images.
+        bgr2rgb (bool): Whether to change bgr to rgb.
+        float32 (bool): Whether to change to float32.
+
+    Returns:
+        list[tensor] | tensor: Tensor images. If returned results only have
+            one element, just return tensor.
+    """
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    else:
+        return _totensor(imgs, bgr2rgb, float32)
+
+
+def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
+    """Convert torch Tensors into image numpy arrays.
+
+    After clamping to [min, max], values will be normalized to [0, 1].
+
+    Args:
+        tensor (Tensor or list[Tensor]): Accept shapes:
+            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
+            2) 3D Tensor of shape (3/1 x H x W);
+            3) 2D Tensor of shape (H x W).
+            Tensor channel should be in RGB order.
+        rgb2bgr (bool): Whether to change rgb to bgr.
+        out_type (numpy type): output types. If ``np.uint8``, transform outputs
+            to uint8 type with range [0, 255]; otherwise, float type with
+            range [0, 1]. Default: ``np.uint8``.
+        min_max (tuple[int]): min and max values for clamp.
+
+    Returns:
+        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
+        shape (H x W). The channel order is BGR.
+    """
+    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
+        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
+
+    if torch.is_tensor(tensor):
+        tensor = [tensor]
+    result = []
+    for _tensor in tensor:
+        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
+        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
+
+        n_dim = _tensor.dim()
+        if n_dim == 4:
+            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if rgb2bgr:
+                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 3:
+            img_np = _tensor.numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if img_np.shape[2] == 1:  # gray image
+                img_np = np.squeeze(img_np, axis=2)
+            else:
+                if rgb2bgr:
+                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 2:
+            img_np = _tensor.numpy()
+        else:
+            raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
+        if out_type == np.uint8:
+            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
+            img_np = (img_np * 255.0).round()
+        img_np = img_np.astype(out_type)
+        result.append(img_np)
+    if len(result) == 1:
+        result = result[0]
+    return result
+
+
+def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)):
+    """This implementation is slightly faster than tensor2img.
+    It now only supports torch tensor with shape (1, c, h, w).
+
+    Args:
+        tensor (Tensor): Now only support torch tensor with (1, c, h, w).
+        rgb2bgr (bool): Whether to change rgb to bgr. Default: True.
+        min_max (tuple[int]): min and max values for clamp.
+    """
+    output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0)
+    output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255
+    output = output.type(torch.uint8).cpu().numpy()
+    if rgb2bgr:
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+    return output
+
+
+def imfrombytes(content, flag='color', float32=False):
+    """Read an image from bytes.
+
+    Args:
+        content (bytes): Image bytes got from files or other streams.
+        flag (str): Flags specifying the color type of a loaded image,
+            candidates are `color`, `grayscale` and `unchanged`.
+        float32 (bool): Whether to change to float32., If True, will also norm
+            to [0, 1]. Default: False.
+
+    Returns:
+        ndarray: Loaded image array.
+    """
+    img_np = np.frombuffer(content, np.uint8)
+    imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}
+    img = cv2.imdecode(img_np, imread_flags[flag])
+    if float32:
+        img = img.astype(np.float32) / 255.
+    return img
+
+
+def imwrite(img, file_path, params=None, auto_mkdir=True):
+    """Write image to file.
+
+    Args:
+        img (ndarray): Image array to be written.
+        file_path (str): Image file path.
+        params (None or list): Same as opencv's :func:`imwrite` interface.
+        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
+            whether to create it automatically.
+
+    Returns:
+        bool: Successful or not.
+    """
+    if auto_mkdir:
+        dir_name = os.path.abspath(os.path.dirname(file_path))
+        os.makedirs(dir_name, exist_ok=True)
+    return cv2.imwrite(file_path, img, params)
+
+
+def crop_border(imgs, crop_border):
+    """Crop borders of images.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
+        crop_border (int): Crop border for each end of height and weight.
+
+    Returns:
+        list[ndarray]: Cropped images.
+    """
+    if crop_border == 0:
+        return imgs
+    else:
+        if isinstance(imgs, list):
+            return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs]
+        else:
+            return imgs[crop_border:-crop_border, crop_border:-crop_border, ...]
diff --git a/basicsr/utils/lmdb_util.py b/basicsr/utils/lmdb_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0a10f60ffca2e36ac5f5564aafd70e79d06a723
--- /dev/null
+++ b/basicsr/utils/lmdb_util.py
@@ -0,0 +1,196 @@
+import cv2
+import lmdb
+import sys
+from multiprocessing import Pool
+from os import path as osp
+from tqdm import tqdm
+
+
+def make_lmdb_from_imgs(data_path,
+                        lmdb_path,
+                        img_path_list,
+                        keys,
+                        batch=5000,
+                        compress_level=1,
+                        multiprocessing_read=False,
+                        n_thread=40,
+                        map_size=None):
+    """Make lmdb from images.
+
+    Contents of lmdb. The file structure is:
+    example.lmdb
+    ├── data.mdb
+    ├── lock.mdb
+    ├── meta_info.txt
+
+    The data.mdb and lock.mdb are standard lmdb files and you can refer to
+    https://lmdb.readthedocs.io/en/release/ for more details.
+
+    The meta_info.txt is a specified txt file to record the meta information
+    of our datasets. It will be automatically created when preparing
+    datasets by our provided dataset tools.
+    Each line in the txt file records 1)image name (with extension),
+    2)image shape, and 3)compression level, separated by a white space.
+
+    For example, the meta information could be:
+    `000_00000000.png (720,1280,3) 1`, which means:
+    1) image name (with extension): 000_00000000.png;
+    2) image shape: (720,1280,3);
+    3) compression level: 1
+
+    We use the image name without extension as the lmdb key.
+
+    If `multiprocessing_read` is True, it will read all the images to memory
+    using multiprocessing. Thus, your server needs to have enough memory.
+
+    Args:
+        data_path (str): Data path for reading images.
+        lmdb_path (str): Lmdb save path.
+        img_path_list (str): Image path list.
+        keys (str): Used for lmdb keys.
+        batch (int): After processing batch images, lmdb commits.
+            Default: 5000.
+        compress_level (int): Compress level when encoding images. Default: 1.
+        multiprocessing_read (bool): Whether use multiprocessing to read all
+            the images to memory. Default: False.
+        n_thread (int): For multiprocessing.
+        map_size (int | None): Map size for lmdb env. If None, use the
+            estimated size from images. Default: None
+    """
+
+    assert len(img_path_list) == len(keys), ('img_path_list and keys should have the same length, '
+                                             f'but got {len(img_path_list)} and {len(keys)}')
+    print(f'Create lmdb for {data_path}, save to {lmdb_path}...')
+    print(f'Totoal images: {len(img_path_list)}')
+    if not lmdb_path.endswith('.lmdb'):
+        raise ValueError("lmdb_path must end with '.lmdb'.")
+    if osp.exists(lmdb_path):
+        print(f'Folder {lmdb_path} already exists. Exit.')
+        sys.exit(1)
+
+    if multiprocessing_read:
+        # read all the images to memory (multiprocessing)
+        dataset = {}  # use dict to keep the order for multiprocessing
+        shapes = {}
+        print(f'Read images with multiprocessing, #thread: {n_thread} ...')
+        pbar = tqdm(total=len(img_path_list), unit='image')
+
+        def callback(arg):
+            """get the image data and update pbar."""
+            key, dataset[key], shapes[key] = arg
+            pbar.update(1)
+            pbar.set_description(f'Read {key}')
+
+        pool = Pool(n_thread)
+        for path, key in zip(img_path_list, keys):
+            pool.apply_async(read_img_worker, args=(osp.join(data_path, path), key, compress_level), callback=callback)
+        pool.close()
+        pool.join()
+        pbar.close()
+        print(f'Finish reading {len(img_path_list)} images.')
+
+    # create lmdb environment
+    if map_size is None:
+        # obtain data size for one image
+        img = cv2.imread(osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED)
+        _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
+        data_size_per_img = img_byte.nbytes
+        print('Data size per image is: ', data_size_per_img)
+        data_size = data_size_per_img * len(img_path_list)
+        map_size = data_size * 10
+
+    env = lmdb.open(lmdb_path, map_size=map_size)
+
+    # write data to lmdb
+    pbar = tqdm(total=len(img_path_list), unit='chunk')
+    txn = env.begin(write=True)
+    txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
+    for idx, (path, key) in enumerate(zip(img_path_list, keys)):
+        pbar.update(1)
+        pbar.set_description(f'Write {key}')
+        key_byte = key.encode('ascii')
+        if multiprocessing_read:
+            img_byte = dataset[key]
+            h, w, c = shapes[key]
+        else:
+            _, img_byte, img_shape = read_img_worker(osp.join(data_path, path), key, compress_level)
+            h, w, c = img_shape
+
+        txn.put(key_byte, img_byte)
+        # write meta information
+        txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n')
+        if idx % batch == 0:
+            txn.commit()
+            txn = env.begin(write=True)
+    pbar.close()
+    txn.commit()
+    env.close()
+    txt_file.close()
+    print('\nFinish writing lmdb.')
+
+
+def read_img_worker(path, key, compress_level):
+    """Read image worker.
+
+    Args:
+        path (str): Image path.
+        key (str): Image key.
+        compress_level (int): Compress level when encoding images.
+
+    Returns:
+        str: Image key.
+        byte: Image byte.
+        tuple[int]: Image shape.
+    """
+
+    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+    if img.ndim == 2:
+        h, w = img.shape
+        c = 1
+    else:
+        h, w, c = img.shape
+    _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
+    return (key, img_byte, (h, w, c))
+
+
+class LmdbMaker():
+    """LMDB Maker.
+
+    Args:
+        lmdb_path (str): Lmdb save path.
+        map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB.
+        batch (int): After processing batch images, lmdb commits.
+            Default: 5000.
+        compress_level (int): Compress level when encoding images. Default: 1.
+    """
+
+    def __init__(self, lmdb_path, map_size=1024**4, batch=5000, compress_level=1):
+        if not lmdb_path.endswith('.lmdb'):
+            raise ValueError("lmdb_path must end with '.lmdb'.")
+        if osp.exists(lmdb_path):
+            print(f'Folder {lmdb_path} already exists. Exit.')
+            sys.exit(1)
+
+        self.lmdb_path = lmdb_path
+        self.batch = batch
+        self.compress_level = compress_level
+        self.env = lmdb.open(lmdb_path, map_size=map_size)
+        self.txn = self.env.begin(write=True)
+        self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
+        self.counter = 0
+
+    def put(self, img_byte, key, img_shape):
+        self.counter += 1
+        key_byte = key.encode('ascii')
+        self.txn.put(key_byte, img_byte)
+        # write meta information
+        h, w, c = img_shape
+        self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n')
+        if self.counter % self.batch == 0:
+            self.txn.commit()
+            self.txn = self.env.begin(write=True)
+
+    def close(self):
+        self.txn.commit()
+        self.env.close()
+        self.txt_file.close()
diff --git a/basicsr/utils/logger.py b/basicsr/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..57b06433312ffff6e4a6cd337ccd28832834352a
--- /dev/null
+++ b/basicsr/utils/logger.py
@@ -0,0 +1,167 @@
+import datetime
+import logging
+import time
+
+from .dist_util import get_dist_info, master_only
+
+
+class MessageLogger():
+    """Message logger for printing.
+
+    Args:
+        opt (dict): Config. It contains the following keys:
+            name (str): Exp name.
+            logger (dict): Contains 'print_freq' (str) for logger interval.
+            train (dict): Contains 'total_iter' (int) for total iters.
+            use_tb_logger (bool): Use tensorboard logger.
+        start_iter (int): Start iter. Default: 1.
+        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
+    """
+
+    def __init__(self, opt, start_iter=1, tb_logger=None):
+        self.exp_name = opt['name']
+        self.interval = opt['logger']['print_freq']
+        self.start_iter = start_iter
+        self.max_iters = opt['train']['total_iter']
+        self.use_tb_logger = opt['logger']['use_tb_logger']
+        self.tb_logger = tb_logger
+        self.start_time = time.time()
+        self.logger = get_root_logger()
+
+    @master_only
+    def __call__(self, log_vars):
+        """Format logging message.
+
+        Args:
+            log_vars (dict): It contains the following keys:
+                epoch (int): Epoch number.
+                iter (int): Current iter.
+                lrs (list): List for learning rates.
+
+                time (float): Iter time.
+                data_time (float): Data time for each iter.
+        """
+        # epoch, iter, learning rates
+        epoch = log_vars.pop('epoch')
+        current_iter = log_vars.pop('iter')
+        lrs = log_vars.pop('lrs')
+
+        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, ' f'iter:{current_iter:8,d}, lr:(')
+        for v in lrs:
+            message += f'{v:.3e},'
+        message += ')] '
+
+        # time and estimated time
+        if 'time' in log_vars.keys():
+            iter_time = log_vars.pop('time')
+            data_time = log_vars.pop('data_time')
+
+            total_time = time.time() - self.start_time
+            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
+            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
+            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
+            message += f'[eta: {eta_str}, '
+            message += f'time (data): {iter_time:.3f} ({data_time:.3f})] '
+
+        # other items, especially losses
+        for k, v in log_vars.items():
+            message += f'{k}: {v:.4e} '
+            # tensorboard logger
+            if self.use_tb_logger and 'debug' not in self.exp_name:
+                if k.startswith('l_'):
+                    self.tb_logger.add_scalar(f'losses/{k}', v, current_iter)
+                else:
+                    self.tb_logger.add_scalar(k, v, current_iter)
+        self.logger.info(message)
+
+
+@master_only
+def init_tb_logger(log_dir):
+    from torch.utils.tensorboard import SummaryWriter
+    tb_logger = SummaryWriter(log_dir=log_dir)
+    return tb_logger
+
+
+@master_only
+def init_wandb_logger(opt):
+    """We now only use wandb to sync tensorboard log."""
