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causalvideovae/dataset/ucf101.py

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+import math
+import os
+
+import decord
+import numpy as np
+import torch
+import torchvision
+from decord import VideoReader, cpu
+from torch.utils.data import Dataset
+from torchvision.transforms import Compose, Lambda, ToTensor
+from torchvision.transforms._transforms_video import NormalizeVideo, RandomCropVideo, RandomHorizontalFlipVideo
+from pytorchvideo.transforms import ApplyTransformToKey, ShortSideScale, UniformTemporalSubsample
+from torch.nn import functional as F
+import random
+
+from ..utils.dataset_utils import DecordInit
+
+
+class UCF101(Dataset):
+    def __init__(self, args, transform, temporal_sample):
+        self.data_path = args.data_path
+        self.num_frames = args.num_frames
+        self.transform = transform
+        self.temporal_sample = temporal_sample
+        self.v_decoder = DecordInit()
+
+        self.classes = sorted(os.listdir(self.data_path))
+        self.class_to_idx = {cls_name: idx for idx, cls_name in enumerate(self.classes)}
+        self.samples = self._make_dataset()
+
+
+    def _make_dataset(self):
+        dataset = []
+        for class_name in self.classes:
+            class_path = os.path.join(self.data_path, class_name)
+            for fname in os.listdir(class_path):
+                if fname.endswith('.avi'):
+                    item = (os.path.join(class_path, fname), self.class_to_idx[class_name])
+                    dataset.append(item)
+        return dataset
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx):
+        video_path, label = self.samples[idx]
+        try:
+            video = self.tv_read(video_path)
+            video = self.transform(video)  # T C H W -> T C H W
+            video = video.transpose(0, 1)  # T C H W -> C T H W
+            return video, label
+        except Exception as e:
+            print(f'Error with {e}, {video_path}')
+            return self.__getitem__(random.randint(0, self.__len__()-1))
+
+    def tv_read(self, path):
+        vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='TCHW')
+        total_frames = len(vframes)
+
+        # Sampling video frames
+        start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
+        # assert end_frame_ind - start_frame_ind >= self.num_frames
+        frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.num_frames, dtype=int)
+        video = vframes[frame_indice]  # (T, C, H, W)
+
+        return video
+
+    def decord_read(self, path):
+        decord_vr = self.v_decoder(path)
+        total_frames = len(decord_vr)
+        # Sampling video frames
+        start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
+        # assert end_frame_ind - start_frame_ind >= self.num_frames
+        frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.num_frames, dtype=int)
+
+        video_data = decord_vr.get_batch(frame_indice).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(0, 3, 1, 2)  # (T, H, W, C) -> (T C H W)
+        return video_data
+

causalvideovae/eval/RAFT/.gitignore

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+*.pyc
+*.egg-info
+dist
+datasets
+pytorch_env
+models
+build
+correlation.egg-info

causalvideovae/eval/RAFT/alt_cuda_corr/correlation.cpp

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+#include <torch/extension.h>
+#include <vector>
+
+// CUDA forward declarations
+std::vector<torch::Tensor> corr_cuda_forward(
+    torch::Tensor fmap1,
+    torch::Tensor fmap2,
+    torch::Tensor coords,
+    int radius);
+
+std::vector<torch::Tensor> corr_cuda_backward(
+  torch::Tensor fmap1,
+  torch::Tensor fmap2,
+  torch::Tensor coords,
+  torch::Tensor corr_grad,
+  int radius);
+
+// C++ interface
+#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)
+
+std::vector<torch::Tensor> corr_forward(
+    torch::Tensor fmap1,
+    torch::Tensor fmap2,
+    torch::Tensor coords,
+    int radius) {
+  CHECK_INPUT(fmap1);
+  CHECK_INPUT(fmap2);
+  CHECK_INPUT(coords);
+
+  return corr_cuda_forward(fmap1, fmap2, coords, radius);
+}
+
+
+std::vector<torch::Tensor> corr_backward(
+    torch::Tensor fmap1,
+    torch::Tensor fmap2,
+    torch::Tensor coords,
+    torch::Tensor corr_grad,
+    int radius) {
+  CHECK_INPUT(fmap1);
+  CHECK_INPUT(fmap2);
+  CHECK_INPUT(coords);
+  CHECK_INPUT(corr_grad);
+
+  return corr_cuda_backward(fmap1, fmap2, coords, corr_grad, radius);
+}
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &corr_forward, "CORR forward");
+  m.def("backward", &corr_backward, "CORR backward");
+}

causalvideovae/eval/RAFT/alt_cuda_corr/setup.py

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+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+
+setup(
+    name='correlation',
+    ext_modules=[
+        CUDAExtension('alt_cuda_corr',
+            sources=['correlation.cpp', 'correlation_kernel.cu'],
+            extra_compile_args={'cxx': [], 'nvcc': ['-O3']}),
+    ],
+    cmdclass={
+        'build_ext': BuildExtension
+    })
+

causalvideovae/eval/RAFT/core/raft.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .update import BasicUpdateBlock, SmallUpdateBlock
+from .extractor import BasicEncoder, SmallEncoder
+from .corr import CorrBlock, AlternateCorrBlock
+from .utils.utils import bilinear_sampler, coords_grid, upflow8
+
+try:
+    autocast = torch.cuda.amp.autocast
+except:
+    # dummy autocast for PyTorch < 1.6
+    class autocast:
+        def __init__(self, enabled):
+            pass
+        def __enter__(self):
+            pass
+        def __exit__(self, *args):
+            pass
+
+
+class RAFT(nn.Module):
+    def __init__(self, args):
+        super(RAFT, self).__init__()
+        self.args = args
+
+        if args.small:
+            self.hidden_dim = hdim = 96
+            self.context_dim = cdim = 64
+            args.corr_levels = 4
+            args.corr_radius = 3
+        
+        else:
+            self.hidden_dim = hdim = 128
+            self.context_dim = cdim = 128
+            args.corr_levels = 4
+            args.corr_radius = 4
+
+        if 'dropout' not in self.args:
+            self.args.dropout = 0
+
+        if 'alternate_corr' not in self.args:
+            self.args.alternate_corr = False
+
+        # feature network, context network, and update block
+        if args.small:
+            self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)        
+            self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
+            self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
+
+        else:
+            self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)        
+            self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
+            self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
+
+    def freeze_bn(self):
+        for m in self.modules():
+            if isinstance(m, nn.BatchNorm2d):
+                m.eval()
+
+    def initialize_flow(self, img):
+        """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
+        N, C, H, W = img.shape
+        coords0 = coords_grid(N, H//8, W//8, device=img.device)
+        coords1 = coords_grid(N, H//8, W//8, device=img.device)
+
+        # optical flow computed as difference: flow = coords1 - coords0
+        return coords0, coords1
+
+    def upsample_flow(self, flow, mask):
+        """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
+        N, _, H, W = flow.shape
+        mask = mask.view(N, 1, 9, 8, 8, H, W)
+        mask = torch.softmax(mask, dim=2)
+
+        up_flow = F.unfold(8 * flow, [3,3], padding=1)
+        up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
+
+        up_flow = torch.sum(mask * up_flow, dim=2)
+        up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
+        return up_flow.reshape(N, 2, 8*H, 8*W)
+
+
+    def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
+        """ Estimate optical flow between pair of frames """
+
+        image1 = 2 * (image1 / 255.0) - 1.0
+        image2 = 2 * (image2 / 255.0) - 1.0
+
+        image1 = image1.contiguous()
+        image2 = image2.contiguous()
+
+        hdim = self.hidden_dim
+        cdim = self.context_dim
+
+        # run the feature network
+        with autocast(enabled=self.args.mixed_precision):
+            fmap1, fmap2 = self.fnet([image1, image2])        
+        
+        fmap1 = fmap1.float()
+        fmap2 = fmap2.float()
+        if self.args.alternate_corr:
+            corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
+        else:
+            corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
+
+        # run the context network
+        with autocast(enabled=self.args.mixed_precision):
+            cnet = self.cnet(image1)
+            net, inp = torch.split(cnet, [hdim, cdim], dim=1)
+            net = torch.tanh(net)
+            inp = torch.relu(inp)
+
+        coords0, coords1 = self.initialize_flow(image1)
+
+        if flow_init is not None:
+            coords1 = coords1 + flow_init
+
+        flow_predictions = []
+        for itr in range(iters):
+            coords1 = coords1.detach()
+            corr = corr_fn(coords1) # index correlation volume
+
+            flow = coords1 - coords0
+            with autocast(enabled=self.args.mixed_precision):
+                net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
+
+            # F(t+1) = F(t) + \Delta(t)
+            coords1 = coords1 + delta_flow
+
+            # upsample predictions
+            if up_mask is None:
+                flow_up = upflow8(coords1 - coords0)
+            else:
+                flow_up = self.upsample_flow(coords1 - coords0, up_mask)
+            
+            flow_predictions.append(flow_up)
+
+        if test_mode:
+            return coords1 - coords0, flow_up
+            
+        return flow_predictions

causalvideovae/eval/RAFT/core/update.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class FlowHead(nn.Module):
+    def __init__(self, input_dim=128, hidden_dim=256):
+        super(FlowHead, self).__init__()
+        self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
+        self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        return self.conv2(self.relu(self.conv1(x)))
+
+class ConvGRU(nn.Module):
+    def __init__(self, hidden_dim=128, input_dim=192+128):
+        super(ConvGRU, self).__init__()
+        self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
+        self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
+        self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
+
+    def forward(self, h, x):
+        hx = torch.cat([h, x], dim=1)
+
+        z = torch.sigmoid(self.convz(hx))
+        r = torch.sigmoid(self.convr(hx))
+        q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
+
+        h = (1-z) * h + z * q
+        return h
+
+class SepConvGRU(nn.Module):
+    def __init__(self, hidden_dim=128, input_dim=192+128):
+        super(SepConvGRU, self).__init__()
+        self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
+        self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
+        self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
+
+        self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
+        self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
+        self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
+
+
+    def forward(self, h, x):
+        # horizontal
+        hx = torch.cat([h, x], dim=1)
+        z = torch.sigmoid(self.convz1(hx))
+        r = torch.sigmoid(self.convr1(hx))
+        q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))        
+        h = (1-z) * h + z * q
+
+        # vertical
+        hx = torch.cat([h, x], dim=1)
+        z = torch.sigmoid(self.convz2(hx))
+        r = torch.sigmoid(self.convr2(hx))
+        q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))       
+        h = (1-z) * h + z * q
+
+        return h
+
+class SmallMotionEncoder(nn.Module):
+    def __init__(self, args):
+        super(SmallMotionEncoder, self).__init__()
+        cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
+        self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
+        self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
+        self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
+        self.conv = nn.Conv2d(128, 80, 3, padding=1)
+
+    def forward(self, flow, corr):
+        cor = F.relu(self.convc1(corr))
+        flo = F.relu(self.convf1(flow))
+        flo = F.relu(self.convf2(flo))
+        cor_flo = torch.cat([cor, flo], dim=1)
+        out = F.relu(self.conv(cor_flo))
+        return torch.cat([out, flow], dim=1)
+
+class BasicMotionEncoder(nn.Module):
+    def __init__(self, args):
+        super(BasicMotionEncoder, self).__init__()
+        cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
+        self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
+        self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
+        self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
+        self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
+        self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1)
+
+    def forward(self, flow, corr):
+        cor = F.relu(self.convc1(corr))
+        cor = F.relu(self.convc2(cor))
+        flo = F.relu(self.convf1(flow))
+        flo = F.relu(self.convf2(flo))
+
+        cor_flo = torch.cat([cor, flo], dim=1)
+        out = F.relu(self.conv(cor_flo))
+        return torch.cat([out, flow], dim=1)
+
+class SmallUpdateBlock(nn.Module):
+    def __init__(self, args, hidden_dim=96):
+        super(SmallUpdateBlock, self).__init__()
+        self.encoder = SmallMotionEncoder(args)
+        self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64)
+        self.flow_head = FlowHead(hidden_dim, hidden_dim=128)
+
+    def forward(self, net, inp, corr, flow):
+        motion_features = self.encoder(flow, corr)
+        inp = torch.cat([inp, motion_features], dim=1)
+        net = self.gru(net, inp)
+        delta_flow = self.flow_head(net)
+
+        return net, None, delta_flow
+
+class BasicUpdateBlock(nn.Module):
+    def __init__(self, args, hidden_dim=128, input_dim=128):
+        super(BasicUpdateBlock, self).__init__()
+        self.args = args
+        self.encoder = BasicMotionEncoder(args)
+        self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
+        self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
+
+        self.mask = nn.Sequential(
+            nn.Conv2d(128, 256, 3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 64*9, 1, padding=0))
+
+    def forward(self, net, inp, corr, flow, upsample=True):
+        motion_features = self.encoder(flow, corr)
+        inp = torch.cat([inp, motion_features], dim=1)
+
+        net = self.gru(net, inp)
+        delta_flow = self.flow_head(net)
+
+        # scale mask to balence gradients
+        mask = .25 * self.mask(net)
+        return net, mask, delta_flow
+
+
+

causalvideovae/eval/RAFT/core/utils/augmentor.py

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+import numpy as np
+import random
+import math
+from PIL import Image
+
+import cv2
+cv2.setNumThreads(0)
+cv2.ocl.setUseOpenCL(False)
+
+import torch
+from torchvision.transforms import ColorJitter
+import torch.nn.functional as F
+
+
+class FlowAugmentor:
+    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True):
+        
+        # spatial augmentation params
+        self.crop_size = crop_size
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.spatial_aug_prob = 0.8
+        self.stretch_prob = 0.8
+        self.max_stretch = 0.2
+
+        # flip augmentation params
+        self.do_flip = do_flip
+        self.h_flip_prob = 0.5
+        self.v_flip_prob = 0.1
+
+        # photometric augmentation params
+        self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14)
+        self.asymmetric_color_aug_prob = 0.2
+        self.eraser_aug_prob = 0.5
+
+    def color_transform(self, img1, img2):
+        """ Photometric augmentation """
+
+        # asymmetric
+        if np.random.rand() < self.asymmetric_color_aug_prob:
+            img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
+            img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
+
+        # symmetric
+        else:
+            image_stack = np.concatenate([img1, img2], axis=0)
+            image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
+            img1, img2 = np.split(image_stack, 2, axis=0)
+
+        return img1, img2
+
+    def eraser_transform(self, img1, img2, bounds=[50, 100]):
+        """ Occlusion augmentation """
+
+        ht, wd = img1.shape[:2]
+        if np.random.rand() < self.eraser_aug_prob:
+            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
+            for _ in range(np.random.randint(1, 3)):
+                x0 = np.random.randint(0, wd)
+                y0 = np.random.randint(0, ht)
+                dx = np.random.randint(bounds[0], bounds[1])
+                dy = np.random.randint(bounds[0], bounds[1])
+                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
+
+        return img1, img2
+
+    def spatial_transform(self, img1, img2, flow):
+        # randomly sample scale
+        ht, wd = img1.shape[:2]
+        min_scale = np.maximum(
+            (self.crop_size[0] + 8) / float(ht), 
+            (self.crop_size[1] + 8) / float(wd))
+
+        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
+        scale_x = scale
+        scale_y = scale
+        if np.random.rand() < self.stretch_prob:
+            scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
+            scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
+        
+        scale_x = np.clip(scale_x, min_scale, None)
+        scale_y = np.clip(scale_y, min_scale, None)
+
+        if np.random.rand() < self.spatial_aug_prob:
+            # rescale the images
+            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            flow = flow * [scale_x, scale_y]
+
+        if self.do_flip:
+            if np.random.rand() < self.h_flip_prob: # h-flip
+                img1 = img1[:, ::-1]
+                img2 = img2[:, ::-1]
+                flow = flow[:, ::-1] * [-1.0, 1.0]
+
+            if np.random.rand() < self.v_flip_prob: # v-flip
+                img1 = img1[::-1, :]
+                img2 = img2[::-1, :]
+                flow = flow[::-1, :] * [1.0, -1.0]
+
+        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
+        x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
+        
+        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+
+        return img1, img2, flow
+
+    def __call__(self, img1, img2, flow):
+        img1, img2 = self.color_transform(img1, img2)
+        img1, img2 = self.eraser_transform(img1, img2)
+        img1, img2, flow = self.spatial_transform(img1, img2, flow)
+
+        img1 = np.ascontiguousarray(img1)
+        img2 = np.ascontiguousarray(img2)
+        flow = np.ascontiguousarray(flow)
+
+        return img1, img2, flow
+
+class SparseFlowAugmentor:
+    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False):
+        # spatial augmentation params
+        self.crop_size = crop_size
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.spatial_aug_prob = 0.8
+        self.stretch_prob = 0.8
+        self.max_stretch = 0.2
+
+        # flip augmentation params
+        self.do_flip = do_flip
+        self.h_flip_prob = 0.5
+        self.v_flip_prob = 0.1
+
+        # photometric augmentation params
+        self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14)
+        self.asymmetric_color_aug_prob = 0.2
+        self.eraser_aug_prob = 0.5
+        
+    def color_transform(self, img1, img2):
+        image_stack = np.concatenate([img1, img2], axis=0)
+        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
+        img1, img2 = np.split(image_stack, 2, axis=0)
+        return img1, img2
+
+    def eraser_transform(self, img1, img2):
+        ht, wd = img1.shape[:2]
+        if np.random.rand() < self.eraser_aug_prob:
+            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
+            for _ in range(np.random.randint(1, 3)):
+                x0 = np.random.randint(0, wd)
+                y0 = np.random.randint(0, ht)
+                dx = np.random.randint(50, 100)
+                dy = np.random.randint(50, 100)
+                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
+
+        return img1, img2
+
+    def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
+        ht, wd = flow.shape[:2]
+        coords = np.meshgrid(np.arange(wd), np.arange(ht))
+        coords = np.stack(coords, axis=-1)
+
+        coords = coords.reshape(-1, 2).astype(np.float32)
+        flow = flow.reshape(-1, 2).astype(np.float32)
+        valid = valid.reshape(-1).astype(np.float32)
+
+        coords0 = coords[valid>=1]
+        flow0 = flow[valid>=1]
+
+        ht1 = int(round(ht * fy))
+        wd1 = int(round(wd * fx))
+
+        coords1 = coords0 * [fx, fy]
+        flow1 = flow0 * [fx, fy]
+
+        xx = np.round(coords1[:,0]).astype(np.int32)
+        yy = np.round(coords1[:,1]).astype(np.int32)
+
+        v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
+        xx = xx[v]
+        yy = yy[v]
+        flow1 = flow1[v]
+
+        flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
+        valid_img = np.zeros([ht1, wd1], dtype=np.int32)
+
+        flow_img[yy, xx] = flow1
+        valid_img[yy, xx] = 1
+
+        return flow_img, valid_img
+
+    def spatial_transform(self, img1, img2, flow, valid):
+        # randomly sample scale
+
+        ht, wd = img1.shape[:2]
+        min_scale = np.maximum(
+            (self.crop_size[0] + 1) / float(ht), 
+            (self.crop_size[1] + 1) / float(wd))
+
+        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
+        scale_x = np.clip(scale, min_scale, None)
+        scale_y = np.clip(scale, min_scale, None)
+
+        if np.random.rand() < self.spatial_aug_prob:
+            # rescale the images
+            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
+
+        if self.do_flip:
+            if np.random.rand() < 0.5: # h-flip
+                img1 = img1[:, ::-1]
+                img2 = img2[:, ::-1]
+                flow = flow[:, ::-1] * [-1.0, 1.0]
+                valid = valid[:, ::-1]
+
+        margin_y = 20
+        margin_x = 50
+
+        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
+        x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
+
+        y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
+        x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
+
+        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        return img1, img2, flow, valid
+
+
+    def __call__(self, img1, img2, flow, valid):
+        img1, img2 = self.color_transform(img1, img2)
+        img1, img2 = self.eraser_transform(img1, img2)
+        img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
+
+        img1 = np.ascontiguousarray(img1)
+        img2 = np.ascontiguousarray(img2)
+        flow = np.ascontiguousarray(flow)
+        valid = np.ascontiguousarray(valid)
+
+        return img1, img2, flow, valid

causalvideovae/eval/RAFT/core/utils/flow_viz.py

@@ -0,0 +1,132 @@
+# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
+
+
+# MIT License
+#
+# Copyright (c) 2018 Tom Runia
+#
+# 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 conditions.
+#
+# Author: Tom Runia
+# Date Created: 2018-08-03
+
+import numpy as np
+
+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)

causalvideovae/eval/RAFT/evaluate.py

@@ -0,0 +1,197 @@
+import sys
+sys.path.append('core')
+
+from PIL import Image
+import argparse
+import os
+import time
+import numpy as np
+import torch
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+
+import datasets
+from utils import flow_viz
+from utils import frame_utils
+
+from raft import RAFT
+from utils.utils import InputPadder, forward_interpolate
+
+
+@torch.no_grad()
+def create_sintel_submission(model, iters=32, warm_start=False, output_path='sintel_submission'):
+    """ Create submission for the Sintel leaderboard """
+    model.eval()
+    for dstype in ['clean', 'final']:
+        test_dataset = datasets.MpiSintel(split='test', aug_params=None, dstype=dstype)
+        
+        flow_prev, sequence_prev = None, None
+        for test_id in range(len(test_dataset)):
+            image1, image2, (sequence, frame) = test_dataset[test_id]
+            if sequence != sequence_prev:
+                flow_prev = None
+            
+            padder = InputPadder(image1.shape)
+            image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
+
+            flow_low, flow_pr = model(image1, image2, iters=iters, flow_init=flow_prev, test_mode=True)
+            flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
+
+            if warm_start:
+                flow_prev = forward_interpolate(flow_low[0])[None].cuda()
+            
+            output_dir = os.path.join(output_path, dstype, sequence)
+            output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame+1))
+
+            if not os.path.exists(output_dir):
+                os.makedirs(output_dir)
+
+            frame_utils.writeFlow(output_file, flow)
+            sequence_prev = sequence
+
+
+@torch.no_grad()
+def create_kitti_submission(model, iters=24, output_path='kitti_submission'):
+    """ Create submission for the Sintel leaderboard """
+    model.eval()
+    test_dataset = datasets.KITTI(split='testing', aug_params=None)
+
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+
+    for test_id in range(len(test_dataset)):
+        image1, image2, (frame_id, ) = test_dataset[test_id]
+        padder = InputPadder(image1.shape, mode='kitti')
+        image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
+
+        _, flow_pr = model(image1, image2, iters=iters, test_mode=True)
+        flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
+
+        output_filename = os.path.join(output_path, frame_id)
+        frame_utils.writeFlowKITTI(output_filename, flow)
+
+
+@torch.no_grad()
+def validate_chairs(model, iters=24):
+    """ Perform evaluation on the FlyingChairs (test) split """
+    model.eval()
+    epe_list = []
+
+    val_dataset = datasets.FlyingChairs(split='validation')
+    for val_id in range(len(val_dataset)):
+        image1, image2, flow_gt, _ = val_dataset[val_id]
+        image1 = image1[None].cuda()
+        image2 = image2[None].cuda()
+
+        _, flow_pr = model(image1, image2, iters=iters, test_mode=True)
+        epe = torch.sum((flow_pr[0].cpu() - flow_gt)**2, dim=0).sqrt()
+        epe_list.append(epe.view(-1).numpy())
+
+    epe = np.mean(np.concatenate(epe_list))
+    print("Validation Chairs EPE: %f" % epe)
+    return {'chairs': epe}
+
+
+@torch.no_grad()
+def validate_sintel(model, iters=32):
+    """ Peform validation using the Sintel (train) split """
+    model.eval()
+    results = {}
+    for dstype in ['clean', 'final']:
+        val_dataset = datasets.MpiSintel(split='training', dstype=dstype)
+        epe_list = []
+
+        for val_id in range(len(val_dataset)):
+            image1, image2, flow_gt, _ = val_dataset[val_id]
+            image1 = image1[None].cuda()
+            image2 = image2[None].cuda()
+
+            padder = InputPadder(image1.shape)
+            image1, image2 = padder.pad(image1, image2)
+
+            flow_low, flow_pr = model(image1, image2, iters=iters, test_mode=True)
+            flow = padder.unpad(flow_pr[0]).cpu()
+
+            epe = torch.sum((flow - flow_gt)**2, dim=0).sqrt()
+            epe_list.append(epe.view(-1).numpy())
+
+        epe_all = np.concatenate(epe_list)
+        epe = np.mean(epe_all)
+        px1 = np.mean(epe_all<1)
+        px3 = np.mean(epe_all<3)
+        px5 = np.mean(epe_all<5)
+
+        print("Validation (%s) EPE: %f, 1px: %f, 3px: %f, 5px: %f" % (dstype, epe, px1, px3, px5))
+        results[dstype] = np.mean(epe_list)
+
+    return results
+
+
+@torch.no_grad()
+def validate_kitti(model, iters=24):
+    """ Peform validation using the KITTI-2015 (train) split """
+    model.eval()
+    val_dataset = datasets.KITTI(split='training')
+
+    out_list, epe_list = [], []
+    for val_id in range(len(val_dataset)):
+        image1, image2, flow_gt, valid_gt = val_dataset[val_id]
+        image1 = image1[None].cuda()
+        image2 = image2[None].cuda()
+
+        padder = InputPadder(image1.shape, mode='kitti')
+        image1, image2 = padder.pad(image1, image2)
+
+        flow_low, flow_pr = model(image1, image2, iters=iters, test_mode=True)
+        flow = padder.unpad(flow_pr[0]).cpu()
+
+        epe = torch.sum((flow - flow_gt)**2, dim=0).sqrt()
+        mag = torch.sum(flow_gt**2, dim=0).sqrt()
+
+        epe = epe.view(-1)
+        mag = mag.view(-1)
+        val = valid_gt.view(-1) >= 0.5
+
+        out = ((epe > 3.0) & ((epe/mag) > 0.05)).float()
+        epe_list.append(epe[val].mean().item())
+        out_list.append(out[val].cpu().numpy())
+
+    epe_list = np.array(epe_list)
+    out_list = np.concatenate(out_list)
+
+    epe = np.mean(epe_list)
+    f1 = 100 * np.mean(out_list)
+
+    print("Validation KITTI: %f, %f" % (epe, f1))
+    return {'kitti-epe': epe, 'kitti-f1': f1}
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', help="restore checkpoint")
+    parser.add_argument('--dataset', help="dataset for evaluation")
+    parser.add_argument('--small', action='store_true', help='use small model')
+    parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
+    parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
+    args = parser.parse_args()
+
+    model = torch.nn.DataParallel(RAFT(args))
+    model.load_state_dict(torch.load(args.model))
+
+    model.cuda()
+    model.eval()
+
+    # create_sintel_submission(model.module, warm_start=True)
+    # create_kitti_submission(model.module)
+
+    with torch.no_grad():
+        if args.dataset == 'chairs':
+            validate_chairs(model.module)
+
+        elif args.dataset == 'sintel':
+            validate_sintel(model.module)
+
+        elif args.dataset == 'kitti':
+            validate_kitti(model.module)
+
+

