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Edit2Perceive/utils/visualize.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+def prepare_image(tensor):
+    """把tensor处理成matplotlib能显示的格式 (H,W,C) or (H,W)"""
+    if torch.is_tensor(tensor):
+        tensor = tensor.detach().cpu()
+    # 如果是 (C,H,W)，转为 (H,W,C)
+    # 如果是 (B,C,H,W)，取第一个样本，转为 (H,W,C)
+    if tensor.ndim == 4:
+        tensor = tensor[0]
+    if tensor.ndim == 3 and tensor.shape[0]>3:
+        # (C,H,W) but C>3, 取前3通道
+        tensor = tensor[:3]
+    if tensor.ndim == 3:
+        # 归一化 -1~1 -> 0~1
+        if tensor.min() < -0.1 and tensor.max() > 1.1:
+            tensor = (tensor + 1) / 2
+            tensor = tensor.clamp(0, 1)
+        tensor = tensor.permute(1, 2, 0)  # (H,W,C)
+    elif tensor.ndim == 2:
+        # mask, 0~1之间
+        if tensor.min() < 0.05 and tensor.min()>-0.05:
+            tensor = tensor.clamp(0, 1)
+        tensor = tensor
+    return (tensor.numpy()*255).astype(np.uint8)
+def visualize_sample(sample, figsize=(12, 4)):
+    """
+    智能可视化函数
+    sample: dict, 包含 'kontext_images', 'image', 'mask'
+    """
+
+    kontext = prepare_image(sample["kontext_images"].to(torch.float32))
+    image   = prepare_image(sample["image"].to(torch.float32))
+    mask    = prepare_image(sample["mask"].to(torch.float32)) if "mask" in sample else None
+    print(f"kontext: {type(kontext)}, shape: {kontext.shape}, dtype: {kontext.dtype}")
+    print(f"image: {type(image)}, shape: {image.shape}, dtype: {image.dtype}")
+
+    fig, axs = plt.subplots(1, 3, figsize=figsize)
+    axs[0].imshow(kontext)
+    axs[0].set_title("kontext_images")
+    axs[0].axis("off")
+
+    axs[1].imshow(image)
+    axs[1].set_title("image")
+    axs[1].axis("off")
+
+    axs[2].imshow((mask).astype(np.uint8), cmap="gray")
+    axs[2].set_title("mask")
+    axs[2].axis("off")
+
+    plt.tight_layout()
+    plt.savefig("tmp.png")
+

Edit2Perceive/vram_management/__init__.py

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+from vram_management.layers import *
+from vram_management.gradient_checkpointing import *

Edit2Perceive/vram_management/gradient_checkpointing.py

@@ -0,0 +1,34 @@
+import torch
+
+
+def create_custom_forward(module):
+    def custom_forward(*inputs, **kwargs):
+        return module(*inputs, **kwargs)
+    return custom_forward
+
+
+def gradient_checkpoint_forward(
+    model,
+    use_gradient_checkpointing,
+    use_gradient_checkpointing_offload,
+    *args,
+    **kwargs,
+):
+    if use_gradient_checkpointing_offload:
+        with torch.autograd.graph.save_on_cpu():
+            model_output = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(model),
+                *args,
+                **kwargs,
+                use_reentrant=False,
+            )
+    elif use_gradient_checkpointing:
+        model_output = torch.utils.checkpoint.checkpoint(
+            create_custom_forward(model),
+            *args,
+            **kwargs,
+            use_reentrant=False,
+        )
+    else:
+        model_output = model(*args, **kwargs)
+    return model_output

Edit2Perceive/vram_management/layers.py

@@ -0,0 +1,213 @@
+import torch, copy
+from models.utils import init_weights_on_device
+
+
+def cast_to(weight, dtype, device):
+    r = torch.empty_like(weight, dtype=dtype, device=device)
+    r.copy_(weight)
+    return r
+
+
+class AutoTorchModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+    def check_free_vram(self):
+        gpu_mem_state = torch.cuda.mem_get_info(self.computation_device)
+        used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024 ** 3)
+        return used_memory < self.vram_limit
+
+    def offload(self):
+        if self.state != 0:
+            self.to(dtype=self.offload_dtype, device=self.offload_device)
+            self.state = 0
+
+    def onload(self):
+        if self.state != 1:
+            self.to(dtype=self.onload_dtype, device=self.onload_device)
+            self.state = 1
+            
+    def keep(self):
+        if self.state != 2:
+            self.to(dtype=self.computation_dtype, device=self.computation_device)
+            self.state = 2
+
+
+class AutoWrappedModule(AutoTorchModule):
+    def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs):
+        super().__init__()
+        self.module = module.to(dtype=offload_dtype, device=offload_device)
+        self.offload_dtype = offload_dtype
+        self.offload_device = offload_device
+        self.onload_dtype = onload_dtype
+        self.onload_device = onload_device
+        self.computation_dtype = computation_dtype
+        self.computation_device = computation_device
+        self.vram_limit = vram_limit
+        self.state = 0
+
+    def forward(self, *args, **kwargs):
+        if self.state == 2:
+            module = self.module
+        else:
+            if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device:
+                module = self.module
+            elif self.vram_limit is not None and self.check_free_vram():
+                self.keep()
+                module = self.module
+            else:
+                module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device)
+        return module(*args, **kwargs)
+    
+
+class WanAutoCastLayerNorm(torch.nn.LayerNorm, AutoTorchModule):
+    def __init__(self, module: torch.nn.LayerNorm, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs):
+        with init_weights_on_device(device=torch.device("meta")):
+            super().__init__(module.normalized_shape, eps=module.eps, elementwise_affine=module.elementwise_affine, bias=module.bias is not None, dtype=offload_dtype, device=offload_device)
+        self.weight = module.weight
+        self.bias = module.bias
+        self.offload_dtype = offload_dtype
+        self.offload_device = offload_device
+        self.onload_dtype = onload_dtype
+        self.onload_device = onload_device
+        self.computation_dtype = computation_dtype
+        self.computation_device = computation_device
+        self.vram_limit = vram_limit
+        self.state = 0
+
+    def forward(self, x, *args, **kwargs):
+        if self.state == 2:
+            weight, bias = self.weight, self.bias
+        else:
+            if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device:
+                weight, bias = self.weight, self.bias
+            elif self.vram_limit is not None and self.check_free_vram():
+                self.keep()
+                weight, bias = self.weight, self.bias
+            else:
+                weight = None if self.weight is None else cast_to(self.weight, self.computation_dtype, self.computation_device)
+                bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device)
+        with torch.amp.autocast(device_type=x.device.type):
+            x = torch.nn.functional.layer_norm(x.float(), self.normalized_shape, weight, bias, self.eps).type_as(x)
+        return x
+    
+
+class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
+    def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, name="", **kwargs):
+        with init_weights_on_device(device=torch.device("meta")):
+            super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device)
+        self.weight = module.weight
+        self.bias = module.bias
+        self.offload_dtype = offload_dtype
+        self.offload_device = offload_device
+        self.onload_dtype = onload_dtype
+        self.onload_device = onload_device
+        self.computation_dtype = computation_dtype
+        self.computation_device = computation_device
+        self.vram_limit = vram_limit
+        self.state = 0
+        self.name = name
+        self.lora_A_weights = []
+        self.lora_B_weights = []
+        self.lora_merger = None
+        self.enable_fp8 = computation_dtype in [torch.float8_e4m3fn, torch.float8_e4m3fnuz]
+        
+    def fp8_linear(
+        self,
+        input: torch.Tensor,
+        weight: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = input.device
+        origin_dtype = input.dtype
+        origin_shape = input.shape
+        input = input.reshape(-1, origin_shape[-1])
+
+        x_max = torch.max(torch.abs(input), dim=-1, keepdim=True).values
+        fp8_max = 448.0
+        # For float8_e4m3fnuz, the maximum representable value is half of that of e4m3fn.
+        # To avoid overflow and ensure numerical compatibility during FP8 computation,
+        # we scale down the input by 2.0 in advance.
+        # This scaling will be compensated later during the final result scaling.
+        if self.computation_dtype == torch.float8_e4m3fnuz:
+            fp8_max = fp8_max / 2.0
+        scale_a = torch.clamp(x_max / fp8_max, min=1.0).float().to(device=device)
+        scale_b = torch.ones((weight.shape[0], 1)).to(device=device)
+        input = input / (scale_a + 1e-8)
+        input = input.to(self.computation_dtype)
+        weight = weight.to(self.computation_dtype)
+        bias = bias.to(torch.bfloat16)
+
+        result = torch._scaled_mm(
+            input,
+            weight.T,
+            scale_a=scale_a,
+            scale_b=scale_b.T,
+            bias=bias,
+            out_dtype=origin_dtype,
+        )
+        new_shape = origin_shape[:-1] + result.shape[-1:]
+        result = result.reshape(new_shape)
+        return result
+
+    def forward(self, x, *args, **kwargs):
+        # VRAM management
+        if self.state == 2:
+            weight, bias = self.weight, self.bias
+        else:
+            if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device:
+                weight, bias = self.weight, self.bias
+            elif self.vram_limit is not None and self.check_free_vram():
+                self.keep()
+                weight, bias = self.weight, self.bias
+            else:
+                weight = cast_to(self.weight, self.computation_dtype, self.computation_device)
+                bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device)
+        
+        # Linear forward
+        if self.enable_fp8:
+            out = self.fp8_linear(x, weight, bias)
+        else:
+            out = torch.nn.functional.linear(x, weight, bias)
+        
+        # LoRA
+        if len(self.lora_A_weights) == 0:
+            # No LoRA
+            return out
+        elif self.lora_merger is None:
+            # Native LoRA inference
+            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
+                out = out + x @ lora_A.T @ lora_B.T
+        else:
+            # LoRA fusion
+            lora_output = []
+            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
+                lora_output.append(x @ lora_A.T @ lora_B.T)
+            lora_output = torch.stack(lora_output)
+            out = self.lora_merger(out, lora_output)
+        return out
+
+
+def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0, vram_limit=None, name_prefix=""):
+    for name, module in model.named_children():
+        layer_name = name if name_prefix == "" else name_prefix + "." + name
+        for source_module, target_module in module_map.items():
+            if isinstance(module, source_module):
+                num_param = sum(p.numel() for p in module.parameters())
+                if max_num_param is not None and total_num_param + num_param > max_num_param:
+                    module_config_ = overflow_module_config
+                else:
+                    module_config_ = module_config
+                module_ = target_module(module, **module_config_, vram_limit=vram_limit, name=layer_name)
+                setattr(model, name, module_)
+                total_num_param += num_param
+                break
+        else:
+            total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param, vram_limit=vram_limit, name_prefix=layer_name)
+    return total_num_param
+
+
+def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, vram_limit=None):
+    enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0, vram_limit=vram_limit)
+    model.vram_management_enabled = True
+