+    import wandb
+    logger = logging.getLogger('basicsr')
+
+    project = opt['logger']['wandb']['project']
+    resume_id = opt['logger']['wandb'].get('resume_id')
+    if resume_id:
+        wandb_id = resume_id
+        resume = 'allow'
+        logger.warning(f'Resume wandb logger with id={wandb_id}.')
+    else:
+        wandb_id = wandb.util.generate_id()
+        resume = 'never'
+
+    wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)
+
+    logger.info(f'Use wandb logger with id={wandb_id}; project={project}.')
+
+
+def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
+    """Get the root logger.
+
+    The logger will be initialized if it has not been initialized. By default a
+    StreamHandler will be added. If `log_file` is specified, a FileHandler will
+    also be added.
+
+    Args:
+        logger_name (str): root logger name. Default: 'basicsr'.
+        log_file (str | None): The log filename. If specified, a FileHandler
+            will be added to the root logger.
+        log_level (int): The root logger level. Note that only the process of
+            rank 0 is affected, while other processes will set the level to
+            "Error" and be silent most of the time.
+
+    Returns:
+        logging.Logger: The root logger.
+    """
+    logger = logging.getLogger(logger_name)
+    # if the logger has been initialized, just return it
+    if logger.hasHandlers():
+        return logger
+
+    format_str = '%(asctime)s %(levelname)s: %(message)s'
+    logging.basicConfig(format=format_str, level=log_level)
+    rank, _ = get_dist_info()
+    if rank != 0:
+        logger.setLevel('ERROR')
+    elif log_file is not None:
+        file_handler = logging.FileHandler(log_file, 'w')
+        file_handler.setFormatter(logging.Formatter(format_str))
+        file_handler.setLevel(log_level)
+        logger.addHandler(file_handler)
+
+    return logger
+
+
+def get_env_info():
+    """Get environment information.
+
+    Currently, only log the software version.
+    """
+    import torch
+    import torchvision
+
+    #from basicsr.version import __version__
+    msg = r"""
+                ____                _       _____  ____
+               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
+              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
+             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
+            /_____/ \__,_//____//_/ \___//____//_/ |_|
+     ______                   __   __                 __      __
+    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
+   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
+  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
+  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
+    """
+    msg += ('\nVersion Information: '
+            f'\n\tPyTorch: {torch.__version__}'
+            f'\n\tTorchVision: {torchvision.__version__}')
+    return msg
diff --git a/basicsr/utils/matlab_functions.py b/basicsr/utils/matlab_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6ce1004a2c9f8521505c4b5889d3c24a909c70d
--- /dev/null
+++ b/basicsr/utils/matlab_functions.py
@@ -0,0 +1,347 @@
+import math
+import numpy as np
+import torch
+
+
+def cubic(x):
+    """cubic function used for calculate_weights_indices."""
+    absx = torch.abs(x)
+    absx2 = absx**2
+    absx3 = absx**3
+    return (1.5 * absx3 - 2.5 * absx2 + 1) * (
+        (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) *
+                                                                                     (absx <= 2)).type_as(absx))
+
+
+def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+    """Calculate weights and indices, used for imresize function.
+
+    Args:
+        in_length (int): Input length.
+        out_length (int): Output length.
+        scale (float): Scale factor.
+        kernel_width (int): Kernel width.
+        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
+    """
+
+    if (scale < 1) and antialiasing:
+        # Use a modified kernel (larger kernel width) to simultaneously
+        # interpolate and antialias
+        kernel_width = kernel_width / scale
+
+    # Output-space coordinates
+    x = torch.linspace(1, out_length, out_length)
+
+    # Input-space coordinates. Calculate the inverse mapping such that 0.5
+    # in output space maps to 0.5 in input space, and 0.5 + scale in output
+    # space maps to 1.5 in input space.
+    u = x / scale + 0.5 * (1 - 1 / scale)
+
+    # What is the left-most pixel that can be involved in the computation?
+    left = torch.floor(u - kernel_width / 2)
+
+    # What is the maximum number of pixels that can be involved in the
+    # computation?  Note: it's OK to use an extra pixel here; if the
+    # corresponding weights are all zero, it will be eliminated at the end
+    # of this function.
+    p = math.ceil(kernel_width) + 2
+
+    # The indices of the input pixels involved in computing the k-th output
+    # pixel are in row k of the indices matrix.
+    indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand(
+        out_length, p)
+
+    # The weights used to compute the k-th output pixel are in row k of the
+    # weights matrix.
+    distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices
+
+    # apply cubic kernel
+    if (scale < 1) and antialiasing:
+        weights = scale * cubic(distance_to_center * scale)
+    else:
+        weights = cubic(distance_to_center)
+
+    # Normalize the weights matrix so that each row sums to 1.
+    weights_sum = torch.sum(weights, 1).view(out_length, 1)
+    weights = weights / weights_sum.expand(out_length, p)
+
+    # If a column in weights is all zero, get rid of it. only consider the
+    # first and last column.
+    weights_zero_tmp = torch.sum((weights == 0), 0)
+    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
+        indices = indices.narrow(1, 1, p - 2)
+        weights = weights.narrow(1, 1, p - 2)
+    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
+        indices = indices.narrow(1, 0, p - 2)
+        weights = weights.narrow(1, 0, p - 2)
+    weights = weights.contiguous()
+    indices = indices.contiguous()
+    sym_len_s = -indices.min() + 1
+    sym_len_e = indices.max() - in_length
+    indices = indices + sym_len_s - 1
+    return weights, indices, int(sym_len_s), int(sym_len_e)
+
+
+@torch.no_grad()
+def imresize(img, scale, antialiasing=True):
+    """imresize function same as MATLAB.
+
+    It now only supports bicubic.
+    The same scale applies for both height and width.
+
+    Args:
+        img (Tensor | Numpy array):
+            Tensor: Input image with shape (c, h, w), [0, 1] range.
+            Numpy: Input image with shape (h, w, c), [0, 1] range.
+        scale (float): Scale factor. The same scale applies for both height
+            and width.
+        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
+            Default: True.
+
+    Returns:
+        Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round.
+    """
+    if type(img).__module__ == np.__name__:  # numpy type
+        numpy_type = True
+        img = torch.from_numpy(img.transpose(2, 0, 1)).float()
+    else:
+        numpy_type = False
+
+    in_c, in_h, in_w = img.size()
+    out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale)
+    kernel_width = 4
+    kernel = 'cubic'
+
+    # get weights and indices
+    weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width,
+                                                                             antialiasing)
+    weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width,
+                                                                             antialiasing)
+    # process H dimension
+    # symmetric copying
+    img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
+    img_aug.narrow(1, sym_len_hs, in_h).copy_(img)
+
+    sym_patch = img[:, :sym_len_hs, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)
+
+    sym_patch = img[:, -sym_len_he:, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)
+
+    out_1 = torch.FloatTensor(in_c, out_h, in_w)
+    kernel_width = weights_h.size(1)
+    for i in range(out_h):
+        idx = int(indices_h[i][0])
+        for j in range(in_c):
+            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i])
+
+    # process W dimension
+    # symmetric copying
+    out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
+    out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)
+
+    sym_patch = out_1[:, :, :sym_len_ws]
+    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(2, inv_idx)
+    out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)
+
+    sym_patch = out_1[:, :, -sym_len_we:]
+    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(2, inv_idx)
+    out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)
+
+    out_2 = torch.FloatTensor(in_c, out_h, out_w)
+    kernel_width = weights_w.size(1)
+    for i in range(out_w):
+        idx = int(indices_w[i][0])
+        for j in range(in_c):
+            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i])
+
+    if numpy_type:
+        out_2 = out_2.numpy().transpose(1, 2, 0)
+    return out_2
+
+
+def rgb2ycbcr(img, y_only=False):
+    """Convert a RGB image to YCbCr image.
+
+    This function produces the same results as Matlab's `rgb2ycbcr` function.
+    It implements the ITU-R BT.601 conversion for standard-definition
+    television. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
+
+    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
+    In OpenCV, it implements a JPEG conversion. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
+
+    Args:
+        img (ndarray): The input image. It accepts:
+            1. np.uint8 type with range [0, 255];
+            2. np.float32 type with range [0, 1].
+        y_only (bool): Whether to only return Y channel. Default: False.
+
+    Returns:
+        ndarray: The converted YCbCr image. The output image has the same type
+            and range as input image.
+    """
+    img_type = img.dtype
+    img = _convert_input_type_range(img)
+    if y_only:
+        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
+    else:
+        out_img = np.matmul(
+            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128]
+    out_img = _convert_output_type_range(out_img, img_type)
+    return out_img
+
+
+def bgr2ycbcr(img, y_only=False):
+    """Convert a BGR image to YCbCr image.
+
+    The bgr version of rgb2ycbcr.
+    It implements the ITU-R BT.601 conversion for standard-definition
+    television. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
+
+    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
+    In OpenCV, it implements a JPEG conversion. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
+
+    Args:
+        img (ndarray): The input image. It accepts:
+            1. np.uint8 type with range [0, 255];
+            2. np.float32 type with range [0, 1].
+        y_only (bool): Whether to only return Y channel. Default: False.
+
+    Returns:
+        ndarray: The converted YCbCr image. The output image has the same type
+            and range as input image.
+    """
+    img_type = img.dtype
+    img = _convert_input_type_range(img)
+    if y_only:
+        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
+    else:
+        out_img = np.matmul(
+            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
+    out_img = _convert_output_type_range(out_img, img_type)
+    return out_img
+
+
+def ycbcr2rgb(img):
+    """Convert a YCbCr image to RGB image.
+
+    This function produces the same results as Matlab's ycbcr2rgb function.
+    It implements the ITU-R BT.601 conversion for standard-definition
+    television. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
+
+    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
+    In OpenCV, it implements a JPEG conversion. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
+
+    Args:
+        img (ndarray): The input image. It accepts:
+            1. np.uint8 type with range [0, 255];
+            2. np.float32 type with range [0, 1].
+
+    Returns:
+        ndarray: The converted RGB image. The output image has the same type
+            and range as input image.
+    """
+    img_type = img.dtype
+    img = _convert_input_type_range(img) * 255
+    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
+                              [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]  # noqa: E126
+    out_img = _convert_output_type_range(out_img, img_type)
+    return out_img
+
+
+def ycbcr2bgr(img):
+    """Convert a YCbCr image to BGR image.
+
+    The bgr version of ycbcr2rgb.
+    It implements the ITU-R BT.601 conversion for standard-definition
+    television. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
+
+    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
+    In OpenCV, it implements a JPEG conversion. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
+
+    Args:
+        img (ndarray): The input image. It accepts:
+            1. np.uint8 type with range [0, 255];
+            2. np.float32 type with range [0, 1].
+
+    Returns:
+        ndarray: The converted BGR image. The output image has the same type
+            and range as input image.
+    """
+    img_type = img.dtype
+    img = _convert_input_type_range(img) * 255
+    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0],
+                              [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921]  # noqa: E126
+    out_img = _convert_output_type_range(out_img, img_type)
+    return out_img
+
+
+def _convert_input_type_range(img):
+    """Convert the type and range of the input image.
+
+    It converts the input image to np.float32 type and range of [0, 1].
+    It is mainly used for pre-processing the input image in colorspace
+    convertion functions such as rgb2ycbcr and ycbcr2rgb.
+
+    Args:
+        img (ndarray): The input image. It accepts:
+            1. np.uint8 type with range [0, 255];
+            2. np.float32 type with range [0, 1].
+
+    Returns:
+        (ndarray): The converted image with type of np.float32 and range of
+            [0, 1].
+    """
+    img_type = img.dtype
+    img = img.astype(np.float32)
+    if img_type == np.float32:
+        pass
+    elif img_type == np.uint8:
+        img /= 255.
+    else:
+        raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
+    return img
+
+
+def _convert_output_type_range(img, dst_type):
+    """Convert the type and range of the image according to dst_type.
+
+    It converts the image to desired type and range. If `dst_type` is np.uint8,
+    images will be converted to np.uint8 type with range [0, 255]. If
+    `dst_type` is np.float32, it converts the image to np.float32 type with
+    range [0, 1].
+    It is mainly used for post-processing images in colorspace convertion
+    functions such as rgb2ycbcr and ycbcr2rgb.
+
+    Args:
+        img (ndarray): The image to be converted with np.float32 type and
+            range [0, 255].
+        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
+            converts the image to np.uint8 type with range [0, 255]. If
+            dst_type is np.float32, it converts the image to np.float32 type
+            with range [0, 1].
+
+    Returns:
+        (ndarray): The converted image with desired type and range.
+    """
+    if dst_type not in (np.uint8, np.float32):
+        raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
+    if dst_type == np.uint8:
+        img = img.round()
+    else:
+        img /= 255.
+    return img.astype(dst_type)
diff --git a/basicsr/utils/misc.py b/basicsr/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..56ee2086be7fa9d68f8e589564e02816f6c72251
--- /dev/null
+++ b/basicsr/utils/misc.py
@@ -0,0 +1,136 @@
+import numpy as np
+import os
+import random
+import time
+import torch
+from os import path as osp
+
+from .dist_util import master_only
+from .logger import get_root_logger
+
+
+def set_random_seed(seed):
+    """Set random seeds."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def get_time_str():
+    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
+
+
+def mkdir_and_rename(path):
+    """mkdirs. If path exists, rename it with timestamp and create a new one.
+
+    Args:
+        path (str): Folder path.
