Init code

.gitignore

@@ -174,3 +174,6 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 
+
+source/
+output/

.python-version

@@ -0,0 +1 @@
+3.12

main.py

@@ -0,0 +1,173 @@
+import logging
+from pathlib import Path
+
+import torch
+import numpy as np
+from omegaconf import OmegaConf, DictConfig
+
+from src.utils import utils
+from src.utils.torch import img2tensor, check_dim_and_resize, tensor2img
+from src.utils.build import build_from_cfg
+from src.utils.padder import InputPadder
+
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+
+class Anchor:
+    def __init__(self, resolution: int, memory: int, memory_bias: int) -> None:
+        self.resolution = resolution
+        self.memory = memory
+        self.memory_bias = memory_bias
+
+    def __str__(self) -> str:
+        return f"Anchor(resolution={self.resolution}, memory={self.memory}, memory_bias={self.memory_bias})"
+
+
+class ModelRunner:
+    def __init__(self, config: Path, ckpt_path: Path, device: torch.device) -> None:
+        """Initializes the ModelRunner with configuration and checkpoint.
+
+        Args:
+            config (Path): Path to model configuration in YAML format
+            ckpt_path (Path): Path to model checkpoint in .pth format
+            device (torch.device): Device to load the model on
+        """
+        omega_config = OmegaConf.load(config)
+        network_config: DictConfig = omega_config.network
+        logging.info(
+            f"Loaded network configuration: {network_config} from [{ckpt_path}]"
+        )
+        model = build_from_cfg(network_config)
+        checkpoint = torch.load(ckpt_path, map_location=device, weights_only=False)
+        model.load_state_dict(checkpoint["state_dict"])
+        model = model.to(get_device())
+        model.eval()
+        self.model = model
+
+
+def get_vram_available(device: torch.device) -> int:
+    """Returns the available VRAM in bytes."""
+    if device.type == "cuda" and torch.cuda.is_available():
+        return torch.cuda.get_device_properties(
+            device
+        ).total_memory - torch.cuda.memory_allocated(device)
+    elif device.type == "mps" and torch.mps.is_available():
+        # MPS does not provide a way to query available memory, so we return a large number to avoid issues
+        return torch.mps.recommended_max_memory()
+    else:
+        return 1
+
+
+def get_device():
+    """Detects and returns the best available device for PyTorch computation.
+
+    Returns:
+        torch.device: CUDA device if available, MPS device for Apple Silicon if available, otherwise CPU.
+    """
+    if torch.cuda.is_available():
+        logging.info("Using CUDA-enabled GPU")
+        return torch.device("cuda")
+    elif torch.mps.is_available():
+        logging.info("Using Apple Silicon GPU (MPS)")
+        return torch.device("mps")
+    logging.info("No GPU available, using CPU")
+    return torch.device("cpu")
+
+
+class ImageInterpolator:
+    def __init__(self, device: torch.device, anchor: Anchor, model_runner: ModelRunner):
+        self.device = device
+        self.anchor = anchor
+        self.vram_available = get_vram_available(device)
+        self.embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
+        self.model_runner = model_runner
+        logging.info(
+            f"Initialized ImageInterpolator with device: {device}, anchor: {anchor}, available VRAM: {self.vram_available} bytes"
+        )
+
+    def interpolate(self, image1: Path, image2: Path, output_path: Path):
+        logging.info(f"Reading images: {image1} and {image2}")
+        tensor1 = img2tensor(utils.read(image1)).to(self.device)
+        tensor2 = img2tensor(utils.read(image2)).to(self.device)
+        logging.info(
+            f"Image shapes after conversion to tensors: {tensor1.shape}, {tensor2.shape}"
+        )
+        tensor1, tensor2 = check_dim_and_resize(tensor1, tensor2)
+        logging.info(f"Image shapes after resizing: {tensor1.shape}, {tensor2.shape}")
+        h, w = tensor1.shape[2], tensor1.shape[3]
+        logging.info(f"Interpolating images of size: {h}x{w}")
+
+        scale = self.scale(h, w)
+        logging.info(f"Calculated scale factor: {scale:.2f}")
+        padding = int(16 / scale)
+        logging.info(f"Calculated padding: {padding} pixels")
+        padder = InputPadder(tensor1.shape, divisor=padding)
+        tensor1_padded, tensor2_padded = padder.pad(tensor1, tensor2)
+        logging.info(
+            f"Image shapes after padding: {tensor1_padded.shape}, {tensor2_padded.shape}"
+        )
+
+        tensor1_padded = tensor1_padded.to(self.device)
+        tensor2_padded = tensor2_padded.to(self.device)
+        logging.info("Running model inference for interpolation")
+        with torch.no_grad():
+            interpolated = self.model_runner.model(
+                tensor1_padded, tensor2_padded, self.embt, scale_factor=scale, eval=True
+            )["imgt_pred"]
+        logging.info(f"Interpolated image shape before unpadding: {interpolated.shape}")
+        (interpolated,) = padder.unpad(interpolated)
+        logging.info(f"Interpolated image shape after unpadding: {interpolated.shape}")
+        utils.write(output_path, tensor2img(interpolated.cpu()))
+        logging.info(f"Saved interpolated image to: {output_path}")
+
+    def scale(self, height: int, width: int) -> float:
+        scale = (
+            self.anchor.resolution
+            / (height * width)
+            * np.sqrt(
+                (self.vram_available - self.anchor.memory_bias) / self.anchor.memory
+            )
+        )
+        scale = 1 if scale > 1 else scale
+        scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
+        if scale < 1:
+            logging.info(
+                f"Due to the limited VRAM, the video will be scaled by {scale:.2f}"
+            )
+        return scale
+
+
+def main():
+    config_path = Path("src/config/AMT-G.yaml")
+    ckpt_path = Path("src/pretrained/amt-g.pth")
+    image1_path = Path("source/img0.png")
+    image2_path = Path("source/img1.png")
+    output_path = Path("output/interpolated_image.png")
+
+    device = get_device()
+    model_runner = ModelRunner(config_path, ckpt_path, device)
+    if device.type in ("cpu", "mps"):
+        if device.type == "mps":
+            logging.warning(
+                "Running on Apple Silicon GPU (MPS) may have limited performance. Consider using a CUDA-enabled GPU for better performance."
+            )
+        else:
+            logging.warning(
+                "Running on CPU may be very slow. Consider using a GPU for better performance."
+            )
+        anchor = Anchor(resolution=8192 * 8192, memory=1, memory_bias=0)
+    elif device.type == "cuda":
+        anchor = Anchor(
+            resolution=1024 * 512, memory=1500 * 1024**2, memory_bias=2500 * 1024**2
+        )
+    else:
+        raise Exception(f"Unsupported device type: {device.type}")
+    interpolator = ImageInterpolator(device, anchor, model_runner)
+    interpolator.interpolate(image1_path, image2_path, output_path)
+
+
+if __name__ == "__main__":
+    main()