Lotus-2/.gitignore

@@ -0,0 +1,7 @@
+__pycache__/
+outputs/
+.DS_Store
+*.tar
+dsine_eval/
+vibe_edit_history/
+.gradio/

Lotus-2/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+
+   9. Accepting Warranty or Additional Liability. While redistributing
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+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
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+
+   Copyright [2025] [Jing He, Haodong Li, Mingzhi Sheng, Ying-Cong Chen]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
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+   You may obtain a copy of the License at
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Lotus-2/README.md

@@ -0,0 +1,100 @@
+# <img src="assets/badges/lotus_icon.png" alt="lotus" style="height:1em; vertical-align:bottom;"/> Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model
+
+[![Page](https://img.shields.io/badge/Project-Website-pink?logo=googlechrome&logoColor=white)](https://lotus-2.github.io/)
+[![Paper](https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2512.01030)
+[![HuggingFace Demo](https://img.shields.io/badge/🤗%20HuggingFace-Demo%20(Depth)-yellow)](https://huggingface.co/spaces/haodongli/Lotus-2_Depth)
+[![HuggingFace Demo](https://img.shields.io/badge/🤗%20HuggingFace-Demo%20(Normal)-yellow)](https://huggingface.co/spaces/haodongli/Lotus-2_Normal)
+[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Weights-blue)](https://huggingface.co/jingheya/Lotus-2/tree/main)
+
+[Jing He](https://scholar.google.com/citations?hl=en&user=RsLS11MAAAAJ)<sup>1</sup>,
+[Haodong Li](https://haodong-li.com/)<sup>12<span>&#10033;</span></sup>,
+[Mingzhi Sheng]()<sup>1<span>&#10033;</span></sup>,
+[Ying-Cong Chen](https://www.yingcong.me/)<sup>13&#9993;</sup>
+
+<span class="author-block"><sup>1</sup>HKUST(GZ)</span>
+<span class="author-block"><sup>2</sup>UC San Diego</span>
+<span class="author-block"><sup>3</sup>HKUST</span><br>
+<span class="author-block">
+    <sup>&#10033;</sup>Both authors contributed equally.
+    <sup>&#9993;</sup>Corresponding author.
+</span>
+
+![teaser](assets/badges/teaser-1.png)
+
+**We present Lotus-2, a two-stage deterministic framework for monocular geometric dense prediction.** Our method leverages pre-trained generative model as a deterministic world prior to achieve **new state-of-the-art accuracy** while requiring **remarkably minimal data** (trained on only **0.66%** of the samples used by MoGe-2). This figure demonstrates Lotus-2's robust zero-shot generalization with sharp geometric details, especially in challenging cases like oil paintings and transparent objects.
+
+🚀🚀🚀 **Please also check the** [**Project Page**](https://lotus3d.github.io/) **and** [**Github Repo**](https://github.com/EnVision-Research/Lotus) **of our prior work: Lotus!** 🚀🚀🚀
+
+## 📢 News
+- 2025-12-01: [Paper](https://arxiv.org/abs/2512.01030) released! <br>
+- 2025-11-28: The inference code and HuggingFace demo ([Depth](https://huggingface.co/spaces/haodongli/Lotus-2_Depth) & [Normal](https://huggingface.co/spaces/haodongli/Lotus-2_Normal)) are available! <br>
+
+## 🛠️ Setup
+This installation was tested on: Ubuntu 20.04 LTS, Python 3.10, CUDA 12.3, NVIDIA A800-SXM4-80GB.  
+1. Be sure you have a GPU with at least **40GB** memory.
+2. Clone the repository (requires git):
+   ```
+   git clone https://github.com/EnVision-Research/Lotus-2.git
+   cd Lotus-2
+   ```
+3. Install dependencies (requires conda):
+   ```
+   conda create -n lotus2 python=3.10 -y
+   conda activate lotus2
+   pip install -r requirements.txt 
+   ```
+4. Be sure you have access to [`black-forest-labs/FLUX.1-dev`](https://huggingface.co/black-forest-labs/FLUX.1-dev).
+5. Login your huggingface account via (if you want to switch account, run `hf auth logout` at first):
+   ```
+   hf auth login
+   ```
+
+## 🤗 Gradio Demo
+
+1. Online demo: [Depth](https://huggingface.co/spaces/haodongli/Lotus-2_Depth) & [Normal](https://huggingface.co/spaces/haodongli/Lotus-2_Normal)
+2. Local demo:
+- For **depth** estimation, run:
+    ```
+    python app.py depth
+    ```
+- For **normal** estimation, run:
+    ```
+    python app.py normal
+    ```
+
+## 🕹️ Inference
+1. Place your images in a directory, for example, under `./assets/in-the-wild_example` (where we have already prepared several examples). 
+2. Run the inference command:
+   ```
+   sh infer.sh
+   ```
+- <b> Note</b>: The inference code will automatically download the required model weights. You also can download them manually using the HuggingFace CLI:
+   ```
+   hf download jingheya/Lotus-2 --local-dir <path/to/your/local/directory>
+   ```
+   Use the following arguments to specify the paths: `--core_predictor_model_path`, `--lcm_model_path`, and `--detail_sharpener_model_path`.
+
+## 🚀 Evaluation
+1. Prepare benchmark datasets:
+- For **depth** estimation, please download the [Marigold evaluation datasets](https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset/) via:
+    ```
+    cd datasets/eval/depth/
+    
+    wget -r -np -nH --cut-dirs=4 -R "index.html*" -P . https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset/
+    ```
+- For **normal** estimation, please (manually) download the  [DSINE evaluation datasets](https://drive.google.com/drive/folders/1t3LMJIIrSnCGwOEf53Cyg0lkSXd3M4Hm?usp=drive_link) (`dsine_eval.zip`)  under: `datasets/eval/normal/` and unzip it. 
+2. Run the evaluation command (modify the `TASK_NAME` in `eval.sh` to switch tasks):
+   ```
+   sh eval.sh
+   ```
+
+## 🎓 Citation
+If you find our work useful in your research, please consider citing our paper:
+```bibtex
+@article{he2025lotus,
+  title={Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model},
+  author={He, Jing and Li, Haodong and Sheng, Mingzhi and Chen, Ying-Cong},
+  journal={arXiv preprint arXiv:2512.01030},
+  year={2025}
+}
+```