causalvideovae/eval/RAFT/train.py

@@ -0,0 +1,247 @@
+from __future__ import print_function, division
+import sys
+sys.path.append('core')
+
+import argparse
+import os
+import cv2
+import time
+import numpy as np
+import matplotlib.pyplot as plt
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+
+from torch.utils.data import DataLoader
+from raft import RAFT
+import evaluate
+import datasets
+
+from torch.utils.tensorboard import SummaryWriter
+
+try:
+    from torch.cuda.amp import GradScaler
+except:
+    # dummy GradScaler for PyTorch < 1.6
+    class GradScaler:
+        def __init__(self):
+            pass
+        def scale(self, loss):
+            return loss
+        def unscale_(self, optimizer):
+            pass
+        def step(self, optimizer):
+            optimizer.step()
+        def update(self):
+            pass
+
+
+# exclude extremly large displacements
+MAX_FLOW = 400
+SUM_FREQ = 100
+VAL_FREQ = 5000
+
+
+def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, max_flow=MAX_FLOW):
+    """ Loss function defined over sequence of flow predictions """
+
+    n_predictions = len(flow_preds)    
+    flow_loss = 0.0
+
+    # exlude invalid pixels and extremely large diplacements
+    mag = torch.sum(flow_gt**2, dim=1).sqrt()
+    valid = (valid >= 0.5) & (mag < max_flow)
+
+    for i in range(n_predictions):
+        i_weight = gamma**(n_predictions - i - 1)
+        i_loss = (flow_preds[i] - flow_gt).abs()
+        flow_loss += i_weight * (valid[:, None] * i_loss).mean()
+
+    epe = torch.sum((flow_preds[-1] - flow_gt)**2, dim=1).sqrt()
+    epe = epe.view(-1)[valid.view(-1)]
+
+    metrics = {
+        'epe': epe.mean().item(),
+        '1px': (epe < 1).float().mean().item(),
+        '3px': (epe < 3).float().mean().item(),
+        '5px': (epe < 5).float().mean().item(),
+    }
+
+    return flow_loss, metrics
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+def fetch_optimizer(args, model):
+    """ Create the optimizer and learning rate scheduler """
+    optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)
+
+    scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100,
+        pct_start=0.05, cycle_momentum=False, anneal_strategy='linear')
+
+    return optimizer, scheduler
+    
+
+class Logger:
+    def __init__(self, model, scheduler):
+        self.model = model
+        self.scheduler = scheduler
+        self.total_steps = 0
+        self.running_loss = {}
+        self.writer = None
+
+    def _print_training_status(self):
+        metrics_data = [self.running_loss[k]/SUM_FREQ for k in sorted(self.running_loss.keys())]
+        training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, self.scheduler.get_last_lr()[0])
+        metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data)
+        
+        # print the training status
+        print(training_str + metrics_str)
+
+        if self.writer is None:
+            self.writer = SummaryWriter()
+
+        for k in self.running_loss:
+            self.writer.add_scalar(k, self.running_loss[k]/SUM_FREQ, self.total_steps)
+            self.running_loss[k] = 0.0
+
+    def push(self, metrics):
+        self.total_steps += 1
+
+        for key in metrics:
+            if key not in self.running_loss:
+                self.running_loss[key] = 0.0
+
+            self.running_loss[key] += metrics[key]
+
+        if self.total_steps % SUM_FREQ == SUM_FREQ-1:
+            self._print_training_status()
+            self.running_loss = {}
+
+    def write_dict(self, results):
+        if self.writer is None:
+            self.writer = SummaryWriter()
+
+        for key in results:
+            self.writer.add_scalar(key, results[key], self.total_steps)
+
+    def close(self):
+        self.writer.close()
+
+
+def train(args):
+
+    model = nn.DataParallel(RAFT(args), device_ids=args.gpus)
+    print("Parameter Count: %d" % count_parameters(model))
+
+    if args.restore_ckpt is not None:
+        model.load_state_dict(torch.load(args.restore_ckpt), strict=False)
+
+    model.cuda()
+    model.train()
+
+    if args.stage != 'chairs':
+        model.module.freeze_bn()
+
+    train_loader = datasets.fetch_dataloader(args)
+    optimizer, scheduler = fetch_optimizer(args, model)
+
+    total_steps = 0
+    scaler = GradScaler(enabled=args.mixed_precision)
+    logger = Logger(model, scheduler)
+
+    VAL_FREQ = 5000
+    add_noise = True
+
+    should_keep_training = True
+    while should_keep_training:
+
+        for i_batch, data_blob in enumerate(train_loader):
+            optimizer.zero_grad()
+            image1, image2, flow, valid = [x.cuda() for x in data_blob]
+
+            if args.add_noise:
+                stdv = np.random.uniform(0.0, 5.0)
+                image1 = (image1 + stdv * torch.randn(*image1.shape).cuda()).clamp(0.0, 255.0)
+                image2 = (image2 + stdv * torch.randn(*image2.shape).cuda()).clamp(0.0, 255.0)
+
+            flow_predictions = model(image1, image2, iters=args.iters)            
+
+            loss, metrics = sequence_loss(flow_predictions, flow, valid, args.gamma)
+            scaler.scale(loss).backward()
+            scaler.unscale_(optimizer)                
+            torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
+            
+            scaler.step(optimizer)
+            scheduler.step()
+            scaler.update()
+
+            logger.push(metrics)
+
+            if total_steps % VAL_FREQ == VAL_FREQ - 1:
+                PATH = 'checkpoints/%d_%s.pth' % (total_steps+1, args.name)
+                torch.save(model.state_dict(), PATH)
+
+                results = {}
+                for val_dataset in args.validation:
+                    if val_dataset == 'chairs':
+                        results.update(evaluate.validate_chairs(model.module))
+                    elif val_dataset == 'sintel':
+                        results.update(evaluate.validate_sintel(model.module))
+                    elif val_dataset == 'kitti':
+                        results.update(evaluate.validate_kitti(model.module))
+
+                logger.write_dict(results)
+                
+                model.train()
+                if args.stage != 'chairs':
+                    model.module.freeze_bn()
+            
+            total_steps += 1
+
+            if total_steps > args.num_steps:
+                should_keep_training = False
+                break
+
+    logger.close()
+    PATH = 'checkpoints/%s.pth' % args.name
+    torch.save(model.state_dict(), PATH)
+
+    return PATH
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--name', default='raft', help="name your experiment")
+    parser.add_argument('--stage', help="determines which dataset to use for training") 
+    parser.add_argument('--restore_ckpt', help="restore checkpoint")
+    parser.add_argument('--small', action='store_true', help='use small model')
+    parser.add_argument('--validation', type=str, nargs='+')
+
+    parser.add_argument('--lr', type=float, default=0.00002)
+    parser.add_argument('--num_steps', type=int, default=100000)
+    parser.add_argument('--batch_size', type=int, default=6)
+    parser.add_argument('--image_size', type=int, nargs='+', default=[384, 512])
+    parser.add_argument('--gpus', type=int, nargs='+', default=[0,1])
+    parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
+
+    parser.add_argument('--iters', type=int, default=12)
+    parser.add_argument('--wdecay', type=float, default=.00005)
+    parser.add_argument('--epsilon', type=float, default=1e-8)
+    parser.add_argument('--clip', type=float, default=1.0)
+    parser.add_argument('--dropout', type=float, default=0.0)
+    parser.add_argument('--gamma', type=float, default=0.8, help='exponential weighting')
+    parser.add_argument('--add_noise', action='store_true')
+    args = parser.parse_args()
+
+    torch.manual_seed(1234)
+    np.random.seed(1234)
+
+    if not os.path.isdir('checkpoints'):
+        os.mkdir('checkpoints')
+
+    train(args)

causalvideovae/eval/RAFT/train_standard.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+mkdir -p checkpoints
+python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 1 --num_steps 100000 --batch_size 10 --lr 0.0004 --image_size 368 496 --wdecay 0.0001
+python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 400 720 --wdecay 0.0001
+python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 368 768 --wdecay 0.00001 --gamma=0.85
+python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 1 --num_steps 50000 --batch_size 6 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85

causalvideovae/eval/cal_flolpips.py

@@ -0,0 +1,83 @@
+import numpy as np
+import torch
+from tqdm import tqdm
+import math
+from einops import rearrange
+import sys
+sys.path.append(".")
+from flolpips.pwcnet import Network as PWCNet
+from flolpips.flolpips import FloLPIPS
+
+loss_fn = FloLPIPS(net='alex', version='0.1').eval().requires_grad_(False)
+flownet = PWCNet().eval().requires_grad_(False)
+
+def trans(x):
+    return x
+
+
+def calculate_flolpips(videos1, videos2, device):
+    global loss_fn, flownet
+    
+    print("calculate_flowlpips...")
+    loss_fn = loss_fn.to(device)
+    flownet = flownet.to(device)
+    
+    if videos1.shape != videos2.shape:
+        print("Warning: the shape of videos are not equal.")
+        min_frames = min(videos1.shape[1], videos2.shape[1])
+        videos1 = videos1[:, :min_frames]
+        videos2 = videos2[:, :min_frames]
+        
+    videos1 = trans(videos1)
+    videos2 = trans(videos2)
+
+    flolpips_results = []
+    for video_num in tqdm(range(videos1.shape[0])):
+        video1 = videos1[video_num].to(device)
+        video2 = videos2[video_num].to(device)
+        frames_rec = video1[:-1]
+        frames_rec_next = video1[1:]
+        frames_gt = video2[:-1]
+        frames_gt_next = video2[1:]
+        t, c, h, w = frames_gt.shape
+        flow_gt = flownet(frames_gt, frames_gt_next)
+        flow_dis = flownet(frames_rec, frames_rec_next)
+        flow_diff = flow_gt - flow_dis
+        flolpips = loss_fn.forward(frames_gt, frames_rec, flow_diff, normalize=True)
+        flolpips_results.append(flolpips.cpu().numpy().tolist())
+        
+    flolpips_results = np.array(flolpips_results) # [batch_size, num_frames]
+    flolpips = {}
+    flolpips_std = {}
+
+    for clip_timestamp in range(flolpips_results.shape[1]):
+        flolpips[clip_timestamp] = np.mean(flolpips_results[:,clip_timestamp], axis=-1)
+        flolpips_std[clip_timestamp] = np.std(flolpips_results[:,clip_timestamp], axis=-1)
+
+    result = {
+        "value": flolpips,
+        "value_std": flolpips_std,
+        "video_setting": video1.shape,
+        "video_setting_name": "time, channel, heigth, width",
+        "result": flolpips_results,
+        "details": flolpips_results.tolist()
+    }
+
+    return result
+
+# test code / using example
+
+def main():
+    NUMBER_OF_VIDEOS = 8
+    VIDEO_LENGTH = 50
+    CHANNEL = 3
+    SIZE = 64
+    videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+    videos2 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+
+    import json
+    result = calculate_flolpips(videos1, videos2, "cuda:0")
+    print(json.dumps(result, indent=4))
+
+if __name__ == "__main__":
+    main()

causalvideovae/eval/cal_fvd.py

@@ -0,0 +1,85 @@
+import numpy as np
+import torch
+from tqdm import tqdm
+
+def trans(x):
+    # if greyscale images add channel
+    if x.shape[-3] == 1:
+        x = x.repeat(1, 1, 3, 1, 1)
+
+    # permute BTCHW -> BCTHW
+    x = x.permute(0, 2, 1, 3, 4) 
+
+    return x
+
+def calculate_fvd(videos1, videos2, device, method='styleganv'):
+
+    if method == 'styleganv':
+        from fvd.styleganv.fvd import get_fvd_feats, frechet_distance, load_i3d_pretrained
+    elif method == 'videogpt':
+        from fvd.videogpt.fvd import load_i3d_pretrained
+        from fvd.videogpt.fvd import get_fvd_logits as get_fvd_feats
+        from fvd.videogpt.fvd import frechet_distance
+
+    print("calculate_fvd...")
+
+    # videos [batch_size, timestamps, channel, h, w]
+    
+    assert videos1.shape == videos2.shape
+
+    i3d = load_i3d_pretrained(device=device)
+    fvd_results = []
+
+    # support grayscale input, if grayscale -> channel*3
+    # BTCHW -> BCTHW
+    # videos -> [batch_size, channel, timestamps, h, w]
+
+    videos1 = trans(videos1)
+    videos2 = trans(videos2)
+
+    fvd_results = {}
+
+    # for calculate FVD, each clip_timestamp must >= 10
+    for clip_timestamp in tqdm(range(10, videos1.shape[-3]+1)):
+       
+        # get a video clip
+        # videos_clip [batch_size, channel, timestamps[:clip], h, w]
+        videos_clip1 = videos1[:, :, : clip_timestamp]
+        videos_clip2 = videos2[:, :, : clip_timestamp]
+
+        # get FVD features
+        feats1 = get_fvd_feats(videos_clip1, i3d=i3d, device=device)
+        feats2 = get_fvd_feats(videos_clip2, i3d=i3d, device=device)
+      
+        # calculate FVD when timestamps[:clip]
+        fvd_results[clip_timestamp] = frechet_distance(feats1, feats2)
+
+    result = {
+        "value": fvd_results,
+        "video_setting": videos1.shape,
+        "video_setting_name": "batch_size, channel, time, heigth, width",
+    }
+
+    return result
+
+# test code / using example
+
+def main():
+    NUMBER_OF_VIDEOS = 8
+    VIDEO_LENGTH = 50
+    CHANNEL = 3
+    SIZE = 64
+    videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+    videos2 = torch.ones(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+    device = torch.device("cuda")
+    # device = torch.device("cpu")
+
+    import json
+    result = calculate_fvd(videos1, videos2, device, method='videogpt')
+    print(json.dumps(result, indent=4))
+
+    result = calculate_fvd(videos1, videos2, device, method='styleganv')
+    print(json.dumps(result, indent=4))
+
+if __name__ == "__main__":
+    main()

causalvideovae/eval/cal_lpips.py

@@ -0,0 +1,97 @@
+import numpy as np
+import torch
+from tqdm import tqdm
+import math
+
+import torch
+import lpips
+
+spatial = True         # Return a spatial map of perceptual distance.
+
+# Linearly calibrated models (LPIPS)
+loss_fn = lpips.LPIPS(net='alex', spatial=spatial) # Can also set net = 'squeeze' or 'vgg'
+# loss_fn = lpips.LPIPS(net='alex', spatial=spatial, lpips=False) # Can also set net = 'squeeze' or 'vgg'
+
+def trans(x):
+    # if greyscale images add channel
+    if x.shape[-3] == 1:
+        x = x.repeat(1, 1, 3, 1, 1)
+
+    # value range [0, 1] -> [-1, 1]
+    x = x * 2 - 1
+
+    return x
+
+def calculate_lpips(videos1, videos2, device):
+    # image should be RGB, IMPORTANT: normalized to [-1,1]
+    print("calculate_lpips...")
+
+    assert videos1.shape == videos2.shape
+
+    # videos [batch_size, timestamps, channel, h, w]
+
+    # support grayscale input, if grayscale -> channel*3
+    # value range [0, 1] -> [-1, 1]
+    videos1 = trans(videos1)
+    videos2 = trans(videos2)
+
+    lpips_results = []
+
+    for video_num in tqdm(range(videos1.shape[0])):
+        # get a video
+        # video [timestamps, channel, h, w]
+        video1 = videos1[video_num]
+        video2 = videos2[video_num]
+
+        lpips_results_of_a_video = []
+        for clip_timestamp in range(len(video1)):
+            # get a img
+            # img [timestamps[x], channel, h, w]
+            # img [channel, h, w] tensor
+
+            img1 = video1[clip_timestamp].unsqueeze(0).to(device)
+            img2 = video2[clip_timestamp].unsqueeze(0).to(device)
+            
+            loss_fn.to(device)
+
+            # calculate lpips of a video
+            lpips_results_of_a_video.append(loss_fn.forward(img1, img2).mean().detach().cpu().tolist())
+        lpips_results.append(lpips_results_of_a_video)
+    
+    lpips_results = np.array(lpips_results)
+    
+    lpips = {}
+    lpips_std = {}
+
+    for clip_timestamp in range(len(video1)):
+        lpips[clip_timestamp] = np.mean(lpips_results[:,clip_timestamp])
+        lpips_std[clip_timestamp] = np.std(lpips_results[:,clip_timestamp])
+
+
+    result = {
+        "value": lpips,
+        "value_std": lpips_std,
+        "video_setting": video1.shape,
+        "video_setting_name": "time, channel, heigth, width",
+    }
+
+    return result
+
+# test code / using example
+
+def main():
+    NUMBER_OF_VIDEOS = 8
+    VIDEO_LENGTH = 50
+    CHANNEL = 3
+    SIZE = 64
+    videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+    videos2 = torch.ones(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+    device = torch.device("cuda")
+    # device = torch.device("cpu")
+
+    import json
+    result = calculate_lpips(videos1, videos2, device)
+    print(json.dumps(result, indent=4))
+
+if __name__ == "__main__":
+    main()

causalvideovae/eval/cal_psnr.py

@@ -0,0 +1,97 @@
+import numpy as np
+import torch
+from tqdm import tqdm
+import math
+
+def img_psnr_cuda(img1, img2):
+    # [0,1]
+    # compute mse
+    # mse = np.mean((img1-img2)**2)
+    mse = torch.mean((img1 / 1.0 - img2 / 1.0) ** 2)
+    # compute psnr
+    if mse < 1e-10:
+        return 100
+    psnr = 20 * torch.log10(1 / torch.sqrt(mse))
+    return psnr
+
+
+def img_psnr(img1, img2):
+    # [0,1]
+    # compute mse
+    # mse = np.mean((img1-img2)**2)
+    mse = np.mean((img1 / 1.0 - img2 / 1.0) ** 2)
+    # compute psnr
+    if mse < 1e-10:
+        return 100
+    psnr = 20 * math.log10(1 / math.sqrt(mse))
+    return psnr
+
+
+def trans(x):
+    return x
+
+def calculate_psnr(videos1, videos2):
+    print("calculate_psnr...")
+
+    # videos [batch_size, timestamps, channel, h, w]
+    
+    assert videos1.shape == videos2.shape
+
+    videos1 = trans(videos1)
+    videos2 = trans(videos2)
+
+    psnr_results = []
+    
+    for video_num in tqdm(range(videos1.shape[0])):
+        # get a video
+        # video [timestamps, channel, h, w]
+        video1 = videos1[video_num]
+        video2 = videos2[video_num]
+
+        psnr_results_of_a_video = []
+        for clip_timestamp in range(len(video1)):
+            # get a img
+            # img [timestamps[x], channel, h, w]
+            # img [channel, h, w] numpy
+
+            img1 = video1[clip_timestamp].numpy()
+            img2 = video2[clip_timestamp].numpy()
+            
+            # calculate psnr of a video
+            psnr_results_of_a_video.append(img_psnr(img1, img2))
+
+        psnr_results.append(psnr_results_of_a_video)
+    
+    psnr_results = np.array(psnr_results) # [batch_size, num_frames]
+    psnr = {}
+    psnr_std = {}
+
+    for clip_timestamp in range(len(video1)):
+        psnr[clip_timestamp] = np.mean(psnr_results[:,clip_timestamp])
+        psnr_std[clip_timestamp] = np.std(psnr_results[:,clip_timestamp])
+
+    result = {
+        "value": psnr,
+        "value_std": psnr_std,
+        "video_setting": video1.shape,
+        "video_setting_name": "time, channel, heigth, width",
+    }
+
+    return result
+
+# test code / using example
+
+def main():
+    NUMBER_OF_VIDEOS = 8
+    VIDEO_LENGTH = 50
+    CHANNEL = 3
+    SIZE = 64
+    videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+    videos2 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
+
+    import json
+    result = calculate_psnr(videos1, videos2)
+    print(json.dumps(result, indent=4))
+
+if __name__ == "__main__":
+    main()

causalvideovae/eval/eval_clip_score.py

@@ -0,0 +1,225 @@
+"""Calculates the CLIP Scores
+
+The CLIP model is a contrasitively learned language-image model. There is
+an image encoder and a text encoder. It is believed that the CLIP model could 
+measure the similarity of cross modalities. Please find more information from 
+https://github.com/openai/CLIP.
+
+The CLIP Score measures the Cosine Similarity between two embedded features.
+This repository utilizes the pretrained CLIP Model to calculate 
+the mean average of cosine similarities. 
+
+See --help to see further details.
+
+Code apapted from https://github.com/mseitzer/pytorch-fid and https://github.com/openai/CLIP.
+
+Copyright 2023 The Hong Kong Polytechnic University
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import os
+import os.path as osp
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+
+import clip
+import torch
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader
+
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If tqdm is not available, provide a mock version of it
+    def tqdm(x):
+        return x
+
+
+IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm',
+                    'tif', 'tiff', 'webp'}
+
+TEXT_EXTENSIONS = {'txt'}
+
+
+class DummyDataset(Dataset):
+    
+    FLAGS = ['img', 'txt']
+    def __init__(self, real_path, generated_path,
+                 real_flag: str = 'img',
+                 generated_flag: str = 'img',
+                 transform = None,
+                 tokenizer = None) -> None:
+        super().__init__()
+        assert real_flag in self.FLAGS and generated_flag in self.FLAGS, \
+            'CLIP Score only support modality of {}. However, get {} and {}'.format(
+                self.FLAGS, real_flag, generated_flag
+            )
+        self.real_folder = self._combine_without_prefix(real_path)
+        self.real_flag = real_flag
+        self.fake_foler = self._combine_without_prefix(generated_path)
+        self.generated_flag = generated_flag
+        self.transform = transform
+        self.tokenizer = tokenizer
+        # assert self._check()
+
+    def __len__(self):
+        return len(self.real_folder)
+
+    def __getitem__(self, index):
+        if index >= len(self):
+            raise IndexError
+        real_path = self.real_folder[index]
+        generated_path = self.fake_foler[index]
+        real_data = self._load_modality(real_path, self.real_flag)
+        fake_data = self._load_modality(generated_path, self.generated_flag)
+
+        sample = dict(real=real_data, fake=fake_data)
+        return sample
+    
+    def _load_modality(self, path, modality):
+        if modality == 'img':
+            data = self._load_img(path)
+        elif modality == 'txt':
+            data = self._load_txt(path)
+        else:
+            raise TypeError("Got unexpected modality: {}".format(modality))
+        return data
+
+    def _load_img(self, path):
+        img = Image.open(path)
+        if self.transform is not None:
+            img = self.transform(img)
+        return img
+    
+    def _load_txt(self, path):
+        with open(path, 'r') as fp:
+            data = fp.read()
+            fp.close()
+        if self.tokenizer is not None:
+            data = self.tokenizer(data).squeeze()
+        return data
+
+    def _check(self):
+        for idx in range(len(self)):
+            real_name = self.real_folder[idx].split('.')
+            fake_name = self.fake_folder[idx].split('.')
+            if fake_name != real_name:
+                return False
+        return True
+    
+    def _combine_without_prefix(self, folder_path, prefix='.'):
+        folder = []
+        for name in os.listdir(folder_path):
+            if name[0] == prefix:
+                continue
+            folder.append(osp.join(folder_path, name))
+        folder.sort()
+        return folder
+
+
+@torch.no_grad()
+def calculate_clip_score(dataloader, model, real_flag, generated_flag):
+    score_acc = 0.
+    sample_num = 0.
+    logit_scale = model.logit_scale.exp()
+    for batch_data in tqdm(dataloader):
+        real = batch_data['real']
+        real_features = forward_modality(model, real, real_flag)
+        fake = batch_data['fake']
+        fake_features = forward_modality(model, fake, generated_flag)
+        
+        # normalize features
+        real_features = real_features / real_features.norm(dim=1, keepdim=True).to(torch.float32)
+        fake_features = fake_features / fake_features.norm(dim=1, keepdim=True).to(torch.float32)
+        
+        # calculate scores
+        # score = logit_scale * real_features @ fake_features.t()
+        # score_acc += torch.diag(score).sum()
+        score = logit_scale * (fake_features * real_features).sum()
+        score_acc += score
+        sample_num += real.shape[0]
+    
+    return score_acc / sample_num
+
+        
+def forward_modality(model, data, flag):
+    device = next(model.parameters()).device
+    if flag == 'img':
+        features = model.encode_image(data.to(device))
+    elif flag == 'txt':
+        features = model.encode_text(data.to(device))
+    else:
+        raise TypeError
+    return features
+
+
+def main():
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--batch-size', type=int, default=50,
+                    help='Batch size to use')
+    parser.add_argument('--clip-model', type=str, default='ViT-B/32',
+                    help='CLIP model to use')
+    parser.add_argument('--num-workers', type=int, default=8,
+                    help=('Number of processes to use for data loading. '
+                          'Defaults to `min(8, num_cpus)`'))
+    parser.add_argument('--device', type=str, default=None,
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+    parser.add_argument('--real_flag', type=str, default='img',
+                    help=('The modality of real path. '
+                          'Default to img'))
+    parser.add_argument('--generated_flag', type=str, default='txt',
+                    help=('The modality of generated path. '
+                          'Default to txt'))
+    parser.add_argument('--real_path', type=str, 
+                    help=('Paths to the real images or '
+                          'to .npz statistic files'))
+    parser.add_argument('--generated_path', type=str,
+                    help=('Paths to the generated images or '
+                          'to .npz statistic files'))
+    args = parser.parse_args()
+
+    if args.device is None:
+        device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
+    else:
+        device = torch.device(args.device)
+
+    if args.num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = args.num_workers
+
+    print('Loading CLIP model: {}'.format(args.clip_model))
+    model, preprocess = clip.load(args.clip_model, device=device)
+    
+    dataset = DummyDataset(args.real_path, args.generated_path,
+                           args.real_flag, args.generated_flag,
+                           transform=preprocess, tokenizer=clip.tokenize)
+    dataloader = DataLoader(dataset, args.batch_size, 
+                            num_workers=num_workers, pin_memory=True)
+    
+    print('Calculating CLIP Score:')
+    clip_score = calculate_clip_score(dataloader, model,
+                                      args.real_flag, args.generated_flag)
+    clip_score = clip_score.cpu().item()
+    print('CLIP Score: ', clip_score)
+
+
+if __name__ == '__main__':
+    main()