networks/AMT-G.py

@@ -0,0 +1,187 @@
+import torch
+import torch.nn as nn
+from networks.blocks.raft import coords_grid, BasicUpdateBlock, BidirCorrBlock
+from networks.blocks.feat_enc import LargeEncoder
+from networks.blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
+from networks.blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
+
+
+class Model(nn.Module):
+    def __init__(
+        self,
+        corr_radius=3,
+        corr_lvls=4,
+        num_flows=5,
+        channels=[84, 96, 112, 128],
+        skip_channels=84,
+    ):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+
+        self.feat_encoder = LargeEncoder(
+            output_dim=128, norm_fn="instance", dropout=0.0
+        )
+        self.encoder = Encoder(channels, large=True)
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(112, None)
+        self.update3_low = self._get_updateblock(96, 2.0)
+        self.update2_low = self._get_updateblock(84, 4.0)
+
+        self.update3_high = self._get_updateblock(96, None)
+        self.update2_high = self._get_updateblock(84, None)
+
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3 * self.num_flows, 6 * self.num_flows, 7, 1, 3),
+            nn.PReLU(6 * self.num_flows),
+            nn.Conv2d(6 * self.num_flows, 3, 7, 1, 3),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return BasicUpdateBlock(
+            cdim=cdim,
+            hidden_dim=192,
+            flow_dim=64,
+            corr_dim=256,
+            corr_dim2=192,
+            fc_dim=188,
+            scale_factor=scale_factor,
+            corr_levels=self.corr_levels,
+            radius=self.radius,
+        )
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1.0 / embt
+        t0_scale = 1.0 / (1.0 - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale)
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = (
+            torch.cat([img0, img1], 2)
+            .mean(1, keepdim=True)
+            .mean(2, keepdim=True)
+            .mean(3, keepdim=True)
+        )
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_])  # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(
+            fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels
+        )
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(
+            corr_fn, coord, up_flow0_4, up_flow1_4, embt, downsample=1
+        )
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(
+            ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4
+        )
+        corr_3, flow_3 = self._corr_scale_lookup(
+            corr_fn, coord, up_flow0_3, up_flow1_3, embt, downsample=2
+        )
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3_low(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+
+        # residue update with lookup corr (hr)
+        corr_3 = resize(corr_3, scale_factor=2.0)
+        up_flow_3 = torch.cat([up_flow0_3, up_flow1_3], dim=1)
+        delta_ft_2_, delta_up_flow_3 = self.update3_high(ft_2_, up_flow_3, corr_3)
+        ft_2_ += delta_ft_2_
+        up_flow0_3 += delta_up_flow_3[:, 0:2]
+        up_flow1_3 += delta_up_flow_3[:, 2:4]
+
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_ = self.decoder2(
+            ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3
+        )
+        corr_2, flow_2 = self._corr_scale_lookup(
+            corr_fn, coord, up_flow0_2, up_flow1_2, embt, downsample=4
+        )
+
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2_low(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+
+        # residue update with lookup corr (hr)
+        corr_2 = resize(corr_2, scale_factor=4.0)
+        up_flow_2 = torch.cat([up_flow0_2, up_flow1_2], dim=1)
+        delta_ft_1_, delta_up_flow_2 = self.update2_high(ft_1_, up_flow_2, corr_2)
+        ft_1_ += delta_ft_1_
+        up_flow0_2 += delta_up_flow_2[:, 0:2]
+        up_flow1_2 += delta_up_flow_2[:, 2:4]
+
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(
+            ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2
+        )
+
+        if scale_factor != 1.0:
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
+                1.0 / scale_factor
+            )
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
+                1.0 / scale_factor
+            )
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
+
+        # Merge multiple predictions
+        imgt_pred = multi_flow_combine(
+            self.comb_block, img0, img1, up_flow0_1, up_flow1_1, mask, img_res, mean_
+        )
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return {"imgt_pred": imgt_pred}
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                "imgt_pred": imgt_pred,
+                "flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                "flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                "ft_pred": [ft_1_, ft_2_, ft_3_],
+            }

