things

main.py

@@ -1,3 +1,4 @@
+import math
 import os
 
 import torch
@@ -12,17 +13,43 @@ from src.dataset.preprocess import denormalize
 from src.model.utransformer import UTransformer
 from src.rf import RF
 
+train_dataset, test_dataset = get_dataset()
+
 device = "cuda:1"
 
+batch_size = 8 * 4
+accumulation_steps = 4
+total_epoch = 500
+
+steps_per_epoch = len(train_dataset) // batch_size
+total_steps = steps_per_epoch * total_epoch
+warmup_steps = int(0.05 * total_steps)
+
+grad_norm = 1.0
+
+
 model = UTransformer.from_pretrained_backbone(
     "facebook/dinov3-vitl16-pretrain-sat493m"
 ).to(device)
-rf = RF(model)
+rf = RF(model, "icfm", "lpips_mse")
-optimizer = optim.AdamW(model.parameters(), lr=1e-4)
+optimizer = optim.AdamW(model.parameters(), lr=3e-4)
 
-train_dataset, test_dataset = get_dataset()
 
-wandb.init(project="cloud-removal-kmu", id="dashing-moon-31", resume="allow")
+# scheduler
+def get_lr(step: int) -> float:
+    if step < warmup_steps:
+        return step / warmup_steps
+    else:
+        progress = (step - warmup_steps) / (total_steps - warmup_steps)
+        return 0.5 * (1 + math.cos(math.pi * progress))
+
+
+scheduler = optim.lr_scheduler.LambdaLR(optimizer, get_lr)
+
+wandb.init(project="cloud-removal-kmu", resume="allow")
+
+# phase 2
+# model.requires_grad_(True)
 
 if not (wandb.run and wandb.run.name):
     raise Exception("nope")
@@ -35,11 +62,11 @@ if os.path.exists(checkpoint_path):
     checkpoint = torch.load(checkpoint_path, map_location=device)
     model.load_state_dict(checkpoint["model_state_dict"])
     optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+    if "scheduler_state_dict" in checkpoint:
+        scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
     start_epoch = checkpoint["epoch"] + 1
 
-batch_size = 8
+
-accumulation_steps = 8
-total_epoch = 1000
 for epoch in range(start_epoch, total_epoch):
     lossbin = {i: 0 for i in range(10)}
     losscnt = {i: 1e-6 for i in range(10)}
@@ -54,15 +81,31 @@ for epoch in range(start_epoch, total_epoch):
         cloud = batch["cloud"].to(device)
         gt = batch["gt"].to(device)
 
-        loss, blsct = rf.forward(gt, cloud)
+        loss, blsct, loss_list = rf.forward(gt, cloud)
         loss = loss / accumulation_steps
         loss.backward()
 
         if (i // batch_size + 1) % accumulation_steps == 0:
+            # total_norm = torch.nn.utils.clip_grad_norm_(
+            #     model.parameters(), max_norm=grad_norm
+            # )
             optimizer.step()
+            scheduler.step()
             optimizer.zero_grad()
 
-        wandb.log({"train/loss": loss.item() * accumulation_steps})
+            # wandb.log(
+            #     {
+            #         "train/grad_norm": total_norm.item(),
+            #     }
+            # )
+
+        wandb.log(
+            {
+                "train/loss": loss.item() * accumulation_steps,
+                "train/lr": scheduler.get_last_lr()[0],
+            }
+        )
+        wandb.log(loss_list)
 
         for t, lss in blsct:
             bin_idx = min(int(t * 10), 9)
@@ -70,14 +113,23 @@ for epoch in range(start_epoch, total_epoch):
             losscnt[bin_idx] += 1
 
     if (len(range(0, len(train_dataset), batch_size)) % accumulation_steps) != 0:
+        # total_norm = torch.nn.utils.clip_grad_norm_(
+        #     model.parameters(), max_norm=grad_norm
+        # )
         optimizer.step()
+        scheduler.step()
         optimizer.zero_grad()
+        # wandb.log(
+        #     {
+        #         "train/grad_norm": total_norm.item(),
+        #     }
+        # )
 
     epoch_metrics = {f"lossbin/lossbin_{i}": lossbin[i] / losscnt[i] for i in range(10)}
     epoch_metrics["epoch"] = epoch
     wandb.log(epoch_metrics)
 