Lotus-2/app.py

@@ -0,0 +1,104 @@
+import spaces  # must be first!
+import sys
+import os
+import torch
+from PIL import Image
+import gradio as gr
+from glob import glob
+from contextlib import nullcontext
+from pipeline import Lotus2Pipeline
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler,
+    FluxTransformer2DModel,
+)
+from infer import (
+    load_lora_and_lcm_weights,
+    process_single_image
+)
+
+os.environ['GRADIO_TEMP_DI']="."
+pipeline = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+weight_dtype = torch.bfloat16
+task = None
+
+@spaces.GPU
+def load_pipeline():
+    global pipeline, device, weight_dtype, task
+    noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+        'black-forest-labs/FLUX.1-dev', subfolder="scheduler", num_train_timesteps=10
+    )
+    transformer = FluxTransformer2DModel.from_pretrained(
+        'black-forest-labs/FLUX.1-dev', subfolder="transformer", revision=None, variant=None
+    )
+    transformer.requires_grad_(False)
+    transformer.to(device=device, dtype=weight_dtype)
+    transformer, local_continuity_module = load_lora_and_lcm_weights(transformer, None, None, None, task)
+    pipeline = Lotus2Pipeline.from_pretrained(
+        'black-forest-labs/FLUX.1-dev',
+        scheduler=noise_scheduler,
+        transformer=transformer,
+        revision=None,
+        variant=None,
+        torch_dtype=weight_dtype,
+    )
+    pipeline.local_continuity_module = local_continuity_module
+    pipeline = pipeline.to(device)
+
+@spaces.GPU
+def fn(image_path):
+    global pipeline, device, task
+    pipeline.set_progress_bar_config(disable=True)
+    with nullcontext():
+        _, output_vis, _ = process_single_image(
+            image_path, pipeline, 
+            task_name=task,
+            device=device,
+            num_inference_steps=10,
+            process_res=1024
+        )
+    return [Image.open(image_path), output_vis]
+
+def build_demo():
+    global task
+    inputs = [
+        gr.Image(label="Image", type="filepath")
+    ]
+    outputs = [
+        gr.ImageSlider(
+            label=f"{task.title()}",
+            type="pil",
+            slider_position=20,
+        )
+    ]
+    examples = glob(f"assets/demo_examples/{task}/*.png") + glob(f"assets/demo_examples/{task}/*.jpg")
+    demo = gr.Interface(
+        fn=fn,
+        title="Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model",
+        description=f"""
+            <strong>Please consider starring <span style="color: orange">&#9733;</span> our <a href="https://github.com/EnVision-Research/Lotus-2" target="_blank" rel="noopener noreferrer">GitHub Repo</a> if you find this demo useful! 😊</strong>
+            <br>
+            <strong>Current Task: </strong><strong style="color: red;">{task.title()}</strong>
+        """,
+        inputs=inputs,
+        outputs=outputs,
+        examples=examples,
+        examples_per_page=10
+    )
+    return demo
+
+def main(task_name):
+    global task
+    task = task_name
+    load_pipeline()
+    demo = build_demo()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=6381,
+    )
+
+if __name__ == "__main__":
+    task_name = sys.argv[-1]
+    if not task_name in ['depth', 'normal']:
+        raise ValueError("Invalid task. Please choose from 'depth' and 'normal'.")
+    main(task_name)

Lotus-2/datasets/eval/depth/configs/data_diode_all.yaml

@@ -0,0 +1,6 @@
+name: diode
+disp_name: diode_val_all
+# dir: diode
+dir: diode/diode_val.tar
+filenames: datasets/eval/depth/data_split/diode/diode_val_all_filename_list.txt 
+processing_res: 640

Lotus-2/datasets/eval/depth/configs/data_eth3d.yaml

@@ -0,0 +1,7 @@
+name: eth3d
+disp_name: eth3d_full
+# dir: eth3d
+dir: eth3d/eth3d.tar
+filenames: datasets/eval/depth/data_split/eth3d/eth3d_filename_list.txt
+processing_res: 768
+alignment_max_res: 1024

Lotus-2/datasets/eval/depth/configs/data_kitti_eigen_test.yaml

@@ -0,0 +1,6 @@
+name: kitti
+disp_name: kitti_eigen_test_full
+dir: kitti/kitti_eigen_split_test.tar
+filenames: datasets/eval/depth/data_split/kitti/eigen_test_files_with_gt.txt
+kitti_bm_crop: true
+valid_mask_crop: eigen

Lotus-2/datasets/eval/depth/configs/data_nyu_test.yaml

@@ -0,0 +1,5 @@
+name: nyu_v2
+disp_name: nyu_test_full
+dir: nyuv2/nyu_labeled_extracted.tar
+filenames: datasets/eval/depth/data_split/nyu/labeled/filename_list_test.txt
+eigen_valid_mask: true

Lotus-2/datasets/eval/depth/configs/data_scannet_val.yaml

@@ -0,0 +1,5 @@
+name: scannet
+disp_name: scannet_val_800_1
+# dir: scannet
+dir: scannet/scannet_val_sampled_800_1.tar
+filenames: datasets/eval/depth/data_split/scannet/scannet_val_sampled_list_800_1.txt

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Lotus-2/eval.py

@@ -0,0 +1,109 @@
+import sys
+import os
+import torch
+from PIL import Image
+import gradio as gr
+from glob import glob
+from contextlib import nullcontext
+from pipeline import Lotus2Pipeline
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler,
+    FluxTransformer2DModel,
+)
+from infer import (
+    load_lora_and_lcm_weights,
+    process_single_image
+)
+from evaluation.evaluation import evaluation_depth, evaluation_normal
+
+
+pipeline = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+weight_dtype = torch.bfloat16
+task = os.environ.get("TASK_NAME", "depth") # or normal
+
+def load_pipeline():
+    global pipeline, device, weight_dtype, task
+    noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+        'black-forest-labs/FLUX.1-dev', subfolder="scheduler", num_train_timesteps=10
+    )
+    transformer = FluxTransformer2DModel.from_pretrained(
+        'black-forest-labs/FLUX.1-dev', subfolder="transformer", revision=None, variant=None
+    )
+    transformer.requires_grad_(False)
+    transformer.to(device=device, dtype=weight_dtype)
+    transformer, local_continuity_module = load_lora_and_lcm_weights(transformer, None, None, None, task)
+    pipeline = Lotus2Pipeline.from_pretrained(
+        'black-forest-labs/FLUX.1-dev',
+        scheduler=noise_scheduler,
+        transformer=transformer,
+        revision=None,
+        variant=None,
+        torch_dtype=weight_dtype,
+    )
+    pipeline.local_continuity_module = local_continuity_module
+    pipeline = pipeline.to(device)
+    pipeline.set_progress_bar_config(disable=True)
+
+def eval():
+    global pipeline, device, weight_dtype, task
+    base_test_data_dir = os.environ.get("TEST_DATA_DIR", "datasets/eval")
+    output_dir = os.environ.get("OUTPUT_DIR", "outputs/eval")
+
+    def gen_fn(rgb_in):
+        if task == "depth":
+            rgb_input = rgb_in / 255.0 * 2.0 - 1.0  #  [0, 255] -> [-1, 1]
+            output_type = "np"
+        elif task == "normal":
+            rgb_input = rgb_in
+            output_type = "pt"
+        else:
+            raise ValueError(f"Invalid task name: {task}")
+
+        prediction = pipeline(
+            rgb_in=rgb_input, 
+            prompt='', 
+            num_inference_steps=10,
+            output_type=output_type,
+            process_res=None
+            ).images[0]
+
+        if task == "depth":
+            output = prediction.mean(axis=-1)
+        elif task == "normal":
+            output = (prediction * 2.0 - 1.0).unsqueeze(0) # [0,1] -> [-1,1], (1, 3, h, w)
+        return output
+
+    with torch.no_grad():
+        if task == 'depth':
+            test_data_dir = os.path.join(base_test_data_dir, task)
+            test_depth_dataset_configs = {
+                "nyuv2": "configs/data_nyu_test.yaml", 
+                "kitti": "configs/data_kitti_eigen_test.yaml",
+                "scannet": "configs/data_scannet_val.yaml",
+                "eth3d": "configs/data_eth3d.yaml",
+                "diode": "configs/data_diode_all.yaml",
+            }
+            for dataset_name, config_path in test_depth_dataset_configs.items():
+                eval_dir = os.path.join(output_dir, task, dataset_name)
+                test_dataset_config = os.path.join(test_data_dir, config_path)
+                alignment_type = "least_square_disparity"
+                metric_tracker = evaluation_depth(eval_dir, test_dataset_config, test_data_dir, eval_mode="generate_prediction",
+                                                  gen_prediction=gen_fn, pipeline=pipeline, alignment=alignment_type, processing_res=None)
+                print(dataset_name,',', 'abs_relative_difference: ', metric_tracker.result()['abs_relative_difference'], 'delta1_acc: ', metric_tracker.result()['delta1_acc'])
+        elif task == 'normal':
+            test_data_dir = os.path.join(base_test_data_dir, task)
+            dataset_split_path = "evaluation/dataset_normal"
+            eval_datasets = [ ('nyuv2', 'test'), ('scannet', 'test'), ('ibims', 'ibims'), ('sintel', 'sintel'),  ('oasis', 'val')]
+            eval_dir = os.path.join(output_dir, task)
+            evaluation_normal(eval_dir, test_data_dir, dataset_split_path, eval_mode="generate_prediction", 
+                              gen_prediction=gen_fn, pipeline=pipeline, eval_datasets=eval_datasets, processing_res=None)
+        else:
+            raise ValueError(f"Not support predicting {task} yet. ")
+        
+        print('==> Evaluation is done. \n==> Results saved to:', output_dir)
+
+
+if __name__ == "__main__":
+    load_pipeline()
+    eval()