+    """
+    if osp.exists(path):
+        new_name = path + '_archived_' + get_time_str()
+        print(f'Path already exists. Rename it to {new_name}', flush=True)
+        os.rename(path, new_name)
+    os.makedirs(path, exist_ok=True)
+
+
+@master_only
+def make_exp_dirs(opt):
+    """Make dirs for experiments."""
+    path_opt = opt['path'].copy()
+    if opt['is_train']:
+        mkdir_and_rename(path_opt.pop('experiments_root'))
+    else:
+        mkdir_and_rename(path_opt.pop('results_root'))
+    for key, path in path_opt.items():
+        if ('strict_load' not in key) and ('pretrain_network' not in key) and ('resume' not in key):
+            os.makedirs(path, exist_ok=True)
+
+
+def scandir(dir_path, suffix=None, recursive=False, full_path=False):
+    """Scan a directory to find the interested files.
+
+    Args:
+        dir_path (str): Path of the directory.
+        suffix (str | tuple(str), optional): File suffix that we are
+            interested in. Default: None.
+        recursive (bool, optional): If set to True, recursively scan the
+            directory. Default: False.
+        full_path (bool, optional): If set to True, include the dir_path.
+            Default: False.
+
+    Returns:
+        A generator for all the interested files with relative pathes.
+    """
+
+    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+        raise TypeError('"suffix" must be a string or tuple of strings')
+
+    root = dir_path
+
+    def _scandir(dir_path, suffix, recursive):
+        import os
+        print('hey...', os.getcwd())
+        for entry in os.scandir(dir_path):
+            if not entry.name.startswith('.') and entry.is_file():
+                if full_path:
+                    return_path = entry.path
+                else:
+                    return_path = osp.relpath(entry.path, root)
+
+                if suffix is None:
+                    yield return_path
+                elif return_path.endswith(suffix):
+                    yield return_path
+            else:
+                if recursive:
+                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
+                else:
+                    continue
+
+    return _scandir(dir_path, suffix=suffix, recursive=recursive)
+
+
+def check_resume(opt, resume_iter):
+    """Check resume states and pretrain_network paths.
+
+    Args:
+        opt (dict): Options.
+        resume_iter (int): Resume iteration.
+    """
+    logger = get_root_logger()
+    if opt['path']['resume_state']:
+        # get all the networks
+        networks = [key for key in opt.keys() if key.startswith('network_')]
+        flag_pretrain = False
+        for network in networks:
+            if opt['path'].get(f'pretrain_{network}') is not None:
+                flag_pretrain = True
+        if flag_pretrain:
+            logger.warning('pretrain_network path will be ignored during resuming.')
+        # set pretrained model paths
+        for network in networks:
+            name = f'pretrain_{network}'
+            basename = network.replace('network_', '')
+            if opt['path'].get('ignore_resume_networks') is None or (basename
+                                                                     not in opt['path']['ignore_resume_networks']):
+                opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth')
+                logger.info(f"Set {name} to {opt['path'][name]}")
+
+
+def sizeof_fmt(size, suffix='B'):
+    """Get human readable file size.
+
+    Args:
+        size (int): File size.
+        suffix (str): Suffix. Default: 'B'.
+
+    Return:
+        str: Formated file siz.
+    """
+    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
+        if abs(size) < 1024.0:
+            return f'{size:3.1f} {unit}{suffix}'
+        size /= 1024.0
+    return f'{size:3.1f} Y{suffix}'
diff --git a/basicsr/utils/options.py b/basicsr/utils/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ade7af32088f62a69ed20daefe2bf2b350220b4
--- /dev/null
+++ b/basicsr/utils/options.py
@@ -0,0 +1,105 @@
+import yaml
+from collections import OrderedDict
+from os import path as osp
+
+
+def ordered_yaml():
+    """Support OrderedDict for yaml.
+
+    Returns:
+        yaml Loader and Dumper.
+    """
+    try:
+        from yaml import CDumper as Dumper
+        from yaml import CLoader as Loader
+    except ImportError:
+        from yaml import Dumper, Loader
+
+    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
+
+    def dict_representer(dumper, data):
+        return dumper.represent_dict(data.items())
+
+    def dict_constructor(loader, node):
+        return OrderedDict(loader.construct_pairs(node))
+
+    Dumper.add_representer(OrderedDict, dict_representer)
+    Loader.add_constructor(_mapping_tag, dict_constructor)
+    return Loader, Dumper
+
+
+def parse(opt_path, root_path, is_train=True):
+    """Parse option file.
+
+    Args:
+        opt_path (str): Option file path.
+        is_train (str): Indicate whether in training or not. Default: True.
+
+    Returns:
+        (dict): Options.
+    """
+    with open(opt_path, mode='r') as f:
+        Loader, _ = ordered_yaml()
+        opt = yaml.load(f, Loader=Loader)
+
+    opt['is_train'] = is_train
+
+    # datasets
+    for phase, dataset in opt['datasets'].items():
+        # for several datasets, e.g., test_1, test_2
+        phase = phase.split('_')[0]
+        dataset['phase'] = phase
+        if 'scale' in opt:
+            dataset['scale'] = opt['scale']
+        if dataset.get('dataroot_gt') is not None:
+            dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
+        if dataset.get('dataroot_lq') is not None:
+            dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])
+
+    # paths
+    for key, val in opt['path'].items():
+        if (val is not None) and ('resume_state' in key or 'pretrain_network' in key):
+            opt['path'][key] = osp.expanduser(val)
+
+    if is_train:
+        experiments_root = osp.join(root_path, 'experiments', opt['name'])
+        opt['path']['experiments_root'] = experiments_root
+        opt['path']['models'] = osp.join(experiments_root, 'models')
+        opt['path']['training_states'] = osp.join(experiments_root, 'training_states')
+        opt['path']['log'] = experiments_root
+        opt['path']['visualization'] = osp.join(experiments_root, 'visualization')
+
+        # change some options for debug mode
+        if 'debug' in opt['name']:
+            if 'val' in opt:
+                opt['val']['val_freq'] = 8
+            opt['logger']['print_freq'] = 1
+            opt['logger']['save_checkpoint_freq'] = 8
+    else:  # test
+        results_root = osp.join(root_path, 'results', opt['name'])
+        opt['path']['results_root'] = results_root
+        opt['path']['log'] = results_root
+        opt['path']['visualization'] = osp.join(results_root, 'visualization')
+
+    return opt
+
+
+def dict2str(opt, indent_level=1):
+    """dict to string for printing options.
+
+    Args:
+        opt (dict): Option dict.
+        indent_level (int): Indent level. Default: 1.
+
+    Return:
+        (str): Option string for printing.
+    """
+    msg = '\n'
+    for k, v in opt.items():
+        if isinstance(v, dict):
+            msg += ' ' * (indent_level * 2) + k + ':['
+            msg += dict2str(v, indent_level + 1)
+            msg += ' ' * (indent_level * 2) + ']\n'
+        else:
+            msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
+    return msg
diff --git a/basicsr/utils/registry.py b/basicsr/utils/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..655753b3b9cbd0cfe73fe93a77cf1fcc3db6d827
--- /dev/null
+++ b/basicsr/utils/registry.py
@@ -0,0 +1,82 @@
+# Modified from: https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/registry.py  # noqa: E501
+
+
+class Registry():
+    """
+    The registry that provides name -> object mapping, to support third-party
+    users' custom modules.
+
+    To create a registry (e.g. a backbone registry):
+
+    .. code-block:: python
+
+        BACKBONE_REGISTRY = Registry('BACKBONE')
+
+    To register an object:
+
+    .. code-block:: python
+
+        @BACKBONE_REGISTRY.register()
+        class MyBackbone():
+            ...
+
+    Or:
+
+    .. code-block:: python
+
+        BACKBONE_REGISTRY.register(MyBackbone)
+    """
+
+    def __init__(self, name):
+        """
+        Args:
+            name (str): the name of this registry
+        """
+        self._name = name
+        self._obj_map = {}
+
+    def _do_register(self, name, obj):
+        assert (name not in self._obj_map), (f"An object named '{name}' was already registered "
+                                             f"in '{self._name}' registry!")
+        self._obj_map[name] = obj
+
+    def register(self, obj=None):
+        """
+        Register the given object under the the name `obj.__name__`.
+        Can be used as either a decorator or not.
+        See docstring of this class for usage.
+        """
+        if obj is None:
+            # used as a decorator
+            def deco(func_or_class):
+                name = func_or_class.__name__
+                self._do_register(name, func_or_class)
+                return func_or_class
+
+            return deco
+
+        # used as a function call
+        name = obj.__name__
+        self._do_register(name, obj)
+
+    def get(self, name):
+        ret = self._obj_map.get(name)
+        if ret is None:
+            raise KeyError(f"No object named '{name}' found in '{self._name}' registry!")
+        return ret
+
+    def __contains__(self, name):
+        return name in self._obj_map
+
+    def __iter__(self):
+        return iter(self._obj_map.items())
+
+    def keys(self):
+        return self._obj_map.keys()
+
+
+DATASET_REGISTRY = Registry('dataset')
+ARCH_REGISTRY = Registry('arch')
+MODEL_REGISTRY = Registry('model')
+LOSS_REGISTRY = Registry('loss')
+METRIC_REGISTRY = Registry('metric')
diff --git a/checkpoints/README.md b/checkpoints/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..757d6b51a3100d81d03d1512b9a3a174adac8806
--- /dev/null
+++ b/checkpoints/README.md
@@ -0,0 +1 @@
+This is the folder for storing Pytorch checkpoints of models.
\ No newline at end of file
diff --git a/checkpoints/esrgan_yunying.pth b/checkpoints/esrgan_yunying.pth
new file mode 100644
index 0000000000000000000000000000000000000000..8ba1419e5bdb46c4797b772a5aeb90e2ff1e4cc0
--- /dev/null
+++ b/checkpoints/esrgan_yunying.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0fe4c6cef4e78a3aeb4ea379dfad753adc4eb44a82313f0775130adaadfdf15
+size 67040989
diff --git a/checkpoints/face_segmentation.pth b/checkpoints/face_segmentation.pth
new file mode 100644
index 0000000000000000000000000000000000000000..ca57f3257ca7715bc340d065764bc249d985c287
--- /dev/null
+++ b/checkpoints/face_segmentation.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:468e13ca13a9b43cc0881a9f99083a430e9c0a38abd935431d1c28ee94b26567
+size 53289463
diff --git a/checkpoints/pretrained.state b/checkpoints/pretrained.state
new file mode 100644
index 0000000000000000000000000000000000000000..9575a40a9f3534189b1649d8279cb9048827a0f8
--- /dev/null
+++ b/checkpoints/pretrained.state
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e8d76d5f8af3ba33332067ed572edb55f3aa868310f6601abf73bfda2e6e208
+size 310688649
diff --git a/checkpoints/wav2lip_gan.pth b/checkpoints/wav2lip_gan.pth
new file mode 100644
index 0000000000000000000000000000000000000000..0b8907735b1aef6adb297f2f6dcbcf8432823de4
--- /dev/null
+++ b/checkpoints/wav2lip_gan.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca9ab7b7b812c0e80a6e70a5977c545a1e8a365a6c49d5e533023c034d7ac3d8
+size 435801865
diff --git a/download_models.py b/download_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..a52e372f9a809bd82e6b641e44ffa3cca044925d
--- /dev/null
+++ b/download_models.py
@@ -0,0 +1,30 @@
+import gdown
+
+######### downloading models ########
+
+urls = {
+    "wav2lip_gan.pth": "10Iu05Modfti3pDbxCFPnofmfVlbkvrCm", 
+    "face_segmentation.pth": "154JgKpzCPW82qINcVieuPH3fZ2e0P812",
+    "esrgan_yunying.pth": "1aB-jqBikcZPJnFrJXWUEpvF2RFCuerSe",
+    "pretrained.state": "1_MGeOLdARWHylC1PCU2p5_FQztD4Bo7B"
+}
+
+for name, id in urls.items():
+    url = f"https://drive.google.com/uc?id={id}"
+    output = f"checkpoints/{name}"
+    gdown.download(url, output, quiet=False)
+    print(f"Loaded {name}")
+
+######### downloading videos ########
+# If you load files from Drive, run this cell
+
+# Paste the filename and Google Drive ID of your video below.