-    if (epoch + 1) % 10 == 0:
+    if (epoch + 1) % 50 == 0:
         rf.model.eval()
         psnr_sum = 0
         ssim_sum = 0
@@ -90,7 +142,7 @@ for epoch in range(start_epoch, total_epoch):
                 desc=f"Benchmark {epoch + 1}/{total_epoch}",
             ):
                 batch = test_dataset[i : i + batch_size]
-                images = rf.sample_heun(batch["cloud"].to(device))
+                images = rf.sample(batch["cloud"].to(device))
                 image = denormalize(images[-1]).clamp(0, 1)
                 original = denormalize(batch["gt"]).clamp(0, 1)
 
@@ -128,24 +180,27 @@ for epoch in range(start_epoch, total_epoch):
                 "epoch": epoch,
                 "model_state_dict": model.state_dict(),
                 "optimizer_state_dict": optimizer.state_dict(),
+                "scheduler_state_dict": scheduler.state_dict(),
             },
             f"artifact/{wandb.run.name}/checkpoint_epoch_{epoch + 1}.pt",
         )
 
-    torch.save(
+        torch.save(
-        {
+            {
-            "epoch": epoch,
+                "epoch": epoch,
-            "model_state_dict": model.state_dict(),
+                "model_state_dict": model.state_dict(),
-            "optimizer_state_dict": optimizer.state_dict(),
+                "optimizer_state_dict": optimizer.state_dict(),
-        },
+                "scheduler_state_dict": scheduler.state_dict(),
-        checkpoint_path,
+            },
-    )
+            checkpoint_path,
+        )
 
 torch.save(
     {
         "epoch": epoch,  # type: ignore
         "model_state_dict": model.state_dict(),
         "optimizer_state_dict": optimizer.state_dict(),
+        "scheduler_state_dict": scheduler.state_dict(),
     },
     f"artifact/{wandb.run.name}/checkpoint_final.pt",
 )

pyproject.toml

@@ -10,9 +10,12 @@ dependencies = [
     "lpips>=0.1.4",
     "pyright>=1.1.405",
     "python-lsp-server>=1.13.1",
+    "pytorch-msssim>=1.0.0",
     "ruff>=0.13.2",
     "safetensors>=0.6.2",
     "torch>=2.8.0",
+    "torchcfm>=1.0.7",
+    "torchdiffeq>=0.2.5",
     "torcheval>=0.0.7",
     "torchmetrics>=1.8.2",
     "torchvision>=0.23.0",

src/dataset/cuhk_cr2.py

@@ -4,7 +4,7 @@ from pathlib import Path
 from datasets import Dataset, DatasetDict, Image
 from src.dataset.preprocess import make_transform
 
-transform = make_transform(512)
+transform = make_transform(256)
 
 
 def get_dataset() -> tuple[Dataset, Dataset]:

src/dataset/preprocess.py

@@ -18,3 +18,9 @@ def denormalize(tensor: torch.Tensor) -> torch.Tensor:
     mean = torch.tensor([0.430, 0.411, 0.296]).view(3, 1, 1).to(tensor.device)
     std = torch.tensor([0.213, 0.156, 0.143]).view(3, 1, 1).to(tensor.device)
     return tensor * std + mean
+
+
+def normalize(tensor: torch.Tensor) -> torch.Tensor:
+    mean = torch.tensor([0.430, 0.411, 0.296]).view(3, 1, 1).to(tensor.device)
+    std = torch.tensor([0.213, 0.156, 0.143]).view(3, 1, 1).to(tensor.device)
+    return (tensor - mean) / std

