Merge pull request #50 from yunyangge/interpolation

[feat]: frame_interpolation

Former-commit-id: 59ac3fd0b48bcd89f2b1f816b74a4e17c83328b2

.gitignore

@@ -1 +1,5 @@
 src/sora/modules/ae/vqvae/videogpt/ucf101_stride4x4x4
+__pycache__
+*.mp4
+.ipynb_checkpoints
+*.pth

src/sora/models/interpolation/cfgs/AMT-G.yaml

@@ -0,0 +1,9 @@
+
+seed: 2023
+
+network:
+  name: networks.AMT-G.Model
+  params:
+    corr_radius: 3
+    corr_lvls: 4
+    num_flows: 5

src/sora/models/interpolation/environment.yaml

@@ -0,0 +1,19 @@
+name: amt
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.8.5
+  - pip=20.3
+  - cudatoolkit=11.3
+  - pytorch=1.11.0
+  - torchvision=0.12.0
+  - numpy=1.21.5
+  - pip:
+    - opencv-python==4.1.2.30
+    - imageio==2.19.3
+    - omegaconf==2.3.0
+    - Pillow==9.4.0
+    - tqdm==4.64.1
+    - wandb==0.12.21

src/sora/models/interpolation/interpolation.py

@@ -0,0 +1,188 @@
+# this script is modified from https://github.com/MCG-NKU/AMT/blob/main/demos/demo_2x.py
+from json import load
+import os
+import cv2
+import sys
+import glob
+import torch
+import argparse
+import numpy as np
+import os.path as osp
+from warnings import warn
+from omegaconf import OmegaConf
+from torchvision.utils import make_grid
+sys.path.append('.')
+from utils.utils import (
+    read, write,
+    img2tensor, tensor2img,
+    check_dim_and_resize
+    )
+from utils.build_utils import build_from_cfg
+from utils.utils import InputPadder
+
+
+def init(device="cuda"):
+
+    '''
+        initialize the device and the anchor resolution.
+    '''
+
+    if device == 'cuda':
+        anchor_resolution = 1024 * 512
+        anchor_memory = 1500 * 1024**2
+        anchor_memory_bias = 2500 * 1024**2
+        vram_avail = torch.cuda.get_device_properties(device).total_memory
+        print("VRAM available: {:.1f} MB".format(vram_avail / 1024 ** 2))
+    else:
+        # Do not resize in cpu mode
+        anchor_resolution = 8192*8192
+        anchor_memory = 1
+        anchor_memory_bias = 0
+        vram_avail = 1
+    
+    return anchor_resolution, anchor_memory, anchor_memory_bias, vram_avail
+
+def get_input_video_from_path(input_path, device="cuda"):
+
+    '''
+        Get the input video from the input_path.
+
+        params:
+            input_path: str, the path of the input video.
+            devices: str, the device to run the model.
+        returns:
+            inputs: list, the list of the input frames.
+            scale: float, the scale of the input frames.
+            padder: InputPadder, the padder to pad the input frames.
+    '''
+
+    anchor_resolution, anchor_memory, anchor_memory_bias, vram_avail = init(device)
+
+    if osp.splitext(input_path)[-1] in ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', 
+                                        '.webm', '.MP4', '.AVI', '.MOV', '.MKV', '.FLV', 
+                                        '.WMV', '.WEBM']:
+
+        vcap = cv2.VideoCapture(input_path)
+
+        inputs = []
+        w = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        h = int(vcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        scale = anchor_resolution / (h * w) * np.sqrt((vram_avail - anchor_memory_bias) / anchor_memory)
+        scale = 1 if scale > 1 else scale
+        scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
+        if scale < 1:
+            print(f"Due to the limited VRAM, the video will be scaled by {scale:.2f}")
+        padding = int(16 / scale)
+        padder = InputPadder((h, w), padding)
+        while True:
+            ret, frame = vcap.read()
+            if ret is False:
+                break
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame_t = img2tensor(frame).to(device)
+            frame_t = padder.pad(frame_t)
+            inputs.append(frame_t)
+        print(f'Loading the [video] from {input_path}, the number of frames [{len(inputs)}]')
+    else:
+        raise TypeError("Input should be a video.")
+    
+    return inputs, scale, padder
+
+
+def load_model(cfg_path, ckpt_path, device="cuda"):
+
+    '''
+        load the frame interpolation model.
+    '''
+ 
+    network_cfg = OmegaConf.load(cfg_path).network
+    network_name = network_cfg.name
+    print(f'Loading [{network_name}] from [{ckpt_path}]...')
+    model = build_from_cfg(network_cfg)
+    ckpt = torch.load(ckpt_path)
+    model.load_state_dict(ckpt['state_dict'])
+    model = model.to(device)
+    model.eval()
+    return model
+
+def interpolater(model, inputs, scale, padder, iters=1):
+
+    '''
+        interpolating with the interpolation model.
+
+        params:
+            model: nn.Module, the frame interpolation model.
+            inputs: list, the list of the input frames.
+            scale: float, the scale of the input frames.
+            iters: int, the number of iterations of interpolation. The final frames model generating is 2 ** iters * (m - 1) + 1 and m is input frames. 
+        returns:
+            outputs: list, the list of the output frames.
+    '''
+
+    print(f'Start frame interpolation:')
+    embt = torch.tensor(1/2).float().view(1, 1, 1, 1).to(device)
+
+    for i in range(iters):
+        print(f'Iter {i+1}. input_frames={len(inputs)} output_frames={2*len(inputs)-1}')
+        outputs = [inputs[0]]
+        for in_0, in_1 in zip(inputs[:-1], inputs[1:]):
+            in_0 = in_0.to(device)
+            in_1 = in_1.to(device)
+            with torch.no_grad():
+                imgt_pred = model(in_0, in_1, embt, scale_factor=scale, eval=True)['imgt_pred']
+            outputs += [imgt_pred.cpu(), in_1.cpu()]
+        inputs = outputs
+
+    outputs = padder.unpad(*outputs)
+
+    return outputs
+
+def write(outputs, output_path, frame_rate=30):
+    '''
+        write results to the output_path.
+    '''
+
+    if osp.exists(output_path) is False:
+        os.makedirs(output_path)
+
+    
+    size = outputs[0].shape[2:][::-1]
+
+    video_count = len(glob.glob(os.path.join(output_path, "*.mp4")))
+
+    save_video_path = f'{output_path}/demo_{video_count:04d}.mp4'
+    writer = cv2.VideoWriter(save_video_path, cv2.VideoWriter_fourcc(*"mp4v"), 
+                        frame_rate, size)
+
+    for i, imgt_pred in enumerate(outputs):
+        imgt_pred = tensor2img(imgt_pred)
+        imgt_pred = cv2.cvtColor(imgt_pred, cv2.COLOR_RGB2BGR)
+        writer.write(imgt_pred)        
+    print(f"Demo video is saved to [{save_video_path}]")
+
+    writer.release()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, default='cfgs/AMT-G.yaml') 
+    parser.add_argument('-p', '--ckpt', type=str, default='amt-g.pth') 
+    parser.add_argument('-n', '--niters', type=int, default=6) 
+    parser.add_argument('-i', '--input', default="test.mp4") 
+    parser.add_argument('-o', '--output_path', type=str, default='results') 
+    parser.add_argument('-r', '--frame_rate', type=int, default=30)
+
+    args = parser.parse_args()
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    cfg_path = args.config
+    ckpt_path = args.ckpt
+    input_path = args.input
+    output_path = args.output_path
+    iters = int(args.niters)
+    frame_rate = int(args.frame_rate)
+
+    inputs, scale, padder = get_input_video_from_path(input_path, device)
+    model = load_model(cfg_path, ckpt_path, device)
+    outputs = interpolater(model, inputs, scale, padder, iters)
+    write(outputs, output_path, frame_rate)