causalvideovae/eval/eval_common_metric.py

@@ -0,0 +1,228 @@
+"""Calculates the CLIP Scores
+
+The CLIP model is a contrasitively learned language-image model. There is
+an image encoder and a text encoder. It is believed that the CLIP model could 
+measure the similarity of cross modalities. Please find more information from 
+https://github.com/openai/CLIP.
+
+The CLIP Score measures the Cosine Similarity between two embedded features.
+This repository utilizes the pretrained CLIP Model to calculate 
+the mean average of cosine similarities. 
+
+See --help to see further details.
+
+Code apapted from https://github.com/mseitzer/pytorch-fid and https://github.com/openai/CLIP.
+
+Copyright 2023 The Hong Kong Polytechnic University
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import os
+import os.path as osp
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader, Subset
+from decord import VideoReader, cpu
+import random
+from pytorchvideo.transforms import ShortSideScale
+from torchvision.io import read_video
+from torchvision.transforms import Lambda, Compose
+from torchvision.transforms._transforms_video import CenterCropVideo
+from cal_lpips import calculate_lpips
+from cal_fvd import calculate_fvd
+from cal_psnr import calculate_psnr
+#from cal_flolpips import calculate_flolpips
+from cal_ssim import calculate_ssim
+from cal_mse import calculate_mse
+
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If tqdm is not available, provide a mock version of it
+    def tqdm(x):
+        return x
+
+class VideoDataset(Dataset):
+    def __init__(self, 
+                 real_video_dir,
+                 generated_video_dir,
+                 num_frames,
+                 sample_rate = 1,
+                 crop_size=None,
+                 resolution=128,
+                 ) -> None:
+        super().__init__()
+        self.real_video_files = []
+        self.generated_video_files = self._combine_without_prefix(generated_video_dir)
+        for video_file in self.generated_video_files:
+            filename = os.path.basename(video_file)
+            self.real_video_files.append(os.path.join(real_video_dir, filename))
+        self.num_frames = num_frames
+        self.sample_rate = sample_rate
+        self.crop_size = crop_size
+        self.short_size = resolution
+
+
+    def __len__(self):
+        return len(self.real_video_files)
+
+    def __getitem__(self, index):
+        if index >= len(self):
+            raise IndexError
+        real_video_file = self.real_video_files[index]
+        generated_video_file = self.generated_video_files[index]
+        real_video_tensor  = self._load_video(real_video_file, self.sample_rate)
+        generated_video_tensor  = self._load_video(generated_video_file, 1)
+        return {'real': real_video_tensor, 'generated':generated_video_tensor }
+
+
+    def _load_video(self, video_path, sample_rate):
+        num_frames = self.num_frames
+        decord_vr = VideoReader(video_path, ctx=cpu(0))
+        total_frames = len(decord_vr)
+        sample_frames_len = sample_rate * num_frames
+
+        if total_frames >= sample_frames_len:
+            s = 0
+            e = s + sample_frames_len
+            num_frames = num_frames
+        else:
+            s = 0
+            e = total_frames
+            num_frames = int(total_frames / sample_frames_len * num_frames)
+            print(f'sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}', video_path,
+                total_frames)
+
+
+        frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+        video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (C, T, H, W)
+        return _preprocess(video_data, short_size=self.short_size, crop_size = self.crop_size)
+
+
+    def _combine_without_prefix(self, folder_path, prefix='.'):
+        folder = []
+        os.makedirs(folder_path, exist_ok=True)
+        for name in os.listdir(folder_path):
+            if name[0] == prefix:
+                continue
+            if osp.isfile(osp.join(folder_path, name)):
+                folder.append(osp.join(folder_path, name))
+        folder.sort()
+        return folder
+
+def _preprocess(video_data, short_size=128, crop_size=None):
+    transform = Compose(
+        [
+            Lambda(lambda x: x / 255.0),
+        ]
+    )
+    video_outputs = transform(video_data)
+    # video_outputs = torch.unsqueeze(video_outputs, 0) # (bz,c,t,h,w)
+    return video_outputs
+
+
+def calculate_common_metric(args, dataloader, device):
+
+    score_list = []
+    for batch_data in tqdm(dataloader): # {'real': real_video_tensor, 'generated':generated_video_tensor }
+        real_videos = batch_data['real'] 
+        generated_videos = batch_data['generated']
+        
+        assert real_videos.shape[2] == generated_videos.shape[2]
+        if args.metric == 'fvd':
+            tmp_list = list(calculate_fvd(real_videos, generated_videos, args.device, method=args.fvd_method)['value'].values())
+        elif args.metric == 'ssim':
+            tmp_list = list(calculate_ssim(real_videos, generated_videos)['value'].values())
+        elif args.metric == 'psnr':
+            tmp_list = list(calculate_psnr(real_videos, generated_videos)['value'].values())
+        elif args.metric == 'mse':
+            tmp_list = list(calculate_mse(real_videos, generated_videos)['value'].values())
+       # elif args.metric == 'flolpips':
+        #    result = calculate_flolpips(real_videos, generated_videos, args.device)
+        #    tmp_list = list(result['value'].values())
+        elif args.metric == 'wraperror':
+            result = calculate_wraperror(real_videos, generated_videos, args.device)
+            tmp_list = list(result['value'].values())
+        else:
+            tmp_list  = list(calculate_lpips(real_videos, generated_videos, args.device)['value'].values())
+        score_list += tmp_list
+    return np.mean(score_list)
+        
+def main():
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--batch_size', type=int, default=2,
+                    help='Batch size to use')
+    parser.add_argument('--real_video_dir', type=str,
+                    help=('the path of real videos`'))
+    parser.add_argument('--generated_video_dir', type=str,
+                    help=('the path of generated videos`'))
+    parser.add_argument('--device', type=str, default=None,
+                    help='Device to use. Like cuda, cuda:0 or cpu')
+    parser.add_argument('--num_workers', type=int, default=8,
+                    help=('Number of processes to use for data loading. '
+                          'Defaults to `min(8, num_cpus)`'))
+    parser.add_argument('--sample_fps', type=int, default=30)
+    parser.add_argument('--resolution', type=int, default=336)
+    parser.add_argument('--crop_size', type=int, default=None)
+    parser.add_argument('--num_frames', type=int, default=100)
+    parser.add_argument('--sample_rate', type=int, default=1)
+    parser.add_argument('--subset_size', type=int, default=None)
+    parser.add_argument("--metric", type=str, default="fvd",choices=['fvd','psnr','ssim','lpips', 'flolpips', 'mse'])
+    parser.add_argument("--fvd_method", type=str, default='styleganv',choices=['styleganv','videogpt'])
+
+
+    args = parser.parse_args()
+
+    if args.device is None:
+        device = torch.device('cuda:1' if (torch.cuda.is_available()) else 'cpu')
+    else:
+        device = torch.device(args.device)
+
+    if args.num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = args.num_workers
+
+    
+    dataset = VideoDataset(args.real_video_dir,
+                           args.generated_video_dir,
+                            num_frames = args.num_frames,
+                            sample_rate = args.sample_rate,
+                            crop_size=args.crop_size,
+                            resolution=args.resolution) 
+
+    if args.subset_size:
+        indices = range(args.subset_size)
+        dataset = Subset(dataset, indices=indices)
+    
+    dataloader = DataLoader(dataset, args.batch_size, 
+                            num_workers=num_workers, pin_memory=True)
+    
+
+    metric_score = calculate_common_metric(args, dataloader,device)
+    print('metric: ', args.metric, " ",metric_score)
+
+if __name__ == '__main__':
+    main()

causalvideovae/eval/flolpips/pwcnet.py

@@ -0,0 +1,344 @@
+#!/usr/bin/env python
+
+import torch
+
+import getopt
+import math
+import numpy
+import os
+import PIL
+import PIL.Image
+import sys
+
+# try:
+from .correlation import correlation # the custom cost volume layer
+# except:
+# 	sys.path.insert(0, './correlation'); import correlation # you should consider upgrading python
+# end
+
+##########################################################
+
+# assert(int(str('').join(torch.__version__.split('.')[0:2])) >= 13) # requires at least pytorch version 1.3.0
+
+# torch.set_grad_enabled(False) # make sure to not compute gradients for computational performance
+
+# torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance
+
+# ##########################################################
+
+# arguments_strModel = 'default' # 'default', or 'chairs-things'
+# arguments_strFirst = './images/first.png'
+# arguments_strSecond = './images/second.png'
+# arguments_strOut = './out.flo'
+
+# for strOption, strArgument in getopt.getopt(sys.argv[1:], '', [ strParameter[2:] + '=' for strParameter in sys.argv[1::2] ])[0]:
+# 	if strOption == '--model' and strArgument != '': arguments_strModel = strArgument # which model to use
+# 	if strOption == '--first' and strArgument != '': arguments_strFirst = strArgument # path to the first frame
+# 	if strOption == '--second' and strArgument != '': arguments_strSecond = strArgument # path to the second frame
+# 	if strOption == '--out' and strArgument != '': arguments_strOut = strArgument # path to where the output should be stored
+# end
+
+##########################################################
+
+
+
+def backwarp(tenInput, tenFlow):
+	backwarp_tenGrid = {}
+	backwarp_tenPartial = {}
+	if str(tenFlow.shape) not in backwarp_tenGrid:
+		tenHor = torch.linspace(-1.0 + (1.0 / tenFlow.shape[3]), 1.0 - (1.0 / tenFlow.shape[3]), tenFlow.shape[3]).view(1, 1, 1, -1).expand(-1, -1, tenFlow.shape[2], -1)
+		tenVer = torch.linspace(-1.0 + (1.0 / tenFlow.shape[2]), 1.0 - (1.0 / tenFlow.shape[2]), tenFlow.shape[2]).view(1, 1, -1, 1).expand(-1, -1, -1, tenFlow.shape[3])
+
+		backwarp_tenGrid[str(tenFlow.shape)] = torch.cat([ tenHor, tenVer ], 1).cuda()
+	# end
+
+	if str(tenFlow.shape) not in backwarp_tenPartial:
+		backwarp_tenPartial[str(tenFlow.shape)] = tenFlow.new_ones([ tenFlow.shape[0], 1, tenFlow.shape[2], tenFlow.shape[3] ])
+	# end
+
+	tenFlow = torch.cat([ tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0) ], 1)
+	tenInput = torch.cat([ tenInput, backwarp_tenPartial[str(tenFlow.shape)] ], 1)
+
+	tenOutput = torch.nn.functional.grid_sample(input=tenInput, grid=(backwarp_tenGrid[str(tenFlow.shape)] + tenFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='zeros', align_corners=False)
+
+	tenMask = tenOutput[:, -1:, :, :]; tenMask[tenMask > 0.999] = 1.0; tenMask[tenMask < 1.0] = 0.0
+
+	return tenOutput[:, :-1, :, :] * tenMask
+# end
+
+##########################################################
+
+class Network(torch.nn.Module):
+	def __init__(self):
+		super(Network, self).__init__()
+
+		class Extractor(torch.nn.Module):
+			def __init__(self):
+				super(Extractor, self).__init__()
+
+				self.netOne = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netTwo = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netThr = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netFou = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=64, out_channels=96, kernel_size=3, stride=2, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=96, out_channels=96, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=96, out_channels=96, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netFiv = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=96, out_channels=128, kernel_size=3, stride=2, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netSix = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=128, out_channels=196, kernel_size=3, stride=2, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=196, out_channels=196, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=196, out_channels=196, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+			# end
+
+			def forward(self, tenInput):
+				tenOne = self.netOne(tenInput)
+				tenTwo = self.netTwo(tenOne)
+				tenThr = self.netThr(tenTwo)
+				tenFou = self.netFou(tenThr)
+				tenFiv = self.netFiv(tenFou)
+				tenSix = self.netSix(tenFiv)
+
+				return [ tenOne, tenTwo, tenThr, tenFou, tenFiv, tenSix ]
+			# end
+		# end
+
+		class Decoder(torch.nn.Module):
+			def __init__(self, intLevel):
+				super(Decoder, self).__init__()
+
+				intPrevious = [ None, None, 81 + 32 + 2 + 2, 81 + 64 + 2 + 2, 81 + 96 + 2 + 2, 81 + 128 + 2 + 2, 81, None ][intLevel + 1]
+				intCurrent = [ None, None, 81 + 32 + 2 + 2, 81 + 64 + 2 + 2, 81 + 96 + 2 + 2, 81 + 128 + 2 + 2, 81, None ][intLevel + 0]
+
+				if intLevel < 6: self.netUpflow = torch.nn.ConvTranspose2d(in_channels=2, out_channels=2, kernel_size=4, stride=2, padding=1)
+				if intLevel < 6: self.netUpfeat = torch.nn.ConvTranspose2d(in_channels=intPrevious + 128 + 128 + 96 + 64 + 32, out_channels=2, kernel_size=4, stride=2, padding=1)
+				if intLevel < 6: self.fltBackwarp = [ None, None, None, 5.0, 2.5, 1.25, 0.625, None ][intLevel + 1]
+
+				self.netOne = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=intCurrent, out_channels=128, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netTwo = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=intCurrent + 128, out_channels=128, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netThr = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=intCurrent + 128 + 128, out_channels=96, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netFou = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96, out_channels=64, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netFiv = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64, out_channels=32, kernel_size=3, stride=1, padding=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
+				)
+
+				self.netSix = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64 + 32, out_channels=2, kernel_size=3, stride=1, padding=1)
+				)
+			# end
+
+			def forward(self, tenFirst, tenSecond, objPrevious):
+				tenFlow = None
+				tenFeat = None
+
+				if objPrevious is None:
+					tenFlow = None
+					tenFeat = None
+
+					tenVolume = torch.nn.functional.leaky_relu(input=correlation.FunctionCorrelation(tenFirst=tenFirst, tenSecond=tenSecond), negative_slope=0.1, inplace=False)
+
+					tenFeat = torch.cat([ tenVolume ], 1)
+
+				elif objPrevious is not None:
+					tenFlow = self.netUpflow(objPrevious['tenFlow'])
+					tenFeat = self.netUpfeat(objPrevious['tenFeat'])
+
+					tenVolume = torch.nn.functional.leaky_relu(input=correlation.FunctionCorrelation(tenFirst=tenFirst, tenSecond=backwarp(tenInput=tenSecond, tenFlow=tenFlow * self.fltBackwarp)), negative_slope=0.1, inplace=False)
+
+					tenFeat = torch.cat([ tenVolume, tenFirst, tenFlow, tenFeat ], 1)
+
+				# end
+
+				tenFeat = torch.cat([ self.netOne(tenFeat), tenFeat ], 1)
+				tenFeat = torch.cat([ self.netTwo(tenFeat), tenFeat ], 1)
+				tenFeat = torch.cat([ self.netThr(tenFeat), tenFeat ], 1)
+				tenFeat = torch.cat([ self.netFou(tenFeat), tenFeat ], 1)
+				tenFeat = torch.cat([ self.netFiv(tenFeat), tenFeat ], 1)
+
+				tenFlow = self.netSix(tenFeat)
+
+				return {
+					'tenFlow': tenFlow,
+					'tenFeat': tenFeat
+				}
+			# end
+		# end
+
+		class Refiner(torch.nn.Module):
+			def __init__(self):
+				super(Refiner, self).__init__()
+
+				self.netMain = torch.nn.Sequential(
+					torch.nn.Conv2d(in_channels=81 + 32 + 2 + 2 + 128 + 128 + 96 + 64 + 32, out_channels=128, kernel_size=3, stride=1, padding=1, dilation=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=2, dilation=2),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=4, dilation=4),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=128, out_channels=96, kernel_size=3, stride=1, padding=8, dilation=8),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=96, out_channels=64, kernel_size=3, stride=1, padding=16, dilation=16),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1, dilation=1),
+					torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
+					torch.nn.Conv2d(in_channels=32, out_channels=2, kernel_size=3, stride=1, padding=1, dilation=1)
+				)
+			# end
+
+			def forward(self, tenInput):
+				return self.netMain(tenInput)
+			# end
+		# end
+
+		self.netExtractor = Extractor()
+
+		self.netTwo = Decoder(2)
+		self.netThr = Decoder(3)
+		self.netFou = Decoder(4)
+		self.netFiv = Decoder(5)
+		self.netSix = Decoder(6)
+
+		self.netRefiner = Refiner()
+
+		self.load_state_dict({ strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.hub.load_state_dict_from_url(url='http://content.sniklaus.com/github/pytorch-pwc/network-' + 'default' + '.pytorch').items() })
+	# end
+
+	def forward(self, tenFirst, tenSecond):
+		intWidth = tenFirst.shape[3]
+		intHeight = tenFirst.shape[2]
+
+		intPreprocessedWidth = int(math.floor(math.ceil(intWidth / 64.0) * 64.0))
+		intPreprocessedHeight = int(math.floor(math.ceil(intHeight / 64.0) * 64.0))
+
+		tenPreprocessedFirst = torch.nn.functional.interpolate(input=tenFirst, size=(intPreprocessedHeight, intPreprocessedWidth), mode='bilinear', align_corners=False)
+		tenPreprocessedSecond = torch.nn.functional.interpolate(input=tenSecond, size=(intPreprocessedHeight, intPreprocessedWidth), mode='bilinear', align_corners=False)
+		
+		tenFirst = self.netExtractor(tenPreprocessedFirst)
+		tenSecond = self.netExtractor(tenPreprocessedSecond)
+
+
+		objEstimate = self.netSix(tenFirst[-1], tenSecond[-1], None)
+		objEstimate = self.netFiv(tenFirst[-2], tenSecond[-2], objEstimate)
+		objEstimate = self.netFou(tenFirst[-3], tenSecond[-3], objEstimate)
+		objEstimate = self.netThr(tenFirst[-4], tenSecond[-4], objEstimate)
+		objEstimate = self.netTwo(tenFirst[-5], tenSecond[-5], objEstimate)
+
+		tenFlow = objEstimate['tenFlow'] + self.netRefiner(objEstimate['tenFeat'])
+		tenFlow = 20.0 * torch.nn.functional.interpolate(input=tenFlow, size=(intHeight, intWidth), mode='bilinear', align_corners=False)
+		tenFlow[:, 0, :, :] *= float(intWidth) / float(intPreprocessedWidth)
+		tenFlow[:, 1, :, :] *= float(intHeight) / float(intPreprocessedHeight)
+
+		return tenFlow
+	# end
+# end
+
+netNetwork = None
+
+##########################################################
+
+def estimate(tenFirst, tenSecond):
+	global netNetwork
+
+	if netNetwork is None:
+		netNetwork = Network().cuda().eval()
+	# end
+
+	assert(tenFirst.shape[1] == tenSecond.shape[1])
+	assert(tenFirst.shape[2] == tenSecond.shape[2])
+
+	intWidth = tenFirst.shape[2]
+	intHeight = tenFirst.shape[1]
+
+	assert(intWidth == 1024) # remember that there is no guarantee for correctness, comment this line out if you acknowledge this and want to continue
+	assert(intHeight == 436) # remember that there is no guarantee for correctness, comment this line out if you acknowledge this and want to continue
+
+	tenPreprocessedFirst = tenFirst.cuda().view(1, 3, intHeight, intWidth)
+	tenPreprocessedSecond = tenSecond.cuda().view(1, 3, intHeight, intWidth)
+
+	intPreprocessedWidth = int(math.floor(math.ceil(intWidth / 64.0) * 64.0))
+	intPreprocessedHeight = int(math.floor(math.ceil(intHeight / 64.0) * 64.0))
+
+	tenPreprocessedFirst = torch.nn.functional.interpolate(input=tenPreprocessedFirst, size=(intPreprocessedHeight, intPreprocessedWidth), mode='bilinear', align_corners=False)
+	tenPreprocessedSecond = torch.nn.functional.interpolate(input=tenPreprocessedSecond, size=(intPreprocessedHeight, intPreprocessedWidth), mode='bilinear', align_corners=False)
+
+	tenFlow = 20.0 * torch.nn.functional.interpolate(input=netNetwork(tenPreprocessedFirst, tenPreprocessedSecond), size=(intHeight, intWidth), mode='bilinear', align_corners=False)
+
+	tenFlow[:, 0, :, :] *= float(intWidth) / float(intPreprocessedWidth)
+	tenFlow[:, 1, :, :] *= float(intHeight) / float(intPreprocessedHeight)
+
+	return tenFlow[0, :, :, :].cpu()
+# end
+
+##########################################################
+
+# if __name__ == '__main__':
+# 	tenFirst = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(arguments_strFirst))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
+# 	tenSecond = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(arguments_strSecond))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
+
+# 	tenOutput = estimate(tenFirst, tenSecond)
+
+# 	objOutput = open(arguments_strOut, 'wb')
+
+# 	numpy.array([ 80, 73, 69, 72 ], numpy.uint8).tofile(objOutput)
+# 	numpy.array([ tenOutput.shape[2], tenOutput.shape[1] ], numpy.int32).tofile(objOutput)
+# 	numpy.array(tenOutput.numpy().transpose(1, 2, 0), numpy.float32).tofile(objOutput)
+
+# 	objOutput.close()
+# end

causalvideovae/eval/flolpips/utils.py

@@ -0,0 +1,95 @@
+import numpy as np
+import cv2
+import torch
+
+
+def normalize_tensor(in_feat,eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True))
+    return in_feat/(norm_factor+eps)
+
+def l2(p0, p1, range=255.):
+    return .5*np.mean((p0 / range - p1 / range)**2)
+
+def dssim(p0, p1, range=255.):
+    from skimage.measure import compare_ssim
+    return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.
+
+def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
+    return image_numpy.astype(imtype)
+
+def tensor2np(tensor_obj):
+    # change dimension of a tensor object into a numpy array
+    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
+
+def np2tensor(np_obj):
+     # change dimenion of np array into tensor array
+    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
+
+def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
+    # image tensor to lab tensor
+    from skimage import color
+
+    img = tensor2im(image_tensor)
+    img_lab = color.rgb2lab(img)
+    if(mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+    if(to_norm and not mc_only):
+        img_lab[:,:,0] = img_lab[:,:,0]-50
+        img_lab = img_lab/100.
+
+    return np2tensor(img_lab)
+
+def read_frame_yuv2rgb(stream, width, height, iFrame, bit_depth, pix_fmt='420'):
+    if pix_fmt == '420':
+        multiplier = 1
+        uv_factor = 2
+    elif pix_fmt == '444':
+        multiplier = 2
+        uv_factor = 1
+    else:
+        print('Pixel format {} is not supported'.format(pix_fmt))
+        return
+
+    if bit_depth == 8:
+        datatype = np.uint8
+        stream.seek(iFrame*1.5*width*height*multiplier)
+        Y = np.fromfile(stream, dtype=datatype, count=width*height).reshape((height, width))
+        
+        # read chroma samples and upsample since original is 4:2:0 sampling
+        U = np.fromfile(stream, dtype=datatype, count=(width//uv_factor)*(height//uv_factor)).\
+                                reshape((height//uv_factor, width//uv_factor))
+        V = np.fromfile(stream, dtype=datatype, count=(width//uv_factor)*(height//uv_factor)).\
+                                reshape((height//uv_factor, width//uv_factor))
+
+    else:
+        datatype = np.uint16
+        stream.seek(iFrame*3*width*height*multiplier)
+        Y = np.fromfile(stream, dtype=datatype, count=width*height).reshape((height, width))
+                
+        U = np.fromfile(stream, dtype=datatype, count=(width//uv_factor)*(height//uv_factor)).\
+                                reshape((height//uv_factor, width//uv_factor))
+        V = np.fromfile(stream, dtype=datatype, count=(width//uv_factor)*(height//uv_factor)).\
+                                reshape((height//uv_factor, width//uv_factor))
+
+    if pix_fmt == '420':
+        yuv = np.empty((height*3//2, width), dtype=datatype)
+        yuv[0:height,:] = Y
+
+        yuv[height:height+height//4,:] = U.reshape(-1, width)
+        yuv[height+height//4:,:] = V.reshape(-1, width)
+
+        if bit_depth != 8:
+            yuv = (yuv/(2**bit_depth-1)*255).astype(np.uint8)
+
+        #convert to rgb
+        rgb = cv2.cvtColor(yuv, cv2.COLOR_YUV2RGB_I420)
+    
+    else:
+        yvu = np.stack([Y,V,U],axis=2)
+        if bit_depth != 8:
+            yvu = (yvu/(2**bit_depth-1)*255).astype(np.uint8)
+        rgb = cv2.cvtColor(yvu, cv2.COLOR_YCrCb2RGB)
+
+    return rgb