src/gan.py

@@ -0,0 +1,154 @@
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+from torchvision import models
+
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        self.register_buffer(
+            "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None]
+        )
+        self.register_buffer(
+            "scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None]
+        )
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple(
+            "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]
+        )
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+        return out
+
+
+class PatchDiscriminator(nn.Module):
+    def __init__(self):
+        super(PatchDiscriminator, self).__init__()
+        self.scaling_layer = ScalingLayer()
+
+        _vgg = models.vgg16(pretrained=True)
+
+        self.slice1 = nn.Sequential(_vgg.features[:4])
+        self.slice2 = nn.Sequential(_vgg.features[4:9])
+        self.slice3 = nn.Sequential(_vgg.features[9:16])
+        self.slice4 = nn.Sequential(_vgg.features[16:23])
+        self.slice5 = nn.Sequential(_vgg.features[23:30])
+
+        self.binary_classifier1 = nn.Sequential(
+            nn.Conv2d(64, 32, kernel_size=4, stride=4, padding=0, bias=True),
+            nn.ReLU(),
+            nn.Conv2d(32, 1, kernel_size=4, stride=4, padding=0, bias=True),
+        )
+        nn.init.zeros_(self.binary_classifier1[-1].weight)
+
+        self.binary_classifier2 = nn.Sequential(
+            nn.Conv2d(128, 64, kernel_size=4, stride=4, padding=0, bias=True),
+            nn.ReLU(),
+            nn.Conv2d(64, 1, kernel_size=2, stride=2, padding=0, bias=True),
+        )
+        nn.init.zeros_(self.binary_classifier2[-1].weight)
+
+        self.binary_classifier3 = nn.Sequential(
+            nn.Conv2d(256, 128, kernel_size=2, stride=2, padding=0, bias=True),
+            nn.ReLU(),
+            nn.Conv2d(128, 1, kernel_size=2, stride=2, padding=0, bias=True),
+        )
+        nn.init.zeros_(self.binary_classifier3[-1].weight)
+
+        self.binary_classifier4 = nn.Sequential(
+            nn.Conv2d(512, 1, kernel_size=2, stride=2, padding=0, bias=True),
+        )
+        nn.init.zeros_(self.binary_classifier4[-1].weight)
+
+        self.binary_classifier5 = nn.Sequential(
+            nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0, bias=True),
+        )
+        nn.init.zeros_(self.binary_classifier5[-1].weight)
+
+    def forward(self, x):
+        x = self.scaling_layer(x)
+        features1 = self.slice1(x)
+        features2 = self.slice2(features1)
+        features3 = self.slice3(features2)
+        features4 = self.slice4(features3)
+        features5 = self.slice5(features4)
+
+        # torch.Size([1, 64, 256, 256]) torch.Size([1, 128, 128, 128]) torch.Size([1, 256, 64, 64]) torch.Size([1, 512, 32, 32]) torch.Size([1, 512, 16, 16])
+
+        bc1 = self.binary_classifier1(features1).flatten(1)
+        bc2 = self.binary_classifier2(features2).flatten(1)
+        bc3 = self.binary_classifier3(features3).flatten(1)
+        bc4 = self.binary_classifier4(features4).flatten(1)
+        bc5 = self.binary_classifier5(features5).flatten(1)
+
+        return bc1 + bc2 + bc3 + bc4 + bc5
+
+
+def gan_disc_loss(real_preds, fake_preds, disc_type="bce"):
+    if disc_type == "bce":
+        real_loss = nn.functional.binary_cross_entropy_with_logits(
+            real_preds, torch.ones_like(real_preds)
+        )
+        fake_loss = nn.functional.binary_cross_entropy_with_logits(
+            fake_preds, torch.zeros_like(fake_preds)
+        )
+        # eval its online performance
+        avg_real_preds = real_preds.mean().item()
+        avg_fake_preds = fake_preds.mean().item()
+
+        with torch.no_grad():
+            acc = (real_preds > 0).sum().item() + (fake_preds < 0).sum().item()
+            acc = acc / (real_preds.numel() + fake_preds.numel())
+
+    if disc_type == "hinge":
+        real_loss = nn.functional.relu(1 - real_preds).mean()
+        fake_loss = nn.functional.relu(1 + fake_preds).mean()
+
+        with torch.no_grad():
+            acc = (real_preds > 0).sum().item() + (fake_preds < 0).sum().item()
+            acc = acc / (real_preds.numel() + fake_preds.numel())
+
+        avg_real_preds = real_preds.mean().item()
+        avg_fake_preds = fake_preds.mean().item()
+
+    return (real_loss + fake_loss) * 0.5, avg_real_preds, avg_fake_preds, acc  # type: ignore

src/model/dino.py

@@ -419,6 +419,7 @@ class DINOv3ViTModel(nn.Module):
         hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
         position_embeddings = self.rope_embeddings(pixel_values)
 
+        latents = []
         for i, layer_module in enumerate(self.layers):
             layer_head_mask = head_mask[i] if head_mask is not None else None
             hidden_states = layer_module(
@@ -426,11 +427,13 @@ class DINOv3ViTModel(nn.Module):
                 attention_mask=layer_head_mask,
                 position_embeddings=position_embeddings,
             )
+            latents.append(hidden_states)
 
         sequence_output = self.norm(hidden_states)
         pooled_output = sequence_output[:, 0, :]
 
         return {
             "last_hidden_state": sequence_output,
+            "latents": latents,
             "pooler_output": pooled_output,
         }