+urls = {
+    "yunying_30s.mp4": "1dggydm07RHrxiFUIH_51RXmkMcD_bMPE",
+}
+
+for name, id in urls.items():
+    url = f"https://drive.google.com/uc?id={id}"
+    output = f"videos/{name}"
+    gdown.download(url, output, quiet=False)
+    print(f"Loaded {name}")
\ No newline at end of file
diff --git a/examples/1_hd.jpg b/examples/1_hd.jpg
new file mode 100644
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diff --git a/examples/1_low.jpg b/examples/1_low.jpg
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diff --git a/examples/kennedy_hd.jpg b/examples/kennedy_hd.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9b782144ce1bc0c02937f4586a8989276be82f08
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diff --git a/examples/kennedy_hd.mkv b/examples/kennedy_hd.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..54b0d01477c056a667168fa3379891c4ed0890b2
--- /dev/null
+++ b/examples/kennedy_hd.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6214a7947520c6726f966bad46b4258bdaa90a8dd31980791f38479e8e3fea59
+size 5976689
diff --git a/examples/kennedy_low.jpg b/examples/kennedy_low.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..97deeeff7cc54a42fc5277e137010debffcbeefd
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diff --git a/examples/kennedy_low.mp4 b/examples/kennedy_low.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..f7d46a74e20d1601f7605995531f8335b717c13a
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diff --git a/examples/mona_hd.jpg b/examples/mona_hd.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa6a410b7201525c58df3826985326737333061e
--- /dev/null
+++ b/examples/mona_hd.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4aa62cb7ab2e8fc62b5dd9a79eed63f4fa5960efce23f21f3bc2431c63201bc
+size 2579624
diff --git a/examples/mona_hd.mkv b/examples/mona_hd.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..28d5eae98433863622fa9bf9266f4bbbddee4887
--- /dev/null
+++ b/examples/mona_hd.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abc1409485f5f887bedec1096af451448a1c88cb1b2c8a27f8fbe6368400e08f
+size 13426056
diff --git a/examples/mona_low.jpg b/examples/mona_low.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dceb3f41097839dced356ce185553c678ea589ca
Binary files /dev/null and b/examples/mona_low.jpg differ
diff --git a/examples/mona_low.mp4 b/examples/mona_low.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..83f1ec7b3e6246f7e6404d7c24fba058995d9873
Binary files /dev/null and b/examples/mona_low.mp4 differ
diff --git a/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth b/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth
new file mode 100644
index 0000000000000000000000000000000000000000..247bf0089f439b0a071dcdf9a3bc9003a759796a
--- /dev/null
+++ b/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:150ff491eae295bc201672cdc613db24cdef58c193ddacfeb906c9be3645192b
+size 66919172
diff --git a/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/readme.md b/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b2c82bbfd645df99063cae52c1aa544326e2a71
--- /dev/null
+++ b/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/readme.md
@@ -0,0 +1,5 @@
+### Downloading weights
+
+| Model        | Description           | Download Link  |
+| :------------- |:-------------| :-----:|
+| ESRGAN  | Download and store in this directory | [Link](https://drive.google.com/file/d/1Al8lEpnx2K-kDX7zL2DBcAuDnSKXACPb/view?usp=sharing) |
diff --git a/face_detection/README.md b/face_detection/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c073376e4eeda6d4b29cc31c50cb7e88ab42bb73
--- /dev/null
+++ b/face_detection/README.md
@@ -0,0 +1 @@
+The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. 
\ No newline at end of file
diff --git a/face_detection/__init__.py b/face_detection/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bae29fd5f85b41e4669302bd2603bc6924eddc7
--- /dev/null
+++ b/face_detection/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+
+__author__ = """Adrian Bulat"""
+__email__ = 'adrian.bulat@nottingham.ac.uk'
+__version__ = '1.0.1'
+
+from .api import FaceAlignment, LandmarksType, NetworkSize
diff --git a/face_detection/__pycache__/__init__.cpython-36.pyc b/face_detection/__pycache__/__init__.cpython-36.pyc
new file mode 100644
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diff --git a/face_detection/__pycache__/utils.cpython-36.pyc b/face_detection/__pycache__/utils.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1f05378f86f642b4ce19e03919a247c36004fa11
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diff --git a/face_detection/__pycache__/utils.cpython-37.pyc b/face_detection/__pycache__/utils.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4b336df4fec8cd57b17bcc7159108fef4d4be04b
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diff --git a/face_detection/api.py b/face_detection/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb02d5252db5362b9985687a992e128a522e5b63
--- /dev/null
+++ b/face_detection/api.py
@@ -0,0 +1,79 @@
+from __future__ import print_function
+import os
+import torch
+from torch.utils.model_zoo import load_url
+from enum import Enum
+import numpy as np
+import cv2
+try:
+    import urllib.request as request_file
+except BaseException:
+    import urllib as request_file
+
+from .models import FAN, ResNetDepth
+from .utils import *
+
+
+class LandmarksType(Enum):
+    """Enum class defining the type of landmarks to detect.
+
+    ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face
+    ``_2halfD`` - this points represent the projection of the 3D points into 3D
+    ``_3D`` - detect the points ``(x,y,z)``` in a 3D space
+
+    """
+    _2D = 1
+    _2halfD = 2
+    _3D = 3
+
+
+class NetworkSize(Enum):
+    # TINY = 1
+    # SMALL = 2
+    # MEDIUM = 3
+    LARGE = 4
+
+    def __new__(cls, value):
+        member = object.__new__(cls)
+        member._value_ = value
+        return member
+
+    def __int__(self):
+        return self.value
+
+ROOT = os.path.dirname(os.path.abspath(__file__))
+
+class FaceAlignment:
+    def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
+                 device='cuda', flip_input=False, face_detector='sfd', verbose=False):
+        self.device = device
+        self.flip_input = flip_input
+        self.landmarks_type = landmarks_type
+        self.verbose = verbose
+
+        network_size = int(network_size)
+
+        if 'cuda' in device:
+            torch.backends.cudnn.benchmark = True
+
+        # Get the face detector
+        face_detector_module = __import__('face_detection.detection.' + face_detector,
+                                          globals(), locals(), [face_detector], 0)
+        self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
+
+    def get_detections_for_batch(self, images):
+        images = images[..., ::-1]
+        detected_faces = self.face_detector.detect_from_batch(images.copy())
+        results = []
+
+        for i, d in enumerate(detected_faces):
+            if len(d) == 0:
+                results.append(None)
+                continue
+            d = d[0]
+            d = np.clip(d, 0, None)
+            
+            x1, y1, x2, y2 = map(int, d[:-1])
+            results.append((x1, y1, x2, y2))
+
+        return results
\ No newline at end of file
diff --git a/face_detection/detection/__init__.py b/face_detection/detection/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a6b0402dae864a3cc5dc2a90a412fd842a0efc7
--- /dev/null
+++ b/face_detection/detection/__init__.py
@@ -0,0 +1 @@
+from .core import FaceDetector
\ No newline at end of file
diff --git a/face_detection/detection/__pycache__/__init__.cpython-37.pyc b/face_detection/detection/__pycache__/__init__.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2e66ab33c00f03fd861702ade51ed8f4c13447fd
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diff --git a/face_detection/detection/__pycache__/core.cpython-37.pyc b/face_detection/detection/__pycache__/core.cpython-37.pyc
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index 0000000000000000000000000000000000000000..d7e1dd24567451123129713fc68ad9b190f4fa74
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diff --git a/face_detection/detection/core.py b/face_detection/detection/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f8275e8e53143f66298f75f0517c234a68778cd
--- /dev/null
+++ b/face_detection/detection/core.py
@@ -0,0 +1,130 @@
+import logging
+import glob
+from tqdm import tqdm
+import numpy as np
+import torch
+import cv2
+
+
+class FaceDetector(object):
+    """An abstract class representing a face detector.
+
+    Any other face detection implementation must subclass it. All subclasses
+    must implement ``detect_from_image``, that return a list of detected
+    bounding boxes. Optionally, for speed considerations detect from path is
+    recommended.
+    """
+
+    def __init__(self, device, verbose):
+        self.device = device
+        self.verbose = verbose
+
+        if verbose:
+            if 'cpu' in device:
+                logger = logging.getLogger(__name__)
+                logger.warning("Detection running on CPU, this may be potentially slow.")
+
+        if 'cpu' not in device and 'cuda' not in device:
+            if verbose:
+                logger.error("Expected values for device are: {cpu, cuda} but got: %s", device)
+            raise ValueError
+
+    def detect_from_image(self, tensor_or_path):
+        """Detects faces in a given image.
+
+        This function detects the faces present in a provided BGR(usually)
+        image. The input can be either the image itself or the path to it.
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path
+            to an image or the image itself.
+
+        Example::
+
+            >>> path_to_image = 'data/image_01.jpg'
+            ...   detected_faces = detect_from_image(path_to_image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+            >>> image = cv2.imread(path_to_image)
+            ...   detected_faces = detect_from_image(image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+
+        """
+        raise NotImplementedError
+
+    def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True):
+        """Detects faces from all the images present in a given directory.
+
+        Arguments:
+            path {string} -- a string containing a path that points to the folder containing the images
+
+        Keyword Arguments:
+            extensions {list} -- list of string containing the extensions to be
+            consider in the following format: ``.extension_name`` (default:
+            {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the
+            folder recursively (default: {False}) show_progress_bar {bool} --
+            display a progressbar (default: {True})
+
+        Example:
+        >>> directory = 'data'
+        ...   detected_faces = detect_from_directory(directory)
+        {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]}
+
+        """
+        if self.verbose:
+            logger = logging.getLogger(__name__)
+
+        if len(extensions) == 0:
+            if self.verbose:
+                logger.error("Expected at list one extension, but none was received.")
+            raise ValueError
+
+        if self.verbose:
+            logger.info("Constructing the list of images.")
+        additional_pattern = '/**/*' if recursive else '/*'
+        files = []
+        for extension in extensions:
+            files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive))
+
+        if self.verbose:
+            logger.info("Finished searching for images. %s images found", len(files))
+            logger.info("Preparing to run the detection.")
+
+        predictions = {}
+        for image_path in tqdm(files, disable=not show_progress_bar):
+            if self.verbose:
+                logger.info("Running the face detector on image: %s", image_path)
+            predictions[image_path] = self.detect_from_image(image_path)
+
+        if self.verbose:
+            logger.info("The detector was successfully run on all %s images", len(files))
+
+        return predictions
+
+    @property
+    def reference_scale(self):
+        raise NotImplementedError
+
+    @property
+    def reference_x_shift(self):
+        raise NotImplementedError
+
+    @property
+    def reference_y_shift(self):
+        raise NotImplementedError
+
+    @staticmethod
+    def tensor_or_path_to_ndarray(tensor_or_path, rgb=True):
+        """Convert path (represented as a string) or torch.tensor to a numpy.ndarray
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself
+        """
+        if isinstance(tensor_or_path, str):
+            return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1]
+        elif torch.is_tensor(tensor_or_path):
+            # Call cpu in case its coming from cuda
+            return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy()
+        elif isinstance(tensor_or_path, np.ndarray):
+            return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path
+        else:
+            raise TypeError
diff --git a/face_detection/detection/sfd/__init__.py b/face_detection/detection/sfd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a63ecd45658f22e66c171ada751fb33764d4559
--- /dev/null
+++ b/face_detection/detection/sfd/__init__.py
@@ -0,0 +1 @@
+from .sfd_detector import SFDDetector as FaceDetector
\ No newline at end of file
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diff --git a/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-37.pyc b/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..526f3ba7d6c8f9dacbde71f601bee4735fcdaf71
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diff --git a/face_detection/detection/sfd/bbox.py b/face_detection/detection/sfd/bbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bd7222e5e5f78a51944cbeed3cccbacddc46bed
--- /dev/null
+++ b/face_detection/detection/sfd/bbox.py
@@ -0,0 +1,129 @@
+from __future__ import print_function
+import os
+import sys
+import cv2
+import random
+import datetime
+import time
+import math
+import argparse
+import numpy as np
+import torch
+
+try:
+    from iou import IOU
+except BaseException:
+    # IOU cython speedup 10x
+    def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
+        sa = abs((ax2 - ax1) * (ay2 - ay1))
+        sb = abs((bx2 - bx1) * (by2 - by1))
+        x1, y1 = max(ax1, bx1), max(ay1, by1)
+        x2, y2 = min(ax2, bx2), min(ay2, by2)
+        w = x2 - x1
+        h = y2 - y1
+        if w < 0 or h < 0:
+            return 0.0
+        else:
+            return 1.0 * w * h / (sa + sb - w * h)
+
+
+def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
+    xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
+    dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
+    dw, dh = math.log(ww / aww), math.log(hh / ahh)
+    return dx, dy, dw, dh
+
+
+def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
+    xc, yc = dx * aww + axc, dy * ahh + ayc
+    ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
+    x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
+    return x1, y1, x2, y2
+
+
+def nms(dets, thresh):
+    if 0 == len(dets):
+        return []
+    x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
+        xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
+
+        w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
+        ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def batch_decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2)
+    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+    boxes[:, :, 2:] += boxes[:, :, :2]
+    return boxes
diff --git a/face_detection/detection/sfd/detect.py b/face_detection/detection/sfd/detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e1cd814603a3d722c2f544d611850f92d4827b5
--- /dev/null
+++ b/face_detection/detection/sfd/detect.py
@@ -0,0 +1,112 @@
+import torch
+import torch.nn.functional as F
+
+import os
+import sys
+import cv2
+import random
+import datetime
+import math
+import argparse
+import numpy as np
+
+import scipy.io as sio
+import zipfile
+from .net_s3fd import s3fd
+from .bbox import *
+
+
+def detect(net, img, device):
+    img = img - np.array([104, 117, 123])
+    img = img.transpose(2, 0, 1)
+    img = img.reshape((1,) + img.shape)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    img = torch.from_numpy(img.copy()).to(device, dtype=torch.float32)
+    BB, CC, HH, WW = img.size()
+    with torch.no_grad():
+        olist = net(img)
+
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    olist = [oelem.data.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[0, 1, hindex, windex]
+            loc = oreg[0, :, hindex, windex].contiguous().view(1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
+            variances = [0.1, 0.2]
+            box = decode(loc, priors, variances)
+            x1, y1, x2, y2 = box[0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append([x1, y1, x2, y2, score])
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, 5))
+
+    return bboxlist
+
+def batch_detect(net, imgs, device):
+    imgs = imgs - np.array([104, 117, 123])
+    imgs = imgs.transpose(0, 3, 1, 2)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    imgs = torch.from_numpy(imgs.copy()).to(device, dtype=torch.float32)
+    BB, CC, HH, WW = imgs.size()
+    with torch.no_grad():
+        olist = net(imgs)
+
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    olist = [oelem.data.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[:, 1, hindex, windex]
+            loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4)
+            variances = [0.1, 0.2]
+            box = batch_decode(loc, priors, variances)