src/sora/models/interpolation/networks/AMT-G.py

@@ -0,0 +1,172 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from networks.blocks.raft import (
+    coords_grid,
+    BasicUpdateBlock, BidirCorrBlock
+)
+from networks.blocks.feat_enc import (
+    LargeEncoder
+)
+from networks.blocks.ifrnet import (
+    resize,
+    Encoder,
+    InitDecoder,
+    IntermediateDecoder
+)
+from networks.blocks.multi_flow import (
+    multi_flow_combine,
+    MultiFlowDecoder
+)
+
+
+class Model(nn.Module):
+    def __init__(self, 
+                 corr_radius=3, 
+                 corr_lvls=4, 
+                 num_flows=5, 
+                 channels=[84, 96, 112, 128], 
+                 skip_channels=84):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+
+        self.feat_encoder = LargeEncoder(output_dim=128, norm_fn='instance', dropout=0.)
+        self.encoder = Encoder(channels, large=True)
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(112, None)
+        self.update3_low = self._get_updateblock(96, 2.0)
+        self.update2_low = self._get_updateblock(84, 4.0)
+        
+        self.update3_high = self._get_updateblock(96, None)
+        self.update2_high = self._get_updateblock(84, None)
+        
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3*self.num_flows, 6*self.num_flows, 7, 1, 3),
+            nn.PReLU(6*self.num_flows),
+            nn.Conv2d(6*self.num_flows, 3, 7, 1, 3),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return BasicUpdateBlock(cdim=cdim, hidden_dim=192, flow_dim=64, 
+                                corr_dim=256, corr_dim2=192, fc_dim=188, 
+                                scale_factor=scale_factor, corr_levels=self.corr_levels, 
+                                radius=self.radius)
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1. / embt
+        t0_scale = 1. / (1. - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+            
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale) 
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+    
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+        
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, 
+                                                 up_flow0_4, up_flow1_4, 
+                                                 embt, downsample=1)
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        corr_3, flow_3 = self._corr_scale_lookup(corr_fn, 
+                                                 coord, up_flow0_3, up_flow1_3, 
+                                                 embt, downsample=2)
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3_low(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+        
+        # residue update with lookup corr (hr)
+        corr_3 = resize(corr_3, scale_factor=2.0)
+        up_flow_3 = torch.cat([up_flow0_3, up_flow1_3], dim=1)
+        delta_ft_2_, delta_up_flow_3 = self.update3_high(ft_2_, up_flow_3, corr_3)
+        ft_2_ += delta_ft_2_
+        up_flow0_3 += delta_up_flow_3[:, 0:2]
+        up_flow1_3 += delta_up_flow_3[:, 2:4]
+        
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_  = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        corr_2, flow_2 = self._corr_scale_lookup(corr_fn, 
+                                                 coord, up_flow0_2, up_flow1_2, 
+                                                 embt, downsample=4)
+        
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2_low(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+        
+        # residue update with lookup corr (hr)
+        corr_2 = resize(corr_2, scale_factor=4.0)
+        up_flow_2 = torch.cat([up_flow0_2, up_flow1_2], dim=1)
+        delta_ft_1_, delta_up_flow_2 = self.update2_high(ft_1_, up_flow_2, corr_2)
+        ft_1_ += delta_ft_1_
+        up_flow0_2 += delta_up_flow_2[:, 0:2]
+        up_flow1_2 += delta_up_flow_2[:, 2:4]
+        
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+        
+        if scale_factor != 1.0: 
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            mask = resize(mask, scale_factor=(1.0/scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0/scale_factor))
+
+        # Merge multiple predictions 
+        imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, 
+                                                                        mask, img_res, mean_)
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return  { 'imgt_pred': imgt_pred, }
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                'imgt_pred': imgt_pred,
+                'flow0_pred': [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                'flow1_pred': [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                'ft_pred': [ft_1_, ft_2_, ft_3_],
+            }