networks/AMT-L.py

@@ -0,0 +1,155 @@
+import torch
+import torch.nn as nn
+from networks.blocks.raft import (
+    coords_grid,
+    BasicUpdateBlock, BidirCorrBlock
+)
+from networks.blocks.feat_enc import (
+    BasicEncoder
+)
+from networks.blocks.ifrnet import (
+    resize,
+    Encoder,
+    InitDecoder,
+    IntermediateDecoder
+)
+from networks.blocks.multi_flow import (
+    multi_flow_combine,
+    MultiFlowDecoder
+)
+
+
+class Model(nn.Module):
+    def __init__(self, 
+                 corr_radius=3, 
+                 corr_lvls=4, 
+                 num_flows=5,
+                 channels=[48, 64, 72, 128], 
+                 skip_channels=48
+                 ):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+
+        self.feat_encoder = BasicEncoder(output_dim=128, norm_fn='instance', dropout=0.)
+        self.encoder = Encoder([48, 64, 72, 128], large=True)
+        
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(72, None)
+        self.update3 = self._get_updateblock(64, 2.0)
+        self.update2 = self._get_updateblock(48, 4.0)
+        
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3*self.num_flows, 6*self.num_flows, 7, 1, 3),
+            nn.PReLU(6*self.num_flows),
+            nn.Conv2d(6*self.num_flows, 3, 7, 1, 3),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return BasicUpdateBlock(cdim=cdim, hidden_dim=128, flow_dim=48, 
+                                corr_dim=256, corr_dim2=160, fc_dim=124, 
+                                scale_factor=scale_factor, corr_levels=self.corr_levels, 
+                                radius=self.radius)
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1. / embt
+        t0_scale = 1. / (1. - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+            
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale) 
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+    
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+        
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, 
+                                                 up_flow0_4, up_flow1_4, 
+                                                 embt, downsample=1)
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        corr_3, flow_3 = self._corr_scale_lookup(corr_fn, 
+                                                 coord, up_flow0_3, up_flow1_3, 
+                                                 embt, downsample=2)
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_  = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        corr_2, flow_2 = self._corr_scale_lookup(corr_fn, 
+                                                 coord, up_flow0_2, up_flow1_2, 
+                                                 embt, downsample=4)
+        
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+        
+        if scale_factor != 1.0: 
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            mask = resize(mask, scale_factor=(1.0/scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0/scale_factor))
+
+        # Merge multiple predictions 
+        imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, 
+                                                                        mask, img_res, mean_)
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return  { 'imgt_pred': imgt_pred, }
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                'imgt_pred': imgt_pred,
+                'flow0_pred': [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                'flow1_pred': [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                'ft_pred': [ft_1_, ft_2_, ft_3_],
+            }
+    