Lotus-2/eval.sh

@@ -0,0 +1,5 @@
+export TASK_NAME="depth" # or normal
+export TEST_DATA_DIR="datasets/eval"
+export OUTPUT_DIR="outputs/eval"
+
+python eval.py

Lotus-2/evaluation/dataset_depth/__init__.py

@@ -0,0 +1,37 @@
+# Author: Bingxin Ke
+# Last modified: 2024-03-30
+
+import os
+
+from .base_depth_dataset import BaseDepthDataset, get_pred_name, DatasetMode  # noqa: F401
+from .eth3d_dataset import ETH3DDataset
+from .kitti_dataset import KITTIDataset
+from .nyu_dataset import NYUDataset
+from .scannet_dataset import ScanNetDataset
+from .diode_dataset import DIODEDataset
+
+dataset_name_class_dict = {
+    "nyu_v2": NYUDataset,
+    "kitti": KITTIDataset,
+    "eth3d": ETH3DDataset,
+    "scannet": ScanNetDataset,
+    "diode": DIODEDataset,
+}
+
+
+def get_dataset(
+    cfg_data_split, base_data_dir: str, mode: DatasetMode, **kwargs
+) -> BaseDepthDataset:
+    if cfg_data_split.name in dataset_name_class_dict.keys():
+        dataset_class = dataset_name_class_dict[cfg_data_split.name]
+        dataset = dataset_class(
+            mode=mode,
+            filename_ls_path=cfg_data_split.filenames,
+            dataset_dir=os.path.join(base_data_dir, cfg_data_split.dir),
+            **cfg_data_split,
+            **kwargs,
+        )
+    else:
+        raise NotImplementedError
+
+    return dataset

Lotus-2/evaluation/dataset_depth/base_depth_dataset.py

@@ -0,0 +1,256 @@
+# Author: Bingxin Ke
+# Last modified: 2024-04-15
+
+import io
+import os
+import random
+import tarfile
+from enum import Enum
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision.transforms import InterpolationMode, Resize
+
+
+class DatasetMode(Enum):
+    RGB_ONLY = "rgb_only"
+    EVAL = "evaluate"
+    TRAIN = "train"
+
+
+def read_image_from_tar(tar_obj, img_rel_path):
+    image = tar_obj.extractfile("./" + img_rel_path)
+    image = image.read()
+    image = Image.open(io.BytesIO(image))
+
+
+class BaseDepthDataset(Dataset):
+    def __init__(
+        self,
+        mode: DatasetMode,
+        filename_ls_path: str,
+        dataset_dir: str,
+        disp_name: str,
+        min_depth,
+        max_depth,
+        has_filled_depth,
+        name_mode,
+        depth_transform=None,
+        augmentation_args: dict = None,
+        resize_to_hw=None,
+        move_invalid_to_far_plane: bool = True,
+        rgb_transform=lambda x: x / 255.0 * 2 - 1,  #  [0, 255] -> [-1, 1],
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.mode = mode
+        # dataset info
+        self.filename_ls_path = filename_ls_path
+        self.dataset_dir = dataset_dir
+        self.disp_name = disp_name
+        self.has_filled_depth = has_filled_depth
+        self.name_mode: DepthFileNameMode = name_mode
+        self.min_depth = min_depth
+        self.max_depth = max_depth
+
+        # training arguments
+        self.depth_transform = depth_transform
+        self.augm_args = augmentation_args
+        self.resize_to_hw = resize_to_hw
+        self.rgb_transform = rgb_transform
+        self.move_invalid_to_far_plane = move_invalid_to_far_plane
+
+        # Load filenames
+        with open(self.filename_ls_path, "r") as f:
+            self.filenames = [
+                s.split() for s in f.readlines()
+            ]  # [['rgb.png', 'depth.tif'], [], ...]
+
+        # Tar dataset
+        self.tar_obj = None
+        self.is_tar = (
+            True
+            if os.path.isfile(dataset_dir) and tarfile.is_tarfile(dataset_dir)
+            else False
+        )
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, index):
+        rasters, other = self._get_data_item(index)
+        if DatasetMode.TRAIN == self.mode:
+            rasters = self._training_preprocess(rasters)
+        # merge
+        outputs = rasters
+        outputs.update(other)
+        return outputs
+
+    def _get_data_item(self, index):
+        rgb_rel_path, depth_rel_path, filled_rel_path = self._get_data_path(index=index)
+
+        rasters = {}
+
+        # RGB data
+        rasters.update(self._load_rgb_data(rgb_rel_path=rgb_rel_path))
+
+        # Depth data
+        if DatasetMode.RGB_ONLY != self.mode:
+            # load data
+            depth_data = self._load_depth_data(
+                depth_rel_path=depth_rel_path, filled_rel_path=filled_rel_path
+            )
+            rasters.update(depth_data)
+            # valid mask
+            rasters["valid_mask_raw"] = self._get_valid_mask(
+                rasters["depth_raw_linear"]
+            ).clone()
+            rasters["valid_mask_filled"] = self._get_valid_mask(
+                rasters["depth_filled_linear"]
+            ).clone()
+
+        other = {"index": index, "rgb_relative_path": rgb_rel_path}
+
+        return rasters, other
+
+    def _load_rgb_data(self, rgb_rel_path):
+        # Read RGB data
+        rgb = self._read_rgb_file(rgb_rel_path)
+
+        outputs = {
+            "rgb_int": torch.from_numpy(rgb).int(),
+        }
+        return outputs
+
+    def _load_depth_data(self, depth_rel_path, filled_rel_path):
+        # Read depth data
+        outputs = {}
+        depth_raw = self._read_depth_file(depth_rel_path).squeeze()
+        depth_raw_linear = torch.from_numpy(depth_raw).float().unsqueeze(0)  # [1, H, W]
+        outputs["depth_raw_linear"] = depth_raw_linear.clone()
+
+        if self.has_filled_depth:
+            depth_filled = self._read_depth_file(filled_rel_path).squeeze()
+            depth_filled_linear = torch.from_numpy(depth_filled).float().unsqueeze(0)
+            outputs["depth_filled_linear"] = depth_filled_linear
+        else:
+            outputs["depth_filled_linear"] = depth_raw_linear.clone()
+
+        return outputs
+
+    def _get_data_path(self, index):
+        filename_line = self.filenames[index]
+
+        # Get data path
+        rgb_rel_path = filename_line[0]
+
+        depth_rel_path, filled_rel_path = None, None
+        if DatasetMode.RGB_ONLY != self.mode:
+            depth_rel_path = filename_line[1]
+            if self.has_filled_depth:
+                filled_rel_path = filename_line[2]
+        return rgb_rel_path, depth_rel_path, filled_rel_path
+
+    def _read_image(self, img_rel_path) -> np.ndarray:
+        if self.is_tar:
+            if self.tar_obj is None:
+                self.tar_obj = tarfile.open(self.dataset_dir)
+            image = self.tar_obj.extractfile("./" + img_rel_path)
+            image = image.read()
+            image = Image.open(io.BytesIO(image))  # [H, W, rgb]
+        else:
+            img_path = os.path.join(self.dataset_dir, img_rel_path)
+            image = Image.open(img_path)
+        image = np.asarray(image)
+        return image
+
+    def _read_rgb_file(self, rel_path) -> np.ndarray:
+        rgb = self._read_image(rel_path)
+        rgb = np.transpose(rgb, (2, 0, 1)).astype(int)  # [rgb, H, W]
+        return rgb
+
+    def _read_depth_file(self, rel_path):
+        depth_in = self._read_image(rel_path)
+        #  Replace code below to decode depth according to dataset definition
+        depth_decoded = depth_in
+
+        return depth_decoded
+
+    def _get_valid_mask(self, depth: torch.Tensor):
+        valid_mask = torch.logical_and(
+            (depth > self.min_depth), (depth < self.max_depth)
+        ).bool()
+        return valid_mask
+
+    def _training_preprocess(self, rasters):
+        # Augmentation
+        if self.augm_args is not None:
+            rasters = self._augment_data(rasters)
+
+        # Normalization
+        rasters["depth_raw_norm"] = self.depth_transform(
+            rasters["depth_raw_linear"], rasters["valid_mask_raw"]
+        ).clone()
+        rasters["depth_filled_norm"] = self.depth_transform(
+            rasters["depth_filled_linear"], rasters["valid_mask_filled"]
+        ).clone()
+
+        # Set invalid pixel to far plane
+        if self.move_invalid_to_far_plane:
+            if self.depth_transform.far_plane_at_max:
+                rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
+                    self.depth_transform.norm_max
+                )
+            else:
+                rasters["depth_filled_norm"][~rasters["valid_mask_filled"]] = (
+                    self.depth_transform.norm_min
+                )
+
+        # Resize
+        if self.resize_to_hw is not None:
+            resize_transform = Resize(
+                size=self.resize_to_hw, interpolation=InterpolationMode.NEAREST_EXACT
+            )
+            rasters = {k: resize_transform(v) for k, v in rasters.items()}
+
+        return rasters
+
+    def _augment_data(self, rasters_dict):
+        # lr flipping
+        lr_flip_p = self.augm_args.lr_flip_p
+        if random.random() < lr_flip_p:
+            rasters_dict = {k: v.flip(-1) for k, v in rasters_dict.items()}
+
+        return rasters_dict
+
+    def __del__(self):
+        if self.tar_obj is not None:
+            self.tar_obj.close()
+            self.tar_obj = None
+
+
+# Prediction file naming modes
+class DepthFileNameMode(Enum):
+    id = 1  # id.png
+    rgb_id = 2  # rgb_id.png
+    i_d_rgb = 3  # i_d_1_rgb.png
+    rgb_i_d = 4
+
+
+def get_pred_name(rgb_basename, name_mode, suffix=".png"):
+    if DepthFileNameMode.rgb_id == name_mode:
+        pred_basename = "pred_" + rgb_basename.split("_")[1]
+    elif DepthFileNameMode.i_d_rgb == name_mode:
+        pred_basename = rgb_basename.replace("_rgb.", "_pred.")
+    elif DepthFileNameMode.id == name_mode:
+        pred_basename = "pred_" + rgb_basename
+    elif DepthFileNameMode.rgb_i_d == name_mode:
+        pred_basename = "pred_" + "_".join(rgb_basename.split("_")[1:])
+    else:
+        raise NotImplementedError
+    # change suffix
+    pred_basename = os.path.splitext(pred_basename)[0] + suffix
+
+    return pred_basename