src/sora/models/interpolation/networks/blocks/feat_enc.py

@@ -0,0 +1,343 @@
+import torch
+import torch.nn as nn
+
+
+class BottleneckBlock(nn.Module):
+    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
+        super(BottleneckBlock, self).__init__()
+  
+        self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0)
+        self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride)
+        self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0)
+        self.relu = nn.ReLU(inplace=True)
+
+        num_groups = planes // 8
+
+        if norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
+            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
+            self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            if not stride == 1:
+                self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+        
+        elif norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(planes//4)
+            self.norm2 = nn.BatchNorm2d(planes//4)
+            self.norm3 = nn.BatchNorm2d(planes)
+            if not stride == 1:
+                self.norm4 = nn.BatchNorm2d(planes)
+        
+        elif norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(planes//4)
+            self.norm2 = nn.InstanceNorm2d(planes//4)
+            self.norm3 = nn.InstanceNorm2d(planes)
+            if not stride == 1:
+                self.norm4 = nn.InstanceNorm2d(planes)
+
+        elif norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+            self.norm2 = nn.Sequential()
+            self.norm3 = nn.Sequential()
+            if not stride == 1:
+                self.norm4 = nn.Sequential()
+
+        if stride == 1:
+            self.downsample = None
+        
+        else:    
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4)
+
+
+    def forward(self, x):
+        y = x
+        y = self.relu(self.norm1(self.conv1(y)))
+        y = self.relu(self.norm2(self.conv2(y)))
+        y = self.relu(self.norm3(self.conv3(y)))
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return self.relu(x+y)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
+        super(ResidualBlock, self).__init__()
+  
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
+        self.relu = nn.ReLU(inplace=True)
+
+        num_groups = planes // 8
+
+        if norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            if not stride == 1:
+                self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+        
+        elif norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(planes)
+            self.norm2 = nn.BatchNorm2d(planes)
+            if not stride == 1:
+                self.norm3 = nn.BatchNorm2d(planes)
+        
+        elif norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(planes)
+            self.norm2 = nn.InstanceNorm2d(planes)
+            if not stride == 1:
+                self.norm3 = nn.InstanceNorm2d(planes)
+
+        elif norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+            self.norm2 = nn.Sequential()
+            if not stride == 1:
+                self.norm3 = nn.Sequential()
+
+        if stride == 1:
+            self.downsample = None
+        
+        else:    
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
+
+
+    def forward(self, x):
+        y = x
+        y = self.relu(self.norm1(self.conv1(y)))
+        y = self.relu(self.norm2(self.conv2(y)))
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return self.relu(x+y)
+
+
+class SmallEncoder(nn.Module):
+    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
+        super(SmallEncoder, self).__init__()
+        self.norm_fn = norm_fn
+
+        if self.norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
+            
+        elif self.norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(32)
+
+        elif self.norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(32)
+
+        elif self.norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.in_planes = 32
+        self.layer1 = self._make_layer(32,  stride=1)
+        self.layer2 = self._make_layer(64, stride=2)
+        self.layer3 = self._make_layer(96, stride=2)
+
+        self.dropout = None
+        if dropout > 0:
+            self.dropout = nn.Dropout2d(p=dropout)
+        
+        self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
+                if m.weight is not None:
+                    nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
+        layers = (layer1, layer2)
+    
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+
+    def forward(self, x):
+
+        # if input is list, combine batch dimension
+        is_list = isinstance(x, tuple) or isinstance(x, list)
+        if is_list:
+            batch_dim = x[0].shape[0]
+            x = torch.cat(x, dim=0)
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.conv2(x)
+
+        if self.training and self.dropout is not None:
+            x = self.dropout(x)
+
+        if is_list:
+            x = torch.split(x, [batch_dim, batch_dim], dim=0)
+
+        return x
+
+class BasicEncoder(nn.Module):
+    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
+        super(BasicEncoder, self).__init__()
+        self.norm_fn = norm_fn
+
+        if self.norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
+            
+        elif self.norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(64)
+
+        elif self.norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(64)
+
+        elif self.norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.in_planes = 64
+        self.layer1 = self._make_layer(64,  stride=1)
+        self.layer2 = self._make_layer(72, stride=2)
+        self.layer3 = self._make_layer(128, stride=2)
+
+        # output convolution
+        self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
+
+        self.dropout = None
+        if dropout > 0:
+            self.dropout = nn.Dropout2d(p=dropout)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
+                if m.weight is not None:
+                    nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
+        layers = (layer1, layer2)
+        
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+
+    def forward(self, x):
+
+        # if input is list, combine batch dimension
+        is_list = isinstance(x, tuple) or isinstance(x, list)
+        if is_list:
+            batch_dim = x[0].shape[0]
+            x = torch.cat(x, dim=0)
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+
+        x = self.conv2(x)
+
+        if self.training and self.dropout is not None:
+            x = self.dropout(x)
+
+        if is_list:
+            x = torch.split(x, [batch_dim, batch_dim], dim=0)
+
+        return x
+
+class LargeEncoder(nn.Module):
+    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
+        super(LargeEncoder, self).__init__()
+        self.norm_fn = norm_fn
+
+        if self.norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
+            
+        elif self.norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(64)
+
+        elif self.norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(64)
+
+        elif self.norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.in_planes = 64
+        self.layer1 = self._make_layer(64, stride=1)
+        self.layer2 = self._make_layer(112, stride=2)
+        self.layer3 = self._make_layer(160, stride=2)
+        self.layer3_2 = self._make_layer(160, stride=1)
+
+        # output convolution
+        self.conv2 = nn.Conv2d(self.in_planes, output_dim, kernel_size=1)
+
+        self.dropout = None
+        if dropout > 0:
+            self.dropout = nn.Dropout2d(p=dropout)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
+                if m.weight is not None:
+                    nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
+        layers = (layer1, layer2)
+        
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+
+    def forward(self, x):
+
+        # if input is list, combine batch dimension
+        is_list = isinstance(x, tuple) or isinstance(x, list)
+        if is_list:
+            batch_dim = x[0].shape[0]
+            x = torch.cat(x, dim=0)
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer3_2(x)
+
+        x = self.conv2(x)
+
+        if self.training and self.dropout is not None:
+            x = self.dropout(x)
+
+        if is_list:
+            x = torch.split(x, [batch_dim, batch_dim], dim=0)
+
+        return x