src/model/hourglass.py

@@ -0,0 +1,144 @@
+from typing import Optional
+
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors import safe_open
+from torch import nn
+from transformers.models.dinov3_vit.configuration_dinov3_vit import DINOv3ViTConfig
+
+from src.model.dino import (
+    DINOv3ViTEmbeddings,
+    DINOv3ViTRopePositionEmbedding,
+)
+from src.model.utransformer import DinoConditionedLayer, TimestepEmbedder
+
+
+class Hourgrass(nn.Module):
+    def __init__(
+        self, config: DINOv3ViTConfig, num_classes: int, scale_factor: int = 4
+    ):
+        super().__init__()
+        self.config = config
+        self.scale_factor = scale_factor
+
+        assert config.num_hidden_layers % scale_factor == 0
+
+        self.embeddings = DINOv3ViTEmbeddings(config)
+        self.rope_embeddings = DINOv3ViTRopePositionEmbedding(config)
+        self.t_embedder = TimestepEmbedder(config.hidden_size)
+
+        self.encoder_layers = nn.ModuleList(
+            [
+                DinoConditionedLayer(config, True)
+                for _ in range(config.num_hidden_layers)
+            ]
+        )
+        self.encoder_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        # freeze pretrained
+        self.embeddings.requires_grad_(False)
+        self.rope_embeddings.requires_grad_(False)
+        self.encoder_norm.requires_grad_(False)
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        time: torch.Tensor,
+        # cond: torch.Tensor,
+        bool_masked_pos: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+    ):
+        if time.dim() == 0:
+            time = time.repeat(pixel_values.shape[0])
+
+        pixel_values = pixel_values.to(self.embeddings.patch_embeddings.weight.dtype)
+        position_embeddings = self.rope_embeddings(pixel_values)
+
+        x = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
+        t = self.t_embedder(time).unsqueeze(1)
+
+        conditioning_input = t.to(x.dtype)
+
+        residual = []
+        for i, layer_module in enumerate(self.encoder_layers):
+            if i % self.scale_factor == 0:
+                residual.append(x)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            x = layer_module(
+                x,
+                conditioning_input=conditioning_input,
+                attention_mask=layer_head_mask,
+                position_embeddings=position_embeddings,
+            )
+
+        x = self.encoder_norm(x)
+
+        reversed_residual = residual[::-1]
+        for i, layer_module in enumerate(self.decoder_layers):
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            x = layer_module(
+                x,
+                conditioning_input=conditioning_input,
+                attention_mask=layer_head_mask,
+                position_embeddings=position_embeddings,
+            )
+            x = x + reversed_residual[i]
+
+        x = self.decoder_norm(x)
+
+        return self.decoder(x, image_size=pixel_values.shape[-2:]), residual
+
+    def get_residual(
+        self,
+        pixel_values: torch.Tensor,
+        time: Optional[torch.Tensor],
+        do_condition: bool,
+    ):
+        pixel_values = pixel_values.to(self.embeddings.patch_embeddings.weight.dtype)
+        position_embeddings = self.rope_embeddings(pixel_values)
+
+        x = self.embeddings(pixel_values, bool_masked_pos=None)
+
+        if do_condition:
+            t = self.t_embedder(time).unsqueeze(1)
+            # y = self.y_embedder(cond, self.training).unsqueeze(1)
+            # conditioning_input = t.to(x.dtype) + y.to(x.dtype)
+            conditioning_input = t.to(x.dtype)
+        else:
+            conditioning_input = None
+
+        residual = []
+        for i, layer_module in enumerate(self.encoder_layers):
+            if i % self.scale_factor == 0:
+                residual.append(x)
+
+            x = layer_module(
+                x,
+                conditioning_input=conditioning_input,
+                attention_mask=None,
+                position_embeddings=position_embeddings,
+                do_condition=do_condition,
+            )
+
+        return residual
+
+    @staticmethod
+    def from_pretrained_backbone(name: str):
+        config = DINOv3ViTConfig.from_pretrained(name)
+        instance = UTransformer(config, 0).to("cuda:1")
+
+        weight_dict = {}
+        with safe_open(
+            hf_hub_download(name, "model.safetensors"), framework="pt", device="cuda:1"
+        ) as f:
+            for key in f.keys():
+                new_key = key.replace("layer.", "encoder_layers.").replace(
+                    "norm.", "encoder_norm."
+                )
+
+                weight_dict[new_key] = f.get_tensor(key)
+
+        instance.load_state_dict(weight_dict, strict=False)
+
+        return instance

src/model/utransformer.py

@@ -188,30 +188,83 @@ class DinoV3ViTDecoder(nn.Module):
 
         self.projection = nn.Linear(
             config.hidden_size,
-            self.num_channels_out * (self.patch_size**2),
+            config.num_channels * (self.patch_size**2),
             bias=True,
         )
-
         self.pixel_shuffle = nn.PixelShuffle(self.patch_size)
+
         nn.init.zeros_(self.projection.weight)
-        nn.init.zeros_(self.projection.bias)
+        nn.init.zeros_(
+            self.projection.bias
+        ) if self.projection.bias is not None else None
 
     def forward(self, x: torch.Tensor, image_size: tuple[int, int]) -> torch.Tensor:
         batch_size = x.shape[0]
 
         x = x[:, 1 + self.config.num_register_tokens :, :]
 