+            box = box[:, 0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy())
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, BB, 5))
+
+    return bboxlist
+
+def flip_detect(net, img, device):
+    img = cv2.flip(img, 1)
+    b = detect(net, img, device)
+
+    bboxlist = np.zeros(b.shape)
+    bboxlist[:, 0] = img.shape[1] - b[:, 2]
+    bboxlist[:, 1] = b[:, 1]
+    bboxlist[:, 2] = img.shape[1] - b[:, 0]
+    bboxlist[:, 3] = b[:, 3]
+    bboxlist[:, 4] = b[:, 4]
+    return bboxlist
+
+
+def pts_to_bb(pts):
+    min_x, min_y = np.min(pts, axis=0)
+    max_x, max_y = np.max(pts, axis=0)
+    return np.array([min_x, min_y, max_x, max_y])
diff --git a/face_detection/detection/sfd/net_s3fd.py b/face_detection/detection/sfd/net_s3fd.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc64313c277ab594d0257585c70f147606693452
--- /dev/null
+++ b/face_detection/detection/sfd/net_s3fd.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class L2Norm(nn.Module):
+    def __init__(self, n_channels, scale=1.0):
+        super(L2Norm, self).__init__()
+        self.n_channels = n_channels
+        self.scale = scale
+        self.eps = 1e-10
+        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
+        self.weight.data *= 0.0
+        self.weight.data += self.scale
+
+    def forward(self, x):
+        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
+        x = x / norm * self.weight.view(1, -1, 1, 1)
+        return x
+
+
+class s3fd(nn.Module):
+    def __init__(self):
+        super(s3fd, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
+        self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
+
+        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
+        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
+
+        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
+        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        self.conv3_3_norm = L2Norm(256, scale=10)
+        self.conv4_3_norm = L2Norm(512, scale=8)
+        self.conv5_3_norm = L2Norm(512, scale=5)
+
+        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
+        self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        h = F.relu(self.conv1_1(x))
+        h = F.relu(self.conv1_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        f3_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        f4_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv5_1(h))
+        h = F.relu(self.conv5_2(h))
+        h = F.relu(self.conv5_3(h))
+        f5_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.fc6(h))
+        h = F.relu(self.fc7(h))
+        ffc7 = h
+        h = F.relu(self.conv6_1(h))
+        h = F.relu(self.conv6_2(h))
+        f6_2 = h
+        h = F.relu(self.conv7_1(h))
+        h = F.relu(self.conv7_2(h))
+        f7_2 = h
+
+        f3_3 = self.conv3_3_norm(f3_3)
+        f4_3 = self.conv4_3_norm(f4_3)
+        f5_3 = self.conv5_3_norm(f5_3)
+
+        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
+        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
+        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
+        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
+        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
+        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
+        cls4 = self.fc7_mbox_conf(ffc7)
+        reg4 = self.fc7_mbox_loc(ffc7)
+        cls5 = self.conv6_2_mbox_conf(f6_2)
+        reg5 = self.conv6_2_mbox_loc(f6_2)
+        cls6 = self.conv7_2_mbox_conf(f7_2)
+        reg6 = self.conv7_2_mbox_loc(f7_2)
+
+        # max-out background label
+        chunk = torch.chunk(cls1, 4, 1)
+        bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
+        cls1 = torch.cat([bmax, chunk[3]], dim=1)
+
+        return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
diff --git a/face_detection/detection/sfd/readme.md b/face_detection/detection/sfd/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..2527ef144cc96ea4e30cd65b00dab68c9e047ced
--- /dev/null
+++ b/face_detection/detection/sfd/readme.md
@@ -0,0 +1,5 @@
+### Downloading weights
+
+| Model        | Description           | Download Link  |
+| :------------- |:-------------| :-----:|
+| Face_Detection    | Download and store in this directory | [Link](https://drive.google.com/file/d/1uNLYCPFFmO-og3WSHyFytJQLLYOwH5uY/view?usp=sharing) |
\ No newline at end of file
diff --git a/face_detection/detection/sfd/s3fd.pth b/face_detection/detection/sfd/s3fd.pth
new file mode 100644
index 0000000000000000000000000000000000000000..895538e7fb6df3ad6e0e80d6d48b3c4e60cd9e6c
--- /dev/null
+++ b/face_detection/detection/sfd/s3fd.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:619a31681264d3f7f7fc7a16a42cbbe8b23f31a256f75a366e5a1bcd59b33543
+size 89843225
diff --git a/face_detection/detection/sfd/sfd_detector.py b/face_detection/detection/sfd/sfd_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbce15253251d403754ab4348f93ae85a6ba2fb
--- /dev/null
+++ b/face_detection/detection/sfd/sfd_detector.py
@@ -0,0 +1,59 @@
+import os
+import cv2
+from torch.utils.model_zoo import load_url
+
+from ..core import FaceDetector
+
+from .net_s3fd import s3fd
+from .bbox import *
+from .detect import *
+
+models_urls = {
+    's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',
+}
+
+
+class SFDDetector(FaceDetector):
+    def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False):
+        super(SFDDetector, self).__init__(device, verbose)
+
+        # Initialise the face detector
+        if not os.path.isfile(path_to_detector):
+            model_weights = load_url(models_urls['s3fd'])
+        else:
+            model_weights = torch.load(path_to_detector)
+
+        self.face_detector = s3fd()
+        self.face_detector.load_state_dict(model_weights)
+        self.face_detector.to(device)
+        self.face_detector.eval()
+
+    def detect_from_image(self, tensor_or_path):
+        image = self.tensor_or_path_to_ndarray(tensor_or_path)
+
+        bboxlist = detect(self.face_detector, image, device=self.device)
+        keep = nms(bboxlist, 0.3)
+        bboxlist = bboxlist[keep, :]
+        bboxlist = [x for x in bboxlist if x[-1] > 0.5]
+
+        return bboxlist
+
+    def detect_from_batch(self, images):
+        bboxlists = batch_detect(self.face_detector, images, device=self.device)
+        keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])]
+        bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)]
+        bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists]
+
+        return bboxlists
+
+    @property
+    def reference_scale(self):
+        return 195
+
+    @property
+    def reference_x_shift(self):
+        return 0
+
+    @property
+    def reference_y_shift(self):
+        return 0
diff --git a/face_detection/models.py b/face_detection/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee2dde32bdf72c25a4600e48efa73ffc0d4a3893
--- /dev/null
+++ b/face_detection/models.py
@@ -0,0 +1,261 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
+                     stride=strd, padding=padding, bias=bias)
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_planes, out_planes):
+        super(ConvBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
+        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
+        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
+        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
+        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
+
+        if in_planes != out_planes:
+            self.downsample = nn.Sequential(
+                nn.BatchNorm2d(in_planes),
+                nn.ReLU(True),
+                nn.Conv2d(in_planes, out_planes,
+                          kernel_size=1, stride=1, bias=False),
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        residual = x
+
+        out1 = self.bn1(x)
+        out1 = F.relu(out1, True)
+        out1 = self.conv1(out1)
+
+        out2 = self.bn2(out1)
+        out2 = F.relu(out2, True)
+        out2 = self.conv2(out2)
+
+        out3 = self.bn3(out2)
+        out3 = F.relu(out3, True)
+        out3 = self.conv3(out3)
+
+        out3 = torch.cat((out1, out2, out3), 1)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        out3 += residual
+
+        return out3
+
+
+class Bottleneck(nn.Module):
+
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class HourGlass(nn.Module):
+    def __init__(self, num_modules, depth, num_features):
+        super(HourGlass, self).__init__()
+        self.num_modules = num_modules
+        self.depth = depth
+        self.features = num_features
+
+        self._generate_network(self.depth)
+
+    def _generate_network(self, level):
+        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features))
+
+        if level > 1:
+            self._generate_network(level - 1)
+        else:
+            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features))
+
+    def _forward(self, level, inp):
+        # Upper branch
+        up1 = inp
+        up1 = self._modules['b1_' + str(level)](up1)
+
+        # Lower branch
+        low1 = F.avg_pool2d(inp, 2, stride=2)
+        low1 = self._modules['b2_' + str(level)](low1)
+
+        if level > 1:
+            low2 = self._forward(level - 1, low1)
+        else:
+            low2 = low1
+            low2 = self._modules['b2_plus_' + str(level)](low2)
+
+        low3 = low2
+        low3 = self._modules['b3_' + str(level)](low3)
+
+        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
+
+        return up1 + up2
+
+    def forward(self, x):
+        return self._forward(self.depth, x)
+
+
+class FAN(nn.Module):
+
+    def __init__(self, num_modules=1):
+        super(FAN, self).__init__()
+        self.num_modules = num_modules
+
+        # Base part
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.conv2 = ConvBlock(64, 128)
+        self.conv3 = ConvBlock(128, 128)
+        self.conv4 = ConvBlock(128, 256)
+
+        # Stacking part
+        for hg_module in range(self.num_modules):
+            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
+            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
+            self.add_module('conv_last' + str(hg_module),
+                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
+            self.add_module('l' + str(hg_module), nn.Conv2d(256,
+                                                            68, kernel_size=1, stride=1, padding=0))
+
+            if hg_module < self.num_modules - 1:
+                self.add_module(
+                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+                self.add_module('al' + str(hg_module), nn.Conv2d(68,
+                                                                 256, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)), True)
+        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        previous = x
+
+        outputs = []
+        for i in range(self.num_modules):
+            hg = self._modules['m' + str(i)](previous)
+
+            ll = hg
+            ll = self._modules['top_m_' + str(i)](ll)
+
+            ll = F.relu(self._modules['bn_end' + str(i)]
+                        (self._modules['conv_last' + str(i)](ll)), True)
+
+            # Predict heatmaps
+            tmp_out = self._modules['l' + str(i)](ll)
+            outputs.append(tmp_out)
+
+            if i < self.num_modules - 1:
+                ll = self._modules['bl' + str(i)](ll)
+                tmp_out_ = self._modules['al' + str(i)](tmp_out)
+                previous = previous + ll + tmp_out_
+
+        return outputs
+
+
+class ResNetDepth(nn.Module):
+
+    def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68):
+        self.inplanes = 64
+        super(ResNetDepth, self).__init__()
+        self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
diff --git a/face_detection/utils.py b/face_detection/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dc4cf3e328efaa227cbcfdd969e1056688adad5
--- /dev/null
+++ b/face_detection/utils.py
@@ -0,0 +1,313 @@
+from __future__ import print_function
+import os
+import sys
+import time
+import torch
+import math
+import numpy as np
+import cv2
+
+
+def _gaussian(
+        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
+        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
+        mean_vert=0.5):
+    # handle some defaults
+    if width is None:
+        width = size
+    if height is None:
+        height = size
+    if sigma_horz is None:
+        sigma_horz = sigma
+    if sigma_vert is None:
+        sigma_vert = sigma
+    center_x = mean_horz * width + 0.5
+    center_y = mean_vert * height + 0.5
+    gauss = np.empty((height, width), dtype=np.float32)
+    # generate kernel
+    for i in range(height):
+        for j in range(width):
+            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
+                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
+    if normalize:
+        gauss = gauss / np.sum(gauss)
+    return gauss
+
+
+def draw_gaussian(image, point, sigma):
+    # Check if the gaussian is inside
+    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
+    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
+    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
+        return image
+    size = 6 * sigma + 1
+    g = _gaussian(size)
+    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
+    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
+    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
+    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
+    assert (g_x[0] > 0 and g_y[1] > 0)
+    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
+          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
+    image[image > 1] = 1
+    return image
+
+
+def transform(point, center, scale, resolution, invert=False):
+    """Generate and affine transformation matrix.
+
+    Given a set of points, a center, a scale and a targer resolution, the
+    function generates and affine transformation matrix. If invert is ``True``
+    it will produce the inverse transformation.
+
+    Arguments:
+        point {torch.tensor} -- the input 2D point
+        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
+        scale {float} -- the scale of the face/object
+        resolution {float} -- the output resolution
+
+    Keyword Arguments:
+        invert {bool} -- define wherever the function should produce the direct or the
+        inverse transformation matrix (default: {False})
+    """
+    _pt = torch.ones(3)
+    _pt[0] = point[0]
+    _pt[1] = point[1]
+
+    h = 200.0 * scale
+    t = torch.eye(3)
+    t[0, 0] = resolution / h
+    t[1, 1] = resolution / h
+    t[0, 2] = resolution * (-center[0] / h + 0.5)
+    t[1, 2] = resolution * (-center[1] / h + 0.5)
+
+    if invert:
+        t = torch.inverse(t)
+
+    new_point = (torch.matmul(t, _pt))[0:2]
+
+    return new_point.int()
+
+
+def crop(image, center, scale, resolution=256.0):
+    """Center crops an image or set of heatmaps
+
+    Arguments:
+        image {numpy.array} -- an rgb image
+        center {numpy.array} -- the center of the object, usually the same as of the bounding box
+        scale {float} -- scale of the face
+
+    Keyword Arguments:
+        resolution {float} -- the size of the output cropped image (default: {256.0})
+
+    Returns:
+        [type] -- [description]
+    """  # Crop around the center point
+    """ Crops the image around the center. Input is expected to be an np.ndarray """
+    ul = transform([1, 1], center, scale, resolution, True)
+    br = transform([resolution, resolution], center, scale, resolution, True)
+    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
+    if image.ndim > 2:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
+                           image.shape[2]], dtype=np.int32)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    else:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    ht = image.shape[0]
+    wd = image.shape[1]
+    newX = np.array(
+        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
+    newY = np.array(
+        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
+    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
+    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
+    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
+           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
+    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
+                        interpolation=cv2.INTER_LINEAR)
+    return newImg
+
+
+def get_preds_fromhm(hm, center=None, scale=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
+    and the scale is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        center {torch.tensor} -- the center of the bounding box (default: {None})
+        scale {float} -- face scale (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if center is not None and scale is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], center, scale, hm.size(2), True)
+
+    return preds, preds_orig
+
+def get_preds_fromhm_batch(hm, centers=None, scales=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the centers
+    and the scales is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        centers {torch.tensor} -- the centers of the bounding box (default: {None})
+        scales {float} -- face scales (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if centers is not None and scales is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], centers[i], scales[i], hm.size(2), True)
+
+    return preds, preds_orig
+
+def shuffle_lr(parts, pairs=None):
+    """Shuffle the points left-right according to the axis of symmetry
+    of the object.