src/sora/models/interpolation/networks/blocks/ifrnet.py

@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from utils.flow_utils import warp
+
+
+def resize(x, scale_factor):
+    return F.interpolate(x, scale_factor=scale_factor, mode="bilinear", align_corners=False)
+
+def convrelu(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, bias=True):
+    return nn.Sequential(
+        nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias=bias), 
+        nn.PReLU(out_channels)
+    )
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channels, side_channels, bias=True):
+        super(ResBlock, self).__init__()
+        self.side_channels = side_channels
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=bias), 
+            nn.PReLU(in_channels)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(side_channels, side_channels, kernel_size=3, stride=1, padding=1, bias=bias), 
+            nn.PReLU(side_channels)
+        )
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=bias), 
+            nn.PReLU(in_channels)
+        )
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(side_channels, side_channels, kernel_size=3, stride=1, padding=1, bias=bias), 
+            nn.PReLU(side_channels)
+        )
+        self.conv5 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=bias)
+        self.prelu = nn.PReLU(in_channels)
+
+    def forward(self, x):
+        out = self.conv1(x)
+
+        res_feat = out[:, :-self.side_channels, ...]
+        side_feat = out[:, -self.side_channels:, :, :]
+        side_feat = self.conv2(side_feat)
+        out = self.conv3(torch.cat([res_feat, side_feat], 1))
+
+        res_feat = out[:, :-self.side_channels, ...]
+        side_feat = out[:, -self.side_channels:, :, :]
+        side_feat = self.conv4(side_feat)
+        out = self.conv5(torch.cat([res_feat, side_feat], 1))
+
+        out = self.prelu(x + out)
+        return out
+    
+class Encoder(nn.Module):
+    def __init__(self, channels, large=False):
+        super(Encoder, self).__init__()
+        self.channels = channels        
+        prev_ch = 3
+        for idx, ch in enumerate(channels, 1):
+            k = 7 if large and idx == 1 else 3
+            p = 3 if k ==7 else 1
+            self.register_module(f'pyramid{idx}', 
+            nn.Sequential(
+                convrelu(prev_ch, ch, k, 2, p),
+                convrelu(ch, ch, 3, 1, 1)
+            ))
+            prev_ch = ch
+                
+    def forward(self, in_x):
+        fs = []
+        for idx in range(len(self.channels)):
+            out_x = getattr(self, f'pyramid{idx+1}')(in_x)
+            fs.append(out_x)
+            in_x = out_x
+        return fs
+    
+class InitDecoder(nn.Module):
+    def __init__(self, in_ch, out_ch, skip_ch) -> None:
+        super().__init__()
+        self.convblock = nn.Sequential(
+            convrelu(in_ch*2+1, in_ch*2), 
+            ResBlock(in_ch*2, skip_ch), 
+            nn.ConvTranspose2d(in_ch*2, out_ch+4, 4, 2, 1, bias=True)
+        )
+    def forward(self, f0, f1, embt):
+        h, w = f0.shape[2:]
+        embt = embt.repeat(1, 1, h, w)
+        out = self.convblock(torch.cat([f0, f1, embt], 1))
+        flow0, flow1 = torch.chunk(out[:, :4, ...], 2, 1)
+        ft_ = out[:, 4:, ...]
+        return flow0, flow1, ft_
+    
+class IntermediateDecoder(nn.Module):
+    def __init__(self, in_ch, out_ch, skip_ch) -> None:
+        super().__init__()
+        self.convblock = nn.Sequential(
+            convrelu(in_ch*3+4, in_ch*3), 
+            ResBlock(in_ch*3, skip_ch), 
+            nn.ConvTranspose2d(in_ch*3, out_ch+4, 4, 2, 1, bias=True)
+        )
+    def forward(self, ft_, f0, f1, flow0_in, flow1_in):
+        f0_warp = warp(f0, flow0_in)
+        f1_warp = warp(f1, flow1_in)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, flow0_in, flow1_in], 1)
+        out = self.convblock(f_in)
+        flow0, flow1 = torch.chunk(out[:, :4, ...], 2, 1)
+        ft_ = out[:, 4:, ...]
+        flow0 = flow0 + 2.0 * resize(flow0_in, scale_factor=2.0)
+        flow1 = flow1 + 2.0 * resize(flow1_in, scale_factor=2.0)
+        return flow0, flow1, ft_

src/sora/models/interpolation/networks/blocks/multi_flow.py

@@ -0,0 +1,69 @@
+import torch
+import torch.nn as nn
+from utils.flow_utils import warp
+from networks.blocks.ifrnet import (
+    convrelu, resize,
+    ResBlock,
+)
+
+
+def multi_flow_combine(comb_block, img0, img1, flow0, flow1, 
+                       mask=None, img_res=None, mean=None):
+        '''
+            A parallel implementation of multiple flow field warping 
+            comb_block: An nn.Seqential object.
+            img shape: [b, c, h, w]
+            flow shape: [b, 2*num_flows, h, w]
+            mask (opt):
+                If 'mask' is None, the function conduct a simple average.
+            img_res (opt):
+                If 'img_res' is None, the function adds zero instead. 
+            mean (opt):
+                If 'mean' is None, the function adds zero instead.       
+        '''
+        b, c, h, w = flow0.shape
+        num_flows = c // 2
+        flow0   =   flow0.reshape(b, num_flows, 2, h, w).reshape(-1, 2, h, w)
+        flow1   =   flow1.reshape(b, num_flows, 2, h, w).reshape(-1, 2, h, w)
+        
+        mask    =    mask.reshape(b, num_flows, 1, h, w
+                            ).reshape(-1, 1, h, w) if mask is not None else None
+        img_res = img_res.reshape(b, num_flows, 3, h, w
+                            ).reshape(-1, 3, h, w)  if img_res is not None else 0
+        img0 = torch.stack([img0] * num_flows, 1).reshape(-1, 3, h, w)
+        img1 = torch.stack([img1] * num_flows, 1).reshape(-1, 3, h, w)
+        mean = torch.stack([mean] * num_flows, 1).reshape(-1, 1, 1, 1
+                                                    ) if mean is not None else 0
+        
+        img0_warp = warp(img0, flow0)
+        img1_warp = warp(img1, flow1)
+        img_warps = mask * img0_warp + (1 - mask) * img1_warp + mean + img_res
+        img_warps = img_warps.reshape(b, num_flows, 3, h, w)
+        imgt_pred = img_warps.mean(1) + comb_block(img_warps.view(b, -1, h, w))
+        return imgt_pred
+
+
+class MultiFlowDecoder(nn.Module):
+    def __init__(self, in_ch, skip_ch, num_flows=3):
+        super(MultiFlowDecoder, self).__init__()
+        self.num_flows = num_flows
+        self.convblock = nn.Sequential(
+            convrelu(in_ch*3+4, in_ch*3), 
+            ResBlock(in_ch*3, skip_ch), 
+            nn.ConvTranspose2d(in_ch*3, 8*num_flows, 4, 2, 1, bias=True)
+        )
+        
+    def forward(self, ft_, f0, f1, flow0, flow1):
+        n = self.num_flows
+        f0_warp = warp(f0, flow0)
+        f1_warp = warp(f1, flow1)
+        out = self.convblock(torch.cat([ft_, f0_warp, f1_warp, flow0, flow1], 1))
+        delta_flow0, delta_flow1, mask, img_res = torch.split(out, [2*n, 2*n, n, 3*n], 1)
+        mask = torch.sigmoid(mask)
+        
+        flow0 = delta_flow0 + 2.0 * resize(flow0, scale_factor=2.0
+                                           ).repeat(1, self.num_flows, 1, 1)
+        flow1 = delta_flow1 + 2.0 * resize(flow1, scale_factor=2.0
+                                           ).repeat(1, self.num_flows, 1, 1)
+        
+        return flow0, flow1, mask, img_res