Lotus-2/evaluation/dataset_depth/diode_dataset.py

@@ -0,0 +1,72 @@
+# Author: Bingxin Ke
+# Last modified: 2024-02-26
+
+import os
+import tarfile
+from io import BytesIO
+
+import numpy as np
+import torch
+
+from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode, DatasetMode
+
+
+class DIODEDataset(BaseDepthDataset):
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            # DIODE data parameter
+            min_depth=0.6,
+            max_depth=350,
+            has_filled_depth=False,
+            name_mode=DepthFileNameMode.id,
+            **kwargs,
+        )
+
+    def _read_npy_file(self, rel_path):
+        if self.is_tar:
+            if self.tar_obj is None:
+                self.tar_obj = tarfile.open(self.dataset_dir)
+            fileobj = self.tar_obj.extractfile("./" + rel_path)
+            npy_path_or_content = BytesIO(fileobj.read())
+        else:
+            npy_path_or_content = os.path.join(self.dataset_dir, rel_path)
+        data = np.load(npy_path_or_content).squeeze()[np.newaxis, :, :]
+        return data
+
+    def _read_depth_file(self, rel_path):
+        depth = self._read_npy_file(rel_path)
+        return depth
+
+    def _get_data_path(self, index):
+        return self.filenames[index]
+
+    def _get_data_item(self, index):
+        # Special: depth mask is read from data
+
+        rgb_rel_path, depth_rel_path, mask_rel_path = self._get_data_path(index=index)
+
+        rasters = {}
+
+        # RGB data
+        rasters.update(self._load_rgb_data(rgb_rel_path=rgb_rel_path))
+
+        # Depth data
+        if DatasetMode.RGB_ONLY != self.mode:
+            # load data
+            depth_data = self._load_depth_data(
+                depth_rel_path=depth_rel_path, filled_rel_path=None
+            )
+            rasters.update(depth_data)
+
+            # valid mask
+            mask = self._read_npy_file(mask_rel_path).astype(bool)
+            mask = torch.from_numpy(mask).bool()
+            rasters["valid_mask_raw"] = mask.clone()
+            rasters["valid_mask_filled"] = mask.clone()
+
+        other = {"index": index, "rgb_relative_path": rgb_rel_path}
+
+        return rasters, other

Lotus-2/evaluation/dataset_depth/eth3d_dataset.py

@@ -0,0 +1,46 @@
+# Author: Bingxin Ke
+# Last modified: 2024-02-08
+
+import torch
+import tarfile
+import os
+import numpy as np
+
+from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode
+
+
+class ETH3DDataset(BaseDepthDataset):
+    HEIGHT, WIDTH = 4032, 6048
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            # ETH3D data parameter
+            min_depth=1e-5,
+            max_depth=torch.inf,
+            has_filled_depth=False,
+            name_mode=DepthFileNameMode.id,
+            **kwargs,
+        )
+
+    def _read_depth_file(self, rel_path):
+        # Read special binary data: https://www.eth3d.net/documentation#format-of-multi-view-data-image-formats
+        if self.is_tar:
+            if self.tar_obj is None:
+                self.tar_obj = tarfile.open(self.dataset_dir)
+            binary_data = self.tar_obj.extractfile("./" + rel_path)
+            binary_data = binary_data.read()
+
+        else:
+            depth_path = os.path.join(self.dataset_dir, rel_path)
+            with open(depth_path, "rb") as file:
+                binary_data = file.read()
+        # Convert the binary data to a numpy array of 32-bit floats
+        depth_decoded = np.frombuffer(binary_data, dtype=np.float32).copy()
+
+        depth_decoded[depth_decoded == torch.inf] = 0.0
+
+        depth_decoded = depth_decoded.reshape((self.HEIGHT, self.WIDTH))
+        return depth_decoded

Lotus-2/evaluation/dataset_depth/kitti_dataset.py

@@ -0,0 +1,105 @@
+# Author: Bingxin Ke
+# Last modified: 2024-02-08
+
+import torch
+
+from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode
+
+
+class KITTIDataset(BaseDepthDataset):
+    def __init__(
+        self,
+        kitti_bm_crop,  # Crop to KITTI benchmark size
+        valid_mask_crop,  # Evaluation mask. [None, garg or eigen]
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            # KITTI data parameter
+            min_depth=1e-5,
+            max_depth=80,
+            has_filled_depth=False,
+            name_mode=DepthFileNameMode.id,
+            **kwargs,
+        )
+        self.kitti_bm_crop = kitti_bm_crop
+        self.valid_mask_crop = valid_mask_crop
+        assert self.valid_mask_crop in [
+            None,
+            "garg",  # set evaluation mask according to Garg  ECCV16
+            "eigen",  # set evaluation mask according to Eigen NIPS14
+        ], f"Unknown crop type: {self.valid_mask_crop}"
+
+        # Filter out empty depth
+        self.filenames = [f for f in self.filenames if "None" != f[1]]
+
+    def _read_depth_file(self, rel_path):
+        depth_in = self._read_image(rel_path)
+        # Decode KITTI depth
+        depth_decoded = depth_in / 256.0
+        return depth_decoded
+
+    def _load_rgb_data(self, rgb_rel_path):
+        rgb_data = super()._load_rgb_data(rgb_rel_path)
+        if self.kitti_bm_crop:
+            rgb_data = {k: self.kitti_benchmark_crop(v) for k, v in rgb_data.items()}
+        return rgb_data
+
+    def _load_depth_data(self, depth_rel_path, filled_rel_path):
+        depth_data = super()._load_depth_data(depth_rel_path, filled_rel_path)
+        if self.kitti_bm_crop:
+            depth_data = {
+                k: self.kitti_benchmark_crop(v) for k, v in depth_data.items()
+            }
+        return depth_data
+
+    @staticmethod
+    def kitti_benchmark_crop(input_img):
+        """
+        Crop images to KITTI benchmark size
+        Args:
+            `input_img` (torch.Tensor): Input image to be cropped.
+
+        Returns:
+            torch.Tensor:Cropped image.
+        """
+        KB_CROP_HEIGHT = 352
+        KB_CROP_WIDTH = 1216
+
+        height, width = input_img.shape[-2:]
+        top_margin = int(height - KB_CROP_HEIGHT)
+        left_margin = int((width - KB_CROP_WIDTH) / 2)
+        if 2 == len(input_img.shape):
+            out = input_img[
+                top_margin : top_margin + KB_CROP_HEIGHT,
+                left_margin : left_margin + KB_CROP_WIDTH,
+            ]
+        elif 3 == len(input_img.shape):
+            out = input_img[
+                :,
+                top_margin : top_margin + KB_CROP_HEIGHT,
+                left_margin : left_margin + KB_CROP_WIDTH,
+            ]
+        return out
+
+    def _get_valid_mask(self, depth: torch.Tensor):
+        # reference: https://github.com/cleinc/bts/blob/master/pytorch/bts_eval.py
+        valid_mask = super()._get_valid_mask(depth)  # [1, H, W]
+
+        if self.valid_mask_crop is not None:
+            eval_mask = torch.zeros_like(valid_mask.squeeze()).bool()
+            gt_height, gt_width = eval_mask.shape
+
+            if "garg" == self.valid_mask_crop:
+                eval_mask[
+                    int(0.40810811 * gt_height) : int(0.99189189 * gt_height),
+                    int(0.03594771 * gt_width) : int(0.96405229 * gt_width),
+                ] = 1
+            elif "eigen" == self.valid_mask_crop:
+                eval_mask[
+                    int(0.3324324 * gt_height) : int(0.91351351 * gt_height),
+                    int(0.0359477 * gt_width) : int(0.96405229 * gt_width),
+                ] = 1
+
+            eval_mask.reshape(valid_mask.shape)
+            valid_mask = torch.logical_and(valid_mask, eval_mask)
+        return valid_mask