+
+    Arguments:
+        parts {torch.tensor} -- a 3D or 4D object containing the
+        heatmaps.
+
+    Keyword Arguments:
+        pairs {list of integers} -- [order of the flipped points] (default: {None})
+    """
+    if pairs is None:
+        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
+                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
+                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
+                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
+                 62, 61, 60, 67, 66, 65]
+    if parts.ndimension() == 3:
+        parts = parts[pairs, ...]
+    else:
+        parts = parts[:, pairs, ...]
+
+    return parts
+
+
+def flip(tensor, is_label=False):
+    """Flip an image or a set of heatmaps left-right
+
+    Arguments:
+        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
+
+    Keyword Arguments:
+        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
+    """
+    if not torch.is_tensor(tensor):
+        tensor = torch.from_numpy(tensor)
+
+    if is_label:
+        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
+    else:
+        tensor = tensor.flip(tensor.ndimension() - 1)
+
+    return tensor
+
+# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py)
+
+
+def appdata_dir(appname=None, roaming=False):
+    """ appdata_dir(appname=None, roaming=False)
+
+    Get the path to the application directory, where applications are allowed
+    to write user specific files (e.g. configurations). For non-user specific
+    data, consider using common_appdata_dir().
+    If appname is given, a subdir is appended (and created if necessary).
+    If roaming is True, will prefer a roaming directory (Windows Vista/7).
+    """
+
+    # Define default user directory
+    userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
+    if userDir is None:
+        userDir = os.path.expanduser('~')
+        if not os.path.isdir(userDir):  # pragma: no cover
+            userDir = '/var/tmp'  # issue #54
+
+    # Get system app data dir
+    path = None
+    if sys.platform.startswith('win'):
+        path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
+        path = (path2 or path1) if roaming else (path1 or path2)
+    elif sys.platform.startswith('darwin'):
+        path = os.path.join(userDir, 'Library', 'Application Support')
+    # On Linux and as fallback
+    if not (path and os.path.isdir(path)):
+        path = userDir
+
+    # Maybe we should store things local to the executable (in case of a
+    # portable distro or a frozen application that wants to be portable)
+    prefix = sys.prefix
+    if getattr(sys, 'frozen', None):
+        prefix = os.path.abspath(os.path.dirname(sys.executable))
+    for reldir in ('settings', '../settings'):
+        localpath = os.path.abspath(os.path.join(prefix, reldir))
+        if os.path.isdir(localpath):  # pragma: no cover
+            try:
+                open(os.path.join(localpath, 'test.write'), 'wb').close()
+                os.remove(os.path.join(localpath, 'test.write'))
+            except IOError:
+                pass  # We cannot write in this directory
+            else:
+                path = localpath
+                break
+
+    # Get path specific for this app
+    if appname:
+        if path == userDir:
+            appname = '.' + appname.lstrip('.')  # Make it a hidden directory
+        path = os.path.join(path, appname)
+        if not os.path.isdir(path):  # pragma: no cover
+            os.mkdir(path)
+
+    # Done
+    return path
diff --git a/face_parsing/README.md b/face_parsing/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed3cf0c437c26109adece95e70d036afe7e7786e
--- /dev/null
+++ b/face_parsing/README.md
@@ -0,0 +1 @@
+Most of the code in this folder was taken from the awesome [face parsing](https://github.com/zllrunning/face-parsing.PyTorch.git) repository.
\ No newline at end of file
diff --git a/face_parsing/__init__.py b/face_parsing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed087a4fc05eda34ea6ddb92c53889f3903c9f9b
--- /dev/null
+++ b/face_parsing/__init__.py
@@ -0,0 +1 @@
+from .swap import init_parser, swap_regions
\ No newline at end of file
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diff --git a/face_parsing/model.py b/face_parsing/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5119e751c3ae18e4dc1eecde7bfcb5bf9c62fb92
--- /dev/null
+++ b/face_parsing/model.py
@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from .resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, n_classes, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()
diff --git a/face_parsing/resnet.py b/face_parsing/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa2bf95130e9815ba378cb6f73207068b81a04b9
--- /dev/null
+++ b/face_parsing/resnet.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self):
+        state_dict = modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()
diff --git a/face_parsing/swap.py b/face_parsing/swap.py
new file mode 100644
index 0000000000000000000000000000000000000000..19bdacdabd6fb05cb46eaa14c455ebc0eab64dfd
--- /dev/null
+++ b/face_parsing/swap.py
@@ -0,0 +1,50 @@
+import torch
+import torchvision.transforms as transforms
+import cv2
+import numpy as np
+
+from .model import BiSeNet
+
+
+def init_parser(pth_path):
+    n_classes = 19
+    net = BiSeNet(n_classes=n_classes)
+    net.cuda()
+    net.load_state_dict(torch.load(pth_path))
+    net.eval()
+    return net
+
+
+def image_to_parsing(img, net):
+    img = cv2.resize(img, (512, 512))
+    img = img[:,:,::-1]
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+    ])
+    img = transform(img.copy())
+    img = torch.unsqueeze(img, 0)
+
+    with torch.no_grad():
+        img = img.cuda()
+        out = net(img)[0]
+        parsing = out.squeeze(0).cpu().numpy().argmax(0)
+        return parsing
+
+
+def get_mask(parsing, classes):
+    res = parsing == classes[0]
+    for val in classes[1:]:
+        res += parsing == val
+    return res
+
+
+def swap_regions(source, target, net):
+    parsing = image_to_parsing(source, net)
+    face_classes = [1, 11, 12, 13]
+
+    mask = get_mask(parsing, face_classes)
+    mask = np.repeat(np.expand_dims(mask, axis=2), 3, 2)
+    result = (1 - mask) * cv2.resize(source, (512, 512)) + mask * cv2.resize(target, (512, 512))
+    result = cv2.resize(result.astype("float32"), (source.shape[1], source.shape[0]))
+    return result
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diff --git a/hparams.py b/hparams.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c019046279f497e4eae3f839f683bc0b1193c6b
--- /dev/null
+++ b/hparams.py
@@ -0,0 +1,101 @@
+from glob import glob
+import os
+
+def get_image_list(data_root, split):
+	filelist = []
+
+	with open('filelists/{}.txt'.format(split)) as f:
+		for line in f:
+			line = line.strip()
+			if ' ' in line: line = line.split()[0]
+			filelist.append(os.path.join(data_root, line))
+
+	return filelist
+
+class HParams:
+	def __init__(self, **kwargs):
+		self.data = {}
+
+		for key, value in kwargs.items():
+			self.data[key] = value
+
+	def __getattr__(self, key):
+		if key not in self.data:
+			raise AttributeError("'HParams' object has no attribute %s" % key)
+		return self.data[key]
+
+	def set_hparam(self, key, value):
+		self.data[key] = value
+
+
+# Default hyperparameters
+hparams = HParams(
+	num_mels=80,  # Number of mel-spectrogram channels and local conditioning dimensionality
+	#  network
+	rescale=True,  # Whether to rescale audio prior to preprocessing
+	rescaling_max=0.9,  # Rescaling value
+	
+	# Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction
+	# It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder
+	# Does not work if n_ffit is not multiple of hop_size!!
+	use_lws=False,
+	
+	n_fft=800,  # Extra window size is filled with 0 paddings to match this parameter
+	hop_size=200,  # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate)
+	win_size=800,  # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate)
+	sample_rate=16000,  # 16000Hz (corresponding to librispeech) (sox --i <filename>)
+	
+	frame_shift_ms=None,  # Can replace hop_size parameter. (Recommended: 12.5)
+	
+	# Mel and Linear spectrograms normalization/scaling and clipping
+	signal_normalization=True,
+	# Whether to normalize mel spectrograms to some predefined range (following below parameters)
+	allow_clipping_in_normalization=True,  # Only relevant if mel_normalization = True
+	symmetric_mels=True,
+	# Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, 
+	# faster and cleaner convergence)
+	max_abs_value=4.,
+	# max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not 
+	# be too big to avoid gradient explosion, 
+	# not too small for fast convergence)
+	# Contribution by @begeekmyfriend
+	# Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude 
+	# levels. Also allows for better G&L phase reconstruction)
+	preemphasize=True,  # whether to apply filter
+	preemphasis=0.97,  # filter coefficient.
+	
+	# Limits
+	min_level_db=-100,
+	ref_level_db=20,
+	fmin=55,
+	# Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To 
+	# test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
+	fmax=7600,  # To be increased/reduced depending on data.
+
+	###################### Our training parameters #################################
+	img_size=96,
+	fps=25,
+	
+	batch_size=16,
+	initial_learning_rate=1e-4,
+	nepochs=200000000000000000,  ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs
+	num_workers=16,
+	checkpoint_interval=3000,
+	eval_interval=3000,
+    save_optimizer_state=True,
+
+    syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. 
+	syncnet_batch_size=64,
+	syncnet_lr=1e-4,
+	syncnet_eval_interval=10000,
+	syncnet_checkpoint_interval=10000,
+
+	disc_wt=0.07,
+	disc_initial_learning_rate=1e-4,
+)
+
+
+def hparams_debug_string():
+	values = hparams.values()
+	hp = ["  %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"]
+	return "Hyperparameters:\n" + "\n".join(hp)
diff --git a/inference.py b/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..25689a2df82ad4e0a03e092652a20797cb6902aa
--- /dev/null
+++ b/inference.py
@@ -0,0 +1,359 @@
+from os import listdir, path
+import numpy as np
+import scipy, cv2, os, sys, argparse, audio
+import json, subprocess, random, string
+from tqdm import tqdm
+from glob import glob
+import torch, face_detection
+from wav2lip_models import Wav2Lip
+import platform
+from face_parsing import init_parser, swap_regions
+from basicsr.apply_sr import init_sr_model, enhance
+
+parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')
+
+parser.add_argument('--checkpoint_path', type=str, 
+					help='Name of saved checkpoint to load weights from', required=True)
+
+parser.add_argument('--segmentation_path', type=str, 
+					help='Name of saved checkpoint of segmentation network', required=True)
+
+parser.add_argument('--sr_path', type=str, 
+					help='Name of saved checkpoint of super-resolution network', required=True)
+
+parser.add_argument('--face', type=str, 
+					help='Filepath of video/image that contains faces to use', required=True)
+parser.add_argument('--audio', type=str, 
+					help='Filepath of video/audio file to use as raw audio source', required=True)
+parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', 
+								default='results/result_voice.mp4')
+
+
+parser.add_argument('--static', type=bool, 
+					help='If True, then use only first video frame for inference', default=False)
+parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', 
+					default=25., required=False)
+
+parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], 
+					help='Padding (top, bottom, left, right). Please adjust to include chin at least')
+
+parser.add_argument('--face_det_batch_size', type=int, 
+					help='Batch size for face detection', default=16)
+parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128)
+
+parser.add_argument('--resize_factor', default=1, type=int, 
+			help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')
+
+parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1], 
+					help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' 
+					'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')
+
+parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], 
+					help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.'
+					'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).')
+
+parser.add_argument('--rotate', default=False, action='store_true',
+					help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.'
+					'Use if you get a flipped result, despite feeding a normal looking video')
+
+parser.add_argument('--nosmooth', default=False, action='store_true',
+					help='Prevent smoothing face detections over a short temporal window')
+parser.add_argument('--no_segmentation', default=False, action='store_true',
+					help='Prevent using face segmentation')
+parser.add_argument('--no_sr', default=False, action='store_true',
+					help='Prevent using super resolution')
+
+parser.add_argument('--save_frames', default=False, action='store_true',
+					help='Save each frame as an image. Use with caution')
+parser.add_argument('--gt_path', type=str, 
+					help='Where to store saved ground truth frames', required=False)
+parser.add_argument('--pred_path', type=str, 
+					help='Where to store frames produced by algorithm', required=False)
+parser.add_argument('--save_as_video', action="store_true", default=False,
+					help='Whether to save frames as video', required=False)
+parser.add_argument('--image_prefix', type=str, default="",
+					help='Prefix to save frames with', required=False)
+
+args = parser.parse_args()
+args.img_size = 96
+
+if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+	args.static = True
+
+def get_smoothened_boxes(boxes, T):
+	for i in range(len(boxes)):
+		if i + T > len(boxes):
+			window = boxes[len(boxes) - T:]
+		else:
+			window = boxes[i : i + T]
+		boxes[i] = np.mean(window, axis=0)
+	return boxes
+
+def face_detect(images):
+	detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, 
+											flip_input=False, device=device)
+
+	batch_size = args.face_det_batch_size
+	
+	while 1:
+		predictions = []
+		try:
+			for i in range(0, len(images), batch_size):
+				predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
+		except RuntimeError:
+			if batch_size == 1: 
+				raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
+			batch_size //= 2
+			print('Recovering from OOM error; New batch size: {}'.format(batch_size))
+			continue
+		break
+
+	results = []
+	pady1, pady2, padx1, padx2 = args.pads
+	for rect, image in zip(predictions, images):
+		if rect is None:
+			cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected.
+			raise ValueError('Face not detected! Ensure the video contains a face in all the frames.')