src/sora/models/interpolation/networks/blocks/raft.py

@@ -0,0 +1,207 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def resize(x, scale_factor):
+    return F.interpolate(x, scale_factor=scale_factor, mode="bilinear", align_corners=False)
+
+
+def bilinear_sampler(img, coords, mask=False):
+    """ Wrapper for grid_sample, uses pixel coordinates """
+    H, W = img.shape[-2:]
+    xgrid, ygrid = coords.split([1,1], dim=-1)
+    xgrid = 2*xgrid/(W-1) - 1
+    ygrid = 2*ygrid/(H-1) - 1
+
+    grid = torch.cat([xgrid, ygrid], dim=-1)
+    img = F.grid_sample(img, grid, align_corners=True)
+
+    if mask:
+        mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
+        return img, mask.float()
+
+    return img
+
+
+def coords_grid(batch, ht, wd, device):
+    coords = torch.meshgrid(torch.arange(ht, device=device), 
+                            torch.arange(wd, device=device), 
+                            indexing='ij')
+    coords = torch.stack(coords[::-1], dim=0).float()
+    return coords[None].repeat(batch, 1, 1, 1)
+
+
+class SmallUpdateBlock(nn.Module):
+    def __init__(self, cdim, hidden_dim, flow_dim, corr_dim, fc_dim,
+                 corr_levels=4, radius=3, scale_factor=None):
+        super(SmallUpdateBlock, self).__init__()
+        cor_planes = corr_levels * (2 * radius + 1) **2
+        self.scale_factor = scale_factor
+
+        self.convc1 = nn.Conv2d(2 * cor_planes, corr_dim, 1, padding=0)
+        self.convf1 = nn.Conv2d(4, flow_dim*2, 7, padding=3)
+        self.convf2 = nn.Conv2d(flow_dim*2, flow_dim, 3, padding=1)
+        self.conv = nn.Conv2d(corr_dim+flow_dim, fc_dim, 3, padding=1)
+
+        self.gru = nn.Sequential(
+            nn.Conv2d(fc_dim+4+cdim, hidden_dim, 3, padding=1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
+        )
+
+        self.feat_head = nn.Sequential(
+            nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(hidden_dim, cdim, 3, padding=1),
+        )
+
+        self.flow_head = nn.Sequential(
+            nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(hidden_dim, 4, 3, padding=1),
+        )
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+            
+    def forward(self, net, flow, corr):
+        net = resize(net, 1 / self.scale_factor
+                      ) if self.scale_factor is not None else net
+        cor = self.lrelu(self.convc1(corr))
+        flo = self.lrelu(self.convf1(flow))
+        flo = self.lrelu(self.convf2(flo))
+        cor_flo = torch.cat([cor, flo], dim=1)
+        inp = self.lrelu(self.conv(cor_flo))
+        inp = torch.cat([inp, flow, net], dim=1)
+
+        out = self.gru(inp)
+        delta_net = self.feat_head(out)
+        delta_flow = self.flow_head(out)
+        
+        if self.scale_factor is not None:
+            delta_net = resize(delta_net, scale_factor=self.scale_factor)
+            delta_flow = self.scale_factor * resize(delta_flow, scale_factor=self.scale_factor)
+        
+        return delta_net, delta_flow
+
+
+class BasicUpdateBlock(nn.Module):
+    def __init__(self, cdim, hidden_dim, flow_dim, corr_dim, corr_dim2, 
+                 fc_dim, corr_levels=4, radius=3, scale_factor=None, out_num=1):
+        super(BasicUpdateBlock, self).__init__()
+        cor_planes = corr_levels * (2 * radius + 1) **2
+
+        self.scale_factor = scale_factor
+        self.convc1 = nn.Conv2d(2 * cor_planes, corr_dim, 1, padding=0)
+        self.convc2 = nn.Conv2d(corr_dim, corr_dim2, 3, padding=1)
+        self.convf1 = nn.Conv2d(4, flow_dim*2, 7, padding=3)
+        self.convf2 = nn.Conv2d(flow_dim*2, flow_dim, 3, padding=1)
+        self.conv = nn.Conv2d(flow_dim+corr_dim2, fc_dim, 3, padding=1)
+
+        self.gru = nn.Sequential(
+            nn.Conv2d(fc_dim+4+cdim, hidden_dim, 3, padding=1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
+        )
+
+        self.feat_head = nn.Sequential(
+            nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(hidden_dim, cdim, 3, padding=1),
+        )
+
+        self.flow_head = nn.Sequential(
+            nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(hidden_dim, 4*out_num, 3, padding=1),
+        )
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+            
+    def forward(self, net, flow, corr):
+        net = resize(net, 1 / self.scale_factor
+                      ) if self.scale_factor is not None else net
+        cor = self.lrelu(self.convc1(corr))
+        cor = self.lrelu(self.convc2(cor))
+        flo = self.lrelu(self.convf1(flow))
+        flo = self.lrelu(self.convf2(flo))
+        cor_flo = torch.cat([cor, flo], dim=1)
+        inp = self.lrelu(self.conv(cor_flo))
+        inp = torch.cat([inp, flow, net], dim=1)
+
+        out = self.gru(inp)
+        delta_net = self.feat_head(out)
+        delta_flow = self.flow_head(out)
+        
+        if self.scale_factor is not None:
+            delta_net = resize(delta_net, scale_factor=self.scale_factor)
+            delta_flow = self.scale_factor * resize(delta_flow, scale_factor=self.scale_factor)
+        return delta_net, delta_flow
+
+
+class BidirCorrBlock:
+    def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
+        self.num_levels = num_levels
+        self.radius = radius
+        self.corr_pyramid = []
+        self.corr_pyramid_T = []
+
+        corr = BidirCorrBlock.corr(fmap1, fmap2)
+        batch, h1, w1, dim, h2, w2 = corr.shape
+        corr_T = corr.clone().permute(0, 4, 5, 3, 1, 2)
+
+        corr = corr.reshape(batch*h1*w1, dim, h2, w2)
+        corr_T = corr_T.reshape(batch*h2*w2, dim, h1, w1)
+        
+        self.corr_pyramid.append(corr)
+        self.corr_pyramid_T.append(corr_T)
+
+        for _ in range(self.num_levels-1):
+            corr = F.avg_pool2d(corr, 2, stride=2)
+            corr_T = F.avg_pool2d(corr_T, 2, stride=2)
+            self.corr_pyramid.append(corr)
+            self.corr_pyramid_T.append(corr_T)
+
+    def __call__(self, coords0, coords1):
+        r = self.radius
+        coords0 = coords0.permute(0, 2, 3, 1)
+        coords1 = coords1.permute(0, 2, 3, 1)
+        assert coords0.shape == coords1.shape, f"coords0 shape: [{coords0.shape}] is not equal to [{coords1.shape}]"
+        batch, h1, w1, _ = coords0.shape
+
+        out_pyramid = []
+        out_pyramid_T = []
+        for i in range(self.num_levels):
+            corr = self.corr_pyramid[i]
+            corr_T = self.corr_pyramid_T[i]
+
+            dx = torch.linspace(-r, r, 2*r+1, device=coords0.device)
+            dy = torch.linspace(-r, r, 2*r+1, device=coords0.device)
+            delta = torch.stack(torch.meshgrid(dy, dx, indexing='ij'), axis=-1)
+            delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
+
+            centroid_lvl_0 = coords0.reshape(batch*h1*w1, 1, 1, 2) / 2**i
+            centroid_lvl_1 = coords1.reshape(batch*h1*w1, 1, 1, 2) / 2**i
+            coords_lvl_0 = centroid_lvl_0 + delta_lvl
+            coords_lvl_1 = centroid_lvl_1 + delta_lvl
+
+            corr = bilinear_sampler(corr, coords_lvl_0)
+            corr_T = bilinear_sampler(corr_T, coords_lvl_1)
+            corr = corr.view(batch, h1, w1, -1)
+            corr_T = corr_T.view(batch, h1, w1, -1)
+            out_pyramid.append(corr)
+            out_pyramid_T.append(corr_T)
+
+        out = torch.cat(out_pyramid, dim=-1)
+        out_T = torch.cat(out_pyramid_T, dim=-1)
+        return out.permute(0, 3, 1, 2).contiguous().float(), out_T.permute(0, 3, 1, 2).contiguous().float()
+
+    @staticmethod
+    def corr(fmap1, fmap2):
+        batch, dim, ht, wd = fmap1.shape
+        fmap1 = fmap1.view(batch, dim, ht*wd)
+        fmap2 = fmap2.view(batch, dim, ht*wd) 
+        
+        corr = torch.matmul(fmap1.transpose(1,2), fmap2)
+        corr = corr.view(batch, ht, wd, 1, ht, wd)
+        return corr  / torch.sqrt(torch.tensor(dim).float())