causalvideovae/eval/fvd/styleganv/fvd.py

@@ -0,0 +1,90 @@
+import torch
+import os
+import math
+import torch.nn.functional as F
+
+# https://github.com/universome/fvd-comparison
+
+
+def load_i3d_pretrained(device=torch.device('cpu')):
+    i3D_WEIGHTS_URL = "https://www.dropbox.com/s/ge9e5ujwgetktms/i3d_torchscript.pt"
+    filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'i3d_torchscript.pt')
+    print(filepath)
+    if not os.path.exists(filepath):
+        print(f"preparing for download {i3D_WEIGHTS_URL}, you can download it by yourself.")
+        os.system(f"wget {i3D_WEIGHTS_URL} -O {filepath}")
+    i3d = torch.jit.load(filepath).eval().to(device)
+    i3d = torch.nn.DataParallel(i3d)
+    return i3d
+    
+
+def get_feats(videos, detector, device, bs=10):
+    # videos : torch.tensor BCTHW [0, 1]
+    detector_kwargs = dict(rescale=False, resize=False, return_features=True) # Return raw features before the softmax layer.
+    feats = np.empty((0, 400))
+    with torch.no_grad():
+        for i in range((len(videos)-1)//bs + 1):
+            feats = np.vstack([feats, detector(torch.stack([preprocess_single(video) for video in videos[i*bs:(i+1)*bs]]).to(device), **detector_kwargs).detach().cpu().numpy()])
+    return feats
+
+
+def get_fvd_feats(videos, i3d, device, bs=10):
+    # videos in [0, 1] as torch tensor BCTHW
+    # videos = [preprocess_single(video) for video in videos]
+    embeddings = get_feats(videos, i3d, device, bs)
+    return embeddings
+
+
+def preprocess_single(video, resolution=224, sequence_length=None):
+    # video: CTHW, [0, 1]
+    c, t, h, w = video.shape
+
+    # temporal crop
+    if sequence_length is not None:
+        assert sequence_length <= t
+        video = video[:, :sequence_length]
+
+    # scale shorter side to resolution
+    scale = resolution / min(h, w)
+    if h < w:
+        target_size = (resolution, math.ceil(w * scale))
+    else:
+        target_size = (math.ceil(h * scale), resolution)
+    video = F.interpolate(video, size=target_size, mode='bilinear', align_corners=False)
+
+    # center crop
+    c, t, h, w = video.shape
+    w_start = (w - resolution) // 2
+    h_start = (h - resolution) // 2
+    video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution]
+
+    # [0, 1] -> [-1, 1]
+    video = (video - 0.5) * 2
+
+    return video.contiguous()
+
+
+"""
+Copy-pasted from https://github.com/cvpr2022-stylegan-v/stylegan-v/blob/main/src/metrics/frechet_video_distance.py
+"""
+from typing import Tuple
+from scipy.linalg import sqrtm
+import numpy as np
+
+
+def compute_stats(feats: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    mu = feats.mean(axis=0) # [d]
+    sigma = np.cov(feats, rowvar=False) # [d, d]
+    return mu, sigma
+
+
+def frechet_distance(feats_fake: np.ndarray, feats_real: np.ndarray) -> float:
+    mu_gen, sigma_gen = compute_stats(feats_fake)
+    mu_real, sigma_real = compute_stats(feats_real)
+    m = np.square(mu_gen - mu_real).sum()
+    if feats_fake.shape[0]>1:
+        s, _ = sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member
+        fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2))
+    else:
+        fid = np.real(m)
+    return float(fid)

causalvideovae/eval/fvd/videogpt/fvd.py

@@ -0,0 +1,137 @@
+import torch
+import os
+import math
+import torch.nn.functional as F
+import numpy as np
+import einops
+
+def load_i3d_pretrained(device=torch.device('cpu')):
+    i3D_WEIGHTS_URL = "https://onedrive.live.com/download?cid=78EEF3EB6AE7DBCB&resid=78EEF3EB6AE7DBCB%21199&authkey=AApKdFHPXzWLNyI"
+    filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'i3d_pretrained_400.pt')
+    print(filepath)
+    if not os.path.exists(filepath):
+        print(f"preparing for download {i3D_WEIGHTS_URL}, you can download it by yourself.")
+        os.system(f"wget {i3D_WEIGHTS_URL} -O {filepath}")
+    from .pytorch_i3d import InceptionI3d
+    i3d = InceptionI3d(400, in_channels=3).eval().to(device)
+    i3d.load_state_dict(torch.load(filepath, map_location=device))
+    i3d = torch.nn.DataParallel(i3d)
+    return i3d
+
+def preprocess_single(video, resolution, sequence_length=None):
+    # video: THWC, {0, ..., 255}
+    video = video.permute(0, 3, 1, 2).float() / 255. # TCHW
+    t, c, h, w = video.shape
+
+    # temporal crop
+    if sequence_length is not None:
+        assert sequence_length <= t
+        video = video[:sequence_length]
+
+    # scale shorter side to resolution
+    scale = resolution / min(h, w)
+    if h < w:
+        target_size = (resolution, math.ceil(w * scale))
+    else:
+        target_size = (math.ceil(h * scale), resolution)
+    video = F.interpolate(video, size=target_size, mode='bilinear',
+                          align_corners=False)
+
+    # center crop
+    t, c, h, w = video.shape
+    w_start = (w - resolution) // 2
+    h_start = (h - resolution) // 2
+    video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution]
+    video = video.permute(1, 0, 2, 3).contiguous() # CTHW
+
+    video -= 0.5
+
+    return video
+
+def preprocess(videos, target_resolution=224):
+    # we should tras videos in [0-1] [b c t h w] as th.float 
+    # -> videos in {0, ..., 255} [b t h w c] as np.uint8 array
+    videos = einops.rearrange(videos, 'b c t h w -> b t h w c')
+    videos = (videos*255).numpy().astype(np.uint8)
+
+    b, t, h, w, c = videos.shape
+    videos = torch.from_numpy(videos)
+    videos = torch.stack([preprocess_single(video, target_resolution) for video in videos])
+    return videos * 2 # [-0.5, 0.5] -> [-1, 1]
+
+def get_fvd_logits(videos, i3d, device, bs=10):
+    videos = preprocess(videos)
+    embeddings = get_logits(i3d, videos, device, bs=10)
+    return embeddings
+
+# https://github.com/tensorflow/gan/blob/de4b8da3853058ea380a6152bd3bd454013bf619/tensorflow_gan/python/eval/classifier_metrics.py#L161
+def _symmetric_matrix_square_root(mat, eps=1e-10):
+    u, s, v = torch.svd(mat)
+    si = torch.where(s < eps, s, torch.sqrt(s))
+    return torch.matmul(torch.matmul(u, torch.diag(si)), v.t())
+
+# https://github.com/tensorflow/gan/blob/de4b8da3853058ea380a6152bd3bd454013bf619/tensorflow_gan/python/eval/classifier_metrics.py#L400
+def trace_sqrt_product(sigma, sigma_v):
+    sqrt_sigma = _symmetric_matrix_square_root(sigma)
+    sqrt_a_sigmav_a = torch.matmul(sqrt_sigma, torch.matmul(sigma_v, sqrt_sigma))
+    return torch.trace(_symmetric_matrix_square_root(sqrt_a_sigmav_a))
+
+# https://discuss.pytorch.org/t/covariance-and-gradient-support/16217/2
+def cov(m, rowvar=False):
+    '''Estimate a covariance matrix given data.
+
+    Covariance indicates the level to which two variables vary together.
+    If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`,
+    then the covariance matrix element `C_{ij}` is the covariance of
+    `x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`.
+
+    Args:
+        m: A 1-D or 2-D array containing multiple variables and observations.
+            Each row of `m` represents a variable, and each column a single
+            observation of all those variables.
+        rowvar: If `rowvar` is True, then each row represents a
+            variable, with observations in the columns. Otherwise, the
+            relationship is transposed: each column represents a variable,
+            while the rows contain observations.
+
+    Returns:
+        The covariance matrix of the variables.
+    '''
+    if m.dim() > 2:
+        raise ValueError('m has more than 2 dimensions')
+    if m.dim() < 2:
+        m = m.view(1, -1)
+    if not rowvar and m.size(0) != 1:
+        m = m.t()
+
+    fact = 1.0 / (m.size(1) - 1) # unbiased estimate
+    m -= torch.mean(m, dim=1, keepdim=True)
+    mt = m.t()  # if complex: mt = m.t().conj()
+    return fact * m.matmul(mt).squeeze()
+
+
+def frechet_distance(x1, x2):
+    x1 = x1.flatten(start_dim=1)
+    x2 = x2.flatten(start_dim=1)
+    m, m_w = x1.mean(dim=0), x2.mean(dim=0)
+    sigma, sigma_w = cov(x1, rowvar=False), cov(x2, rowvar=False)
+    mean = torch.sum((m - m_w) ** 2)
+    if x1.shape[0]>1:
+        sqrt_trace_component = trace_sqrt_product(sigma, sigma_w)
+        trace = torch.trace(sigma + sigma_w) - 2.0 * sqrt_trace_component
+        fd = trace + mean
+    else:
+        fd = np.real(mean)
+    return float(fd)
+
+
+def get_logits(i3d, videos, device, bs=10):
+    # assert videos.shape[0] % 16 == 0
+    with torch.no_grad():
+        logits = []
+        for i in range(0, videos.shape[0], bs):
+            batch = videos[i:i + bs].to(device)
+            # logits.append(i3d.module.extract_features(batch)) # wrong
+            logits.append(i3d(batch)) # right
+        logits = torch.cat(logits, dim=0)
+        return logits

causalvideovae/eval/script/cal_lpips.sh

@@ -0,0 +1,8 @@
+python eval_common_metric.py \
+    --real_video_dir path/to/imageA\
+    --generated_video_dir path/to/imageB \
+    --batch_size 10 \
+    --num_frames 20 \
+    --crop_size 64 \
+    --device 'cuda' \
+    --metric 'lpips'

causalvideovae/model/losses/perceptual_loss.py

@@ -0,0 +1,522 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from .lpips import LPIPS
+from einops import rearrange
+from .discriminator import NLayerDiscriminator, weights_init, NLayerDiscriminator3D
+
+
+def hinge_d_loss(logits_real, logits_fake):
+    loss_real = torch.mean(F.relu(1.0 - logits_real))
+    loss_fake = torch.mean(F.relu(1.0 + logits_fake))
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+    d_loss = 0.5 * (
+        torch.mean(torch.nn.functional.softplus(-logits_real))
+        + torch.mean(torch.nn.functional.softplus(logits_fake))
+    )
+    return d_loss
+
+
+def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
+    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
+    loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3])
+    loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3])
+    loss_real = (weights * loss_real).sum() / weights.sum()
+    loss_fake = (weights * loss_fake).sum() / weights.sum()
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def adopt_weight(weight, global_step, threshold=0, value=0.0):
+    if global_step < threshold:
+        weight = value
+    return weight
+
+
+def measure_perplexity(predicted_indices, n_embed):
+    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
+    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
+    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
+    avg_probs = encodings.mean(0)
+    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
+    cluster_use = torch.sum(avg_probs > 0)
+    return perplexity, cluster_use
+
+
+def l1(x, y):
+    return torch.abs(x - y)
+
+
+def l2(x, y):
+    return torch.pow((x - y), 2)
+
+def l1_10(x, y):
+    return torch.pow((x - y), 0.1)
+
+
+class LPIPSWithDiscriminator(nn.Module):
+    def __init__(
+        self,
+        disc_start,
+        logvar_init=0.0,
+        kl_weight=1.0,
+        pixelloss_weight=1.0,
+        perceptual_weight=1.0,
+        # --- Discriminator Loss ---
+        disc_num_layers=3,
+        disc_in_channels=3,
+        disc_factor=1.0,
+        disc_weight=1.0,
+        use_actnorm=False,
+        disc_conditional=False,
+        disc_loss="hinge",
+        loss_type: str = "l1"
+    ):
+
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.kl_weight = kl_weight
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
+
+        self.discriminator = NLayerDiscriminator(
+            input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm
+        ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+        self.loss_func = l1 if loss_type == "l1" else l2
+        
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        layer = last_layer if last_layer is not None else self.last_layer[0]
+
+        nll_grads = torch.autograd.grad(nll_loss, layer, retain_graph=True)[0]
+        g_grads = torch.autograd.grad(g_loss, layer, retain_graph=True)[0]
+        
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        inputs,
+        reconstructions,
+        posteriors,
+        optimizer_idx,
+        global_step,
+        split="train",
+        weights=None,
+        last_layer=None,
+        cond=None,
+    ):
+        # GAN Part
+        inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
+        reconstructions = rearrange(
+            reconstructions, "b c t h w -> (b t) c h w"
+        ).contiguous()
+        if optimizer_idx == 0:
+            rec_loss = self.loss_func(inputs, reconstructions)
+            if self.perceptual_weight > 0:
+                p_loss = self.perceptual_loss(inputs, reconstructions)
+                rec_loss = rec_loss + self.perceptual_weight * p_loss
+            nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+            weighted_nll_loss = nll_loss
+            if weights is not None:
+                weighted_nll_loss = weights * nll_loss
+            weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+            nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+            kl_loss = posteriors.kl()
+            kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+            
+            logits_fake = self.discriminator(reconstructions)
+            g_loss = -torch.mean(logits_fake)
+            if global_step >= self.discriminator_iter_start:
+                if self.disc_factor > 0.0:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+                
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            loss = (
+                weighted_nll_loss
+                + self.kl_weight * kl_loss
+                + d_weight * disc_factor * g_loss
+            )
+            log = {
+                "{}/total_loss".format(split): loss.clone().detach().mean(),
+                "{}/logvar".format(split): self.logvar.detach(),
+                "{}/kl_loss".format(split): kl_loss.detach().mean(),
+                "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                "{}/d_weight".format(split): d_weight.detach(),
+                "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                "{}/g_loss".format(split): g_loss.detach().mean(),
+            }
+            return loss, log
+
+        if optimizer_idx == 1:
+            if cond is None:
+                logits_real = self.discriminator(inputs.contiguous().detach())
+                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+            else:
+                logits_real = self.discriminator(
+                    torch.cat((inputs.contiguous().detach(), cond), dim=1)
+                )
+                logits_fake = self.discriminator(
+                    torch.cat((reconstructions.contiguous().detach(), cond), dim=1)
+                )
+
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {
+                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                "{}/logits_real".format(split): logits_real.detach().mean(),
+                "{}/logits_fake".format(split): logits_fake.detach().mean(),
+            }
+            return d_loss, log
+
+
+class LPIPSWithDiscriminator3D(nn.Module):
+    def __init__(
+        self,
+        disc_start,
+        logvar_init=0.0,
+        kl_weight=1.0,
+        pixelloss_weight=1.0,
+        perceptual_weight=1.0,
+        # --- Discriminator Loss ---
+        disc_num_layers=3,
+        disc_in_channels=3,
+        disc_factor=1.0,
+        disc_weight=1.0,
+        use_actnorm=False,
+        disc_conditional=False,
+        disc_loss="hinge",
+        learn_logvar: bool = False,
+        loss_type: str = "l1"
+    ):
+
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.kl_weight = kl_weight
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.logvar = nn.Parameter(
+            torch.full((), logvar_init), requires_grad=learn_logvar
+        )
+        self.discriminator = NLayerDiscriminator3D(
+            input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm
+        ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+        self.loss_func = l1 if loss_type == "l1" else l2
+        
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        layer = last_layer if last_layer is not None else self.last_layer[0]
+
+        nll_grads = torch.autograd.grad(nll_loss, layer, retain_graph=True)[0]
+        g_grads = torch.autograd.grad(g_loss, layer, retain_graph=True)[0]
+        
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        inputs,
+        reconstructions,
+        posteriors,
+        optimizer_idx,
+        global_step,
+        split="train",
+        weights=None,
+        last_layer=None,
+        cond=None
+    ):
+        
+        t = inputs.shape[2]
+        # GAN Part
+        if optimizer_idx == 0:
+            inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
+            reconstructions = rearrange(
+                reconstructions, "b c t h w -> (b t) c h w"
+            ).contiguous()
+            rec_loss = self.loss_func(inputs, reconstructions)
+            if self.perceptual_weight > 0:
+                p_loss = self.perceptual_loss(inputs, reconstructions)
+                rec_loss = rec_loss + self.perceptual_weight * p_loss
+            nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+            weighted_nll_loss = nll_loss
+            if weights is not None:
+                weighted_nll_loss = weights * nll_loss
+            weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+            nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+            kl_loss = posteriors.kl()
+            kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+
+            inputs = rearrange(inputs, "(b t) c h w -> b c t h w", t=t).contiguous()
+            reconstructions = rearrange(
+                reconstructions, "(b t) c h w -> b c t h w", t=t
+            ).contiguous()
+            
+            logits_fake = self.discriminator(reconstructions)
+            g_loss = -torch.mean(logits_fake)
+            if global_step >= self.discriminator_iter_start:
+                if self.disc_factor > 0.0:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+                
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            loss = (
+                weighted_nll_loss
+                + self.kl_weight * kl_loss
+                + d_weight * disc_factor * g_loss
+            )
+            log = {
+                "{}/total_loss".format(split): loss.clone().detach().mean(),
+                "{}/logvar".format(split): self.logvar.detach(),
+                "{}/kl_loss".format(split): kl_loss.detach().mean(),
+                "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                "{}/d_weight".format(split): d_weight.detach(),
+                "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                "{}/g_loss".format(split): g_loss.detach().mean(),
+            }
+            return loss, log
+        elif optimizer_idx == 1:
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {
+                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                "{}/logits_real".format(split): logits_real.detach().mean(),
+                "{}/logits_fake".format(split): logits_fake.detach().mean(),
+            }
+            return d_loss, log
+
+
+class LPIPSWithDiscriminator3Drefiner(nn.Module):
+    def __init__(
+        self,
+        disc_start,
+        logvar_init=0.0,
+        pixelloss_weight=1.0,
+        perceptual_weight=1.0,
+        # --- Discriminator Loss ---
+        disc_num_layers=3,
+        disc_in_channels=3,
+        disc_factor=1.0,
+        disc_weight=1.0,
+        use_actnorm=False,
+        disc_conditional=False,
+        disc_loss="hinge",
+        learn_logvar: bool = False,
+        loss_type: str = "l1"
+    ):
+
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.logvar = nn.Parameter(
+            torch.full((), logvar_init), requires_grad=learn_logvar
+        )
+        self.discriminator = NLayerDiscriminator3D(
+            input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm
+        ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+        self.loss_func = l1 if loss_type == "l1" else l2
+        
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        layer = last_layer if last_layer is not None else self.last_layer[0]
+
+        nll_grads = torch.autograd.grad(nll_loss, layer, retain_graph=True)[0]
+        g_grads = torch.autograd.grad(g_loss, layer, retain_graph=True)[0]
+        
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        inputs,
+        reconstructions,
+        optimizer_idx,
+        global_step,
+        split="train",
+        weights=None,
+        last_layer=None,
+        cond=None
+    ):
+        
+        t = inputs.shape[2]
+        # GAN Part
+        if optimizer_idx == 0:
+            inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
+            reconstructions = rearrange(
+                reconstructions, "b c t h w -> (b t) c h w"
+            ).contiguous()
+            rec_loss = self.loss_func(inputs, reconstructions)
+            if self.perceptual_weight > 0:
+                p_loss = self.perceptual_loss(inputs, reconstructions)
+                rec_loss = rec_loss + self.perceptual_weight * p_loss
+            nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+            weighted_nll_loss = nll_loss
+            if weights is not None:
+                weighted_nll_loss = weights * nll_loss
+            weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+            nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+
+            inputs = rearrange(inputs, "(b t) c h w -> b c t h w", t=t).contiguous()
+            reconstructions = rearrange(
+                reconstructions, "(b t) c h w -> b c t h w", t=t
+            ).contiguous()
+            
+            logits_fake = self.discriminator(reconstructions)
+            g_loss = -torch.mean(logits_fake)
+            if global_step >= self.discriminator_iter_start:
+                if self.disc_factor > 0.0:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+                
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            loss = (
+                weighted_nll_loss
+                + d_weight * disc_factor * g_loss
+            )
+            log = {
+                "{}/total_loss".format(split): loss.clone().detach().mean(),
+                "{}/logvar".format(split): self.logvar.detach(),
+                "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                "{}/d_weight".format(split): d_weight.detach(),
+                "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                "{}/g_loss".format(split): g_loss.detach().mean(),
+            }
+            return loss, log
+        elif optimizer_idx == 1:
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+            disc_factor = adopt_weight(
+                self.disc_factor, global_step, threshold=self.discriminator_iter_start
+            )
+            
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {
+                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                "{}/logits_real".format(split): logits_real.detach().mean(),
+                "{}/logits_fake".format(split): logits_fake.detach().mean(),
+            }
+            return d_loss, log
+
+class SimpleLPIPS(nn.Module):
+    def __init__(
+        self,
+        logvar_init=0.0,
+        kl_weight=1.0,
+        pixelloss_weight=1.0,
+        perceptual_weight=1.0,
+        disc_loss="hinge",
+        learn_logvar: bool = False,
+        **kwargs
+    ):
+
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.kl_weight = kl_weight
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.logvar = nn.Parameter(
+            torch.full((), logvar_init), requires_grad=learn_logvar
+        )
+
+    def forward(
+        self,
+        inputs,
+        reconstructions,
+        posteriors,
+        split="train",
+        weights=None,
+    ):
+        inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
+        reconstructions = rearrange(
+            reconstructions, "b c t h w -> (b t) c h w"
+        ).contiguous()
+        rec_loss = torch.abs(inputs - reconstructions)
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(inputs, reconstructions)
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+        weighted_nll_loss = nll_loss
+        if weights is not None:
+            weighted_nll_loss = weights * nll_loss
+        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+        kl_loss = posteriors.kl()
+        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+        loss = weighted_nll_loss + self.kl_weight * kl_loss
+        log = {
+            "{}/total_loss".format(split): loss.clone().detach().mean(),
+            "{}/logvar".format(split): self.logvar.detach(),
+            "{}/kl_loss".format(split): kl_loss.detach().mean(),
+            "{}/nll_loss".format(split): nll_loss.detach().mean(),
+            "{}/rec_loss".format(split): rec_loss.detach().mean(),
+        }
+        if self.perceptual_weight > 0:
+            log.update({"{}/p_loss".format(split): p_loss.detach().mean()})
+        return loss, log