+
+		y1 = max(0, rect[1] - pady1)
+		y2 = min(image.shape[0], rect[3] + pady2)
+		x1 = max(0, rect[0] - padx1)
+		x2 = min(image.shape[1], rect[2] + padx2)
+		
+		results.append([x1, y1, x2, y2])
+
+	boxes = np.array(results)
+	if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5)
+	results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
+
+	del detector
+	return results 
+
+def datagen(mels):
+	img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
+
+	"""
+	if args.box[0] == -1:
+		if not args.static:
+			face_det_results = face_detect(frames) # BGR2RGB for CNN face detection
+		else:
+			face_det_results = face_detect([frames[0]])
+	else:
+		print('Using the specified bounding box instead of face detection...')
+		y1, y2, x1, x2 = args.box
+		face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]
+	"""
+
+	reader = read_frames()
+
+	for i, m in enumerate(mels):
+		try:
+			frame_to_save = next(reader)
+		except StopIteration:
+			reader = read_frames()
+			frame_to_save = next(reader)
+
+		face, coords = face_detect([frame_to_save])[0]
+
+		face = cv2.resize(face, (args.img_size, args.img_size))
+			
+		img_batch.append(face)
+		mel_batch.append(m)
+		frame_batch.append(frame_to_save)
+		coords_batch.append(coords)
+
+		if len(img_batch) >= args.wav2lip_batch_size:
+			img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+
+			img_masked = img_batch.copy()
+			img_masked[:, args.img_size//2:] = 0
+
+			img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
+			mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+			yield img_batch, mel_batch, frame_batch, coords_batch
+			img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
+
+	if len(img_batch) > 0:
+		img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+
+		img_masked = img_batch.copy()
+		img_masked[:, args.img_size//2:] = 0
+
+		img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
+		mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+		yield img_batch, mel_batch, frame_batch, coords_batch
+
+mel_step_size = 16
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print('Using {} for inference.'.format(device))
+
+def _load(checkpoint_path):
+	if device == 'cuda':
+		checkpoint = torch.load(checkpoint_path)
+	else:
+		checkpoint = torch.load(checkpoint_path,
+								map_location=lambda storage, loc: storage)
+	return checkpoint
+
+def load_model(path):
+	model = Wav2Lip()
+	print("Load checkpoint from: {}".format(path))
+	checkpoint = _load(path)
+	s = checkpoint["state_dict"]
+	new_s = {}
+	for k, v in s.items():
+		new_s[k.replace('module.', '')] = v
+	model.load_state_dict(new_s)
+
+	model = model.to(device)
+	return model.eval()
+
+def read_frames():
+	if args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+		face = cv2.imread(args.face)
+		while 1:
+			yield face
+
+	video_stream = cv2.VideoCapture(args.face)
+	fps = video_stream.get(cv2.CAP_PROP_FPS)
+
+	print('Reading video frames from start...')
+
+	while 1:
+		still_reading, frame = video_stream.read()
+		if not still_reading:
+			video_stream.release()
+			break
+		if args.resize_factor > 1:
+			frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor))
+
+		if args.rotate:
+			frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
+
+		y1, y2, x1, x2 = args.crop
+		if x2 == -1: x2 = frame.shape[1]
+		if y2 == -1: y2 = frame.shape[0]
+
+		frame = frame[y1:y2, x1:x2]
+
+		yield frame
+
+def main():
+	if not os.path.isfile(args.face):
+		raise ValueError('--face argument must be a valid path to video/image file')
+
+	elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+		fps = args.fps
+	else:
+		video_stream = cv2.VideoCapture(args.face)
+		fps = video_stream.get(cv2.CAP_PROP_FPS)
+		video_stream.release()
+
+
+	if not args.audio.endswith('.wav'):
+		print('Extracting raw audio...')
+		command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')
+
+		subprocess.call(command, shell=True)
+		args.audio = 'temp/temp.wav'
+
+	wav = audio.load_wav(args.audio, 16000)
+	mel = audio.melspectrogram(wav)
+	print(mel.shape)
+
+	if np.isnan(mel.reshape(-1)).sum() > 0:
+		raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
+
+	mel_chunks = []
+	mel_idx_multiplier = 80./fps 
+	i = 0
+	while 1:
+		start_idx = int(i * mel_idx_multiplier)
+		if start_idx + mel_step_size > len(mel[0]):
+			mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+			break
+		mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+		i += 1
+
+	print("Length of mel chunks: {}".format(len(mel_chunks)))
+
+	batch_size = args.wav2lip_batch_size
+	gen = datagen(mel_chunks)
+
+
+
+	if args.save_as_video:
+		gt_out = cv2.VideoWriter("temp/gt.avi", cv2.VideoWriter_fourcc(*'DIVX'), fps, (384, 384))
+		pred_out = cv2.VideoWriter("temp/pred.avi", cv2.VideoWriter_fourcc(*'DIVX'), fps, (96, 96))
+
+	abs_idx = 0
+	for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, 
+											total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
+		if i == 0:
+			print("Loading segmentation network...")
+			seg_net = init_parser(args.segmentation_path)
+
+			print("Loading super resolution model...")
+			sr_net = init_sr_model(args.sr_path)
+
+			model = load_model(args.checkpoint_path)
+			print ("Model loaded")
+
+			frame_h, frame_w = next(read_frames()).shape[:-1]
+			out = cv2.VideoWriter('temp/result.avi', 
+									cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
+
+		img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+		mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+
+		with torch.no_grad():
+			pred = model(mel_batch, img_batch)
+
+		pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+		
+		for p, f, c in zip(pred, frames, coords):
+			y1, y2, x1, x2 = c
+
+			if args.save_frames:
+				print("saving frames or video...")
+				if args.save_as_video:
+					print("videos...")
+					pred_out.write(p.astype(np.uint8))
+					gt_out.write(cv2.resize(f[y1:y2, x1:x2], (384, 384)))
+				else:
+					print("frames...")
+					print(f"{args.gt_path}/{args.image_prefix}{abs_idx}.png")
+					cv2.imwrite(f"{args.gt_path}/{args.image_prefix}{abs_idx}.png", f[y1:y2, x1:x2])
+					cv2.imwrite(f"{args.pred_path}/{args.image_prefix}{abs_idx}.png", p)
+					abs_idx += 1
+
+			if not args.no_sr:
+				p = enhance(sr_net, p)
+			p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
+			
+			if not args.no_segmentation:
+				p = swap_regions(f[y1:y2, x1:x2], p, seg_net)
+
+			f[y1:y2, x1:x2] = p
+			out.write(f)
+
+	out.release()
+
+	command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile)
+	subprocess.call(command, shell=platform.system() != 'Windows')
+
+	if args.save_frames and args.save_as_video:
+		gt_out.release()
+		pred_out.release()
+
+		command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/gt.avi', args.gt_path)
+		subprocess.call(command, shell=platform.system() != 'Windows')
+
+		command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/pred.avi', args.pred_path)
+		subprocess.call(command, shell=platform.system() != 'Windows')
+
+
+if __name__ == '__main__':
+	main()
diff --git a/input_audios/part_000_RVC_1.wav b/input_audios/part_000_RVC_1.wav
new file mode 100644
index 0000000000000000000000000000000000000000..25ffbc430daae76d9eb422b784007e41db90fc7c
--- /dev/null
+++ b/input_audios/part_000_RVC_1.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c2719b938c959c052b34b28bffd040f23b22d89e70b98e341baf557f391a6e2
+size 57592364
diff --git a/input_videos/MC.mp4 b/input_videos/MC.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..ae80d86d1d30248826d39de6a7fe3ec11878cfcc
--- /dev/null
+++ b/input_videos/MC.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44696c3eb58ddd0a02e0da838b3363d6f83558bd86b28b254dfcbf02d56bb034
+size 3362862
diff --git a/input_videos/README.md b/input_videos/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..eeb9deded1abea9b1181a0075c0e0cc0538f601b
--- /dev/null
+++ b/input_videos/README.md
@@ -0,0 +1 @@
+This is the folder where you can place all your files for inference.
\ No newline at end of file
diff --git a/output_videos_hd/kennedy.mkv b/output_videos_hd/kennedy.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..54b0d01477c056a667168fa3379891c4ed0890b2
--- /dev/null
+++ b/output_videos_hd/kennedy.mkv
@@ -0,0 +1,3 @@
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+oid sha256:6214a7947520c6726f966bad46b4258bdaa90a8dd31980791f38479e8e3fea59
+size 5976689
diff --git a/output_videos_hd/mona.mkv b/output_videos_hd/mona.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..28d5eae98433863622fa9bf9266f4bbbddee4887
--- /dev/null
+++ b/output_videos_hd/mona.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abc1409485f5f887bedec1096af451448a1c88cb1b2c8a27f8fbe6368400e08f
+size 13426056
diff --git a/output_videos_wav2lip/kennedy.mp4 b/output_videos_wav2lip/kennedy.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..f7d46a74e20d1601f7605995531f8335b717c13a
Binary files /dev/null and b/output_videos_wav2lip/kennedy.mp4 differ
diff --git a/output_videos_wav2lip/mona.mp4 b/output_videos_wav2lip/mona.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..83f1ec7b3e6246f7e6404d7c24fba058995d9873
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2d449fe25dadd164f1787d2c21e0e43c303b880d
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,14 @@
+addict
+future
+librosa==0.7.0
+lmdb
+numba==0.48
+numpy
+Pillow
+pyyaml
+requests
+scikit-image
+scipy
+tb-nightly
+tqdm
+yapf
\ No newline at end of file
diff --git a/resizeframes.py b/resizeframes.py
new file mode 100644
index 0000000000000000000000000000000000000000..20c7f4b9204566d1d1bd2991d2f1ffb6713e03c1
--- /dev/null
+++ b/resizeframes.py
@@ -0,0 +1,10 @@
+import os
+from tqdm import tqdm
+import cv2
+
+paths = os.listdir("basicsr/data/gt")
+
+for img_path in tqdm(paths):
+    img = cv2.imread("basicsr/data/gt/" + img_path)
+    img = cv2.resize(img, (384, 384))
+    cv2.imwrite("basicsr/data/hq/" + img_path, img)
\ No newline at end of file
diff --git a/results/README.md b/results/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..bcda506bb66017995df8d830735e1b162aad1742
--- /dev/null
+++ b/results/README.md
@@ -0,0 +1 @@
+This is a folder for result videos.
\ No newline at end of file
diff --git a/run_final.sh b/run_final.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f98bc0ec81277a4833816eb9968147704d6c4341
--- /dev/null
+++ b/run_final.sh
@@ -0,0 +1,14 @@
+export filename=mona
+export input_video=input_videos/MC.mp4
+export input_audio=input_audios/part_000_RVC_1.wav
+export frames_wav2lip=frames_wav2lip
+export frames_hd=frames_hd
+export output_videos_wav2lip=output_videos_wav2lip
+export output_videos_hd=output_videos_hd
+export back_dir=..
+
+python3 inference.py --checkpoint_path "checkpoints/wav2lip_gan.pth" --segmentation_path "checkpoints/face_segmentation.pth" --sr_path "checkpoints/esrgan_yunying.pth" --face ${input_video}/${filename}.mp4 --audio ${input_audio} --save_frames --gt_path "data/gt" --pred_path "data/lq" --no_sr --no_segmentation --outfile ${output_videos_wav2lip}/${filename}.mp4
+python video2frames.py --input_video ${output_videos_wav2lip}/${filename}.mp4 --frames_path ${frames_wav2lip}/${filename}
+cd Real-ESRGAN
+python inference_realesrgan.py -n RealESRGAN_x4plus -i ${back_dir}/${frames_wav2lip}/${filename} --output ${back_dir}/${frames_hd}/${filename} --outscale 3.5 --face_enhance
+ffmpeg -r 20 -i ${back_dir}/${frames_hd}/${filename}/frame_%05d_out.jpg -i ${back_dir}/${input_audios} -vcodec libx264 -crf 25 -preset veryslow -acodec copy ${back_dir}/${output_videos_hd}/${filename}.mkv
\ No newline at end of file
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diff --git a/temp/README.md b/temp/README.md
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+This is a folder for various temporary stuff.
\ No newline at end of file
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diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ff7d6efc5ed92db0021c7b2c14091b2bd20437a
--- /dev/null
+++ b/train.py
@@ -0,0 +1,221 @@
+import argparse
+import datetime
+import logging
+import math
+import random
+import time
+import torch
+from os import path as osp
+
+from basicsr.data import build_dataloader, build_dataset
+from basicsr.data.data_sampler import EnlargedSampler
+from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
+from basicsr.models import build_model
+from basicsr.utils import (MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, init_tb_logger,
+                           init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed)
+from basicsr.utils.dist_util import get_dist_info, init_dist
+from basicsr.utils.options import dict2str, parse
+
+
+def parse_options(root_path, is_train=True):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.')
+    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    opt = parse(args.opt, root_path, is_train=is_train)
+
+    # distributed settings
+    if args.launcher == 'none':
+        opt['dist'] = False
+        print('Disable distributed.', flush=True)
+    else:
+        opt['dist'] = True
+        if args.launcher == 'slurm' and 'dist_params' in opt:
+            init_dist(args.launcher, **opt['dist_params'])
+        else:
+            init_dist(args.launcher)
+
+    opt['rank'], opt['world_size'] = get_dist_info()
+
+    # random seed
+    seed = opt.get('manual_seed')
+    if seed is None:
+        seed = random.randint(1, 10000)
+        opt['manual_seed'] = seed
+    set_random_seed(seed + opt['rank'])
+
+    return opt
+
+
+def init_loggers(opt):
+    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log")
+    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
+    logger.info(get_env_info())
+    logger.info(dict2str(opt))
+
+    # initialize wandb logger before tensorboard logger to allow proper sync:
+    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project')
+                                                     is not None) and ('debug' not in opt['name']):
+        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
+        init_wandb_logger(opt)
+    tb_logger = None
+    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
+        tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name']))
+    return logger, tb_logger
+
+
+def create_train_val_dataloader(opt, logger):
+    # create train and val dataloaders
+    train_loader, val_loader = None, None
+    for phase, dataset_opt in opt['datasets'].items():
+        if phase == 'train':
+            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
+            train_set = build_dataset(dataset_opt)
+            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
+            train_loader = build_dataloader(
+                train_set,
+                dataset_opt,
+                num_gpu=opt['num_gpu'],
+                dist=opt['dist'],
+                sampler=train_sampler,
+                seed=opt['manual_seed'])
+
+            num_iter_per_epoch = math.ceil(
+                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
+            total_iters = int(opt['train']['total_iter'])
+            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
+            logger.info('Training statistics:'
+                        f'\n\tNumber of train images: {len(train_set)}'
+                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
+                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
+                        f'\n\tWorld size (gpu number): {opt["world_size"]}'
+                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
+                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
+
+        elif phase == 'val':
+            val_set = build_dataset(dataset_opt)
+            val_loader = build_dataloader(
+                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
+            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: ' f'{len(val_set)}')
+        else:
+            raise ValueError(f'Dataset phase {phase} is not recognized.')