networks/AMT-S.py

@@ -0,0 +1,154 @@
+import torch
+import torch.nn as nn
+from networks.blocks.raft import (
+    coords_grid,
+    SmallUpdateBlock, BidirCorrBlock
+)
+from networks.blocks.feat_enc import (
+    SmallEncoder
+)
+from networks.blocks.ifrnet import (
+    resize,
+    Encoder,
+    InitDecoder,
+    IntermediateDecoder
+)
+from networks.blocks.multi_flow import (
+    multi_flow_combine,
+    MultiFlowDecoder
+)
+
+
+class Model(nn.Module):
+    def __init__(self, 
+                 corr_radius=3, 
+                 corr_lvls=4, 
+                 num_flows=3, 
+                 channels=[20, 32, 44, 56], 
+                 skip_channels=20):
+        super(Model, self).__init__()
+        self.radius = corr_radius
+        self.corr_levels = corr_lvls
+        self.num_flows = num_flows
+        self.channels = channels
+        self.skip_channels = skip_channels
+
+        self.feat_encoder = SmallEncoder(output_dim=84, norm_fn='instance', dropout=0.)
+        self.encoder = Encoder(channels)
+
+        self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
+        self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
+        self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
+        self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
+
+        self.update4 = self._get_updateblock(44)
+        self.update3 = self._get_updateblock(32, 2)
+        self.update2 = self._get_updateblock(20, 4)
+        
+        self.comb_block = nn.Sequential(
+            nn.Conv2d(3*num_flows, 6*num_flows, 3, 1, 1),
+            nn.PReLU(6*num_flows),
+            nn.Conv2d(6*num_flows, 3, 3, 1, 1),
+        )
+
+    def _get_updateblock(self, cdim, scale_factor=None):
+        return SmallUpdateBlock(cdim=cdim, hidden_dim=76, flow_dim=20, corr_dim=64, 
+                                fc_dim=68, scale_factor=scale_factor, 
+                                corr_levels=self.corr_levels, radius=self.radius)
+
+    def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
+        # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
+        # based on linear assumption
+        t1_scale = 1. / embt
+        t0_scale = 1. / (1. - embt)
+        if downsample != 1:
+            inv = 1 / downsample
+            flow0 = inv * resize(flow0, scale_factor=inv)
+            flow1 = inv * resize(flow1, scale_factor=inv)
+            
+        corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale) 
+        corr = torch.cat([corr0, corr1], dim=1)
+        flow = torch.cat([flow0, flow1], dim=1)
+        return corr, flow
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+        b, _, h, w = img0_.shape
+        coord = coords_grid(b, h // 8, w // 8, img0.device)
+        
+        fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [1, 128, H//8, W//8]
+        corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
+
+        # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
+        # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        ######################################### the 4th decoder #########################################
+        up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
+        corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, 
+                                                 up_flow0_4, up_flow1_4, 
+                                                 embt, downsample=1)
+
+        # residue update with lookup corr
+        delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
+        delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
+        up_flow0_4 = up_flow0_4 + delta_flow0_4
+        up_flow1_4 = up_flow1_4 + delta_flow1_4
+        ft_3_ = ft_3_ + delta_ft_3_
+
+        ######################################### the 3rd decoder #########################################
+        up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        corr_3, flow_3 = self._corr_scale_lookup(corr_fn, 
+                                                 coord, up_flow0_3, up_flow1_3, 
+                                                 embt, downsample=2)
+
+        # residue update with lookup corr
+        delta_ft_2_, delta_flow_3 = self.update3(ft_2_, flow_3, corr_3)
+        delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
+        up_flow0_3 = up_flow0_3 + delta_flow0_3
+        up_flow1_3 = up_flow1_3 + delta_flow1_3
+        ft_2_ = ft_2_ + delta_ft_2_
+
+        ######################################### the 2nd decoder #########################################
+        up_flow0_2, up_flow1_2, ft_1_  = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        corr_2, flow_2 = self._corr_scale_lookup(corr_fn, 
+                                                 coord, up_flow0_2, up_flow1_2, 
+                                                 embt, downsample=4)
+        
+        # residue update with lookup corr
+        delta_ft_1_, delta_flow_2 = self.update2(ft_1_, flow_2, corr_2)
+        delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
+        up_flow0_2 = up_flow0_2 + delta_flow0_2
+        up_flow1_2 = up_flow1_2 + delta_flow1_2
+        ft_1_ = ft_1_ + delta_ft_1_
+
+        ######################################### the 1st decoder #########################################
+        up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+        
+        if scale_factor != 1.0: 
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            mask = resize(mask, scale_factor=(1.0/scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0/scale_factor))
+        
+        # Merge multiple predictions 
+        imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, 
+                                                                        mask, img_res, mean_)
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return  { 'imgt_pred': imgt_pred, }
+        else:
+            up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
+            up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
+            return {
+                'imgt_pred': imgt_pred,
+                'flow0_pred': [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                'flow1_pred': [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                'ft_pred': [ft_1_, ft_2_, ft_3_],
+            }