causalvideovae/model/modules/attention.py

@@ -0,0 +1,287 @@
+import torch.nn as nn
+from .normalize import Normalize
+from .conv import CausalConv3d
+import torch
+import numpy as np
+from einops import rearrange
+from .block import Block
+from .ops import video_to_image
+
+class LinearAttention(Block):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(
+            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
+        )
+        k = k.softmax(dim=-1)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        out = rearrange(
+            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
+        )
+        return self.to_out(out)
+
+
+class LinAttnBlock(LinearAttention):
+    """to match AttnBlock usage"""
+
+    def __init__(self, in_channels):
+        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
+
+
+class AttnBlock3D(Block):
+    """Compatible with old versions, there are issues, use with caution."""
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.k = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.v = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.proj_out = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, t, h, w = q.shape
+        q = q.reshape(b * t, c, h * w)
+        q = q.permute(0, 2, 1)  # b,hw,c
+        k = k.reshape(b * t, c, h * w)  # b,c,hw
+        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b * t, c, h * w)
+        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b, c, t, h, w)
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+class AttnBlock3DFixNorm(nn.Module):
+    """
+    Thanks to https://github.com/PKU-YuanGroup/Open-Sora-Plan/pull/172.
+    """
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.k = torch.nn.Conv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.v = torch.nn.Conv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.proj_out = torch.nn.Conv3d(in_channels, in_channels, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        h_ = x
+        h_ = h_.permute(0, 2, 3, 4, 1)
+        h_ = self.norm(h_)
+        h_ = h_.permute(0, 4, 1, 2, 3)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        # q: (b c t h w) -> (b t c h w) -> (b*t c h*w) -> (b*t h*w c)
+        b, c, t, h, w = q.shape
+        q = q.permute(0, 2, 1, 3, 4)
+        q = q.reshape(b * t, c, h * w)
+        q = q.permute(0, 2, 1)
+
+        # k: (b c t h w) -> (b t c h w) -> (b*t c h*w)
+        k = k.permute(0, 2, 1, 3, 4)
+        k = k.reshape(b * t, c, h * w)
+
+        # w: (b*t hw hw)
+        w_ = torch.bmm(q, k)
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        # v: (b c t h w) -> (b t c h w) -> (bt c hw)
+        # w_: (bt hw hw) -> (bt hw hw)
+        v = v.permute(0, 2, 1, 3, 4)
+        v = v.reshape(b * t, c, h * w)
+        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+
+        # h_: (b*t c hw) -> (b t c h w) -> (b c t h w)
+        h_ = h_.reshape(b, t, c, h, w)
+        h_ = h_.permute(0, 2, 1, 3 ,4)
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+class AttnBlock3DFix(nn.Module):
+    """
+    Thanks to https://github.com/PKU-YuanGroup/Open-Sora-Plan/pull/172.
+    """
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.k = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.v = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+        self.proj_out = CausalConv3d(in_channels, in_channels, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        h = x
+        h = h.permute(0, 2, 3, 4, 1)
+        h = self.norm(h)
+        h_ = h.permute(0, 4, 1, 2, 3)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        # q: (b c t h w) -> (b t c h w) -> (b*t c h*w) -> (b*t h*w c)
+        b, c, t, h, w = q.shape
+        q = q.permute(0, 2, 1, 3, 4)
+        q = q.reshape(b * t, c, h * w)
+        q = q.permute(0, 2, 1)
+
+        # k: (b c t h w) -> (b t c h w) -> (b*t c h*w)
+        k = k.permute(0, 2, 1, 3, 4)
+        k = k.reshape(b * t, c, h * w)
+
+        # w: (b*t hw hw)
+        w_ = torch.bmm(q, k)
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        # v: (b c t h w) -> (b t c h w) -> (bt c hw)
+        # w_: (bt hw hw) -> (bt hw hw)
+        v = v.permute(0, 2, 1, 3, 4)
+        v = v.reshape(b * t, c, h * w)
+        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+
+        # h_: (b*t c hw) -> (b t c h w) -> (b c t h w)
+        h_ = h_.reshape(b, t, c, h, w)
+        h_ = h_.permute(0, 2, 1, 3 ,4)
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+class AttnBlock(Block):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        
+    @video_to_image
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = q.reshape(b, c, h * w)
+        q = q.permute(0, 2, 1)  # b,hw,c
+        k = k.reshape(b, c, h * w)  # b,c,hw
+        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h * w)
+        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b, c, h, w)
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+class TemporalAttnBlock(Block):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv3d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = torch.nn.Conv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv3d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, t, h, w = q.shape
+        q = rearrange(q, "b c t h w -> (b h w) t c")
+        k = rearrange(k, "b c t h w -> (b h w) c t")
+        v = rearrange(v, "b c t h w -> (b h w) c t")
+        w_ = torch.bmm(q, k)
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        w_ = w_.permute(0, 2, 1)
+        h_ = torch.bmm(v, w_)
+        h_ = rearrange(h_, "(b h w) c t -> b c t h w", h=h, w=w)
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+def make_attn(in_channels, attn_type="vanilla"):
+    assert attn_type in ["vanilla", "linear", "none", "vanilla3D"], f"attn_type {attn_type} unknown"
+    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    print(attn_type)
+    if attn_type == "vanilla":
+        return AttnBlock(in_channels)
+    elif attn_type == "vanilla3D":
+        return AttnBlock3D(in_channels)
+    elif attn_type == "none":
+        return nn.Identity(in_channels)
+    else:
+        return LinAttnBlock(in_channels)

causalvideovae/model/modules/normalize.py

@@ -0,0 +1,104 @@
+import torch
+import torch.nn as nn
+from .block import Block
+
+class GroupNorm(Block):
+    def __init__(self, num_channels, num_groups=32, eps=1e-6, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.norm = torch.nn.GroupNorm(
+            num_groups=num_groups, num_channels=num_channels, eps=1e-6, affine=True
+        )
+    def forward(self, x):
+        return self.norm(x)
+
+def Normalize(in_channels, num_groups=32):
+    """
+    return torch.nn.GroupNorm(
+        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
+    )
+    """
+    return torch.nn.LayerNorm(in_channels, eps=1e-12, elementwise_affine=True)
+
+class ActNorm(nn.Module):
+    def __init__(self, num_features, logdet=False, affine=True,
+                 allow_reverse_init=False):
+        assert affine
+        super().__init__()
+        self.logdet = logdet
+        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
+        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
+        self.allow_reverse_init = allow_reverse_init
+
+        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
+
+    def initialize(self, input):
+        with torch.no_grad():
+            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
+            mean = (
+                flatten.mean(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+            std = (
+                flatten.std(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+
+            self.loc.data.copy_(-mean)
+            self.scale.data.copy_(1 / (std + 1e-6))
+
+    def forward(self, input, reverse=False):
+        if reverse:
+            return self.reverse(input)
+        if len(input.shape) == 2:
+            input = input[:,:,None,None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        _, _, height, width = input.shape
+
+        if self.training and self.initialized.item() == 0:
+            self.initialize(input)
+            self.initialized.fill_(1)
+
+        h = self.scale * (input + self.loc)
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+
+        if self.logdet:
+            log_abs = torch.log(torch.abs(self.scale))
+            logdet = height*width*torch.sum(log_abs)
+            logdet = logdet * torch.ones(input.shape[0]).to(input)
+            return h, logdet
+
+        return h
+
+    def reverse(self, output):
+        if self.training and self.initialized.item() == 0:
+            if not self.allow_reverse_init:
+                raise RuntimeError(
+                    "Initializing ActNorm in reverse direction is "
+                    "disabled by default. Use allow_reverse_init=True to enable."
+                )
+            else:
+                self.initialize(output)
+                self.initialized.fill_(1)
+
+        if len(output.shape) == 2:
+            output = output[:,:,None,None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        h = output / self.scale - self.loc
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+        return h

causalvideovae/model/modules/ops.py

@@ -0,0 +1,40 @@
+import torch
+from einops import rearrange
+
+def video_to_image(func):
+    def wrapper(self, x, *args, **kwargs):
+        if x.dim() == 5:
+            t = x.shape[2]
+            x = rearrange(x, "b c t h w -> (b t) c h w")
+            x = func(self, x, *args, **kwargs)
+            x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
+        return x
+    return wrapper
+
+def nonlinearity(x):
+    return x * torch.sigmoid(x)
+
+def cast_tuple(t, length=1):
+    return t if isinstance(t, tuple) or isinstance(t, list) else ((t,) * length)
+
+def shift_dim(x, src_dim=-1, dest_dim=-1, make_contiguous=True):
+    n_dims = len(x.shape)
+    if src_dim < 0:
+        src_dim = n_dims + src_dim
+    if dest_dim < 0:
+        dest_dim = n_dims + dest_dim
+    assert 0 <= src_dim < n_dims and 0 <= dest_dim < n_dims
+    dims = list(range(n_dims))
+    del dims[src_dim]
+    permutation = []
+    ctr = 0
+    for i in range(n_dims):
+        if i == dest_dim:
+            permutation.append(src_dim)
+        else:
+            permutation.append(dims[ctr])
+            ctr += 1
+    x = x.permute(permutation)
+    if make_contiguous:
+        x = x.contiguous()
+    return x

causalvideovae/model/refiner/__init__.py

@@ -0,0 +1,2 @@
+from .modeling_refiner import Refiner
+

causalvideovae/model/utils/distrib_utils.py

@@ -0,0 +1,42 @@
+import torch
+import numpy as np
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean

causalvideovae/model/utils/module_utils.py

@@ -0,0 +1,17 @@
+import importlib
+
+Module = str
+MODULES_BASE = "causalvideovae.model.modules."
+
+def resolve_str_to_obj(str_val, append=True):
+    if append:
+        str_val = MODULES_BASE + str_val
+    module_name, class_name = str_val.rsplit('.', 1)
+    module = importlib.import_module(module_name)
+    return getattr(module, class_name)
+
+def create_instance(module_class_str: str, **kwargs):
+    module_name, class_name = module_class_str.rsplit('.', 1)
+    module = importlib.import_module(module_name)
+    class_ = getattr(module, class_name)
+    return class_(**kwargs)

causalvideovae/utils/dataset_utils.py

@@ -0,0 +1,160 @@
+import math
+from einops import rearrange
+import decord
+from torch.nn import functional as F
+import torch
+
+
+IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG']
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+class DecordInit(object):
+    """Using Decord(https://github.com/dmlc/decord) to initialize the video_reader."""
+
+    def __init__(self, num_threads=1):
+        self.num_threads = num_threads
+        self.ctx = decord.cpu(0)
+
+    def __call__(self, filename):
+        """Perform the Decord initialization.
+        Args:
+            results (dict): The resulting dict to be modified and passed
+                to the next transform in pipeline.
+        """
+        reader = decord.VideoReader(filename,
+                                    ctx=self.ctx,
+                                    num_threads=self.num_threads)
+        return reader
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'sr={self.sr},'
+                    f'num_threads={self.num_threads})')
+        return repr_str
+
+def pad_to_multiple(number, ds_stride):
+    remainder = number % ds_stride
+    if remainder == 0:
+        return number
+    else:
+        padding = ds_stride - remainder
+        return number + padding
+
+class Collate:
+    def __init__(self, args):
+        self.max_image_size = args.max_image_size
+        self.ae_stride = args.ae_stride
+        self.ae_stride_t = args.ae_stride_t
+        self.ae_stride_thw = (self.ae_stride_t, self.ae_stride, self.ae_stride)
+        self.ae_stride_1hw = (1, self.ae_stride, self.ae_stride)
+
+        self.patch_size = args.patch_size
+        self.patch_size_t = args.patch_size_t
+        self.patch_size_thw = (self.patch_size_t, self.patch_size, self.patch_size)
+        self.patch_size_1hw = (1, self.patch_size, self.patch_size)
+
+        self.num_frames = args.num_frames
+        self.use_image_num = args.use_image_num
+        self.max_thw = (self.num_frames, self.max_image_size, self.max_image_size)
+        self.max_1hw = (1, self.max_image_size, self.max_image_size)
+
+    def package(self, batch):
+        # import ipdb;ipdb.set_trace()
+        batch_tubes_vid = [i['video_data']['video'] for i in batch]  # b [c t h w]
+        input_ids_vid = torch.stack([i['video_data']['input_ids'] for i in batch])  # b 1 l
+        cond_mask_vid = torch.stack([i['video_data']['cond_mask'] for i in batch])  # b 1 l
+        batch_tubes_img, input_ids_img, cond_mask_img = None, None, None
+        if self.use_image_num != 0: 
+            batch_tubes_img = [j for i in batch for j in i['image_data']['image']]  # b*num_img [c 1 h w]
+            input_ids_img = torch.stack([i['image_data']['input_ids'] for i in batch])  # b image_num l
+            cond_mask_img = torch.stack([i['image_data']['cond_mask'] for i in batch])  # b image_num l
+        return batch_tubes_vid, input_ids_vid, cond_mask_vid, batch_tubes_img, input_ids_img, cond_mask_img
+
+    def __call__(self, batch):
+        batch_tubes_vid, input_ids_vid, cond_mask_vid, batch_tubes_img, input_ids_img, cond_mask_img = self.package(batch)
+
+        # import ipdb;ipdb.set_trace()
+        ds_stride = self.ae_stride * self.patch_size
+        t_ds_stride = self.ae_stride_t * self.patch_size_t
+        if self.use_image_num == 0:
+            pad_batch_tubes, attention_mask = self.process(batch_tubes_vid, t_ds_stride, ds_stride, 
+                                                      self.max_thw, self.ae_stride_thw, self.patch_size_thw, extra_1=True)
+            # attention_mask: b t h w
+            input_ids, cond_mask = input_ids_vid.squeeze(1), cond_mask_vid.squeeze(1)  # b 1 l -> b l
+        else:
+            pad_batch_tubes_vid, attention_mask_vid = self.process(batch_tubes_vid, t_ds_stride, ds_stride, 
+                                                                   self.max_thw, self.ae_stride_thw, self.patch_size_thw, extra_1=True)
+            # attention_mask_vid: b t h w
+            pad_batch_tubes_img, attention_mask_img = self.process(batch_tubes_img, 1, ds_stride, 
+                                                                   self.max_1hw, self.ae_stride_1hw, self.patch_size_1hw, extra_1=False)
+            pad_batch_tubes_img = rearrange(pad_batch_tubes_img, '(b i) c 1 h w -> b c i h w', i=self.use_image_num)
+            attention_mask_img = rearrange(attention_mask_img, '(b i) 1 h w -> b i h w', i=self.use_image_num)
+            pad_batch_tubes = torch.cat([pad_batch_tubes_vid, pad_batch_tubes_img], dim=2)  # concat at temporal, video first
+            # attention_mask_img: b num_img h w
+            attention_mask = torch.cat([attention_mask_vid, attention_mask_img], dim=1)  # b t+num_img h w
+            input_ids = torch.cat([input_ids_vid, input_ids_img], dim=1)  # b 1+num_img hw
+            cond_mask = torch.cat([cond_mask_vid, cond_mask_img], dim=1)  # b 1+num_img hw
+        return pad_batch_tubes, attention_mask, input_ids, cond_mask
+
+    def process(self, batch_tubes, t_ds_stride, ds_stride, max_thw, ae_stride_thw, patch_size_thw, extra_1):
+        
+        # pad to max multiple of ds_stride
+        batch_input_size = [i.shape for i in batch_tubes]  # [(c t h w), (c t h w)]
+        max_t, max_h, max_w = max_thw
+        pad_max_t, pad_max_h, pad_max_w = pad_to_multiple(max_t-1 if extra_1 else max_t, t_ds_stride), \
+                                          pad_to_multiple(max_h, ds_stride), \
+                                          pad_to_multiple(max_w, ds_stride)
+        pad_max_t = pad_max_t + 1 if extra_1 else pad_max_t
+        each_pad_t_h_w = [[pad_max_t - i.shape[1],
+                           pad_max_h - i.shape[2],
+                           pad_max_w - i.shape[3]] for i in batch_tubes]
+        pad_batch_tubes = [F.pad(im,
+                                 (0, pad_w,
+                                  0, pad_h,
+                                  0, pad_t), value=0) for (pad_t, pad_h, pad_w), im in zip(each_pad_t_h_w, batch_tubes)]
+        pad_batch_tubes = torch.stack(pad_batch_tubes, dim=0)
+
+        # make attention_mask
+        # first_channel_first_frame, first_channel_other_frame = pad_batch_tubes[:, :1, :1], pad_batch_tubes[:, :1, 1:]  # first channel to make attention_mask
+        # attention_mask_first_frame = F.max_pool3d(first_channel_first_frame, kernel_size=(1, *ae_stride_thw[1:]), stride=(1, *ae_stride_thw[1:]))
+        # if first_channel_other_frame.numel() != 0:
+        #     attention_mask_other_frame = F.max_pool3d(first_channel_other_frame, kernel_size=ae_stride_thw, stride=ae_stride_thw)
+        #     attention_mask = torch.cat([attention_mask_first_frame, attention_mask_other_frame], dim=2)
+        # else:
+        #     attention_mask = attention_mask_first_frame
+        # attention_mask_ = attention_mask[:, 0].bool().float()  # b t h w, do not channel
+
+        # import ipdb;ipdb.set_trace()
+        max_tube_size = [pad_max_t, pad_max_h, pad_max_w]
+        max_latent_size = [((max_tube_size[0]-1) // ae_stride_thw[0] + 1) if extra_1 else (max_tube_size[0] // ae_stride_thw[0]),
+                           max_tube_size[1] // ae_stride_thw[1],
+                           max_tube_size[2] // ae_stride_thw[2]]
+        valid_latent_size = [[int(math.ceil((i[1]-1) / ae_stride_thw[0])) + 1 if extra_1 else int(math.ceil(i[1] / ae_stride_thw[0])),
+                            int(math.ceil(i[2] / ae_stride_thw[1])),
+                            int(math.ceil(i[3] / ae_stride_thw[2]))] for i in batch_input_size]
+        attention_mask = [F.pad(torch.ones(i),
+                                (0, max_latent_size[2] - i[2],
+                                 0, max_latent_size[1] - i[1],
+                                 0, max_latent_size[0] - i[0]), value=0) for i in valid_latent_size]
+        attention_mask = torch.stack(attention_mask)  # b t h w
+
+
+        # max_tube_size = [pad_max_t, pad_max_h, pad_max_w]
+        # max_latent_size = [((max_tube_size[0]-1) // ae_stride_thw[0] + 1) if extra_1 else (max_tube_size[0] // ae_stride_thw[0]),
+        #                    max_tube_size[1] // ae_stride_thw[1],
+        #                    max_tube_size[2] // ae_stride_thw[2]]
+        # max_patchify_latent_size = [((max_latent_size[0]-1) // patch_size_thw[0] + 1) if extra_1 else (max_latent_size[0] // patch_size_thw[0]),
+        #                             max_latent_size[1] // patch_size_thw[1],
+        #                             max_latent_size[2] // patch_size_thw[2]]
+        # valid_patchify_latent_size = [[int(math.ceil((i[1]-1) / t_ds_stride)) + 1 if extra_1 else int(math.ceil(i[1] / t_ds_stride)),
+        #                                int(math.ceil(i[2] / ds_stride)),
+        #                                int(math.ceil(i[3] / ds_stride))] for i in batch_input_size]
+        # attention_mask = [F.pad(torch.ones(i),
+        #                         (0, max_patchify_latent_size[2] - i[2],
+        #                          0, max_patchify_latent_size[1] - i[1],
+        #                          0, max_patchify_latent_size[0] - i[0]), value=0) for i in valid_patchify_latent_size]
+        # attention_mask = torch.stack(attention_mask)  # b t h w
+
+        return pad_batch_tubes, attention_mask