-        x = self.projection(x)
-
         p = self.config.patch_size
         h_grid = image_size[0] // p
         w_grid = image_size[1] // p
 
         assert x.shape[1] == h_grid * w_grid
 
+        x = self.projection(x)
+
         x = x.reshape(batch_size, h_grid, w_grid, -1).permute(0, 3, 1, 2)
 
-        return self.pixel_shuffle(x)
+        x = self.pixel_shuffle(x)
+
+        return x
+
+
+# how about transposed conv decoderclass DinoV3ViTDecoder(nn.Module):
+# class DinoV3ViTDecoder(nn.Module):
+#     def __init__(self, config: DINOv3ViTConfig):
+#         super().__init__()
+#         self.config = config
+#         self.num_channels_out = config.num_channels
+#         self.patch_size = config.patch_size
+
+#         intermediate_channels = config.hidden_size // 4
+
+#         self.decoder_block = nn.Sequential(
+#             nn.ConvTranspose2d(
+#                 in_channels=config.hidden_size,
+#                 out_channels=intermediate_channels,
+#                 kernel_size=self.patch_size,
+#                 stride=self.patch_size,
+#                 bias=True,
+#             ),
+#             nn.LayerNorm(intermediate_channels),
+#             nn.GELU(),
+#             nn.Conv2d(
+#                 in_channels=intermediate_channels,
+#                 out_channels=config.num_channels,
+#                 kernel_size=1,
+#                 bias=True,
+#             ),
+#         )
+
+#         nn.init.zeros_(self.decoder_block[-1].weight)
+#         nn.init.zeros_(self.decoder_block[-1].bias)
+
+#     def forward(self, x: torch.Tensor, image_size: tuple[int, int]) -> torch.Tensor:
+#         batch_size = x.shape[0]
+
+#         x = x[:, 1 + self.config.num_register_tokens :, :]
+
+#         p = self.config.patch_size
+#         h_grid = image_size[0] // p
+#         w_grid = image_size[1] // p
+#         assert x.shape[1] == h_grid * w_grid
+
+#         x = x.transpose(1, 2).reshape(
+#             batch_size, self.config.hidden_size, h_grid, w_grid
+#         )
+#         x = self.decoder_block(x)
+
+#         return x
 
 
 class UTransformer(nn.Module):
@@ -261,6 +314,9 @@ class UTransformer(nn.Module):
         bool_masked_pos: Optional[torch.Tensor] = None,
         head_mask: Optional[torch.Tensor] = None,
     ):
+        if time.dim() == 0:
+            time = time.repeat(pixel_values.shape[0])
+
         pixel_values = pixel_values.to(self.embeddings.patch_embeddings.weight.dtype)
         position_embeddings = self.rope_embeddings(pixel_values)
 

src/rf.py

@@ -1,117 +1,175 @@
 import lpips
 import torch
+import torch.optim as optim
+from pytorch_msssim import ms_ssim
+from torchcfm.conditional_flow_matching import (
+    ConditionalFlowMatcher,
+    ExactOptimalTransportConditionalFlowMatcher,
+    TargetConditionalFlowMatcher,
+    VariancePreservingConditionalFlowMatcher,
+)
+from torchdiffeq import odeint
 
+import wandb
 from src.dataset.preprocess import denormalize
+from src.gan import PatchDiscriminator, gan_disc_loss
 
-
+lecam_loss_weight = 0.1
-def pseudo_huber_loss(x: torch.Tensor, c=0.00054):
+lecam_anchor_real_logits = 0.0
-    """Loss = sqrt(||x||₂² + c²) - c"""
+lecam_anchor_fake_logits = 0.0
-    d = x.shape[1:].numel()
+lecam_beta = 0.9
-    c = c * (d**0.5)
+use_lecam = True
-    x = torch.linalg.vector_norm(x.flatten(1), ord=2, dim=1)
-    return torch.sqrt(x**2 + c**2) - c
 
 
 class RF:
-    def __init__(self, model, ln=False, ushaped=True, loss_fn="lpips_mse_enhanced"):
+    def __init__(self, model, fm="otcfm", loss="mse"):
         self.model = model
-        self.ln = ln
+        self.loss = loss
-        self.ushaped = ushaped
-        self.loss_fn = loss_fn
 