Lotus-2/evaluation/dataset_depth/nyu_dataset.py

@@ -0,0 +1,43 @@
+# Author: Bingxin Ke
+# Last modified: 2024-02-08
+
+
+import torch
+
+from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode
+
+
+class NYUDataset(BaseDepthDataset):
+    def __init__(
+        self,
+        eigen_valid_mask: bool,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            # NYUv2 dataset parameter
+            min_depth=1e-3,
+            max_depth=10.0,
+            has_filled_depth=True,
+            name_mode=DepthFileNameMode.rgb_id,
+            **kwargs,
+        )
+
+        self.eigen_valid_mask = eigen_valid_mask
+
+    def _read_depth_file(self, rel_path):
+        depth_in = self._read_image(rel_path)
+        # Decode NYU depth
+        depth_decoded = depth_in / 1000.0
+        return depth_decoded
+
+    def _get_valid_mask(self, depth: torch.Tensor):
+        valid_mask = super()._get_valid_mask(depth)
+
+        # Eigen crop for evaluation
+        if self.eigen_valid_mask:
+            eval_mask = torch.zeros_like(valid_mask.squeeze()).bool()
+            eval_mask[45:471, 41:601] = 1
+            eval_mask.reshape(valid_mask.shape)
+            valid_mask = torch.logical_and(valid_mask, eval_mask)
+
+        return valid_mask

Lotus-2/evaluation/dataset_depth/scannet_dataset.py

@@ -0,0 +1,25 @@
+# Author: Bingxin Ke
+# Last modified: 2024-02-08
+
+from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode
+
+
+class ScanNetDataset(BaseDepthDataset):
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            # ScanNet data parameter
+            min_depth=1e-3,
+            max_depth=10,
+            has_filled_depth=False,
+            name_mode=DepthFileNameMode.id,
+            **kwargs,
+        )
+
+    def _read_depth_file(self, rel_path):
+        depth_in = self._read_image(rel_path)
+        # Decode ScanNet depth
+        depth_decoded = depth_in / 1000.0
+        return depth_decoded

Lotus-2/evaluation/dataset_normal/__init__.py

@@ -0,0 +1,27 @@
+""" data sample
+"""
+class Sample():
+    def __init__(self, img=None, 
+                 depth=None, depth_mask=None,
+                 normal=None, normal_mask=None, 
+                 intrins=None, flipped=False,
+                 dataset_name='dataset', scene_name='scene', img_name='img', 
+                 info={}):
+ 
+        self.img = img                  # input image
+
+        self.depth = depth              # depth - GT
+        self.depth_mask = depth_mask    # depth - valid_mask
+
+        self.normal = normal            # surface normals - GT
+        self.normal_mask = normal_mask  # surface normals - valid_mask
+        
+        self.intrins = intrins          # camera intrinsics
+        self.flipped = flipped          # True when the image is flipped during augmentation
+
+        self.dataset_name = dataset_name
+        self.scene_name = scene_name
+        self.img_name = img_name
+
+        # other info (this is a dict containing any additional information)
+        self.info = info