causalvideovae/utils/utils.py

@@ -0,0 +1,627 @@
+import os
+
+import torch
+
+import os
+import math
+import torch
+import logging
+import random
+import subprocess
+import numpy as np
+import torch.distributed as dist
+
+# from torch._six import inf
+from torch import inf
+from PIL import Image
+from typing import Union, Iterable
+from collections import OrderedDict
+from torch.utils.tensorboard import SummaryWriter
+
+from diffusers.utils import is_bs4_available, is_ftfy_available
+
+import html
+import re
+import urllib.parse as ul
+
+from torch.utils.data import Dataset
+from torchvision.transforms import Lambda, Compose
+from decord import VideoReader, cpu
+
+from torchvision.transforms._transforms_video import CenterCropVideo
+from torch.nn import functional as F
+import cv2
+import numpy.typing as npt
+
+if is_bs4_available():
+    from bs4 import BeautifulSoup
+
+if is_ftfy_available():
+    import ftfy
+
+_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
+
+def find_model(model_name):
+    """
+    Finds a pre-trained Latte model, downloading it if necessary. Alternatively, loads a model from a local path.
+    """
+    assert os.path.isfile(model_name), f'Could not find Latte checkpoint at {model_name}'
+    checkpoint = torch.load(model_name, map_location=lambda storage, loc: storage)
+
+    # if "ema" in checkpoint:  # supports checkpoints from train.py
+    #     print('Using Ema!')
+    #     checkpoint = checkpoint["ema"]
+    # else:
+    print('Using model!')
+    checkpoint = checkpoint['model']
+    return checkpoint
+
+#################################################################################
+#                             Training Clip Gradients                           #
+#################################################################################
+#import deepspeed
+def print_grad_norm(model):
+    # 计算并打印梯度范数
+    # model_engine = accelerator.deepspeed_engine_wrapped.engine
+    # gradients = model_engine.get_gradients()
+    # grad_norm = get_grad_norm(gradients)
+    # 计算并打印梯度范数
+    grad_norm = 0
+    n_grad = 0
+    for name, param in model.named_parameters():
+        grad_data = deepspeed.utils.safe_get_full_grad(param)
+        # self.print_tensor_stats(grad_data, name=name)
+
+        if grad_data is not None:
+            param_norm = grad_data.norm(2)
+            grad_norm += param_norm.item() ** 2
+            n_grad += 1
+    grad_norm = (grad_norm / n_grad) ** (1. / 2)
+
+    # self.print_msg('=' * 50)
+    print(f'Gradient Norm is : {grad_norm}')
+
+def get_grad_norm(
+        parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor:
+    r"""
+    Copy from torch.nn.utils.clip_grad_norm_
+
+    Clips gradient norm of an iterable of parameters.
+
+    The norm is computed over all gradients together, as if they were
+    concatenated into a single vector. Gradients are modified in-place.
+
+    Args:
+        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+            single Tensor that will have gradients normalized
+        max_norm (float or int): max norm of the gradients
+        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+            infinity norm.
+        error_if_nonfinite (bool): if True, an error is thrown if the total
+            norm of the gradients from :attr:`parameters` is ``nan``,
+            ``inf``, or ``-inf``. Default: False (will switch to True in the future)
+
+    Returns:
+        Total norm of the parameter gradients (viewed as a single vector).
+    """
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    grads = [p.grad for p in parameters if p.grad is not None]
+    norm_type = float(norm_type)
+    if len(grads) == 0:
+        return torch.tensor(0.)
+    device = grads[0].device
+    if norm_type == inf:
+        norms = [g.detach().abs().max().to(device) for g in grads]
+        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
+    else:
+        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
+    return total_norm
+
+
+def clip_grad_norm_(
+        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0,
+        error_if_nonfinite: bool = False, clip_grad=True) -> torch.Tensor:
+    r"""
+    Copy from torch.nn.utils.clip_grad_norm_
+
+    Clips gradient norm of an iterable of parameters.
+
+    The norm is computed over all gradients together, as if they were
+    concatenated into a single vector. Gradients are modified in-place.
+
+    Args:
+        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+            single Tensor that will have gradients normalized
+        max_norm (float or int): max norm of the gradients
+        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+            infinity norm.
+        error_if_nonfinite (bool): if True, an error is thrown if the total
+            norm of the gradients from :attr:`parameters` is ``nan``,
+            ``inf``, or ``-inf``. Default: False (will switch to True in the future)
+
+    Returns:
+        Total norm of the parameter gradients (viewed as a single vector).
+    """
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    grads = [p.grad for p in parameters if p.grad is not None]
+    max_norm = float(max_norm)
+    norm_type = float(norm_type)
+    if len(grads) == 0:
+        return torch.tensor(0.)
+    device = grads[0].device
+    if norm_type == inf:
+        norms = [g.detach().abs().max().to(device) for g in grads]
+        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
+    else:
+        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
+
+    if clip_grad:
+        if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
+            raise RuntimeError(
+                f'The total norm of order {norm_type} for gradients from '
+                '`parameters` is non-finite, so it cannot be clipped. To disable '
+                'this error and scale the gradients by the non-finite norm anyway, '
+                'set `error_if_nonfinite=False`')
+        clip_coef = max_norm / (total_norm + 1e-6)
+        # Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
+        # avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
+        # when the gradients do not reside in CPU memory.
+        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+        for g in grads:
+            g.detach().mul_(clip_coef_clamped.to(g.device))
+        # gradient_cliped = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
+        # print(gradient_cliped)
+    return total_norm
+
+
+def get_experiment_dir(root_dir, args):
+    # if args.pretrained is not None and 'Latte-XL-2-256x256.pt' not in args.pretrained:
+    #     root_dir += '-WOPRE'
+    if args.use_compile:
+        root_dir += '-Compile'  # speedup by torch compile
+    if args.attention_mode:
+        root_dir += f'-{args.attention_mode.upper()}'
+    # if args.enable_xformers_memory_efficient_attention:
+    #     root_dir += '-Xfor'
+    if args.gradient_checkpointing:
+        root_dir += '-Gc'
+    if args.mixed_precision:
+        root_dir += f'-{args.mixed_precision.upper()}'
+    root_dir += f'-{args.max_image_size}'
+    return root_dir
+
+def get_precision(args):
+    if args.mixed_precision == "bf16":
+        dtype = torch.bfloat16
+    elif args.mixed_precision == "fp16":
+        dtype = torch.float16
+    else:
+        dtype = torch.float32
+    return dtype
+
+#################################################################################
+#                             Training Logger                                   #
+#################################################################################
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            # format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            format='[%(asctime)s] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+
+def create_tensorboard(tensorboard_dir):
+    """
+    Create a tensorboard that saves losses.
+    """
+    if dist.get_rank() == 0:  # real tensorboard
+        # tensorboard
+        writer = SummaryWriter(tensorboard_dir)
+
+    return writer
+
+
+def write_tensorboard(writer, *args):
+    '''
+    write the loss information to a tensorboard file.
+    Only for pytorch DDP mode.
+    '''
+    if dist.get_rank() == 0:  # real tensorboard
+        writer.add_scalar(args[0], args[1], args[2])
+
+
+#################################################################################
+#                      EMA Update/ DDP Training Utils                           #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def setup_distributed(backend="nccl", port=None):
+    """Initialize distributed training environment.
+    support both slurm and torch.distributed.launch
+    see torch.distributed.init_process_group() for more details
+    """
+    num_gpus = torch.cuda.device_count()
+
+    if "SLURM_JOB_ID" in os.environ:
+        rank = int(os.environ["SLURM_PROCID"])
+        world_size = int(os.environ["SLURM_NTASKS"])
+        node_list = os.environ["SLURM_NODELIST"]
+        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" not in os.environ:
+            # os.environ["MASTER_PORT"] = "29566"
+            os.environ["MASTER_PORT"] = str(29567 + num_gpus)
+        if "MASTER_ADDR" not in os.environ:
+            os.environ["MASTER_ADDR"] = addr
+        os.environ["WORLD_SIZE"] = str(world_size)
+        os.environ["LOCAL_RANK"] = str(rank % num_gpus)
+        os.environ["RANK"] = str(rank)
+    else:
+        rank = int(os.environ["RANK"])
+        world_size = int(os.environ["WORLD_SIZE"])
+
+    # torch.cuda.set_device(rank % num_gpus)
+
+    dist.init_process_group(
+        backend=backend,
+        world_size=world_size,
+        rank=rank,
+    )
+
+
+#################################################################################
+#                             Testing  Utils                                    #
+#################################################################################
+
+def save_video_grid(video, nrow=None):
+    b, t, h, w, c = video.shape
+
+    if nrow is None:
+        nrow = math.ceil(math.sqrt(b))
+    ncol = math.ceil(b / nrow)
+    padding = 1
+    video_grid = torch.zeros((t, (padding + h) * nrow + padding,
+                              (padding + w) * ncol + padding, c), dtype=torch.uint8)
+
+    print(video_grid.shape)
+    for i in range(b):
+        r = i // ncol
+        c = i % ncol
+        start_r = (padding + h) * r
+        start_c = (padding + w) * c
+        video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i]
+
+    return video_grid
+
+
+#################################################################################
+#                             MMCV  Utils                                    #
+#################################################################################
+
+
+def collect_env():
+    # Copyright (c) OpenMMLab. All rights reserved.
+    from mmcv.utils import collect_env as collect_base_env
+    from mmcv.utils import get_git_hash
+    """Collect the information of the running environments."""
+
+    env_info = collect_base_env()
+    env_info['MMClassification'] = get_git_hash()[:7]
+
+    for name, val in env_info.items():
+        print(f'{name}: {val}')
+
+    print(torch.cuda.get_arch_list())
+    print(torch.version.cuda)
+
+
+#################################################################################
+#                          Pixart-alpha  Utils                                  #
+#################################################################################
+
+
+bad_punct_regex = re.compile(r'['+'#®•©™&@·º½¾¿¡§~'+'\)'+'\('+'\]'+'\['+'\}'+'\{'+'\|'+'\\'+'\/'+'\*' + r']{1,}')  # noqa
+
+def text_preprocessing(text):
+    # The exact text cleaning as was in the training stage:
+    text = clean_caption(text)
+    text = clean_caption(text)
+    return text
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+def clean_caption(caption):
+    caption = str(caption)
+    caption = ul.unquote_plus(caption)
+    caption = caption.strip().lower()
+    caption = re.sub('<person>', 'person', caption)
+    # urls:
+    caption = re.sub(
+        r'\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))',  # noqa
+        '', caption)  # regex for urls
+    caption = re.sub(
+        r'\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))',  # noqa
+        '', caption)  # regex for urls
+    # html:
+    caption = BeautifulSoup(caption, features='html.parser').text
+
+    # @<nickname>
+    caption = re.sub(r'@[\w\d]+\b', '', caption)
+
+    # 31C0—31EF CJK Strokes
+    # 31F0—31FF Katakana Phonetic Extensions
+    # 3200—32FF Enclosed CJK Letters and Months
+    # 3300—33FF CJK Compatibility
+    # 3400—4DBF CJK Unified Ideographs Extension A
+    # 4DC0—4DFF Yijing Hexagram Symbols
+    # 4E00—9FFF CJK Unified Ideographs
+    caption = re.sub(r'[\u31c0-\u31ef]+', '', caption)
+    caption = re.sub(r'[\u31f0-\u31ff]+', '', caption)
+    caption = re.sub(r'[\u3200-\u32ff]+', '', caption)
+    caption = re.sub(r'[\u3300-\u33ff]+', '', caption)
+    caption = re.sub(r'[\u3400-\u4dbf]+', '', caption)
+    caption = re.sub(r'[\u4dc0-\u4dff]+', '', caption)
+    caption = re.sub(r'[\u4e00-\u9fff]+', '', caption)
+    #######################################################
+
+    # все виды тире / all types of dash --> "-"
+    caption = re.sub(
+        r'[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+',  # noqa
+        '-', caption)
+
+    # кавычки к одному стандарту
+    caption = re.sub(r'[`´«»“”¨]', '"', caption)
+    caption = re.sub(r'[‘’]', "'", caption)
+
+    # &quot;
+    caption = re.sub(r'&quot;?', '', caption)
+    # &amp
+    caption = re.sub(r'&amp', '', caption)
+
+    # ip adresses:
+    caption = re.sub(r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}', ' ', caption)
+
+    # article ids:
+    caption = re.sub(r'\d:\d\d\s+$', '', caption)
+
+    # \n
+    caption = re.sub(r'\\n', ' ', caption)
+
+    # "#123"
+    caption = re.sub(r'#\d{1,3}\b', '', caption)
+    # "#12345.."
+    caption = re.sub(r'#\d{5,}\b', '', caption)
+    # "123456.."
+    caption = re.sub(r'\b\d{6,}\b', '', caption)
+    # filenames:
+    caption = re.sub(r'[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)', '', caption)
+
+    #
+    caption = re.sub(r'[\"\']{2,}', r'"', caption)  # """AUSVERKAUFT"""
+    caption = re.sub(r'[\.]{2,}', r' ', caption)  # """AUSVERKAUFT"""
+
+    caption = re.sub(bad_punct_regex, r' ', caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
+    caption = re.sub(r'\s+\.\s+', r' ', caption)  # " . "
+
+    # this-is-my-cute-cat / this_is_my_cute_cat
+    regex2 = re.compile(r'(?:\-|\_)')
+    if len(re.findall(regex2, caption)) > 3:
+        caption = re.sub(regex2, ' ', caption)
+
+    caption = basic_clean(caption)
+
+    caption = re.sub(r'\b[a-zA-Z]{1,3}\d{3,15}\b', '', caption)  # jc6640
+    caption = re.sub(r'\b[a-zA-Z]+\d+[a-zA-Z]+\b', '', caption)  # jc6640vc
+    caption = re.sub(r'\b\d+[a-zA-Z]+\d+\b', '', caption)  # 6640vc231
+
+    caption = re.sub(r'(worldwide\s+)?(free\s+)?shipping', '', caption)
+    caption = re.sub(r'(free\s)?download(\sfree)?', '', caption)
+    caption = re.sub(r'\bclick\b\s(?:for|on)\s\w+', '', caption)
+    caption = re.sub(r'\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?', '', caption)
+    caption = re.sub(r'\bpage\s+\d+\b', '', caption)
+
+    caption = re.sub(r'\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b', r' ', caption)  # j2d1a2a...
+
+    caption = re.sub(r'\b\d+\.?\d*[xх×]\d+\.?\d*\b', '', caption)
+
+    caption = re.sub(r'\b\s+\:\s+', r': ', caption)
+    caption = re.sub(r'(\D[,\./])\b', r'\1 ', caption)
+    caption = re.sub(r'\s+', ' ', caption)
+
+    caption.strip()
+
+    caption = re.sub(r'^[\"\']([\w\W]+)[\"\']$', r'\1', caption)
+    caption = re.sub(r'^[\'\_,\-\:;]', r'', caption)
+    caption = re.sub(r'[\'\_,\-\:\-\+]$', r'', caption)
+    caption = re.sub(r'^\.\S+$', '', caption)
+
+    return caption.strip()
+
+
+#################################################################################
+#                          eval PSNR when training                                  #
+#################################################################################
+
+def resize(x, resolution):
+    height, width = x.shape[-2:]
+    aspect_ratio = width / height
+    if width <= height:
+        new_width = resolution
+        new_height = int(resolution / aspect_ratio)
+    else:
+        new_height = resolution
+        new_width = int(resolution * aspect_ratio)
+    resized_x = F.interpolate(x, size=(new_height, new_width), mode='bilinear', align_corners=True, antialias=True)
+    return resized_x
+
+def _preprocess(video_data, short_size=128, crop_size=None):
+    transform = Compose(
+        [
+            Lambda(lambda x: (x / 255.0)*2-1),
+            Lambda(lambda x: resize(x, short_size)),
+            (
+                CenterCropVideo(crop_size=crop_size)
+                if crop_size is not None
+                else Lambda(lambda x: x)
+            ),
+        ]
+    )
+    video_outputs = transform(video_data)
+    video_outputs = _format_video_shape(video_outputs)
+    return video_outputs
+
+def _format_video_shape(video, time_compress=4, spatial_compress=8):
+    time = video.shape[1]
+    height = video.shape[2]
+    width = video.shape[3]
+    new_time = (
+        (time - (time - 1) % time_compress)
+        if (time - 1) % time_compress != 0
+        else time
+    )
+    new_height = (
+        (height - (height) % spatial_compress)
+        if height % spatial_compress != 0
+        else height
+    )
+    new_width = (
+        (width - (width) % spatial_compress) if width % spatial_compress != 0 else width
+    )
+    return video[:, :new_time, :new_height, :new_width]
+
+def array_to_video(
+    image_array: npt.NDArray, fps: float = 30.0, output_file: str = "output_video.mp4"
+) -> None:
+    height, width, channels = image_array[0].shape
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height))
+
+    for image in image_array:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        video_writer.write(image_rgb)
+
+    video_writer.release()
+
+
+def custom_to_video(
+    x: torch.Tensor, fps: float = 2.0, output_file: str = "output_video.mp4"
+) -> None:
+    x = x.detach().cpu()
+    x = torch.clamp(x, -1, 1)
+    x = (x + 1) / 2
+    x = x.permute(1, 2, 3, 0).float().numpy()
+    x = (255 * x).astype(np.uint8)
+    array_to_video(x, fps=fps, output_file=output_file)
+    return
+
+class RealVideoDataset(Dataset):
+    def __init__(
+        self,
+        real_video_dir,
+        num_frames,
+        sample_rate=1,
+        crop_size=None,
+        resolution=128,
+    ) -> None:
+        super().__init__()
+        self.real_video_files = self._combine_without_prefix(real_video_dir)
+        self.num_frames = num_frames
+        self.sample_rate = sample_rate
+        self.crop_size = crop_size
+        self.short_size = resolution
+
+    def __len__(self):
+        return len(self.real_video_files)
+
+    def __getitem__(self, index):
+        if index >= len(self):
+            raise IndexError
+        real_video_file = self.real_video_files[index]
+        real_video_tensor = self._load_video(real_video_file)
+        video_name = os.path.basename(real_video_file)
+        return {'video': real_video_tensor, 'file_name': video_name }
+
+    def _load_video(self, video_path):
+        num_frames = self.num_frames
+        sample_rate = self.sample_rate
+        decord_vr = VideoReader(video_path, ctx=cpu(0))
+        total_frames = len(decord_vr)
+        sample_frames_len = sample_rate * num_frames
+
+        if total_frames > sample_frames_len:
+            s = 0
+            e = s + sample_frames_len
+            num_frames = num_frames
+        else:
+            s = 0
+            e = total_frames
+            num_frames = int(total_frames / sample_frames_len * num_frames)
+            print(
+                f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+                video_path,
+                total_frames,
+            )
+
+        frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+        video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(3, 0, 1, 2)
+        return _preprocess(
+            video_data, short_size=self.short_size, crop_size=self.crop_size
+        )
+
+    def _combine_without_prefix(self, folder_path, prefix="."):
+        folder = []
+        for name in os.listdir(folder_path):
+            if name[0] == prefix:
+                continue
+            folder.append(os.path.join(folder_path, name))
+        folder.sort()
+        return folder

scripts/CogVideovae_gen_video.sh

@@ -0,0 +1,39 @@
+# NCCL setting
+export GLOO_SOCKET_IFNAME=bond0
+export NCCL_SOCKET_IFNAME=bond0
+export NCCL_IB_HCA=mlx5_10:1,mlx5_11:1,mlx5_12:1,mlx5_13:1
+export NCCL_IB_GID_INDEX=3
+export NCCL_IB_TC=162
+export NCCL_IB_TIMEOUT=22
+export NCCL_PXN_DISABLE=0
+export NCCL_IB_QPS_PER_CONNECTION=4
+# export NCCL_ALGO=Ring
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export NCCL_ALGO=Tree
+REAL_DATASET_DIR=/storage/dataset/train
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=65
+RESOLUTION=720
+SUBSET_SIZE=100
+CKPT=/storage/clh/models/CogVideo
+torchrun \
+    --nnodes=1 --nproc_per_node=8 \
+    --rdzv_endpoint=localhost:29504 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    scripts/rec_CogVideo_vae.py \
+    --batch_size 1 \
+    --real_video_dir ${REAL_DATASET_DIR} \
+    --generated_video_dir /storage/clh/gen/CogVideo_vae/train\
+    --device cuda \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT} \
+    --output_origin \
+    #--enable_tiling \

scripts/causalvideovae_gen_video.sh

@@ -0,0 +1,42 @@
+# NCCL setting
+export GLOO_SOCKET_IFNAME=bond0
+export NCCL_SOCKET_IFNAME=bond0
+export NCCL_IB_HCA=mlx5_10:1,mlx5_11:1,mlx5_12:1,mlx5_13:1
+export NCCL_IB_GID_INDEX=3
+export NCCL_IB_TC=162
+export NCCL_IB_TIMEOUT=22
+export NCCL_PXN_DISABLE=0
+export NCCL_IB_QPS_PER_CONNECTION=4
+# export NCCL_ALGO=Ring
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export NCCL_ALGO=Tree
+REAL_DATASET_DIR=/storage/dataset/vae_eval/panda70m
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=33
+RESOLUTION=256
+SUBSET_SIZE=1000
+CKPT=/storage/clh/models/488dim8
+
+torchrun \
+    --nnodes=1 --nproc_per_node=8 \
+    --rdzv_endpoint=localhost:29504 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    scripts/rec_causalvideo_vae.py \
+    --batch_size 1 \
+    --real_video_dir ${REAL_DATASET_DIR} \
+    --generated_video_dir /storage/clh/gen/488dim8 \
+    --device cuda \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT} \
+    --output_origin \
+    --subset_size 1000\
+    #--change_decoder \
+    #--decoder_dir /remote-home1/clh/Causal-Video-VAE/results/decoder_only-lr1.00e-05-bs1-rs248-sr2-fr25/checkpoint-5000.ckpt \

scripts/easyanimate_gen_video.sh

@@ -0,0 +1,40 @@
+# NCCL setting
+export GLOO_SOCKET_IFNAME=bond0
+export NCCL_SOCKET_IFNAME=bond0
+export NCCL_IB_HCA=mlx5_10:1,mlx5_11:1,mlx5_12:1,mlx5_13:1
+export NCCL_IB_GID_INDEX=3
+export NCCL_IB_TC=162
+export NCCL_IB_TIMEOUT=22
+export NCCL_PXN_DISABLE=0
+export NCCL_IB_QPS_PER_CONNECTION=4
+# export NCCL_ALGO=Ring
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export NCCL_ALGO=Tree
+REAL_DATASET_DIR=/storage/dataset/train
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=17
+RESOLUTION=720
+SUBSET_SIZE=100
+CKPT=/storage/clh/models/488dim8
+
+torchrun \
+    --nnodes=1 --nproc_per_node=8 \
+    --rdzv_endpoint=localhost:29503 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    scripts/rec_easyanimate_vae.py \
+    --batch_size 1 \
+    --real_video_dir ${REAL_DATASET_DIR} \
+    --generated_video_dir /storage/clh/gen/easyanimate_vae/train \
+    --device cuda \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT} \
+    --output_origin \
+    #--enable_tiling \

scripts/rec_CogVideo_vae.py

@@ -0,0 +1,315 @@
+import random
+import argparse
+import cv2
+from tqdm import tqdm
+import numpy as np
+import numpy.typing as npt
+import torch
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler, Subset
+from decord import VideoReader, cpu
+from torch.nn import functional as F
+from pytorchvideo.transforms import ShortSideScale
+from torchvision.transforms import Lambda, Compose
+from torchvision.transforms._transforms_video import CenterCropVideo
+import sys
+from torch.utils.data import Dataset, DataLoader, Subset
+import os
+import glob
+sys.path.append(".")
+from diffusers import AutoencoderKLCogVideoX
+
+
+def ddp_setup():
+    dist.init_process_group(backend="nccl")
+    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
+
+def array_to_video(
+    image_array: npt.NDArray, fps: float = 30.0, output_file: str = "output_video.mp4"
+) -> None:
+    height, width, channels = image_array[0].shape
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height))
+    for image in image_array:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        video_writer.write(image_rgb)
+
+    video_writer.release()
+
+
+def custom_to_video(
+    x: torch.Tensor, fps: float = 2.0, output_file: str = "output_video.mp4"
+) -> None:
+    x = x.detach().cpu()
+    x = torch.clamp(x, -1, 1)
+    x = (x + 1) / 2
+    x = x.permute(1, 2, 3, 0).float().numpy()
+    x = (255 * x).astype(np.uint8)
+    array_to_video(x, fps=fps, output_file=output_file)
+    return
+
+
+def read_video(video_path: str, num_frames: int, sample_rate: int) -> torch.Tensor:
+    decord_vr = VideoReader(video_path, ctx=cpu(0), num_threads=8)
+    total_frames = len(decord_vr)
+    sample_frames_len = sample_rate * num_frames
+
+    if total_frames > sample_frames_len:
+        s = 0
+        e = s + sample_frames_len
+        num_frames = num_frames
+    else:
+        s = 0
+        e = total_frames
+        num_frames = int(total_frames / sample_frames_len * num_frames)
+        print(
+            f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+            video_path,
+            total_frames,
+        )
+
+    frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+    video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+    video_data = torch.from_numpy(video_data)
+    video_data = video_data.permute(3, 0, 1, 2)  # (T, H, W, C) -> (C, T, H, W)
+    return video_data
+
+
+class RealVideoDataset(Dataset):
+    video_exts = ['avi', 'mp4', 'webm']
+    
+    def __init__(
+        self,
+        real_video_dir,
+        num_frames,
+        sample_rate=1,
+        crop_size=None,
+        resolution=128,
+    ) -> None:
+        super().__init__()
+        self.real_video_files = self._combine_without_prefix(real_video_dir)
+        self.num_frames = num_frames
+        self.sample_rate = sample_rate
+        self.crop_size = crop_size
+        self.short_size = resolution
+
+    def __len__(self):
+        return len(self.real_video_files)
+
+    def __getitem__(self, index):
+        try:
+            if index >= len(self):
+                raise IndexError
+            real_video_file = self.real_video_files[index]
+            real_video_tensor = self._load_video(real_video_file)
+            video_name = os.path.basename(real_video_file)
+        except:
+            if index >= len(self):
+                raise IndexError
+            real_video_file = self.real_video_files[random.randint(1,index-1)]
+            real_video_tensor = self._load_video(real_video_file)
+            video_name = os.path.basename(real_video_file)
+        return {'video': real_video_tensor, 'file_name': video_name }
+
+    def _load_video(self, video_path):
+        num_frames = self.num_frames
+        sample_rate = self.sample_rate
+        decord_vr = VideoReader(video_path, ctx=cpu(0))
+        total_frames = len(decord_vr)
+        sample_frames_len = sample_rate * num_frames
+        s = 0
+        e = s + sample_frames_len
+        num_frames = num_frames
+        """
+        if total_frames > sample_frames_len: 
+            s = 0
+            e = s + sample_frames_len
+            num_frames = num_frames
+        
+        else:
+            s = 0
+            e = total_frames
+            num_frames = int(total_frames / sample_frames_len * num_frames)
+            print(
+                f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+                video_path,
+                total_frames,
+            )
+        """
+        frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+        video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(3, 0, 1, 2)
+        return _preprocess(
+            video_data, short_size=self.short_size, crop_size=self.crop_size
+        )
+
+    def _combine_without_prefix(self, folder_path):
+        samples = []
+        samples += sum([glob.glob(os.path.join(folder_path, '**', f'*.{ext}'), recursive=True)
+                            for ext in self.video_exts], [])
+        samples.sort()
+        return samples
+
+def resize(x, resolution):
+    height, width = x.shape[-2:]
+    
+    aspect_ratio = width / height
+    if width <= height:
+        new_width = resolution
+        new_height = int(resolution / aspect_ratio)
+    else:
+        new_height = resolution
+        new_width = int(resolution * aspect_ratio)
+    resized_x = F.interpolate(x, size=(new_height, new_width), mode='bilinear', align_corners=True, antialias=True)
+    return resized_x
+
+def _preprocess(video_data, short_size=128, crop_size=None):
+    transform = Compose(
+        [ 
+            Lambda(lambda x: ((x / 255.0) * 2 - 1)),
+            
+            Lambda(lambda x: resize(x, short_size)),
+            (
+                CenterCropVideo(crop_size=crop_size)
+                if crop_size is not None
+                else Lambda(lambda x: x)
+            ),
+        ]
+        
+    )
+    video_outputs = transform(video_data)
+    video_outputs = _format_video_shape(video_outputs)
+    return video_outputs
+
+
+def _format_video_shape(video, time_compress=4, spatial_compress=8):
+    time = video.shape[1]
+    height = video.shape[2]
+    width = video.shape[3]
+    new_time = (
+        (time - (time - 1) % time_compress)
+        if (time - 1) % time_compress != 0
+        else time
+    )
+    new_height = (
+        (height - (height) % spatial_compress)
+        if height % spatial_compress != 0
+        else height
+    )
+    new_width = (
+        (width - (width) % spatial_compress) if width % spatial_compress != 0 else width
+    )
+    return video[:, :new_time, :new_height, :new_width]
+
+
+@torch.no_grad()
+def main(args: argparse.Namespace):
+    real_video_dir = args.real_video_dir
+    generated_video_dir = args.generated_video_dir
+    ckpt = args.ckpt
+    sample_rate = args.sample_rate
+    resolution = args.resolution
+    crop_size = args.crop_size
+    num_frames = args.num_frames
+    sample_rate = args.sample_rate
+    device = args.device
+    sample_fps = args.sample_fps
+    batch_size = args.batch_size
+    num_workers = args.num_workers
+    subset_size = args.subset_size
+    
+    if not os.path.exists(args.generated_video_dir):
+        os.makedirs(os.path.join(generated_video_dir, "vae_gen/"), exist_ok=True)
+    
+    data_type = torch.bfloat16
+    
+    ddp_setup()
+    rank = int(os.environ["LOCAL_RANK"])
+    
+    # ---- Load Model ----
+    device = args.device
+    vqvae = AutoencoderKLCogVideoX.from_pretrained(args.ckpt)
+    if args.change_decoder:
+        sd = torch.load(args.decoder_dir, map_location="cpu")
+        
+        if "ema_state_dict" in sd and len(sd['ema_state_dict']) > 0 and os.environ.get("NOT_USE_EMA_MODEL", 0) == 0:
+            print("Load from ema model!")
+            sd = sd["ema_state_dict"]
+            sd = {key.replace("module.", ""): value for key, value in sd.items()}
+        elif "state_dict" in sd:
+            print("Load from normal model!")
+            if "gen_model" in sd["state_dict"]:
+                sd = sd["state_dict"]["gen_model"]
+            else:
+                sd = sd["state_dict"]
+        vqvae.load_state_dict(sd, strict=False)
+    #print(vqvae)
+    if args.enable_tiling:
+        vqvae.enable_tiling()
+        vqvae.tile_overlap_factor = args.tile_overlap_factor
+    vqvae = vqvae.to(rank).to(data_type)
+    vqvae.eval()
+    
+    # ---- Load Model ----
+
+    # ---- Prepare Dataset ----
+    dataset = RealVideoDataset(
+        real_video_dir=real_video_dir,
+        num_frames=num_frames,
+        sample_rate=sample_rate,
+        crop_size=crop_size,
+        resolution=resolution,
+    )
+    
+    if subset_size:
+        indices = range(subset_size)
+        dataset = Subset(dataset, indices=indices)
+    ddp_sampler = DistributedSampler(dataset)
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, sampler=ddp_sampler ,pin_memory=True, num_workers=num_workers
+    )
+    # ---- Prepare Dataset
+    # ---- Inference ----
+    for batch in tqdm(dataloader):
+        x, file_names = batch['video'], batch['file_name']
+        
+        x = x.to(device=device, dtype=data_type)  # b c t h w
+        video_recon = vqvae(x)[0]
+        for idx, video in enumerate(video_recon):
+            output_path = os.path.join(generated_video_dir, "vae_gen/", file_names[idx])
+            if args.output_origin:
+                os.makedirs(os.path.join(generated_video_dir, "origin/"), exist_ok=True)
+                origin_output_path = os.path.join(generated_video_dir, "origin/", file_names[idx])
+                custom_to_video(
+                    x[idx], fps=sample_fps / sample_rate, output_file=origin_output_path
+                )
+            custom_to_video(
+                video, fps=sample_fps / sample_rate, output_file=output_path
+            )
+
+    # ---- Inference ----
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--real_video_dir", type=str, default="")
+    parser.add_argument("--generated_video_dir", type=str, default="")
+    parser.add_argument("--decoder_dir", type=str, default="")
+    parser.add_argument("--ckpt", type=str, default="")
+    parser.add_argument("--sample_fps", type=int, default=30)
+    parser.add_argument("--resolution", type=int, default=336)
+    parser.add_argument("--crop_size", type=int, default=None)
+    parser.add_argument("--num_frames", type=int, default=17)
+    parser.add_argument("--sample_rate", type=int, default=1)
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_workers", type=int, default=8)
+    parser.add_argument("--subset_size", type=int, default=None)
+    parser.add_argument("--tile_overlap_factor", type=float, default=0.25)
+    parser.add_argument('--enable_tiling', action='store_true')
+    parser.add_argument('--output_origin', action='store_true')
+    parser.add_argument('--change_decoder', action='store_true')
+    parser.add_argument("--device", type=str, default="cuda")
+
+    args = parser.parse_args()
+    main(args)