networks/IFRNet.py

@@ -0,0 +1,169 @@
+import torch
+import torch.nn as nn
+from src.utils.flow_utils import warp
+from networks.blocks.ifrnet import (
+    convrelu, resize,
+    ResBlock, 
+)
+
+
+class Encoder(nn.Module):
+    def __init__(self):
+        super(Encoder, self).__init__()
+        self.pyramid1 = nn.Sequential(
+            convrelu(3, 32, 3, 2, 1), 
+            convrelu(32, 32, 3, 1, 1)
+        )
+        self.pyramid2 = nn.Sequential(
+            convrelu(32, 48, 3, 2, 1), 
+            convrelu(48, 48, 3, 1, 1)
+        )
+        self.pyramid3 = nn.Sequential(
+            convrelu(48, 72, 3, 2, 1), 
+            convrelu(72, 72, 3, 1, 1)
+        )
+        self.pyramid4 = nn.Sequential(
+            convrelu(72, 96, 3, 2, 1), 
+            convrelu(96, 96, 3, 1, 1)
+        )
+        
+    def forward(self, img):
+        f1 = self.pyramid1(img)
+        f2 = self.pyramid2(f1)
+        f3 = self.pyramid3(f2)
+        f4 = self.pyramid4(f3)
+        return f1, f2, f3, f4
+
+
+class Decoder4(nn.Module):
+    def __init__(self):
+        super(Decoder4, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(192+1, 192), 
+            ResBlock(192, 32), 
+            nn.ConvTranspose2d(192, 76, 4, 2, 1, bias=True)
+        )
+        
+    def forward(self, f0, f1, embt):
+        b, c, h, w = f0.shape
+        embt = embt.repeat(1, 1, h, w)
+        f_in = torch.cat([f0, f1, embt], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Decoder3(nn.Module):
+    def __init__(self):
+        super(Decoder3, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(220, 216), 
+            ResBlock(216, 32), 
+            nn.ConvTranspose2d(216, 52, 4, 2, 1, bias=True)
+        )
+
+    def forward(self, ft_, f0, f1, up_flow0, up_flow1):
+        f0_warp = warp(f0, up_flow0)
+        f1_warp = warp(f1, up_flow1)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, up_flow0, up_flow1], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Decoder2(nn.Module):
+    def __init__(self):
+        super(Decoder2, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(148, 144), 
+            ResBlock(144, 32), 
+            nn.ConvTranspose2d(144, 36, 4, 2, 1, bias=True)
+        )
+
+    def forward(self, ft_, f0, f1, up_flow0, up_flow1):
+        f0_warp = warp(f0, up_flow0)
+        f1_warp = warp(f1, up_flow1)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, up_flow0, up_flow1], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Decoder1(nn.Module):
+    def __init__(self):
+        super(Decoder1, self).__init__()
+        self.convblock = nn.Sequential(
+            convrelu(100, 96), 
+            ResBlock(96, 32), 
+            nn.ConvTranspose2d(96, 8, 4, 2, 1, bias=True)
+        )
+        
+    def forward(self, ft_, f0, f1, up_flow0, up_flow1):
+        f0_warp = warp(f0, up_flow0)
+        f1_warp = warp(f1, up_flow1)
+        f_in = torch.cat([ft_, f0_warp, f1_warp, up_flow0, up_flow1], 1)
+        f_out = self.convblock(f_in)
+        return f_out
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.encoder = Encoder()
+        self.decoder4 = Decoder4()
+        self.decoder3 = Decoder3()
+        self.decoder2 = Decoder2()
+        self.decoder1 = Decoder1()
+
+    def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
+        mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
+        img0 = img0 - mean_
+        img1 = img1 - mean_
+        
+        img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
+        img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
+            
+        f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
+        f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
+
+        out4 = self.decoder4(f0_4, f1_4, embt)
+        up_flow0_4 = out4[:, 0:2]
+        up_flow1_4 = out4[:, 2:4]
+        ft_3_ = out4[:, 4:]
+
+        out3 = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
+        up_flow0_3 = out3[:, 0:2] + 2.0 * resize(up_flow0_4, scale_factor=2.0)
+        up_flow1_3 = out3[:, 2:4] + 2.0 * resize(up_flow1_4, scale_factor=2.0)
+        ft_2_ = out3[:, 4:]
+
+        out2 = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
+        up_flow0_2 = out2[:, 0:2] + 2.0 * resize(up_flow0_3, scale_factor=2.0)
+        up_flow1_2 = out2[:, 2:4] + 2.0 * resize(up_flow1_3, scale_factor=2.0)
+        ft_1_ = out2[:, 4:]
+
+        out1 = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
+        up_flow0_1 = out1[:, 0:2] + 2.0 * resize(up_flow0_2, scale_factor=2.0)
+        up_flow1_1 = out1[:, 2:4] + 2.0 * resize(up_flow1_2, scale_factor=2.0)
+        up_mask_1 = torch.sigmoid(out1[:, 4:5])
+        up_res_1 = out1[:, 5:]
+        
+        if scale_factor != 1.0:
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
+            up_mask_1 = resize(up_mask_1, scale_factor=(1.0/scale_factor))
+            up_res_1 = resize(up_res_1, scale_factor=(1.0/scale_factor))
+            
+        img0_warp = warp(img0, up_flow0_1)
+        img1_warp = warp(img1, up_flow1_1)
+        imgt_merge = up_mask_1 * img0_warp + (1 - up_mask_1) * img1_warp + mean_
+        imgt_pred = imgt_merge + up_res_1
+        imgt_pred = torch.clamp(imgt_pred, 0, 1)
+
+        if eval:
+            return  { 'imgt_pred': imgt_pred, }
+        else:
+            return {
+                'imgt_pred': imgt_pred,
+                'flow0_pred': [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
+                'flow1_pred': [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
+                'ft_pred': [ft_1_, ft_2_, ft_3_],
+                'img0_warp': img0_warp,
+                'img1_warp': img1_warp
+            }