Lotus-2/evaluation/dataset_normal/aug_basic.py

@@ -0,0 +1,239 @@
+""" basic augmentations
+"""
+import random
+import numpy as np
+
+import torch
+from torchvision import transforms
+import torch.nn.functional as F
+import torchvision.transforms.functional as TF
+
+import logging
+logger = logging.getLogger('root')
+
+
+def resize(sample, new_H, new_W):
+    _, orig_H, orig_W = sample.img.shape
+    sample.img = F.interpolate(sample.img.unsqueeze(0), size=(new_H, new_W), mode='bilinear', align_corners=False, antialias=True).squeeze(0)
+    if sample.depth is not None:
+        sample.depth = F.interpolate(sample.depth.unsqueeze(0), size=(new_H, new_W), mode='nearest').squeeze(0)
+    if sample.depth_mask is not None:
+        sample.depth_mask = F.interpolate(sample.depth_mask.unsqueeze(0).float(), size=(new_H, new_W), mode='nearest').squeeze(0) > 0.5
+    if sample.normal is not None:
+        sample.normal = F.interpolate(sample.normal.unsqueeze(0), size=(new_H, new_W), mode='nearest').squeeze(0)
+    if sample.normal_mask is not None:
+        sample.normal_mask = F.interpolate(sample.normal_mask.unsqueeze(0).float(), size=(new_H, new_W), mode='nearest').squeeze(0) > 0.5
+    if sample.intrins is not None:
+        # NOTE: top-left is (0,0)
+        sample.intrins[0, 0] = sample.intrins[0, 0] * (new_W / orig_W)     # fx
+        sample.intrins[1, 1] = sample.intrins[1, 1] * (new_H / orig_H)     # fy
+        sample.intrins[0, 2] = (sample.intrins[0, 2] + 0.5) * (new_W / orig_W) - 0.5     # cx
+        sample.intrins[1, 2] = (sample.intrins[1, 2] + 0.5) * (new_H / orig_H) - 0.5     # cy
+    return sample
+
+
+def pad(sample, lrtb):
+    l, r, t, b = lrtb
+    sample.img = F.pad(sample.img, (l, r, t, b), mode="constant", value=0)
+    if sample.depth is not None:
+        sample.depth = F.pad(sample.depth, (l, r, t, b), mode="constant", value=0)
+    if sample.depth_mask is not None:
+        sample.depth_mask = F.pad(sample.depth_mask, (l, r, t, b), mode="constant", value=False)
+    if sample.normal is not None:
+        sample.normal = F.pad(sample.normal, (l, r, t, b), mode="constant", value=0)
+    if sample.normal_mask is not None:
+        sample.normal_mask = F.pad(sample.normal_mask, (l, r, t, b), mode="constant", value=False)
+    if sample.intrins is not None:
+        sample.intrins[0, 2] = sample.intrins[0, 2] + l
+        sample.intrins[1, 2] = sample.intrins[1, 2] + t
+    return sample
+
+
+def crop(sample, y, H, x, W):
+    sample.img = sample.img[:, y:y+H, x:x+W]
+    if sample.depth is not None:
+        sample.depth = sample.depth[:, y:y+H, x:x+W]
+    if sample.depth_mask is not None:
+        sample.depth_mask = sample.depth_mask[:, y:y+H, x:x+W]
+    if sample.normal is not None:
+        sample.normal = sample.normal[:, y:y+H, x:x+W]
+    if sample.normal_mask is not None:
+        sample.normal_mask = sample.normal_mask[:, y:y+H, x:x+W]
+    if sample.intrins is not None:
+        sample.intrins[0, 2] = sample.intrins[0, 2] - x
+        sample.intrins[1, 2] = sample.intrins[1, 2] - y
+    return sample
+
+
+class ToTensor():
+    """ numpy arrays to torch tensors
+    """
+    def __call__(self, sample):
+        sample.img = torch.from_numpy(sample.img).permute(2, 0, 1)                      # (3, H, W)
+        if sample.depth is not None:
+            sample.depth = torch.from_numpy(sample.depth).permute(2, 0, 1)              # (1, H, W)
+        if sample.depth_mask is not None:
+            sample.depth_mask = torch.from_numpy(sample.depth_mask).permute(2, 0, 1)    # (1, H, W)
+        if sample.normal is not None:
+            sample.normal = torch.from_numpy(sample.normal).permute(2, 0, 1)            # (3, H, W)
+        if sample.normal_mask is not None:
+            sample.normal_mask = torch.from_numpy(sample.normal_mask).permute(2, 0, 1)  # (1, H, W)
+        if sample.intrins is not None:
+            sample.intrins = torch.from_numpy(sample.intrins)                           # (3, 3)
+        return sample
+
+
+class RandomIntrins():
+    """ randomize intrinsics
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __call__(self, sample):
+        assert 'crop_H' in sample.info.keys() 
+        assert 'crop_W' in sample.info.keys()
+        crop_H = sample.info['crop_H']
+        crop_W = sample.info['crop_W']
+
+        # height-based resizing
+        _, orig_H, orig_W = sample.img.shape
+        new_H = random.randrange(min(orig_H, crop_H), max(orig_H, crop_H)+1)
+        new_W = round((new_H / orig_H) * orig_W)
+        sample = resize(sample, new_H=new_H, new_W=new_W)
+
+        # pad if necessary
+        orig_H, orig_W = sample.img.shape[1], sample.img.shape[2]
+        l, r, t, b = 0, 0, 0, 0
+        if crop_H > orig_H:
+            t = b = crop_H - orig_H
+        if crop_W > orig_W:
+            l = r = crop_W - orig_W
+        sample = pad(sample, (l, r, t, b))
+
+        # crop
+        assert sample.img.shape[1] >= crop_H
+        assert sample.img.shape[2] >= crop_W
+        x = random.randint(0, sample.img.shape[2] - crop_W)
+        y = random.randint(0, sample.img.shape[1] - crop_H)
+        sample = crop(sample, y=y, H=crop_H, x=x, W=crop_W)
+
+        return sample
+
+
+class Resize():
+    """ resize to (H, W)
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __init__(self, H=480, W=640):
+        self.H = H
+        self.W = W
+
+    def __call__(self, sample):
+        return resize(sample, new_H=self.H, new_W=self.W)
+
+
+class RandomCrop():
+    """ random crop
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __init__(self, H=416, W=544):
+        self.H = H
+        self.W = W
+
+    def __call__(self, sample):
+        assert sample.img.shape[1] >= self.H
+        assert sample.img.shape[2] >= self.W
+        x = random.randint(0, sample.img.shape[2] - self.W)
+        y = random.randint(0, sample.img.shape[1] - self.H)
+        return crop(sample, y=y, H=self.H, x=x, W=self.W)
+    
+
+class NyuCrop():
+    """ crop image border for NYUv2 images
+        W = 43:608 / H = 45:472
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __call__(self, sample):
+        return crop(sample, y=45, H=472-45, x=43, W=608-43)
+
+
+class HorizontalFlip():
+    """ random horizontal flipping
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, sample):
+        if random.random() < self.p:
+            sample.img = TF.hflip(sample.img)
+            if sample.depth is not None:
+                sample.depth = TF.hflip(sample.depth)
+            if sample.depth_mask is not None:
+                sample.depth_mask = TF.hflip(sample.depth_mask)
+            if sample.normal is not None:
+                sample.normal = TF.hflip(sample.normal)
+                sample.normal[0, :, :] = -sample.normal[0, :, :]
+            if sample.normal_mask is not None:
+                sample.normal_mask = TF.hflip(sample.normal_mask)
+            if sample.intrins is not None:
+                # NOTE: top-left is (0,0)
+                _, H, W = sample.img.shape
+                sample.intrins[0, 2] = sample.intrins[0, 2] + 0.5   # top-left is (0.5, 0.5)
+                sample.intrins[0, 2] = W - sample.intrins[0, 2]
+                sample.intrins[0, 2] = sample.intrins[0, 2] - 0.5   # top-left is (0, 0)
+            sample.flipped = True
+        return sample
+
+
+class ColorAugmentation():
+    """ color augmentation
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __init__(self, gamma_range=(0.9, 1.1),
+                       brightness_range=(0.75, 1.25),
+                       color_range=(0.9, 1.1),
+                       p=0.5):
+        self.gamma_range = gamma_range
+        self.brightness_range = brightness_range
+        self.color_range = color_range
+        self.p = p
+
+    def __call__(self, sample):
+        if random.random() < self.p:
+            # gamma augmentation
+            gamma = random.uniform(*self.gamma_range)
+            sample.img = sample.img ** gamma
+
+            # brightness augmentation
+            brightness = random.uniform(*self.brightness_range)
+            sample.img = sample.img * brightness
+
+            # color augmentation
+            colors = np.random.uniform(*self.color_range, size=3).astype(np.float32)
+            colors = torch.from_numpy(colors).view(3, 1, 1)
+            sample.img = sample.img * colors
+
+            # clip
+            sample.img = torch.clip(sample.img, 0, 1)
+
+        return sample
+    
+
+class Normalize():
+    """ mean & std: for image normalization
+        sample.img is a torch tensor of shape (3, H, W), normalized to [0, 1]
+    """
+    def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+        self.normalize = transforms.Normalize(mean=mean, std=std)
+
+    def __call__(self, sample):
+        sample.img = self.normalize(torch.clip(sample.img, min=0.0, max=1.0))
+        return sample
+
+
+class ToDict():
+    def __call__(self, sample):
+        data_dict = {}
+        for k, v in vars(sample).items():
+            if v is not None:
+                data_dict[k] = v
+        return data_dict

Lotus-2/evaluation/dataset_normal/ibims/__init__.py

@@ -0,0 +1,47 @@
+""" Get samples from iBims-1 (https://paperswithcode.com/dataset/ibims-1)
+    NOTE: We computed the GT surface normals by doing discontinuity-aware plane fitting
+"""
+import os
+import cv2
+import numpy as np
+os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
+
+from evaluation.dataset_normal import Sample
+
+def get_sample(base_data_dir, sample_path, info):
+    # e.g. sample_path = "ibims/corridor_01_img.png"
+    scene_name = sample_path.split('/')[0]
+    img_name, img_ext = sample_path.split('/')[1].split('_img')
+
+    dataset_path = os.path.join(base_data_dir, 'dsine_eval', 'ibims')
+    img_path = '%s/%s' % (dataset_path, sample_path)
+    normal_path = img_path.replace('_img'+img_ext, '_normal.exr')
+    intrins_path = img_path.replace('_img'+img_ext, '_intrins.npy')
+    assert os.path.exists(img_path)
+    assert os.path.exists(normal_path)
+    assert os.path.exists(intrins_path)
+
+    # read image (H, W, 3)
+    img = cv2.cvtColor(cv2.imread(img_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    img = img.astype(np.float32) / 255.0
+
+    # read normal (H, W, 3)
+    normal = cv2.cvtColor(cv2.imread(normal_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    normal_mask = np.linalg.norm(normal, axis=2, keepdims=True) > 0.5
+
+    # read intrins (3, 3)
+    intrins = np.load(intrins_path)
+
+    sample = Sample(
+        img=img,
+        normal=normal,
+        normal_mask=normal_mask,
+        intrins=intrins,
+
+        dataset_name='ibims',
+        scene_name=scene_name,
+        img_name=img_name,
+        info=info
+    )
+
+    return sample