scripts/rec_easyanimate_vae.py

@@ -0,0 +1,321 @@
+import random
+import argparse
+import cv2
+from tqdm import tqdm
+import numpy as np
+import numpy.typing as npt
+import torch
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler, Subset
+from decord import VideoReader, cpu
+from torch.nn import functional as F
+from pytorchvideo.transforms import ShortSideScale
+from torchvision.transforms import Lambda, Compose
+from torchvision.transforms._transforms_video import CenterCropVideo
+import sys
+from torch.utils.data import Dataset, DataLoader, Subset
+import os
+import glob
+sys.path.append(".")
+from diffusers import (AutoencoderKL, DDIMScheduler,
+                       DPMSolverMultistepScheduler,
+                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
+                       PNDMScheduler)
+from omegaconf import OmegaConf
+from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
+from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
+from easyanimate.utils.utils import save_videos_grid, get_image_to_video_latent
+
+def ddp_setup():
+    dist.init_process_group(backend="nccl")
+    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
+
+def array_to_video(
+    image_array: npt.NDArray, fps: float = 30.0, output_file: str = "output_video.mp4"
+) -> None:
+    height, width, channels = image_array[0].shape
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height))
+    for image in image_array:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        video_writer.write(image_rgb)
+
+    video_writer.release()
+
+
+def custom_to_video(
+    x: torch.Tensor, fps: float = 2.0, output_file: str = "output_video.mp4"
+) -> None:
+    x = x.detach().cpu()
+    x = torch.clamp(x, -1, 1)
+    x = (x + 1) / 2
+    x = x.permute(1, 2, 3, 0).float().numpy()
+    x = (255 * x).astype(np.uint8)
+    array_to_video(x, fps=fps, output_file=output_file)
+    return
+
+
+def read_video(video_path: str, num_frames: int, sample_rate: int) -> torch.Tensor:
+    decord_vr = VideoReader(video_path, ctx=cpu(0), num_threads=8)
+    total_frames = len(decord_vr)
+    sample_frames_len = sample_rate * num_frames
+
+    if total_frames > sample_frames_len:
+        s = 0
+        e = s + sample_frames_len
+        num_frames = num_frames
+    else:
+        s = 0
+        e = total_frames
+        num_frames = int(total_frames / sample_frames_len * num_frames)
+        print(
+            f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+            video_path,
+            total_frames,
+        )
+
+    frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+    video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+    video_data = torch.from_numpy(video_data)
+    video_data = video_data.permute(3, 0, 1, 2)  # (T, H, W, C) -> (C, T, H, W)
+    return video_data
+
+
+class RealVideoDataset(Dataset):
+    video_exts = ['avi', 'mp4', 'webm']
+    
+    def __init__(
+        self,
+        real_video_dir,
+        num_frames,
+        sample_rate=1,
+        crop_size=None,
+        resolution=128,
+    ) -> None:
+        super().__init__()
+        self.real_video_files = self._combine_without_prefix(real_video_dir)
+        self.num_frames = num_frames
+        self.sample_rate = sample_rate
+        self.crop_size = crop_size
+        self.short_size = resolution
+
+    def __len__(self):
+        return len(self.real_video_files)
+
+    def __getitem__(self, index):
+        try:
+            if index >= len(self):
+                raise IndexError
+            real_video_file = self.real_video_files[index]
+            real_video_tensor = self._load_video(real_video_file)
+            video_name = os.path.basename(real_video_file)
+        except:
+            if index >= len(self):
+                raise IndexError
+            real_video_file = self.real_video_files[random.randint(1,index-1)]
+            real_video_tensor = self._load_video(real_video_file)
+            video_name = os.path.basename(real_video_file)
+        return {'video': real_video_tensor, 'file_name': video_name }
+
+    def _load_video(self, video_path):
+        num_frames = self.num_frames
+        sample_rate = self.sample_rate
+        decord_vr = VideoReader(video_path, ctx=cpu(0))
+        total_frames = len(decord_vr)
+        sample_frames_len = sample_rate * num_frames
+        s = 0
+        e = s + sample_frames_len
+        num_frames = num_frames
+        """
+        if total_frames > sample_frames_len: 
+            s = 0
+            e = s + sample_frames_len
+            num_frames = num_frames
+        
+        else:
+            s = 0
+            e = total_frames
+            num_frames = int(total_frames / sample_frames_len * num_frames)
+            print(
+                f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+                video_path,
+                total_frames,
+            )
+        """
+        frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+        video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(3, 0, 1, 2)
+        return _preprocess(
+            video_data, short_size=self.short_size, crop_size=self.crop_size
+        )
+
+    def _combine_without_prefix(self, folder_path):
+        samples = []
+        samples += sum([glob.glob(os.path.join(folder_path, '**', f'*.{ext}'), recursive=True)
+                            for ext in self.video_exts], [])
+        samples.sort()
+        return samples
+
+def resize(x, resolution):
+    height, width = x.shape[-2:]
+    
+    aspect_ratio = width / height
+    if width <= height:
+        new_width = resolution
+        new_height = int(resolution / aspect_ratio)
+    else:
+        new_height = resolution
+        new_width = int(resolution * aspect_ratio)
+    resized_x = F.interpolate(x, size=(new_height, new_width), mode='bilinear', align_corners=True, antialias=True)
+    return resized_x
+
+def _preprocess(video_data, short_size=128, crop_size=None):
+    transform = Compose(
+        [ 
+            Lambda(lambda x: ((x / 255.0) * 2 - 1)),
+            
+            Lambda(lambda x: resize(x, short_size)),
+            (
+                CenterCropVideo(crop_size=crop_size)
+                if crop_size is not None
+                else Lambda(lambda x: x)
+            ),
+        ]
+        
+    )
+    video_outputs = transform(video_data)
+    video_outputs = _format_video_shape(video_outputs)
+    return video_outputs
+
+
+def _format_video_shape(video, time_compress=4, spatial_compress=8):
+    time = video.shape[1]
+    height = video.shape[2]
+    width = video.shape[3]
+    new_time = (
+        (time - (time - 1) % time_compress)
+        if (time - 1) % time_compress != 0
+        else time
+    )
+    new_height = (
+        (height - (height) % spatial_compress)
+        if height % spatial_compress != 0
+        else height
+    )
+    new_width = (
+        (width - (width) % spatial_compress) if width % spatial_compress != 0 else width
+    )
+    return video[:, :new_time, :new_height, :new_width]
+
+
+@torch.no_grad()
+def main(args: argparse.Namespace):
+    real_video_dir = args.real_video_dir
+    generated_video_dir = args.generated_video_dir
+    ckpt = args.ckpt
+    sample_rate = args.sample_rate
+    resolution = args.resolution
+    crop_size = args.crop_size
+    num_frames = args.num_frames
+    sample_rate = args.sample_rate
+    device = args.device
+    sample_fps = args.sample_fps
+    batch_size = args.batch_size
+    num_workers = args.num_workers
+    subset_size = args.subset_size
+    
+    if not os.path.exists(args.generated_video_dir):
+        os.makedirs(os.path.join(generated_video_dir, "vae_gen/"), exist_ok=True)
+    
+    data_type = torch.bfloat16
+    
+    ddp_setup()
+    rank = int(os.environ["LOCAL_RANK"])
+    
+    # ---- Load Model ----
+    device = args.device
+    config_path = "/storage/clh/Causal-Video-VAE/easyanimate/easyanimate_video_slicevae_motion_module_v3.yaml"
+    config = OmegaConf.load(config_path)
+    model_name = '/storage/clh/EasyAnimate/models/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-512x512'
+    if OmegaConf.to_container(config['vae_kwargs'])['enable_magvit']:
+        Choosen_AutoencoderKL = AutoencoderKLMagvit
+    else:
+        Choosen_AutoencoderKL = AutoencoderKL
+    vqvae = Choosen_AutoencoderKL.from_pretrained(
+        model_name, 
+        subfolder="vae", 
+    )
+        
+    #print(vqvae)
+    if args.enable_tiling:
+        vqvae.enable_tiling()
+        vqvae.tile_overlap_factor = args.tile_overlap_factor
+    vqvae = vqvae.to(rank).to(data_type)
+    vqvae.eval()
+    
+    # ---- Load Model ----
+
+    # ---- Prepare Dataset ----
+    dataset = RealVideoDataset(
+        real_video_dir=real_video_dir,
+        num_frames=num_frames,
+        sample_rate=sample_rate,
+        crop_size=crop_size,
+        resolution=resolution,
+    )
+    
+    if subset_size:
+        indices = range(subset_size)
+        dataset = Subset(dataset, indices=indices)
+    ddp_sampler = DistributedSampler(dataset)
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, sampler=ddp_sampler ,pin_memory=True, num_workers=num_workers
+    )
+    # ---- Prepare Dataset
+    # ---- Inference ----
+    for batch in tqdm(dataloader):
+        x, file_names = batch['video'], batch['file_name']
+        
+        x = x.to(device=device, dtype=data_type)  # b c t h 
+        latents = vqvae.encode(x).latent_dist.sample().to(data_type)
+        print(latents.shape)
+        video_recon = vqvae.decode(latents)[0]
+        for idx, video in enumerate(video_recon):
+            output_path = os.path.join(generated_video_dir, "vae_gen/", file_names[idx])
+            if args.output_origin:
+                os.makedirs(os.path.join(generated_video_dir, "origin/"), exist_ok=True)
+                origin_output_path = os.path.join(generated_video_dir, "origin/", file_names[idx])
+                custom_to_video(
+                    x[idx], fps=sample_fps / sample_rate, output_file=origin_output_path
+                )
+            custom_to_video(
+                video, fps=sample_fps / sample_rate, output_file=output_path
+            )
+
+    # ---- Inference ----
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--real_video_dir", type=str, default="")
+    parser.add_argument("--generated_video_dir", type=str, default="")
+    parser.add_argument("--decoder_dir", type=str, default="")
+    parser.add_argument("--ckpt", type=str, default="")
+    parser.add_argument("--sample_fps", type=int, default=30)
+    parser.add_argument("--resolution", type=int, default=336)
+    parser.add_argument("--crop_size", type=int, default=None)
+    parser.add_argument("--num_frames", type=int, default=17)
+    parser.add_argument("--sample_rate", type=int, default=1)
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_workers", type=int, default=8)
+    parser.add_argument("--subset_size", type=int, default=None)
+    parser.add_argument("--tile_overlap_factor", type=float, default=0.25)
+    parser.add_argument('--enable_tiling', action='store_true')
+    parser.add_argument('--output_origin', action='store_true')
+    parser.add_argument('--change_decoder', action='store_true')
+    parser.add_argument("--device", type=str, default="cuda")
+
+    args = parser.parse_args()
+    main(args)
+    

scripts/rec_vqgan_vae.py

@@ -0,0 +1,318 @@
+import random
+import argparse
+import cv2
+from tqdm import tqdm
+import numpy as np
+import numpy.typing as npt
+import torch
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler, Subset
+from decord import VideoReader, cpu
+from torch.nn import functional as F
+from pytorchvideo.transforms import ShortSideScale
+from torchvision.transforms import Lambda, Compose
+from torchvision.transforms._transforms_video import CenterCropVideo
+import sys
+from torch.utils.data import Dataset, DataLoader, Subset
+import os
+import glob
+sys.path.append(".")
+import torch.nn as nn
+import yaml
+from omegaconf import OmegaConf
+from einops import rearrange
+from taming.models.vqgan import VQModel, GumbelVQ
+
+def ddp_setup():
+    dist.init_process_group(backend="nccl")
+    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
+
+def array_to_video(
+    image_array: npt.NDArray, fps: float = 30.0, output_file: str = "output_video.mp4"
+) -> None:
+    height, width, channels = image_array[0].shape
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height))
+
+    for image in image_array:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        video_writer.write(image_rgb)
+
+    video_writer.release()
+
+
+def custom_to_video(
+    x: torch.Tensor, fps: float = 2.0, output_file: str = "output_video.mp4"
+) -> None:
+    x = x.detach().cpu()
+    x = torch.clamp(x, -1, 1)
+    x = (x + 1) / 2
+    x = x.permute(1, 2, 3, 0).float().numpy()
+    x = (255 * x).astype(np.uint8)
+    array_to_video(x, fps=fps, output_file=output_file)
+    return
+
+
+def read_video(video_path: str, num_frames: int, sample_rate: int) -> torch.Tensor:
+    decord_vr = VideoReader(video_path, ctx=cpu(0), num_threads=8)
+    total_frames = len(decord_vr)
+    sample_frames_len = sample_rate * num_frames
+
+    if total_frames > sample_frames_len:
+        s = 0
+        e = s + sample_frames_len
+        num_frames = num_frames
+    else:
+        s = 0
+        e = total_frames
+        num_frames = int(total_frames / sample_frames_len * num_frames)
+        print(
+            f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+            video_path,
+            total_frames,
+        )
+
+    frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+    video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+    video_data = torch.from_numpy(video_data)
+    video_data = video_data.permute(3, 0, 1, 2)  # (T, H, W, C) -> (C, T, H, W)
+    return video_data
+
+
+class RealVideoDataset(Dataset):
+    video_exts = ['avi', 'mp4', 'webm']
+    
+    def __init__(
+        self,
+        real_video_dir,
+        num_frames,
+        sample_rate=1,
+        crop_size=None,
+        resolution=128,
+    ) -> None:
+        super().__init__()
+        self.real_video_files = self._combine_without_prefix(real_video_dir)
+        self.num_frames = num_frames
+        self.sample_rate = sample_rate
+        self.crop_size = crop_size
+        self.short_size = resolution
+
+    def __len__(self):
+        return len(self.real_video_files)
+
+    def __getitem__(self, index):
+        try:
+            if index >= len(self):
+                raise IndexError
+            real_video_file = self.real_video_files[index]
+            real_video_tensor = self._load_video(real_video_file)
+            video_name = os.path.basename(real_video_file)
+        except:
+            if index >= len(self):
+                raise IndexError
+            real_video_file = self.real_video_files[random.randint(1,index-1)]
+            real_video_tensor = self._load_video(real_video_file)
+            video_name = os.path.basename(real_video_file)
+        return {'video': real_video_tensor, 'file_name': video_name }
+
+    def _load_video(self, video_path):
+        num_frames = self.num_frames
+        sample_rate = self.sample_rate
+        decord_vr = VideoReader(video_path, ctx=cpu(0))
+        total_frames = len(decord_vr)
+        sample_frames_len = sample_rate * num_frames
+        s = 0
+        e = s + sample_frames_len
+        num_frames = num_frames
+        """
+        if total_frames > sample_frames_len: 
+            s = 0
+            e = s + sample_frames_len
+            num_frames = num_frames
+        
+        else:
+            s = 0
+            e = total_frames
+            num_frames = int(total_frames / sample_frames_len * num_frames)
+            print(
+                f"sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}",
+                video_path,
+                total_frames,
+            )
+        """
+        frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
+        video_data = decord_vr.get_batch(frame_id_list).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(3, 0, 1, 2)
+        return _preprocess(
+            video_data, short_size=self.short_size, crop_size=self.crop_size
+        )
+
+    def _combine_without_prefix(self, folder_path):
+        samples = []
+        samples += sum([glob.glob(os.path.join(folder_path, '**', f'*.{ext}'), recursive=True)
+                            for ext in self.video_exts], [])
+        samples.sort()
+        return samples
+
+def resize(x, resolution):
+    height, width = x.shape[-2:]
+    aspect_ratio = width / height
+    if width <= height:
+        new_width = resolution
+        new_height = int(resolution / aspect_ratio)
+    else:
+        new_height = resolution
+        new_width = int(resolution * aspect_ratio)
+    resized_x = F.interpolate(x, size=(new_height, new_width), mode='bilinear', align_corners=True, antialias=True)
+    return resized_x
+
+def _preprocess(video_data, short_size=128, crop_size=None):
+    transform = Compose(
+        
+        [
+            
+            Lambda(lambda x: ((x / 255.0) * 2 - 1)),
+            Lambda(lambda x: resize(x, short_size)),
+            (
+                CenterCropVideo(crop_size=crop_size)
+                if crop_size is not None
+                else Lambda(lambda x: x)
+            ),
+            
+        ]
+        
+    )
+    video_outputs = transform(video_data)
+    video_outputs = _format_video_shape(video_outputs)
+    return video_outputs
+
+
+def _format_video_shape(video, time_compress=4, spatial_compress=8):
+    time = video.shape[1]
+    height = video.shape[2]
+    width = video.shape[3]
+    new_time = (
+        (time - (time - 1) % time_compress)
+        if (time - 1) % time_compress != 0
+        else time
+    )
+    new_height = (
+        (height - (height) % spatial_compress)
+        if height % spatial_compress != 0
+        else height
+    )
+    new_width = (
+        (width - (width) % spatial_compress) if width % spatial_compress != 0 else width
+    )
+    return video[:, :new_time, :new_height, :new_width]
+
+def load_config(config_path, display=False):
+  config = OmegaConf.load(config_path)
+  if display:
+    print(yaml.dump(OmegaConf.to_container(config)))
+  return config
+
+def load_vqgan(config, ckpt_path=None, is_gumbel=False):
+  if is_gumbel:
+    model = GumbelVQ(**config.model.params)
+  else:
+    model = VQModel(**config.model.params)
+  if ckpt_path is not None:
+    sd = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+    missing, unexpected = model.load_state_dict(sd, strict=False)
+  return model.eval()
+
+@torch.no_grad()
+def main(args: argparse.Namespace):
+    real_video_dir = args.real_video_dir
+    generated_video_dir = args.generated_video_dir
+    ckpt = args.ckpt
+    config = args.config
+    sample_rate = args.sample_rate
+    resolution = args.resolution
+    crop_size = args.crop_size
+    num_frames = args.num_frames
+    sample_rate = args.sample_rate
+    sample_fps = args.sample_fps
+    batch_size = args.batch_size
+    num_workers = args.num_workers
+    subset_size = args.subset_size
+    
+    if not os.path.exists(args.generated_video_dir):
+        os.makedirs(os.path.join(generated_video_dir, "vae_gen/"), exist_ok=True)
+    
+    data_type = torch.bfloat16
+    
+    ddp_setup()
+    rank = int(os.environ["LOCAL_RANK"])
+    
+    # ---- Load Model ----
+    vqgan_config = load_config(config, display=False)
+    vqgan = load_vqgan(vqgan_config, ckpt_path=ckpt, is_gumbel=True)
+    print(vqgan)
+    vqgan = vqgan.to(rank).to(data_type)
+    vqgan.eval()
+    # ---- Load Model ----
+
+    # ---- Prepare Dataset ----
+    dataset = RealVideoDataset(
+        real_video_dir=real_video_dir,
+        num_frames=num_frames,
+        sample_rate=sample_rate,
+        crop_size=crop_size,
+        resolution=resolution,
+    )
+    
+    if subset_size:
+        indices = range(subset_size)
+        dataset = Subset(dataset, indices=indices)
+    ddp_sampler = DistributedSampler(dataset)
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, sampler=ddp_sampler ,pin_memory=True, num_workers=num_workers
+    )
+    # ---- Prepare Dataset
+
+    # ---- Inference ----
+    for batch in tqdm(dataloader):
+        x, file_names = batch['video'], batch['file_name']
+        
+        x = x.to(rank).to(data_type)  # b c t h w
+        t = x.shape[2]
+        x = rearrange(x, "b c t h w -> (b t) c h w", t=t)
+        latents, _, [_, _, indices] = vqgan.encode(x)
+        video_recon = vqgan.decode(latents.to(data_type))
+        video_recon = rearrange(video_recon, "(b t) c h w -> b c t h w", t=t)
+        x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
+        for idx, video in enumerate(video_recon):
+            output_path = os.path.join(generated_video_dir, "vae_gen/", file_names[idx])
+            if args.output_origin:
+                os.makedirs(os.path.join(generated_video_dir, "origin/"), exist_ok=True)
+                origin_output_path = os.path.join(generated_video_dir, "origin/", file_names[idx])
+                custom_to_video(
+                    x[idx], fps=sample_fps / sample_rate, output_file=origin_output_path
+                )
+            custom_to_video(
+                video, fps=sample_fps / sample_rate, output_file=output_path
+            )
+    # ---- Inference ----
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--real_video_dir", type=str, default="")
+    parser.add_argument("--generated_video_dir", type=str, default="")
+    parser.add_argument("--ckpt", type=str, default="")
+    parser.add_argument("--sample_fps", type=int, default=30)
+    parser.add_argument("--resolution", type=int, default=336)
+    parser.add_argument("--crop_size", type=int, default=None)
+    parser.add_argument("--num_frames", type=int, default=17)
+    parser.add_argument("--sample_rate", type=int, default=1)
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_workers", type=int, default=8)
+    parser.add_argument("--subset_size", type=int, default=None)
+    parser.add_argument('--output_origin', action='store_true')
+    parser.add_argument("--config", type=str, default="")
+
+    args = parser.parse_args()
+    main(args)

scripts/refine_video.sh

@@ -0,0 +1,21 @@
+export CUDA_VISIBLE_DEVICES=1
+VAE_DATASET_DIR=/remote-home1/clh/test/vae
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=65
+RESOLUTION=512
+SUBSET_SIZE=1
+CKPT=/remote-home1/clh/Causal-Video-VAE/results/refiner-h64-122-Res6-fjunresizefromistock150k-lr1.00e-05-bs1-rs256-sr1-fr23
+
+python scripts/refine_video.py \
+    --batch_size 1 \
+    --real_video_dir ${VAE_DATASET_DIR} \
+    --generated_video_dir /remote-home1/clh/test/refiner-h64-122-Res6-fjunres\
+    --device cuda \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT}

scripts/sd2_1_gen_video.sh

@@ -0,0 +1,30 @@
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export NCCL_DEBUG=INFO
+export NCCL_SOCKET_IFNAME=ibs11
+export NCCL_IB_DISABLE=1
+REAL_DATASET_DIR=/remote-home1/clh/dataset/panda70m_val
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=33
+RESOLUTION=256
+SUBSET_SIZE=100
+CKPT=/remote-home1/clh/sd2_1
+
+torchrun \
+    --nnodes=1 --nproc_per_node=8 \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=localhost:29508 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    scripts/rec_sd2_1_vae.py \
+    --batch_size 1 \
+    --real_video_dir ${REAL_DATASET_DIR} \
+    --generated_video_dir /remote-home1/clh/gen/sd2_1/panda70m \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT} \
+    --output_origin \

scripts/svd_gen_video.sh

@@ -0,0 +1,30 @@
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export NCCL_DEBUG=INFO
+export NCCL_SOCKET_IFNAME=ibs11
+export NCCL_IB_DISABLE=1
+REAL_DATASET_DIR=/remote-home1/clh/dataset/panda70m_val
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=33
+RESOLUTION=256
+SUBSET_SIZE=100
+CKPT=/remote-home1/clh/svd
+
+torchrun \
+    --nnodes=1 --nproc_per_node=8 \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=localhost:29509 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    scripts/rec_svd_vae.py \
+    --batch_size 8 \
+    --real_video_dir ${REAL_DATASET_DIR} \
+    --generated_video_dir /remote-home1/clh/gen/svd/panda70m \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT} \
+    --output_origin \

scripts/tats_gen_video.sh

@@ -0,0 +1,30 @@
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export NCCL_DEBUG=INFO
+export NCCL_SOCKET_IFNAME=ibs11
+export NCCL_IB_DISABLE=1
+REAL_DATASET_DIR=/remote-home1/clh/dataset/panda70m_val
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=32
+RESOLUTION=256
+SUBSET_SIZE=100
+CKPT=/remote-home1/clh/TATS/vqgan_sky_128_488_epoch_12-step_29999-train.ckpt
+
+torchrun \
+    --nnodes=1 --nproc_per_node=8 \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=localhost:29501 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    scripts/rec_TATS_vae.py \
+    --batch_size 1 \
+    --real_video_dir ${REAL_DATASET_DIR} \
+    --generated_video_dir /remote-home1/clh/gen/TATS/panda70m \
+    --sample_fps 24 \
+    --sample_rate ${SAMPLE_RATE} \
+    --num_frames ${NUM_FRAMES} \
+    --resolution ${RESOLUTION} \
+    --crop_size ${RESOLUTION} \
+    --num_workers 8 \
+    --ckpt ${CKPT} \
+    --output_origin \