networks/blocks/feat_enc.py

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

networks/blocks/ifrnet.py

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

networks/blocks/multi_flow.py

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

networks/blocks/raft.py

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

pyproject.toml

@@ -0,0 +1,12 @@
+[project]
+name = "amt-apple"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "imageio>=2.37.3",
+    "numpy>=2.4.4",
+    "omegaconf>=2.3.0",
+    "torch>=2.11.0",
+]

src/config/AMT-G.yaml

@@ -0,0 +1,62 @@
+exp_name: floloss1e-2_300epoch_bs24_lr1p5e-4
+seed: 2023
+epochs: 300
+distributed: true
+lr: 1.5e-4
+lr_min: 2e-5
+weight_decay: 0.0
+resume_state: null
+save_dir: work_dir
+eval_interval: 1
+
+network:
+  name: networks.AMT-G.Model
+  params:
+    corr_radius: 3
+    corr_lvls: 4
+    num_flows: 5
+data:
+  train: 
+    name: datasets.vimeo_datasets.Vimeo90K_Train_Dataset
+    params: 
+      dataset_dir: data/vimeo_triplet
+  val:
+    name: datasets.vimeo_datasets.Vimeo90K_Test_Dataset
+    params: 
+      dataset_dir: data/vimeo_triplet
+  train_loader:
+    batch_size: 24
+    num_workers: 12
+  val_loader:
+    batch_size: 24
+    num_workers: 3
+
+logger:
+  use_wandb: true  
+  resume_id: null
+
+losses:
+  - {
+    name: losses.loss.CharbonnierLoss,
+    nickname: l_rec,
+    params: {
+      loss_weight: 1.0,
+      keys: [imgt_pred, imgt]
+    }
+  }
+  - {
+    name: losses.loss.TernaryLoss,
+    nickname: l_ter,
+    params: {
+      loss_weight: 1.0,
+      keys: [imgt_pred, imgt]
+    }
+  }
+  - {
+    name: losses.loss.MultipleFlowLoss,
+    nickname: l_flo,
+    params: {
+      loss_weight: 0.005,
+      keys: [flow0_pred, flow1_pred, flow]
+    }
+  }

src/config/AMT-S.yaml

@@ -0,0 +1,63 @@
+exp_name: floloss1e-2_300epoch_bs24_lr2e-4
+seed: 2023
+epochs: 300
+distributed: true
+lr: 2e-4
+lr_min: 2e-5
+weight_decay: 0.0
+resume_state: null
+save_dir: work_dir
+eval_interval: 1
+
+network:
+  name: networks.AMT-S.Model
+  params:
+    corr_radius: 3
+    corr_lvls: 4
+    num_flows: 3
+
+data:
+  train: 
+    name: datasets.vimeo_datasets.Vimeo90K_Train_Dataset
+    params: 
+      dataset_dir: data/vimeo_triplet
+  val:
+    name: datasets.vimeo_datasets.Vimeo90K_Test_Dataset
+    params: 
+      dataset_dir: data/vimeo_triplet
+  train_loader:
+    batch_size: 24
+    num_workers: 12
+  val_loader:
+    batch_size: 24
+    num_workers: 3
+
+logger:
+  use_wandb: false  
+  resume_id: null
+
+losses:
+  - {
+    name: losses.loss.CharbonnierLoss,
+    nickname: l_rec,
+    params: {
+      loss_weight: 1.0,
+      keys: [imgt_pred, imgt]
+    }
+  }
+  - {
+    name: losses.loss.TernaryLoss,
+    nickname: l_ter,
+    params: {
+      loss_weight: 1.0,
+      keys: [imgt_pred, imgt]
+    }
+  }
+  - {
+    name: losses.loss.MultipleFlowLoss,
+    nickname: l_flo,
+    params: {
+      loss_weight: 0.002,
+      keys: [flow0_pred, flow1_pred, flow]
+    }
+  }

src/utils/build.py

@@ -0,0 +1,15 @@
+from typing import TYPE_CHECKING
+import importlib
+
+if TYPE_CHECKING:
+    from omegaconf import DictConfig
+
+
+def base_build_fn(module: str, cls: str, params: dict):
+    return getattr(importlib.import_module(module, package=None), cls)(**params)
+
+
+def build_from_cfg(config: "DictConfig"):
+    module, cls = config["name"].rsplit(".", 1)
+    params: dict = config.get("params", {})
+    return base_build_fn(module, cls, params)