-        self.lpips = lpips.LPIPS(net="vgg").to("cuda:1") if "lpips" in loss_fn else None
+        sigma = 0.0
-
+        if fm == "otcfm":
-    def forward(self, x0, z1):
+            self.fm = ExactOptimalTransportConditionalFlowMatcher(sigma=sigma)
-        # x0 is gt / z is noise
+        elif fm == "icfm":
-        b = x0.size(0)
+            self.fm = ConditionalFlowMatcher(sigma=sigma)
-        if self.ushaped:
+        elif fm == "fm":
-            a = 4.0  # HYPERPARMS
+            self.fm = TargetConditionalFlowMatcher(sigma=sigma)
-            u = torch.rand((b,), device=x0.device)
+        elif fm == "si":
-            t = torch.acosh(1 + (torch.cosh(torch.tensor(a)) - 1) * u) / a
+            self.fm = VariancePreservingConditionalFlowMatcher(sigma=sigma)
-            t = t * (torch.randint(0, 2, (b,), device=x0.device) * 2 - 1) * 0.5 + 0.5
-        elif self.ln:
-            nt = torch.randn((b,)).to(x0.device)
-            t = torch.sigmoid(nt)
         else:
-            t = torch.rand((b,)).to(x0.device)
+            raise NotImplementedError(
-        texp = t.view([b, *([1] * len(x0.shape[1:]))])
+                f"Unknown model {fm}, must be one of ['otcfm', 'icfm', 'fm', 'si']"
-        zt = (1 - texp) * x0 + texp * z1
+            )
 
-        vtheta, residual = self.model(zt, t)
+        self.lpips = lpips.LPIPS(net="vgg").to("cuda:1")
+        self.lpips2 = lpips.LPIPS(net="alex").to("cuda:1")
 
-        if self.loss_fn == "lpips_huber":
+        discriminator = PatchDiscriminator().to("cuda:1")
-            # https://ar5iv.labs.arxiv.org/html/2405.20320v1 /  (z - x) - v_θ(x_t, t)
+        discriminator.requires_grad_(True)
-            if not self.lpips:
+        self.discriminator = discriminator
-                raise Exception
+        self.optimizer_D = optim.AdamW(
+            discriminator.parameters(),
+            lr=2e-4,
+            weight_decay=1e-3,
+            betas=(0.9, 0.95),
+        )
 