src/sora/models/interpolation/utils/build_utils.py

@@ -0,0 +1,12 @@
+import importlib
+
+
+def base_build_fn(module, cls, params):
+    return getattr(importlib.import_module(
+                    module, package=None), cls)(**params)
+
+
+def build_from_cfg(config):
+    module, cls = config['name'].rsplit(".", 1)
+    params = config.get('params', {})
+    return base_build_fn(module, cls, params)

src/sora/models/interpolation/utils/dist_utils.py

@@ -0,0 +1,48 @@
+import os
+import torch
+
+
+def get_world_size():
+    """Find OMPI world size without calling mpi functions
+    :rtype: int
+    """
+    if os.environ.get('PMI_SIZE') is not None:
+        return int(os.environ.get('PMI_SIZE') or 1)
+    elif os.environ.get('OMPI_COMM_WORLD_SIZE') is not None:
+        return int(os.environ.get('OMPI_COMM_WORLD_SIZE') or 1)
+    else:
+        return torch.cuda.device_count()
+
+
+def get_global_rank():
+    """Find OMPI world rank without calling mpi functions
+    :rtype: int
+    """
+    if os.environ.get('PMI_RANK') is not None:
+        return int(os.environ.get('PMI_RANK') or 0)
+    elif os.environ.get('OMPI_COMM_WORLD_RANK') is not None:
+        return int(os.environ.get('OMPI_COMM_WORLD_RANK') or 0)
+    else:
+        return 0
+
+
+def get_local_rank():
+    """Find OMPI local rank without calling mpi functions
+    :rtype: int
+    """
+    if os.environ.get('MPI_LOCALRANKID') is not None:
+        return int(os.environ.get('MPI_LOCALRANKID') or 0)
+    elif os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') is not None:
+        return int(os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') or 0)
+    else:
+        return 0
+
+
+def get_master_ip():
+    if os.environ.get('AZ_BATCH_MASTER_NODE') is not None:
+        return os.environ.get('AZ_BATCH_MASTER_NODE').split(':')[0]
+    elif os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE') is not None:
+        return os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE')
+    else:
+        return "127.0.0.1"
+