src/utils/flow_utils.py

@@ -0,0 +1,25 @@
+import torch
+import torch.nn.functional as F
+
+
+def warp(img, flow):
+    B, _, H, W = flow.shape
+    xx = torch.linspace(-1.0, 1.0, W).view(1, 1, 1, W).expand(B, -1, H, -1)
+    yy = torch.linspace(-1.0, 1.0, H).view(1, 1, H, 1).expand(B, -1, -1, W)
+    grid = torch.cat([xx, yy], 1).to(img)
+    flow_ = torch.cat(
+        [
+            flow[:, 0:1, :, :] / ((W - 1.0) / 2.0),
+            flow[:, 1:2, :, :] / ((H - 1.0) / 2.0),
+        ],
+        1,
+    )
+    grid_ = (grid + flow_).permute(0, 2, 3, 1)
+    output = F.grid_sample(
+        input=img,
+        grid=grid_,
+        mode="bilinear",
+        padding_mode="border",
+        align_corners=True,
+    )
+    return output

src/utils/padder.py

@@ -0,0 +1,35 @@
+from typing import TYPE_CHECKING
+
+import torch.nn.functional as F
+
+if TYPE_CHECKING:
+    import torch
+
+
+class InputPadder:
+    """Pads images such that dimensions are divisible by divisor"""
+
+    def __init__(self, dims: "torch.Size", divisor=16):
+        self.ht, self.wd = dims[-2:]
+        pad_ht = (((self.ht // divisor) + 1) * divisor - self.ht) % divisor
+        pad_wd = (((self.wd // divisor) + 1) * divisor - self.wd) % divisor
+        self._pad = [
+            pad_wd // 2,
+            pad_wd - pad_wd // 2,
+            pad_ht // 2,
+            pad_ht - pad_ht // 2,
+        ]
+
+    def pad(self, *inputs: "torch.Tensor"):
+        if len(inputs) == 1:
+            return F.pad(inputs[0], self._pad, mode="replicate")
+        else:
+            return [F.pad(x, self._pad, mode="replicate") for x in inputs]
+
+    def unpad(self, *inputs: "torch.Tensor"):
+        return [self._unpad(x) for x in inputs]
+
+    def _unpad(self, x: "torch.Tensor"):
+        ht, wd = x.shape[-2:]
+        c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
+        return x[..., c[0] : c[1], c[2] : c[3]]

src/utils/torch.py

@@ -0,0 +1,56 @@
+import logging
+
+import torch
+import numpy as np
+
+
+def tensor2img(tensor: torch.Tensor):
+    return (
+        (tensor * 255.0)
+        .detach()
+        .squeeze(0)
+        .permute(1, 2, 0)
+        .cpu()
+        .numpy()
+        .clip(0, 255)
+        .astype(np.uint8)
+    )
+
+
+def img2tensor(img: np.ndarray) -> torch.Tensor:
+    logging.debug(f"Converting image of shape {img.shape} to tensor")
+    if img.shape[-1] > 3:
+        img = img[:, :, :3]
+    return torch.tensor(img).permute(2, 0, 1).unsqueeze(0) / 255.0
+
+
+def check_dim_and_resize(*args: torch.Tensor) -> list[torch.Tensor]:
+    logging.debug("Checking dimensions of input tensors")
+    shape_list = []
+    result = list(args)
+    for t in args:
+        logging.debug(f"Tensor shape: {t.shape}")
+        shape_list.append(t.shape[2:])
+
+    if len(set(shape_list)) > 1:
+        logging.warning(
+            "Inconsistent tensor shapes detected. Resizing tensors to the same shape."
+        )
+        desired_shape = shape_list[0]
+        logging.info(
+            f"Inconsistent size of input video frames. All frames will be resized to {desired_shape}"
+        )
+
+        resize_tensor_list = []
+        for t in args:
+            resize_tensor_list.append(
+                torch.nn.functional.interpolate(
+                    input=t,
+                    size=tuple(desired_shape),
+                    mode="bilinear",
+                )
+            )
+
+        result = resize_tensor_list
+
+    return result