+
+    return train_loader, train_sampler, val_loader, total_epochs, total_iters
+
+
+def train_pipeline(root_path):
+    # parse options, set distributed setting, set ramdom seed
+    opt = parse_options(root_path, is_train=True)
+
+    torch.backends.cudnn.benchmark = True
+    # torch.backends.cudnn.deterministic = True
+
+    # load resume states if necessary
+    if opt['path'].get('resume_state'):
+        device_id = torch.cuda.current_device()
+        resume_state = torch.load(
+            opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id))
+    else:
+        resume_state = None
+
+    # mkdir for experiments and logger
+    if resume_state is None:
+        make_exp_dirs(opt)
+        if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0:
+            mkdir_and_rename(osp.join('tb_logger', opt['name']))
+
+    # initialize loggers
+    logger, tb_logger = init_loggers(opt)
+
+    # create train and validation dataloaders
+    result = create_train_val_dataloader(opt, logger)
+    train_loader, train_sampler, val_loader, total_epochs, total_iters = result
+
+    # create model
+    if resume_state:  # resume training
+        check_resume(opt, resume_state['iter'])
+        model = build_model(opt)
+        model.resume_training(resume_state)  # handle optimizers and schedulers
+        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
+        start_epoch = resume_state['epoch']
+        current_iter = resume_state['iter']
+    else:
+        model = build_model(opt)
+        start_epoch = 0
+        current_iter = 0
+
+    # create message logger (formatted outputs)
+    msg_logger = MessageLogger(opt, current_iter, tb_logger)
+
+    # dataloader prefetcher
+    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
+    if prefetch_mode is None or prefetch_mode == 'cpu':
+        prefetcher = CPUPrefetcher(train_loader)
+    elif prefetch_mode == 'cuda':
+        prefetcher = CUDAPrefetcher(train_loader, opt)
+        logger.info(f'Use {prefetch_mode} prefetch dataloader')
+        if opt['datasets']['train'].get('pin_memory') is not True:
+            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
+    else:
+        raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.")
+
+    # training
+    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')
+    data_time, iter_time = time.time(), time.time()
+    start_time = time.time()
+
+    for epoch in range(start_epoch, total_epochs + 1):
+        train_sampler.set_epoch(epoch)
+        prefetcher.reset()
+        train_data = prefetcher.next()
+
+        while train_data is not None:
+            data_time = time.time() - data_time
+
+            current_iter += 1
+            if current_iter > total_iters:
+                break
+            # update learning rate
+            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
+            # training
+            model.feed_data(train_data)
+            model.optimize_parameters(current_iter)
+            iter_time = time.time() - iter_time
+            # log
+            if current_iter % opt['logger']['print_freq'] == 0:
+                log_vars = {'epoch': epoch, 'iter': current_iter}
+                log_vars.update({'lrs': model.get_current_learning_rate()})
+                log_vars.update({'time': iter_time, 'data_time': data_time})
+                log_vars.update(model.get_current_log())
+                msg_logger(log_vars)
+
+            # save models and training states
+            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
+                logger.info('Saving models and training states.')
+                model.save(epoch, current_iter)
+
+            # validation
+            if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0):
+                model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
+
+            data_time = time.time()
+            iter_time = time.time()
+            train_data = prefetcher.next()
+        # end of iter
+
+    # end of epoch
+
+    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
+    logger.info(f'End of training. Time consumed: {consumed_time}')
+    logger.info('Save the latest model.')
+    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
+    if opt.get('val') is not None:
+        model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
+    if tb_logger:
+        tb_logger.close()
+
+
+if __name__ == '__main__':
+    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
+    train_pipeline(root_path)
diff --git a/train_basicsr.yml b/train_basicsr.yml
new file mode 100644
index 0000000000000000000000000000000000000000..c4cd1ca2230589f5338f7a93e92d49f15c829779
--- /dev/null
+++ b/train_basicsr.yml
@@ -0,0 +1,120 @@
+# general settings
+name: 001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb
+model_type: ESRGANModel
+scale: 4
+num_gpu: 1  # set num_gpu: 0 for cpu mode
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: face_dataset
+    type: PairedImageDataset
+    dataroot_gt: basicsr/data/hq
+    dataroot_lq: basicsr/data/lq
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    gt_size: 384
+    use_flip: true
+    use_rot: true
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 1
+    batch_size_per_gpu: 4
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 23
+
+network_d:
+  type: VGGStyleDiscriminator128
+  num_in_ch: 3
+  num_feat: 64
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: checkpoints/pretrained.state
+
+# training settings
+train:
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+  optim_d:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [50000, 100000, 200000, 300000]
+    gamma: 0.5
+
+  total_iter: 150000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: !!float 1e-2
+    reduction: mean
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      'conv5_4': 1  # before relu
+    vgg_type: vgg19
+    use_input_norm: true
+    range_norm: false
+    perceptual_weight: 1.0
+    style_weight: 0
+    criterion: l1
+  gan_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 5e-3
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+
+# validation settings
+val:
+  val_freq: !!float 25e2
+  save_img: true
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 4
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 25e2
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500
+
+
diff --git a/video2frames.py b/video2frames.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c42829b61014bbe9edee935da25f6bbf2beaa0c
--- /dev/null
+++ b/video2frames.py
@@ -0,0 +1,42 @@
+import cv2
+import os
+import argparse
+
+parser = argparse.ArgumentParser(description='code for extracting frames from video')
+
+parser.add_argument('--input_video', type=str, help='Video path to save result. See default for an e.g.', 
+                                default='output_videos_wav2lip/1.mp4')
+
+parser.add_argument('--frames_path', type=str, help='Video path to save result. See default for an e.g.', 
+                                default='frames_wav2lip/1/')
+
+args = parser.parse_args()
+
+# Read the video file
+video_path = args.input_video
+video = cv2.VideoCapture(video_path)
+
+# Get the frames per second (fps) and duration of the video
+fps = int(video.get(cv2.CAP_PROP_FPS))
+duration = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+# Create a folder to store the extracted frames
+frame_folder = args.frames_path
+os.makedirs(frame_folder, exist_ok=True)
+
+# Initialize a counter for the frame index
+frame_index = 0
+
+# Loop through each frame of the video and save it as an image file
+for i in range(duration):
+    ret, frame = video.read()
+    if not ret:
+        break
+    # Save the frame as an image file in the frame folder
+    frame_file = os.path.join(frame_folder, f'frame_{frame_index:05d}.jpg')
+    cv2.imwrite(frame_file, frame)
+    frame_index += 1
+
+print("Frames extracted and stored at ", args.frames_path)
+# Release the video object
+video.release()
\ No newline at end of file
diff --git a/wav2lip_models/README.md b/wav2lip_models/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6d8718af5dc19be1a7bbd65962caf259a8eb6dd1
--- /dev/null
+++ b/wav2lip_models/README.md
@@ -0,0 +1 @@
+Code in this folder is taken from the Wav2Lip repository.
\ No newline at end of file
diff --git a/wav2lip_models/__init__.py b/wav2lip_models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4374370494b65f10b76c70a2d4f731c238cfa54c
--- /dev/null
+++ b/wav2lip_models/__init__.py
@@ -0,0 +1,2 @@
+from .wav2lip import Wav2Lip, Wav2Lip_disc_qual
+from .syncnet import SyncNet_color
\ No newline at end of file
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diff --git a/wav2lip_models/conv.py b/wav2lip_models/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed83da00cb199e027ef217fd360352d91a7891ff
--- /dev/null
+++ b/wav2lip_models/conv.py
@@ -0,0 +1,44 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+class Conv2d(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+                            nn.BatchNorm2d(cout)
+                            )
+        self.act = nn.ReLU()
+        self.residual = residual
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        if self.residual:
+            out += x
+        return self.act(out)
+
+class nonorm_Conv2d(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+                            )
+        self.act = nn.LeakyReLU(0.01, inplace=True)
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        return self.act(out)
+
+class Conv2dTranspose(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding),
+                            nn.BatchNorm2d(cout)
+                            )
+        self.act = nn.ReLU()
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        return self.act(out)
diff --git a/wav2lip_models/syncnet.py b/wav2lip_models/syncnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..e773cdca675236745a379a776b7c07d7d353f590
--- /dev/null
+++ b/wav2lip_models/syncnet.py
@@ -0,0 +1,66 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .conv import Conv2d
+
+class SyncNet_color(nn.Module):
+    def __init__(self):
+        super(SyncNet_color, self).__init__()
+
+        self.face_encoder = nn.Sequential(
+            Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3),
+
+            Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
+
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
+
+    def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T)
+        face_embedding = self.face_encoder(face_sequences)
+        audio_embedding = self.audio_encoder(audio_sequences)
+
+        audio_embedding = audio_embedding.view(audio_embedding.size(0), -1)
+        face_embedding = face_embedding.view(face_embedding.size(0), -1)
+
+        audio_embedding = F.normalize(audio_embedding, p=2, dim=1)
+        face_embedding = F.normalize(face_embedding, p=2, dim=1)
+
+
+        return audio_embedding, face_embedding
diff --git a/wav2lip_models/wav2lip.py b/wav2lip_models/wav2lip.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae5d6919169ec497f0f0815184f5db8ba9108fbd
--- /dev/null
+++ b/wav2lip_models/wav2lip.py
@@ -0,0 +1,184 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+import math
+
+from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d
+
+class Wav2Lip(nn.Module):
+    def __init__(self):
+        super(Wav2Lip, self).__init__()
+
+        self.face_encoder_blocks = nn.ModuleList([
+            nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96
+
+            nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1),    # 24,24
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1),   # 12,12
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1),       # 6,6
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1),     # 3,3
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),),
+            
+            nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0),     # 1, 1
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),])
+
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
+
+        self.face_decoder_blocks = nn.ModuleList([
+            nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),),
+
+            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),),
+
+            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6
+
+            nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1),
+            Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12
+
+            nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24
+
+            nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), 
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48
+
+            nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96
+
+        self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
+            nn.Sigmoid()) 
+
+    def forward(self, audio_sequences, face_sequences):
+        # audio_sequences = (B, T, 1, 80, 16)
+        B = audio_sequences.size(0)
+
+        input_dim_size = len(face_sequences.size())
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
+            face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+
+        audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
+
+        feats = []
+        x = face_sequences
+        for f in self.face_encoder_blocks:
+            x = f(x)
+            feats.append(x)
+
+        x = audio_embedding
+        for f in self.face_decoder_blocks:
+            x = f(x)
+            try:
+                x = torch.cat((x, feats[-1]), dim=1)
+            except Exception as e:
+                print(x.size())
+                print(feats[-1].size())
+                raise e
+            
+            feats.pop()
+
+        x = self.output_block(x)
+
+        if input_dim_size > 4:
+            x = torch.split(x, B, dim=0) # [(B, C, H, W)]
+            outputs = torch.stack(x, dim=2) # (B, C, T, H, W)
+
+        else:
+            outputs = x
+            
+        return outputs
+
+class Wav2Lip_disc_qual(nn.Module):
+    def __init__(self):
+        super(Wav2Lip_disc_qual, self).__init__()
+
+        self.face_encoder_blocks = nn.ModuleList([
+            nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)), # 48,96
+
+            nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), # 48,48
+            nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)),
+
+            nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2),    # 24,24
+            nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)),
+
+            nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2),   # 12,12
+            nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)),
+
+            nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1),       # 6,6
+            nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)),
+
+            nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1),     # 3,3
+            nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1),),
+            
+            nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=0),     # 1, 1
+            nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),])
+
+        self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid())
+        self.label_noise = .0
+
+    def get_lower_half(self, face_sequences):
+        return face_sequences[:, :, face_sequences.size(2)//2:]
+
+    def to_2d(self, face_sequences):
+        B = face_sequences.size(0)
+        face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+        return face_sequences
+
+    def perceptual_forward(self, false_face_sequences):
+        false_face_sequences = self.to_2d(false_face_sequences)
+        false_face_sequences = self.get_lower_half(false_face_sequences)
+
+        false_feats = false_face_sequences
+        for f in self.face_encoder_blocks:
+            false_feats = f(false_feats)
+
+        false_pred_loss = F.binary_cross_entropy(self.binary_pred(false_feats).view(len(false_feats), -1), 
+                                        torch.ones((len(false_feats), 1)).cuda())
+
+        return false_pred_loss
+
+    def forward(self, face_sequences):
+        face_sequences = self.to_2d(face_sequences)
+        face_sequences = self.get_lower_half(face_sequences)
+
+        x = face_sequences
+        for f in self.face_encoder_blocks:
+            x = f(x)
+
+        return self.binary_pred(x).view(len(x), -1)