-            huber = torch.nn.functional.huber_loss(
+    def gan_loss(self, real, fake):
-                z1 - x0, vtheta, reduction="none"
+        global lecam_beta, lecam_anchor_real_logits, lecam_anchor_fake_logits, use_lecam
-            ).mean(dim=list(range(1, len(x0.shape))))
+
+        real_preds = self.discriminator(real)
+        fake_preds = self.discriminator(fake.detach())
+        d_loss, avg_real_logits, avg_fake_logits, disc_acc = gan_disc_loss(
+            real_preds, fake_preds, "hinge"
+        )
+
+        lecam_anchor_real_logits = (
+            lecam_beta * lecam_anchor_real_logits + (1 - lecam_beta) * avg_real_logits
+        )
+        lecam_anchor_fake_logits = (
+            lecam_beta * lecam_anchor_fake_logits + (1 - lecam_beta) * avg_fake_logits
+        )
+        total_d_loss = d_loss.mean()
+        d_loss_item = total_d_loss.item()
+        if use_lecam:
+            # penalize the real logits to fake and fake logits to real.
+            lecam_loss = (real_preds - lecam_anchor_fake_logits).pow(2).mean() + (
+                fake_preds - lecam_anchor_real_logits
+            ).pow(2).mean()
+            lecam_loss_item = lecam_loss.item()
+            total_d_loss = total_d_loss + lecam_loss * lecam_loss_weight
+
+            wandb.log(
+                {
+                    "gan/lecam_loss": lecam_loss_item,
+                    "gan/lecam_anchor_real_logits": lecam_anchor_real_logits,
+                    "gan/lecam_anchor_fake_logits": lecam_anchor_fake_logits,
+                }
+            )
+
+        wandb.log(
+            {
+                "gan/discriminator_loss": d_loss_item,
+                "gan/discriminator_accuracy": disc_acc,
+            }
+        )
+
+        self.optimizer_D.zero_grad()
+        total_d_loss.backward(retain_graph=True)
+        self.optimizer_D.step()
+
+    def forward(self, gt, cloud):
+        t, xt, ut = self.fm.sample_location_and_conditional_flow(cloud, gt)  # type: ignore
+        vt, _ = self.model(xt, t)
+
+        if self.loss == "mse":
+            loss = ((vt - ut) ** 2).mean(dim=list(range(1, len(gt.shape))))
+            loss_list = {"train/mse": loss.mean().item()}
+        elif self.loss == "lpips_mse":
+            mse = ((vt - ut) ** 2).mean(dim=list(range(1, len(gt.shape))))
             lpips = self.lpips(
-                denormalize(x0) * 2 - 1, (denormalize(zt - texp * vtheta) * 2 - 1)
+                denormalize(gt) * 2 - 1,
+                denormalize(xt + (1 - t[:, None, None, None]) * vt) * 2 - 1,
             )
-            weight = t.view(-1)
+            loss_list = {
-
+                "train/mse": mse.mean().item(),
-            loss = (1 - weight) * huber + lpips
+                "train/lpips": lpips.mean().item(),
-        elif self.loss_fn == "lpips_mse":
+            }
-            if not self.lpips:
+            loss = mse + lpips * 2.0
-                raise Exception
+        elif self.loss == "gan_lpips_mse":
-
+            self.gan_loss(
+                denormalize(gt),
+                denormalize(xt + (1 - t[:, None, None, None]) * vt),
+            )
+            mse = ((vt - ut) ** 2).mean(dim=list(range(1, len(gt.shape))))
             lpips = self.lpips(
-                denormalize(x0) * 2 - 1, (denormalize(zt - texp * vtheta) * 2 - 1)
+                denormalize(gt) * 2 - 1,
+                denormalize(xt + (1 - t[:, None, None, None]) * vt) * 2 - 1,
             )
-            loss = ((z1 - x0 - vtheta) ** 2).mean(
+            alexlpips = self.lpips2(
-                dim=list(range(1, len(x0.shape)))
+                denormalize(gt) * 2 - 1,
-            ) + 2.0 * lpips
+                denormalize(xt + (1 - t[:, None, None, None]) * vt) * 2 - 1,
-        elif self.loss_fn == "lpips_mse_enhanced":
-            if not self.lpips:
-                raise Exception
-
-            lpips = self.lpips(
-                denormalize(x0) * 2 - 1, (denormalize(zt - texp * vtheta) * 2 - 1)
             )
-            dino_loss = torch.stack(
+            gan = (
-                [
+                -self.discriminator(
-                    (
+                    denormalize(xt + (1 - t[:, None, None, None]) * vt),
-                        1
+                ).mean(-1)
-                        - torch.nn.functional.cosine_similarity(
+                * 0.01
-                            v_residual, x0_residual, dim=-1
-                        )
-                    ).mean(dim=-1)
-                    for v_residual, x0_residual in zip(
-                        residual, self.model.get_residual(x0, None, False)
-                    )
-                ]
-            ).mean(dim=0) * (2 - t.view(-1))
-            loss = (
-                ((z1 - x0 - vtheta) ** 2).mean(dim=list(range(1, len(x0.shape))))
-                + 2.0 * lpips
-                + dino_loss
             )
+            ssim = 1 - ms_ssim(
+                denormalize(gt),
+                denormalize(xt + (1 - t[:, None, None, None]) * vt),
+                data_range=1.0,
+                size_average=False,
+            )
+            loss_list = {
+                "train/mse": mse.mean().item(),
+                "train/lpips": lpips.mean().item(),
+                "train/alexlpips": alexlpips.mean().item(),
+                "train/gan": gan.mean().item(),
+                "train/ssim": ssim.mean().item(),
+            }
+            loss = mse + lpips * 4.0 + gan + alexlpips + ssim
         else:
-            loss = ((z1 - x0 - vtheta) ** 2).mean(dim=list(range(1, len(x0.shape))))
+            raise Exception
 
         tlist = loss.detach().cpu().reshape(-1).tolist()
         ttloss = [(tv, tloss) for tv, tloss in zip(t, tlist)]
 
-        return loss.mean(), ttloss
+        return loss.mean(), ttloss, loss_list
 
     @torch.no_grad()
-    def sample(self, z1, sample_steps=5):
+    def sample(self, cloud, tol=1e-5, integration="dopri5") -> torch.Tensor:
-        b = z1.size(0)
+        t_span = torch.linspace(0, 1, 2, device=cloud.device)
-        dt = 1.0 / sample_steps
+        traj = odeint(
-        dt = torch.tensor([dt] * b).to(z1.device).view([b, *([1] * len(z1.shape[1:]))])
+            lambda t, x: self.model(x, t)[0],
-        images = [z1]
+            cloud,
-        z = z1
+            t_span,
-        for i in range(sample_steps, 0, -1):
+            rtol=tol,
-            t = i / sample_steps
+            atol=tol,
-            t = torch.tensor([t] * b).to(z.device)
+            method=integration,
+        )
 