src/utils/utils.py

@@ -0,0 +1,199 @@
+import re
+import sys
+from pathlib import Path
+
+import numpy as np
+from imageio import imread, imwrite
+
+
+def read(file: Path) -> np.ndarray:
+    readers = {
+        ".float3": readFloat,
+        ".flo": readFlow,
+        ".ppm": readImage,
+        ".pgm": readImage,
+        ".png": readImage,
+        ".jpg": readImage,
+        ".pfm": lambda f: readPFM(f)[0],
+    }
+    func = readers.get(file.suffix.lower())
+    if func is None:
+        raise Exception("don't know how to read %s" % file)
+    return func(file)
+
+
+def write(file: Path, data: np.ndarray) -> None:
+    writers = {
+        ".float3": writeFloat,
+        ".flo": writeFlow,
+        ".ppm": writeImage,
+        ".pgm": writeImage,
+        ".png": writeImage,
+        ".jpg": writeImage,
+        ".pfm": writePFM,
+    }
+    func = writers.get(file.suffix.lower())
+    if func is None:
+        raise Exception("don't know how to write %s" % file)
+    return func(file, data)
+
+
+def readPFM(file: Path):
+    data = open(file, "rb")
+
+    color = None
+    width = None
+    height = None
+    scale = None
+    endian = None
+
+    header = data.readline().rstrip()
+    if header.decode("ascii") == "PF":
+        color = True
+    elif header.decode("ascii") == "Pf":
+        color = False
+    else:
+        raise Exception("Not a PFM file.")
+
+    dim_match = re.match(r"^(\d+)\s(\d+)\s$", data.readline().decode("ascii"))
+    if dim_match:
+        width, height = list(map(int, dim_match.groups()))
+    else:
+        raise Exception("Malformed PFM header.")
+
+    scale = float(data.readline().decode("ascii").rstrip())
+    if scale < 0:
+        endian = "<"
+        scale = -scale
+    else:
+        endian = ">"
+
+    result = np.fromfile(data, endian + "f")
+    shape = (height, width, 3) if color else (height, width)
+
+    result = np.reshape(result, shape)
+    result = np.flipud(result)
+    return result, scale
+
+
+def writePFM(file: Path, image: np.ndarray, scale=1):
+    data = open(file, "wb")
+
+    color = None
+
+    if image.dtype.name != "float32":
+        raise Exception("Image dtype must be float32.")
+
+    image = np.flipud(image)
+
+    if len(image.shape) == 3 and image.shape[2] == 3:
+        color = True
+    elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1:
+        color = False
+    else:
+        raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+    data.write("PF\n" if color else "Pf\n".encode())  # type: ignore
+    data.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+    endian = image.dtype.byteorder
+
+    if endian == "<" or endian == "=" and sys.byteorder == "little":
+        scale = -scale
+
+    data.write("%f\n".encode() % scale)
+
+    image.tofile(data)
+
+
+def readFlow(file: Path):
+    if file.suffix.lower() == ".pfm":
+        return readPFM(file)[0][:, :, 0:2]
+
+    f = open(file, "rb")
+
+    header = f.read(4)
+    if header.decode("utf-8") != "PIEH":
+        raise Exception("Flow file header does not contain PIEH")
+
+    width = np.fromfile(f, np.int32, 1).squeeze()
+    height = np.fromfile(f, np.int32, 1).squeeze()
+
+    flow = np.fromfile(f, np.float32, width * height * 2).reshape((height, width, 2))
+
+    return flow.astype(np.float32)
+
+
+def readImage(file: Path):
+    if file.suffix.lower() == ".pfm":
+        data = readPFM(file)[0]
+        if len(data.shape) == 3:
+            return data[:, :, 0:3]
+        else:
+            return data
+    return imread(file)
+
+
+def writeImage(file: Path, data: np.ndarray):
+    if file.suffix.lower() == ".pfm":
+        return writePFM(file, data, 1)
+    return imwrite(file, data)
+
+
+def writeFlow(file: Path, flow: np.ndarray):
+    f = open(file, "wb")
+    f.write("PIEH".encode("utf-8"))
+    np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
+    flow = flow.astype(np.float32)
+    flow.tofile(f)
+
+
+def readFloat(file: Path):
+    f = open(file, "rb")
+
+    if (f.readline().decode("utf-8")) != "float\n":
+        raise Exception("float file %s did not contain <float> keyword" % file)
+
+    dim = int(f.readline())
+
+    dims = []
+    count = 1
+    for _ in range(0, dim):
+        d = int(f.readline())
+        dims.append(d)
+        count *= d
+
+    dims = list(reversed(dims))
+
+    data = np.fromfile(f, np.float32, count).reshape(dims)
+    if dim > 2:
+        data = np.transpose(data, (2, 1, 0))
+        data = np.transpose(data, (1, 0, 2))
+
+    return data
+
+
+def writeFloat(file: Path, data: np.ndarray):
+    f = open(file, "wb")
+
+    dim = len(data.shape)
+    if dim > 3:
+        raise Exception("bad float file dimension: %d" % dim)
+
+    f.write(("float\n").encode("ascii"))
+    f.write(("%d\n" % dim).encode("ascii"))
+
+    if dim == 1:
+        f.write(("%d\n" % data.shape[0]).encode("ascii"))
+    else:
+        f.write(("%d\n" % data.shape[1]).encode("ascii"))
+        f.write(("%d\n" % data.shape[0]).encode("ascii"))
+        for i in range(2, dim):
+            f.write(("%d\n" % data.shape[i]).encode("ascii"))
+
+    data = data.astype(np.float32)
+    if dim == 2:
+        data.tofile(f)
+
+    else:
+        np.transpose(data, (2, 0, 1)).tofile(f)

uv.lock

@@ -0,0 +1,649 @@
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+revision = 1
+requires-python = ">=3.12"
+
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+    { name = "imageio" },
+    { name = "numpy" },
+    { name = "omegaconf" },
+    { name = "torch" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "imageio", specifier = ">=2.37.3" },
+    { name = "numpy", specifier = ">=2.4.4" },
+    { name = "omegaconf", specifier = ">=2.3.0" },
+    { name = "torch", specifier = ">=2.11.0" },
+]
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