scripts/train_ddp.sh

@@ -0,0 +1,42 @@
+export https_proxy=127.0.0.1:7890
+export http_proxy=127.0.0.1:7890
+export GLOO_SOCKET_IFNAME=bond0
+export NCCL_SOCKET_IFNAME=bond0
+export NCCL_IB_HCA=mlx5_10:1,mlx5_11:1,mlx5_12:1,mlx5_13:1
+export NCCL_IB_GID_INDEX=3
+export NCCL_IB_TC=162
+export NCCL_IB_TIMEOUT=22
+export NCCL_PXN_DISABLE=0
+export NCCL_IB_QPS_PER_CONNECTION=4
+# export NCCL_ALGO=Ring
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export NCCL_ALGO=Tree
+EXP_NAME=464641024layerNorm-reusuem1
+
+torchrun \
+    --nnodes=1 --nproc_per_node=6 \
+    --rdzv_endpoint=localhost:29503 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    train_ddp.py \
+    --exp_name ${EXP_NAME} \
+    --pretrained_model_name_or_path /storage/clh/Causal-Video-VAE/results/464641024layerNorm-lr1.00e-05-bs1-rs256-sr2-fr25/r\
+    --video_path /storage/dataset/pexels \
+    --eval_video_path /storage/dataset/vae_eval/webvid \
+    --resolution 256 \
+    --num_frames 25 \
+    --batch_size 1 \
+    --disc_start 5000 \
+    --save_ckpt_step 1000 \
+    --eval_steps 1000 \
+    --eval_batch_size 1 \
+    --eval_num_frames 33 \
+    --eval_sample_rate 1 \
+    --eval_subset_size 100 \
+    --eval_lpips \
+    --ema \
+    --ema_decay 0.999 \
+    --perceptual_weight 1.0 \
+    --loss_type l1 \
+    --disc_cls causalvideovae.model.losses.LPIPSWithDiscriminator3D 

scripts/train_ddp_refiner.sh

@@ -0,0 +1,41 @@
+# export https_proxy=http://127.0.0.1:8998
+# export http_proxy=http://127.0.0.1:8998
+unset https_proxy
+unset http_proxy
+export WANDB_PROJECT=causalvideovae
+export CUDA_VISIBLE_DEVICES=6,7
+export NCCL_DEBUG=INFO
+export NCCL_SOCKET_IFNAME=ibs11
+export NCCL_IB_DISABLE=1
+
+EXP_NAME=refiner-h64-122-Res6-fjunresizefromistock150k
+
+torchrun \
+    --nnodes=1 --nproc_per_node=2 \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=localhost:29507 \
+    --master_addr=localhost \
+    --master_port=29600 \
+    train_ddp_refiner.py \
+    --exp_name ${EXP_NAME} \
+    --model_config /remote-home1/clh/config.json \
+    --vae_path /remote-home1/clh/models/4_8_8_4_startgan10k_25_sr2_istockfromfjandk400andistock_3DUD_mse/test140k\
+    --video_path /remote-home1/dataset/data_split_1024/ \
+    --eval_video_path /remote-home1/clh/dataset/webvid/videos \
+    --resolution 256 \
+    --num_frames 25 \
+    --batch_size 1 \
+    --sample_rate 1 \
+    --disc_start 100000 \
+    --save_ckpt_step 10000 \
+    --eval_steps 500 \
+    --eval_batch_size 1 \
+    --eval_num_frames 33 \
+    --eval_sample_rate 1 \
+    --eval_lpips \
+    --eval_subset_size 100 \
+    --ema \
+    --ema_decay 0.999 \
+    --perceptual_weight 1.0 \
+    --loss_type l1 \
+    --disc_cls causalvideovae.model.losses.LPIPSWithDiscriminator3Drefiner

scripts/vae_demo.sh

@@ -0,0 +1,15 @@
+export NCCL_DEBUG=INFO
+export NCCL_SOCKET_IFNAME=ibs11
+export NCCL_IB_DISABLE=1
+REAL_DATASET_DIR=/remote-home1/clh/dataset/panda70m_val
+EXP_NAME=test_train
+SAMPLE_RATE=1
+NUM_FRAMES=33
+RESOLUTION=256
+SUBSET_SIZE=100
+CKPT=/storage/clh/Open-Sora/OpenSora-VAE-v1.2
+
+python scripts/vae_demo.py\
+    --ckpt ${CKPT} \
+    --config /storage/clh/Causal-Video-VAE/opensora/video.py\
+    --enable_tiling \

test.py

@@ -0,0 +1,2 @@
+import lpips
+lpips.LPIPS(net='alex', spatial=True)

train_ddp.py

@@ -0,0 +1,586 @@
+import os
+import torch
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, DistributedSampler, Subset
+import argparse
+import logging
+from colorlog import ColoredFormatter
+import tqdm
+from itertools import chain
+import wandb
+import random
+import numpy as np
+from pathlib import Path
+from einops import rearrange
+from causalvideovae.model import CausalVAEModel, EMA
+from causalvideovae.utils.utils import RealVideoDataset
+from causalvideovae.model.dataset_videobase import VideoDataset
+from causalvideovae.model.utils.module_utils import resolve_str_to_obj
+from causalvideovae.model.utils.video_utils import tensor_to_video
+import time
+try:
+    import lpips
+except:
+    raise Exception("Need lpips to valid.")
+
+def set_random_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def ddp_setup():
+    dist.init_process_group(backend="nccl")
+    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
+
+
+def setup_logger(rank):
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+    formatter = ColoredFormatter(
+        f"[rank{rank}] %(log_color)s%(asctime)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        log_colors={
+            "DEBUG": "cyan",
+            "INFO": "green",
+            "WARNING": "yellow",
+            "ERROR": "red",
+            "CRITICAL": "bold_red",
+        },
+        reset=True,
+        style="%",
+    )
+    stream_handler = logging.StreamHandler()
+    stream_handler.setLevel(logging.DEBUG)
+    stream_handler.setFormatter(formatter)
+
+    if not logger.handlers:
+        logger.addHandler(stream_handler)
+
+    return logger
+
+
+def check_unused_params(model):
+    unused_params = []
+    for name, param in model.named_parameters():
+        if param.grad is None:
+            unused_params.append(name)
+    return unused_params
+
+
+def set_requires_grad_optimizer(optimizer, requires_grad):
+    for param_group in optimizer.param_groups:
+        for param in param_group["params"]:
+            param.requires_grad = requires_grad
+
+
+def total_params(model):
+    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    total_params_in_millions = total_params / 1e6
+    return int(total_params_in_millions)
+
+
+def get_exp_name(args):
+    return f"{args.exp_name}-lr{args.lr:.2e}-bs{args.batch_size}-rs{args.resolution}-sr{args.sample_rate}-fr{args.num_frames}"
+
+def set_train(modules):
+    for module in modules:
+        module.train()
+            
+def set_eval(modules):
+    for module in modules:
+        module.eval()
+
+def set_modules_requires_grad(modules, requires_grad):
+    for module in modules:
+        module.requires_grad_(requires_grad)
+
+def save_checkpoint(
+    epoch,
+    batch_idx,
+    optimizer_state,
+    state_dict,
+    scaler_state,
+    checkpoint_dir,
+    filename="checkpoint.ckpt",
+    ema_state_dict={}
+):
+    filepath = checkpoint_dir / Path(filename)
+    torch.save(
+        {
+            "epoch": epoch,
+            "batch_idx": batch_idx,
+            "optimizer_state": optimizer_state,
+            "state_dict": state_dict,
+            "ema_state_dict": ema_state_dict,
+            "scaler_state": scaler_state,
+        },
+        filepath,
+    )
+    return filepath
+
+
+def valid(rank, model, val_dataloader, precision, args):
+    if args.eval_lpips:
+        lpips_model = lpips.LPIPS(net='alex', spatial=True)
+        lpips_model.to(rank)
+        lpips_model = DDP(lpips_model, device_ids=[rank])
+        lpips_model.requires_grad_(False)
+        lpips_model.eval()
+        
+    bar = None
+    if rank == 0:
+        bar = tqdm.tqdm(total=len(val_dataloader), desc="Validation...")
+        
+    psnr_list = []
+    lpips_list = []
+    video_log = []
+    num_video_log = args.eval_num_video_log
+    
+    with torch.no_grad():
+        for batch_idx, batch in enumerate(val_dataloader):
+            inputs = batch['video'].to(rank)
+            with torch.cuda.amp.autocast(dtype=precision):
+                video_recon, _ = model(inputs)
+            
+            # Upload videos
+            if rank == 0:
+                for i in range(len(video_recon)):
+                    if num_video_log <= 0:
+                        break
+                    video = tensor_to_video(video_recon[i])
+                    video_log.append(video)
+                    num_video_log -= 1
+                    
+            inputs = (rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()+1)/2.0
+            video_recon = (rearrange(video_recon, "b c t h w -> (b t) c h w").contiguous()+1)/2.0
+            
+            # Calculate PSNR
+            mse = torch.mean(torch.square(inputs - video_recon), dim=(1,2,3))
+            psnr = 20 * torch.log10(1 / torch.sqrt(mse))
+            psnr = psnr.mean().detach().cpu().item()
+            
+            # Calculate LPIPS
+            if args.eval_lpips:
+                lpips_score = lpips_model.forward(inputs, video_recon).mean().detach().cpu().item()
+                lpips_list.append(lpips_score)
+                
+            psnr_list.append(psnr)
+            if rank == 0:
+                bar.update()
+    # Release gpus memory
+    torch.cuda.empty_cache()
+    return psnr_list, lpips_list, video_log
+
+def gather_valid_result(psnr_list, lpips_list, video_log_list, rank, world_size):
+    gathered_psnr_list = [None for _ in range(world_size)]
+    gathered_lpips_list = [None for _ in range(world_size)]
+    gathered_video_logs = [None for _ in range(world_size)]
+    
+    dist.all_gather_object(gathered_psnr_list, psnr_list)
+    dist.all_gather_object(gathered_lpips_list, lpips_list)
+    dist.all_gather_object(gathered_video_logs, video_log_list)
+    return np.array(gathered_psnr_list).mean(), np.array(gathered_lpips_list).mean(), list(chain(*gathered_video_logs))
+
+def train(args):
+    # Setup logger
+    ddp_setup()
+    rank = int(os.environ["LOCAL_RANK"])
+    logger = setup_logger(rank)
+
+    # Init
+    ckpt_dir = Path(args.ckpt_dir) / Path(get_exp_name(args))
+    if rank == 0:
+        try:
+            ckpt_dir.mkdir(exist_ok=False, parents=True)
+        except:
+            logger.warning(f"`{ckpt_dir}` exists!")
+            time.sleep(5)
+            
+        logger.warning("Connecting to WANDB...")
+        wandb.init(
+            project=os.environ.get("WANDB_PROJECT", "causalvideovae"),
+            config=args,
+            name=get_exp_name(args)
+        )
+    dist.barrier()
+    
+    # Load generator model
+    if args.pretrained_model_name_or_path is not None:
+        if rank == 0:
+            logger.warning(
+                f"You are loading a checkpoint from `{args.pretrained_model_name_or_path}`."
+            )
+        model = CausalVAEModel.from_pretrained(
+            args.pretrained_model_name_or_path, ignore_mismatched_sizes=False
+        )
+    elif args.model_config is not None:
+        if rank == 0:
+            logger.warning(f"Model will be inited randomly.")
+        model = CausalVAEModel.from_config(args.model_config)
+    else:
+        raise Exception(
+            "You should set either `--pretrained_model_name_or_path` or `--model_config`"
+        )
+
+    # Load discriminator model
+    disc_cls = resolve_str_to_obj(args.disc_cls, append=False)
+    logger.warning(f"disc_class: {args.disc_cls} perceptual_weight: {args.perceptual_weight}  loss_type: {args.loss_type}")
+    disc = disc_cls(
+        disc_start=args.disc_start,
+        disc_weight=args.disc_weight,
+        kl_weight=args.kl_weight,
+        logvar_init=args.logvar_init,
+        perceptual_weight=args.perceptual_weight,
+        loss_type=args.loss_type
+    )
+
+    # DDP
+    model = model.to(rank)
+    model = DDP(
+        model, device_ids=[rank], find_unused_parameters=args.find_unused_parameters
+    )
+    disc = disc.to(rank)
+    disc = DDP(
+        disc, device_ids=[rank], find_unused_parameters=args.find_unused_parameters
+    )
+    
+    # Load dataset
+    dataset = VideoDataset(
+        args.video_path,
+        sequence_length=args.num_frames,
+        resolution=args.resolution,
+        sample_rate=args.sample_rate,
+        dynamic_sample=args.dynamic_sample,
+    )
+    ddp_sampler = DistributedSampler(dataset)
+    dataloader = DataLoader(
+        dataset, batch_size=args.batch_size, sampler=ddp_sampler, pin_memory=True, num_workers=args.dataset_num_worker
+    )
+
+    val_dataset = RealVideoDataset(
+        real_video_dir=args.eval_video_path,
+        num_frames=args.eval_num_frames,
+        sample_rate=args.eval_sample_rate,
+        crop_size=args.eval_resolution,
+        resolution=args.eval_resolution,
+    )
+    indices = range(args.eval_subset_size)
+    val_dataset = Subset(val_dataset, indices=indices)
+    val_sampler = DistributedSampler(val_dataset)
+    val_dataloader = DataLoader(val_dataset, batch_size=args.eval_batch_size, sampler=val_sampler, pin_memory=True)
+    
+    
+    # Optimizer
+    modules_to_train = [module for module in model.module.get_decoder()]
+    if not args.freeze_encoder:
+        modules_to_train += [module for module in model.module.get_encoder()]
+    else:
+        for module in model.module.get_encoder():
+            module.eval()
+            module.requires_grad_(False)
+        logger.warning("Encoder is freezed!")
+        
+    parameters_to_train = []
+    for module in modules_to_train:
+        parameters_to_train += module.parameters()
+        
+    gen_optimizer = torch.optim.Adam(parameters_to_train, lr=args.lr)
+    disc_optimizer = torch.optim.Adam(
+        disc.module.discriminator.parameters(), lr=args.lr
+    )
+
+    # AMP scaler
+    scaler = torch.cuda.amp.GradScaler()
+    precision = torch.bfloat16
+    if args.mix_precision == "fp16":
+        precision = torch.float16
+    elif args.mix_precision == "fp32":
+        precision = torch.float32
+
+    
+    
+    if args.resume_from_checkpoint:
+        if not os.path.isfile(args.resume_from_checkpoint):
+            raise Exception(
+                f"Make sure `{args.resume_from_checkpoint}` is a ckpt file."
+            )
+        checkpoint = torch.load(args.resume_from_checkpoint, map_location="cpu")
+        
+        if "ema_state_dict" in checkpoint and len(checkpoint['ema_state_dict']) > 0 and os.environ.get("NOT_USE_EMA_MODEL", 0) == 0:
+            sd = checkpoint["ema_state_dict"]
+            sd = {key.replace("module.", ""): value for key, value in sd.items()}
+            model.module.load_state_dict(sd, strict=True)
+            logger.info("Load from EMA state dict! If you want to load from original state dict, you should set NOT_USE_EMA_MODEL=1.")
+        else:
+            model.module.load_state_dict(checkpoint["state_dict"]["gen_model"])
+        disc.module.load_state_dict(checkpoint["state_dict"]["dics_model"], strict=False)
+        if not args.not_resume_training_process:
+            scaler.load_state_dict(checkpoint["scaler_state"])
+            gen_optimizer.load_state_dict(checkpoint["optimizer_state"]["gen_optimizer"])
+            disc_optimizer.load_state_dict(checkpoint["optimizer_state"]["disc_optimizer"])
+            start_epoch = checkpoint["epoch"]
+            start_batch_idx = checkpoint.get("batch_idx", 0)
+            logger.info(
+                f"Checkpoint loaded from {args.resume_from_checkpoint}, starting from epoch {start_epoch} batch {start_batch_idx}"
+            )
+        else:
+            logger.warning(
+                f"Checkpoint loaded from {args.resume_from_checkpoint}, starting from epoch {start_epoch} batch {start_batch_idx}. But training process is not resumed."
+            )
+    
+    if args.ema:
+        logger.warning(f"Start with EMA. EMA decay = {args.ema_decay}.")
+        ema = EMA(model, args.ema_decay)
+        ema.register()
+            
+    # Training loop
+    logger.info("Prepared!")
+    dist.barrier()
+    if rank == 0:
+        logger.info(f"=== Model Params ===")
+        logger.info(f"Generator:\t\t{total_params(model.module)}M")
+        logger.info(f"\t- Encoder:\t{total_params(model.module.encoder):d}M")
+        logger.info(f"\t- Decoder:\t{total_params(model.module.decoder):d}M")
+        logger.info(f"Discriminator:\t{total_params(disc.module):d}M")
+        logger.info(f"===========")
+        logger.info(f"Precision is set to: {args.mix_precision}!")
+        logger.info("Start training!")
+
+    # Training Bar
+    bar_desc = ""
+    bar = None
+    if rank == 0:
+        max_steps = (
+            args.epochs * len(dataloader) if args.max_steps is None else args.max_steps
+        )
+        bar = tqdm.tqdm(total=max_steps, desc=bar_desc.format(current_epoch=0, loss=0))
+        bar_desc = "Epoch: {current_epoch}, Loss: {loss}"
+        logger.warning("Training Details: ")
+        logger.warning(f" Max steps: {max_steps}")
+        logger.warning(f" Dataset Samples: {len(dataloader)}")
+        logger.warning(
+            f" Total Batch Size: {args.batch_size} * {os.environ['WORLD_SIZE']}"
+        )
+    dist.barrier()
+
+    # Training Loop
+    num_epochs = args.epochs
+    current_step = 1
+
+    def update_bar(bar):
+        if rank == 0:
+            bar.desc = bar_desc.format(current_epoch=epoch, loss=f"-")
+            bar.update()
+    for epoch in range(num_epochs):
+        """
+        if epoch < start_epoch:
+            update_bar(bar)
+            current_step += len(dataloader)
+            continue
+        """
+        set_train(modules_to_train)
+        ddp_sampler.set_epoch(epoch)  # Shuffle data at every epoch
+        for batch_idx, batch in enumerate(dataloader):
+            """
+            if epoch == start_epoch and batch_idx < start_batch_idx:
+                update_bar(bar)
+                current_step += 1
+                continue
+            """
+            inputs = batch["video"].to(rank)
+            if (
+                current_step % 2 == 1
+                and current_step >= disc.module.discriminator_iter_start
+            ):
+                set_modules_requires_grad(modules_to_train, False)
+                step_gen = False
+                step_dis = True
+            else:
+                set_modules_requires_grad(modules_to_train, True)
+                step_gen = True
+                step_dis = False
+
+            assert (
+                step_gen or step_dis
+            ), "You should backward either Gen or Dis in a step."
+
+            with torch.cuda.amp.autocast(dtype=precision):
+                outputs, posterior = model(inputs)
+
+            # Generator Step
+            if step_gen:
+                with torch.cuda.amp.autocast(dtype=precision):
+                    g_loss, g_log = disc(
+                        inputs,
+                        outputs,
+                        posterior,
+                        optimizer_idx=0,
+                        global_step=current_step,
+                        last_layer=model.module.get_last_layer(),
+                        split="train",
+                    )
+                gen_optimizer.zero_grad()
+                scaler.scale(g_loss).backward()
+                scaler.step(gen_optimizer)
+                scaler.update()
+                if args.ema:
+                    ema.update()
+                if rank == 0 and current_step % args.log_steps == 0:
+                    wandb.log({"train/generator_loss": g_loss.item()}, step=current_step)
+
+            # Discriminator Step
+            if step_dis:
+                with torch.cuda.amp.autocast(dtype=precision):
+                    d_loss, d_log = disc(
+                        inputs,
+                        outputs,
+                        posterior,
+                        optimizer_idx=1,
+                        global_step=current_step,
+                        last_layer=None,
+                        split="train",
+                    )
+                disc_optimizer.zero_grad()
+                scaler.scale(d_loss).backward()
+                scaler.step(disc_optimizer)
+                scaler.update()
+                if rank == 0 and current_step % args.log_steps == 0:
+                    wandb.log({"train/discriminator_loss": d_loss.item()}, step=current_step)
+
+            def valid_model(model, name=""):
+                set_eval(modules_to_train)
+                psnr_list, lpips_list, video_log = valid(rank, model, val_dataloader, precision, args)
+                valid_psnr, valid_lpips, valid_video_log = gather_valid_result(psnr_list, lpips_list, video_log, rank, dist.get_world_size())
+                if rank == 0:
+                    name = "_" + name if name != "" else name
+                    wandb.log({f"val{name}/recon": wandb.Video(np.array(valid_video_log), fps=10)}, step=current_step)
+                    wandb.log({f"val{name}/psnr": valid_psnr}, step=current_step)
+                    wandb.log({f"val{name}/lpips": valid_lpips}, step=current_step)
+                    logger.info(f"{name} Validation done.")
+            
+            if current_step % args.eval_steps == 0 or current_step == 1:
+                if rank == 0:
+                    logger.info("Starting validation...")
+                valid_model(model)
+                if args.ema:
+                    ema.apply_shadow()
+                    valid_model(model, "ema")
+                    ema.restore()
+                        
+            # Checkpoint
+            if current_step % args.save_ckpt_step == 0 and rank == 0:
+                file_path = save_checkpoint(
+                    epoch,
+                    batch_idx,
+                    {
+                        "gen_optimizer": gen_optimizer.state_dict(),
+                        "disc_optimizer": disc_optimizer.state_dict(),
+                    },
+                    {
+                        "gen_model": model.module.state_dict(),
+                        "dics_model": disc.module.state_dict(),
+                    },
+                    scaler.state_dict(),
+                    ckpt_dir,
+                    f"checkpoint-{current_step}.ckpt",
+                    ema_state_dict=ema.shadow if args.ema else {}
+                )
+                logger.info(f"Checkpoint has been saved to `{file_path}`.")
+
+            # Update step
+            update_bar(bar)
+            current_step += 1
+
+    dist.destroy_process_group()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Distributed Training")
+    # Exp setting
+    parser.add_argument(
+        "--exp_name", type=str, default="test", help="number of epochs to train"
+    )
+    parser.add_argument("--seed", type=int, default=1234, help="seed")
+    # Training setting
+    parser.add_argument(
+        "--epochs", type=int, default=10, help="number of epochs to train"
+    )
+    parser.add_argument(
+        "--max_steps", type=int, default=None, help="number of epochs to train"
+    )
+    parser.add_argument("--save_ckpt_step", type=int, default=1000, help="")
+    parser.add_argument("--ckpt_dir", type=str, default="./results/", help="")
+    parser.add_argument(
+        "--batch_size", type=int, default=1, help="batch size for training"
+    )
+    parser.add_argument("--lr", type=float, default=1e-5, help="learning rate")
+    parser.add_argument("--log_steps", type=int, default=5, help="log steps")
+    parser.add_argument("--freeze_encoder", action="store_true", help="")
+    
+    # Data
+    parser.add_argument("--video_path", type=str, default=None, help="")
+    parser.add_argument("--num_frames", type=int, default=17, help="")
+    parser.add_argument("--resolution", type=int, default=256, help="")
+    parser.add_argument("--sample_rate", type=int, default=2, help="")
+    parser.add_argument("--dynamic_sample", type=bool, default=False, help="")
+    # Generator model
+    parser.add_argument("--find_unused_parameters", action="store_true", help="")
+    parser.add_argument(
+        "--pretrained_model_name_or_path", type=str, default=None, help=""
+    )
+    parser.add_argument("--resume_from_checkpoint", type=str, default=None, help="")
+    parser.add_argument("--not_resume_training_process", action="store_true", help="")
+    parser.add_argument("--model_config", type=str, default=None, help="")
+    parser.add_argument(
+        "--mix_precision",
+        type=str,
+        default="bf16",
+        choices=["fp16", "bf16", "fp32"],
+        help="precision for training",
+    )
+
+    # Discriminator Model
+    parser.add_argument("--load_disc_from_checkpoint", type=str, default=None, help="")
+    parser.add_argument(
+        "--disc_cls",
+        type=str,
+        default="causalvideovae.model.losses.LPIPSWithDiscriminator3D",
+        help="",
+    )
+    parser.add_argument("--disc_start", type=int, default=5, help="")
+    parser.add_argument("--disc_weight", type=float, default=0.5, help="")
+    parser.add_argument("--kl_weight", type=float, default=1e-06, help="")
+    parser.add_argument("--perceptual_weight", type=float, default=1.0, help="")
+    parser.add_argument("--loss_type", type=str, default="l1", help="")
+    parser.add_argument("--logvar_init", type=float, default=0.0, help="")
+
+    # Validation
+    parser.add_argument("--eval_steps", type=int, default=1000, help="")
+    parser.add_argument("--eval_video_path", type=str, default=None, help="")
+    parser.add_argument("--eval_num_frames", type=int, default=17, help="")
+    parser.add_argument("--eval_resolution", type=int, default=256, help="")
+    parser.add_argument("--eval_sample_rate", type=int, default=1, help="")
+    parser.add_argument("--eval_batch_size", type=int, default=8, help="")
+    parser.add_argument("--eval_subset_size", type=int, default=100, help="")
+    parser.add_argument("--eval_num_video_log", type=int, default=2, help="")
+    parser.add_argument("--eval_lpips", action="store_true", help="")
+    
+    # Dataset
+    parser.add_argument("--dataset_num_worker", type=int, default=16, help="")
+    
+    # EMA
+    parser.add_argument("--ema", action="store_true", help="")
+    parser.add_argument("--ema_decay", type=float, default=0.999, help="")
+
+    args = parser.parse_args()
+
+    set_random_seed(args.seed)
+    train(args)
+
+
+if __name__ == "__main__":
+    main()