src/sora/models/interpolation/utils/flow_utils.py

@@ -0,0 +1,122 @@
+import numpy as np
+import torch
+from PIL import ImageFile
+import torch.nn.functional as F
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+def warp(img, flow):
+    B, _, H, W = flow.shape
+    xx = torch.linspace(-1.0, 1.0, W).view(1, 1, 1, W).expand(B, -1, H, -1)
+    yy = torch.linspace(-1.0, 1.0, H).view(1, 1, H, 1).expand(B, -1, -1, W)
+    grid = torch.cat([xx, yy], 1).to(img)
+    flow_ = torch.cat([flow[:, 0:1, :, :] / ((W - 1.0) / 2.0), flow[:, 1:2, :, :] / ((H - 1.0) / 2.0)], 1)
+    grid_ = (grid + flow_).permute(0, 2, 3, 1)
+    output = F.grid_sample(input=img, grid=grid_, mode='bilinear', padding_mode='border', align_corners=True)
+    return output
+
+
+def make_colorwheel():
+    """
+    Generates a color wheel for optical flow visualization as presented in:
+        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
+        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
+    Code follows the original C++ source code of Daniel Scharstein.
+    Code follows the the Matlab source code of Deqing Sun.
+    Returns:
+        np.ndarray: Color wheel
+    """
+
+    RY = 15
+    YG = 6
+    GC = 4
+    CB = 11
+    BM = 13
+    MR = 6
+
+    ncols = RY + YG + GC + CB + BM + MR
+    colorwheel = np.zeros((ncols, 3))
+    col = 0
+
+    # RY
+    colorwheel[0:RY, 0] = 255
+    colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
+    col = col+RY
+    # YG
+    colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
+    colorwheel[col:col+YG, 1] = 255
+    col = col+YG
+    # GC
+    colorwheel[col:col+GC, 1] = 255
+    colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
+    col = col+GC
+    # CB
+    colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
+    colorwheel[col:col+CB, 2] = 255
+    col = col+CB
+    # BM
+    colorwheel[col:col+BM, 2] = 255
+    colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
+    col = col+BM
+    # MR
+    colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
+    colorwheel[col:col+MR, 0] = 255
+    return colorwheel
+
+def flow_uv_to_colors(u, v, convert_to_bgr=False):
+    """
+    Applies the flow color wheel to (possibly clipped) flow components u and v.
+    According to the C++ source code of Daniel Scharstein
+    According to the Matlab source code of Deqing Sun
+    Args:
+        u (np.ndarray): Input horizontal flow of shape [H,W]
+        v (np.ndarray): Input vertical flow of shape [H,W]
+        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
+    Returns:
+        np.ndarray: Flow visualization image of shape [H,W,3]
+    """
+    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
+    colorwheel = make_colorwheel()  # shape [55x3]
+    ncols = colorwheel.shape[0]
+    rad = np.sqrt(np.square(u) + np.square(v))
+    a = np.arctan2(-v, -u)/np.pi
+    fk = (a+1) / 2*(ncols-1)
+    k0 = np.floor(fk).astype(np.int32)
+    k1 = k0 + 1
+    k1[k1 == ncols] = 0
+    f = fk - k0
+    for i in range(colorwheel.shape[1]):
+        tmp = colorwheel[:,i]
+        col0 = tmp[k0] / 255.0
+        col1 = tmp[k1] / 255.0
+        col = (1-f)*col0 + f*col1
+        idx = (rad <= 1)
+        col[idx]  = 1 - rad[idx] * (1-col[idx])
+        col[~idx] = col[~idx] * 0.75   # out of range
+        # Note the 2-i => BGR instead of RGB
+        ch_idx = 2-i if convert_to_bgr else i
+        flow_image[:,:,ch_idx] = np.floor(255 * col)
+    return flow_image
+
+def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
+    """
+    Expects a two dimensional flow image of shape.
+    Args:
+        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
+        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
+        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
+    Returns:
+        np.ndarray: Flow visualization image of shape [H,W,3]
+    """
+    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
+    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
+    if clip_flow is not None:
+        flow_uv = np.clip(flow_uv, 0, clip_flow)
+    u = flow_uv[:,:,0]
+    v = flow_uv[:,:,1]
+    rad = np.sqrt(np.square(u) + np.square(v))
+    rad_max = np.max(rad)
+    epsilon = 1e-5
+    u = u / (rad_max + epsilon)
+    v = v / (rad_max + epsilon)
+    return flow_uv_to_colors(u, v, convert_to_bgr)