-            vc, _ = self.model(z, t)
+        return [traj[i] for i in range(traj.shape[0])]  # type: ignore
-
-            z = z - dt * vc
-            images.append(z)
-        return images
 
     @torch.no_grad()
     def sample_heun(self, z1, sample_steps=50):

uv.lock

@@ -6,6 +6,15 @@ resolution-markers = [
     "sys_platform != 'linux'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -88,6 +97,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978, upload-time = "2024-11-30T04:30:14.439Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918, upload-time = "2024-11-30T04:30:10.946Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "25.3.0"
@@ -273,9 +300,12 @@ dependencies = [
     { name = "lpips" },
     { name = "pyright" },
     { name = "python-lsp-server" },
+    { name = "pytorch-msssim" },
     { name = "ruff" },
     { name = "safetensors" },
     { name = "torch" },
+    { name = "torchcfm" },
+    { name = "torchdiffeq" },
     { name = "torcheval" },
     { name = "torchmetrics" },
     { name = "torchvision" },
@@ -291,9 +321,12 @@ requires-dist = [
     { name = "lpips", specifier = ">=0.1.4" },
     { name = "pyright", specifier = ">=1.1.405" },
     { name = "python-lsp-server", specifier = ">=1.13.1" },
+    { name = "pytorch-msssim", specifier = ">=1.0.0" },
     { name = "ruff", specifier = ">=0.13.2" },
     { name = "safetensors", specifier = ">=0.6.2" },
     { name = "torch", specifier = ">=2.8.0" },
+    { name = "torchcfm", specifier = ">=1.0.7" },
+    { name = "torchdiffeq", specifier = ">=0.2.5" },
     { name = "torcheval", specifier = ">=0.0.7" },
     { name = "torchmetrics", specifier = ">=1.8.2" },
     { name = "torchvision", specifier = ">=0.23.0" },
@@ -311,6 +344,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "comm"
+version = "0.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
+]
+
 [[package]]
 name = "contourpy"
 version = "1.3.3"
@@ -377,6 +419,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" },
 ]
 
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
+]
+
 [[package]]
 name = "datasets"
 version = "4.1.1"
@@ -401,6 +452,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f4/c8/09012ac195a0aab58755800d2efdc0e7d5905053509f12cb5d136c911cda/datasets-4.1.1-py3-none-any.whl", hash = "sha256:62e4f6899a36be9ec74a7e759a6951253cc85b3fcfa0a759b0efa8353b149dac", size = 503623, upload-time = "2025-09-18T13:14:25.111Z" },
 ]
 
+[[package]]
+name = "debugpy"
+version = "1.8.17"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/15/ad/71e708ff4ca377c4230530d6a7aa7992592648c122a2cd2b321cf8b35a76/debugpy-1.8.17.tar.gz", hash = "sha256:fd723b47a8c08892b1a16b2c6239a8b96637c62a59b94bb5dab4bac592a58a8e", size = 1644129, upload-time = "2025-09-17T16:33:20.633Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/2b/9d8e65beb2751876c82e1aceb32f328c43ec872711fa80257c7674f45650/debugpy-1.8.17-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:f14467edef672195c6f6b8e27ce5005313cb5d03c9239059bc7182b60c176e2d", size = 2549522, upload-time = "2025-09-17T16:33:38.466Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/78/eb0d77f02971c05fca0eb7465b18058ba84bd957062f5eec82f941ac792a/debugpy-1.8.17-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:24693179ef9dfa20dca8605905a42b392be56d410c333af82f1c5dff807a64cc", size = 4309417, upload-time = "2025-09-17T16:33:41.299Z" },
+    { url = "https://files.pythonhosted.org/packages/37/42/c40f1d8cc1fed1e75ea54298a382395b8b937d923fcf41ab0797a554f555/debugpy-1.8.17-cp312-cp312-win32.whl", hash = "sha256:6a4e9dacf2cbb60d2514ff7b04b4534b0139facbf2abdffe0639ddb6088e59cf", size = 5277130, upload-time = "2025-09-17T16:33:43.554Z" },
+    { url = "https://files.pythonhosted.org/packages/72/22/84263b205baad32b81b36eac076de0cdbe09fe2d0637f5b32243dc7c925b/debugpy-1.8.17-cp312-cp312-win_amd64.whl", hash = "sha256:e8f8f61c518952fb15f74a302e068b48d9c4691768ade433e4adeea961993464", size = 5319053, upload-time = "2025-09-17T16:33:53.033Z" },
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