src/sora/models/interpolation/utils/utils.py

@@ -0,0 +1,297 @@
+import re
+import sys
+import torch
+import random
+import numpy as np
+from PIL import ImageFile
+import torch.nn.functional as F
+from imageio import imread, imwrite
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class AverageMeter():
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0.
+        self.avg = 0.
+        self.sum = 0.
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class AverageMeterGroups:
+    def __init__(self) -> None:
+        self.meter_dict = dict()
+    
+    def update(self, dict, n=1):
+        for name, val in dict.items():
+            if self.meter_dict.get(name) is None:
+                self.meter_dict[name] = AverageMeter()
+            self.meter_dict[name].update(val, n)
+    
+    def reset(self, name=None):
+        if name is None:
+            for v in self.meter_dict.values():
+                v.reset()
+        else:
+            meter = self.meter_dict.get(name)
+            if meter is not None:
+                meter.reset()
+    
+    def avg(self, name):
+        meter = self.meter_dict.get(name)
+        if meter is not None:
+            return meter.avg
+
+
+class InputPadder:
+    """ Pads images such that dimensions are divisible by divisor """
+    def __init__(self, dims, divisor=16):
+        self.ht, self.wd = dims[-2:]
+        pad_ht = (((self.ht // divisor) + 1) * divisor - self.ht) % divisor
+        pad_wd = (((self.wd // divisor) + 1) * divisor - self.wd) % divisor
+        self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
+
+    def pad(self, *inputs):
+        if len(inputs) == 1:
+            return F.pad(inputs[0], self._pad, mode='replicate')
+        else:
+            return [F.pad(x, self._pad, mode='replicate') for x in inputs]
+
+    def unpad(self, *inputs):
+        if len(inputs) == 1:
+            return self._unpad(inputs[0])
+        else:
+            return [self._unpad(x) for x in inputs]
+    
+    def _unpad(self, x):
+        ht, wd = x.shape[-2:]
+        c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
+        return x[..., c[0]:c[1], c[2]:c[3]]
+
+
+def img2tensor(img):
+    if img.shape[-1] > 3:
+        img = img[:,:,:3]
+    return torch.tensor(img).permute(2, 0, 1).unsqueeze(0) / 255.0
+
+
+def tensor2img(img_t):
+    return (img_t * 255.).detach(
+                        ).squeeze(0).permute(1, 2, 0).cpu().numpy(
+                        ).clip(0, 255).astype(np.uint8)
+
+def seed_all(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def read(file):
+    if file.endswith('.float3'): return readFloat(file)
+    elif file.endswith('.flo'): return readFlow(file)
+    elif file.endswith('.ppm'): return readImage(file)
+    elif file.endswith('.pgm'): return readImage(file)
+    elif file.endswith('.png'): return readImage(file)
+    elif file.endswith('.jpg'): return readImage(file)
+    elif file.endswith('.pfm'): return readPFM(file)[0]
+    else: raise Exception('don\'t know how to read %s' % file)
+
+
+def write(file, data):
+    if file.endswith('.float3'): return writeFloat(file, data)
+    elif file.endswith('.flo'): return writeFlow(file, data)
+    elif file.endswith('.ppm'): return writeImage(file, data)
+    elif file.endswith('.pgm'): return writeImage(file, data)
+    elif file.endswith('.png'): return writeImage(file, data)
+    elif file.endswith('.jpg'): return writeImage(file, data)
+    elif file.endswith('.pfm'): return writePFM(file, data)
+    else: raise Exception('don\'t know how to write %s' % file)
+
+
+def readPFM(file):
+    file = open(file, 'rb')
+
+    color = None
+    width = None
+    height = None
+    scale = None
+    endian = None
+
+    header = file.readline().rstrip()
+    if header.decode("ascii") == 'PF':
+        color = True
+    elif header.decode("ascii") == 'Pf':
+        color = False
+    else:
+        raise Exception('Not a PFM file.')
+
+    dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode("ascii"))
+    if dim_match:
+        width, height = list(map(int, dim_match.groups()))
+    else:
+        raise Exception('Malformed PFM header.')
+
+    scale = float(file.readline().decode("ascii").rstrip())
+    if scale < 0:
+        endian = '<'
+        scale = -scale
+    else:
+        endian = '>'
+
+    data = np.fromfile(file, endian + 'f')
+    shape = (height, width, 3) if color else (height, width)
+
+    data = np.reshape(data, shape)
+    data = np.flipud(data)
+    return data, scale
+
+
+def writePFM(file, image, scale=1):
+    file = open(file, 'wb')
+
+    color = None
+
+    if image.dtype.name != 'float32':
+        raise Exception('Image dtype must be float32.')
+
+    image = np.flipud(image)
+
+    if len(image.shape) == 3 and image.shape[2] == 3:
+        color = True
+    elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1:
+        color = False
+    else:
+        raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.')
+
+    file.write('PF\n' if color else 'Pf\n'.encode())
+    file.write('%d %d\n'.encode() % (image.shape[1], image.shape[0]))
+
+    endian = image.dtype.byteorder
+
+    if endian == '<' or endian == '=' and sys.byteorder == 'little':
+        scale = -scale
+
+    file.write('%f\n'.encode() % scale)
+
+    image.tofile(file)
+
+
+def readFlow(name):
+    if name.endswith('.pfm') or name.endswith('.PFM'):
+        return readPFM(name)[0][:,:,0:2]
+
+    f = open(name, 'rb')
+
+    header = f.read(4)
+    if header.decode("utf-8") != 'PIEH':
+        raise Exception('Flow file header does not contain PIEH')
+
+    width = np.fromfile(f, np.int32, 1).squeeze()
+    height = np.fromfile(f, np.int32, 1).squeeze()
+
+    flow = np.fromfile(f, np.float32, width * height * 2).reshape((height, width, 2))
+
+    return flow.astype(np.float32)
+
+
+def readImage(name):
+    if name.endswith('.pfm') or name.endswith('.PFM'):
+        data = readPFM(name)[0]
+        if len(data.shape)==3:
+            return data[:,:,0:3]
+        else:
+            return data
+    return imread(name)
+
+
+def writeImage(name, data):
+    if name.endswith('.pfm') or name.endswith('.PFM'):
+        return writePFM(name, data, 1)
+    return imwrite(name, data)
+
+
+def writeFlow(name, flow):
+    f = open(name, 'wb')
+    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)
+
+
+def readFloat(name):
+    f = open(name, 'rb')
+
+    if(f.readline().decode("utf-8"))  != 'float\n':
+        raise Exception('float file %s did not contain <float> keyword' % name)
+
+    dim = int(f.readline())
+
+    dims = []
+    count = 1
+    for i in range(0, dim):
+        d = int(f.readline())
+        dims.append(d)
+        count *= d
+
+    dims = list(reversed(dims))
+
+    data = np.fromfile(f, np.float32, count).reshape(dims)
+    if dim > 2:
+        data = np.transpose(data, (2, 1, 0))
+        data = np.transpose(data, (1, 0, 2))
+
+    return data
+
+
+def writeFloat(name, data):
+    f = open(name, 'wb')
+
+    dim=len(data.shape)
+    if dim>3:
+        raise Exception('bad float file dimension: %d' % dim)
+
+    f.write(('float\n').encode('ascii'))
+    f.write(('%d\n' % dim).encode('ascii'))
+
+    if dim == 1:
+        f.write(('%d\n' % data.shape[0]).encode('ascii'))
+    else:
+        f.write(('%d\n' % data.shape[1]).encode('ascii'))
+        f.write(('%d\n' % data.shape[0]).encode('ascii'))
+        for i in range(2, dim):
+            f.write(('%d\n' % data.shape[i]).encode('ascii'))
+
+    data = data.astype(np.float32)
+    if dim==2:
+        data.tofile(f)
+
+    else:
+        np.transpose(data, (2, 0, 1)).tofile(f)
+
+
+def check_dim_and_resize(tensor_list):
+    shape_list = []
+    for t in tensor_list:
+        shape_list.append(t.shape[2:])
+
+    if len(set(shape_list)) > 1:
+        desired_shape = shape_list[0]
+        print(f'Inconsistent size of input video frames. All frames will be resized to {desired_shape}')
+        
+        resize_tensor_list = []
+        for t in tensor_list:
+            resize_tensor_list.append(torch.nn.functional.interpolate(t, size=tuple(desired_shape), mode='bilinear'))
+
+        tensor_list = resize_tensor_list
+
+    return tensor_list
+