Lotus-2/evaluation/dataset_normal/ibims/split/ibims.txt

@@ -0,0 +1,100 @@
+ibims/corridor_01_img.png
+ibims/corridor_02_img.png
+ibims/corridor_03_img.png
+ibims/corridor_04_img.png
+ibims/corridor_05_img.png
+ibims/corridor_06_img.png
+ibims/corridor_07_img.png
+ibims/corridor_08_img.png
+ibims/corridor_09_img.png
+ibims/corridor_10_img.png
+ibims/factory_01_img.png
+ibims/factory_02_img.png
+ibims/factory_03_img.png
+ibims/factory_04_img.png
+ibims/factory_05_img.png
+ibims/factory_06_img.png
+ibims/factory_07_img.png
+ibims/factory_08_img.png
+ibims/kitchen_01_img.png
+ibims/kitchen_02_img.png
+ibims/kitchen_03_img.png
+ibims/kitchen_04_img.png
+ibims/kitchen_05_img.png
+ibims/kitchen_06_img.png
+ibims/kitchen_07_img.png
+ibims/kitchen_08_img.png
+ibims/lab_01_img.png
+ibims/lab_02_img.png
+ibims/lab_03_img.png
+ibims/lab_04_img.png
+ibims/lab_05_img.png
+ibims/lab_06_img.png
+ibims/lab_07_img.png
+ibims/lab_08_img.png
+ibims/lab_09_img.png
+ibims/lab_10_img.png
+ibims/lab_11_img.png
+ibims/lectureroom_01_img.png
+ibims/lectureroom_02_img.png
+ibims/lectureroom_03_img.png
+ibims/lectureroom_04_img.png
+ibims/lectureroom_05_img.png
+ibims/lectureroom_06_img.png
+ibims/lectureroom_07_img.png
+ibims/lectureroom_08_img.png
+ibims/lectureroom_09_img.png
+ibims/lectureroom_10_img.png
+ibims/livingroom_01_img.png
+ibims/livingroom_02_img.png
+ibims/livingroom_03_img.png
+ibims/livingroom_04_img.png
+ibims/livingroom_05_img.png
+ibims/livingroom_06_img.png
+ibims/livingroom_07_img.png
+ibims/livingroom_08_img.png
+ibims/livingroom_09_img.png
+ibims/livingroom_10_img.png
+ibims/livingroom_11_img.png
+ibims/livingroom_12_img.png
+ibims/livingroom_13_img.png
+ibims/livingroom_14_img.png
+ibims/livingroom_15_img.png
+ibims/meetingroom_01_img.png
+ibims/meetingroom_02_img.png
+ibims/meetingroom_03_img.png
+ibims/meetingroom_04_img.png
+ibims/meetingroom_05_img.png
+ibims/meetingroom_06_img.png
+ibims/meetingroom_07_img.png
+ibims/meetingroom_08_img.png
+ibims/office_01_img.png
+ibims/office_02_img.png
+ibims/office_03_img.png
+ibims/office_04_img.png
+ibims/office_05_img.png
+ibims/office_06_img.png
+ibims/office_07_img.png
+ibims/office_08_img.png
+ibims/restaurant_01_img.png
+ibims/restaurant_02_img.png
+ibims/restaurant_03_img.png
+ibims/restaurant_04_img.png
+ibims/restaurant_05_img.png
+ibims/restaurant_06_img.png
+ibims/restaurant_07_img.png
+ibims/restaurant_08_img.png
+ibims/restaurant_09_img.png
+ibims/restaurant_10_img.png
+ibims/restaurant_11_img.png
+ibims/restaurant_12_img.png
+ibims/restroom_01_img.png
+ibims/restroom_02_img.png
+ibims/storageroom_01_img.png
+ibims/storageroom_02_img.png
+ibims/storageroom_03_img.png
+ibims/storageroom_04_img.png
+ibims/storageroom_05_img.png
+ibims/storageroom_06_img.png
+ibims/storageroom_07_img.png
+ibims/storageroom_08_img.png

Lotus-2/evaluation/dataset_normal/normal_dataloader.py

@@ -0,0 +1,79 @@
+import os
+import random
+
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+from . import aug_basic
+
+import logging
+logger = logging.getLogger('root')
+
+
+def get_transform(dataset_name='hypersim', mode='test'):
+    assert mode in ['test']
+    logger.info('Defining %s transform for %s dataset' % (mode, dataset_name))
+    tf_list = [
+        aug_basic.ToTensor(),
+    ]
+    tf_list += [
+        aug_basic.Normalize(mean=[0.5],std=[0.5]),
+        aug_basic.ToDict(),
+    ]
+    logger.info('Defining %s transform for %s dataset ... DONE' % (mode, dataset_name))
+    return transforms.Compose(tf_list)
+
+
+class NormalDataset(Dataset):
+    def __init__(self, base_data_dir, dataset_split_path, dataset_name='nyuv2', split='test', mode='test',  epoch=0):
+        self.split = split
+        self.mode = mode
+        self.base_data_dir = base_data_dir 
+        assert mode in ['test']
+
+        # data split
+        split_path = os.path.join(dataset_split_path, dataset_name, 'split', split+'.txt') # dataset_split_path: eval/dataset_normal/
+        assert os.path.exists(split_path)
+        with open(split_path, 'r') as f:
+            self.filenames = [i.strip() for i in f.readlines()]
+        self.split_path = split_path
+
+        # get_sample function
+        if dataset_name == 'nyuv2':
+            from evaluation.dataset_normal.nyuv2 import get_sample
+        elif dataset_name == 'scannet':
+            from evaluation.dataset_normal.scannet import get_sample
+        elif dataset_name == 'ibims':
+            from evaluation.dataset_normal.ibims import get_sample
+        elif dataset_name == 'sintel':
+            from evaluation.dataset_normal.sintel import get_sample
+        elif dataset_name == 'vkitti':
+            from evaluation.dataset_normal.vkitti import get_sample
+        elif dataset_name == 'oasis':
+            from evaluation.dataset_normal.oasis import get_sample
+        self.get_sample = get_sample
+
+        # data preprocessing/augmentation
+        self.transform = get_transform(dataset_name=dataset_name, mode=mode)
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, index):
+        info = {}
+
+        sample = self.transform(self.get_sample(
+            base_data_dir = self.base_data_dir,
+            sample_path=self.filenames[index], 
+            info=info)
+        )
+
+        return sample            
+    
+class TestLoader(object):
+    def __init__(self, base_data_dir, dataset_split_path, dataset_name_test, test_split):
+        self.test_samples = NormalDataset(base_data_dir, dataset_split_path, dataset_name=dataset_name_test, 
+                                          split=test_split, mode='test', epoch=None)
+        self.data = DataLoader(self.test_samples, 1, shuffle=False, num_workers=1, pin_memory=True)

Lotus-2/evaluation/dataset_normal/nyuv2/__init__.py

@@ -0,0 +1,48 @@
+""" Get samples from NYUv2 (https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)
+    NOTE: GT surface normals are from GeoNet (CVPR 2018) - https://github.com/xjqi/GeoNet
+"""
+import os
+import cv2
+import numpy as np
+
+from evaluation.dataset_normal import Sample
+
+
+def get_sample(base_data_dir, sample_path, info):
+    # e.g. sample_path = "test/000000_img.png"
+    scene_name = sample_path.split('/')[0]
+    img_name, img_ext = sample_path.split('/')[1].split('_img')
+
+    dataset_path = os.path.join(base_data_dir, 'dsine_eval', 'nyuv2')
+    img_path = '%s/%s' % (dataset_path, sample_path)
+    normal_path = img_path.replace('_img'+img_ext, '_normal.png')
+    intrins_path = img_path.replace('_img'+img_ext, '_intrins.npy')
+    assert os.path.exists(img_path)
+    assert os.path.exists(normal_path)
+    assert os.path.exists(intrins_path)
+
+    # read image (H, W, 3)
+    img = cv2.cvtColor(cv2.imread(img_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    img = img.astype(np.float32) / 255.0
+
+    # read normal (H, W, 3)
+    normal = cv2.cvtColor(cv2.imread(normal_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    normal_mask = np.sum(normal, axis=2, keepdims=True) > 0
+    normal = (normal.astype(np.float32) / 255.0) * 2.0 - 1.0
+
+    # read intrins (3, 3)
+    intrins = np.load(intrins_path)
+
+    sample = Sample(
+        img=img,
+        normal=normal,
+        normal_mask=normal_mask,
+        intrins=intrins,
+
+        dataset_name='nyuv2',
+        scene_name=scene_name,
+        img_name=img_name,
+        info=info
+    )
+
+    return sample

Lotus-2/evaluation/dataset_normal/nyuv2/split/test.txt

@@ -0,0 +1,654 @@
+test/000000_img.png
+test/000001_img.png
+test/000008_img.png
+test/000013_img.png
+test/000014_img.png
+test/000015_img.png
+test/000016_img.png
+test/000017_img.png
+test/000020_img.png
+test/000027_img.png
+test/000028_img.png
+test/000029_img.png
+test/000030_img.png
+test/000031_img.png
+test/000032_img.png
+test/000033_img.png
+test/000034_img.png
+test/000035_img.png
+test/000036_img.png
+test/000037_img.png
+test/000038_img.png
+test/000039_img.png
+test/000040_img.png
+test/000041_img.png
+test/000042_img.png
+test/000045_img.png
+test/000046_img.png
+test/000055_img.png
+test/000056_img.png
+test/000058_img.png
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Lotus-2/evaluation/dataset_normal/oasis/__init__.py

@@ -0,0 +1,76 @@
+""" Get samples from OASIS validation set (https://pvl.cs.princeton.edu/OASIS/)
+"""
+import os
+import cv2
+import numpy as np
+import pickle
+
+from evaluation.dataset_normal import Sample
+
+
+def read_normal(path, h, w):
+    normal_dict = pickle.load(open(path, 'rb'))
+
+    mask = np.zeros((h,w))
+    normal = np.zeros((h,w,3))
+
+    # Stuff ROI normal into bounding box
+    min_y = normal_dict['min_y']
+    max_y = normal_dict['max_y']
+    min_x = normal_dict['min_x']
+    max_x = normal_dict['max_x']
+    roi_normal = normal_dict['normal']
+
+    # to LUB
+    normal[min_y:max_y+1, min_x:max_x+1, :] = roi_normal
+    normal = normal.astype(np.float32)
+    normal[:,:,0] *= -1
+    normal[:,:,1] *= -1
+
+    # Make mask
+    roi_mask = np.logical_or(np.logical_or(roi_normal[:,:,0] != 0, roi_normal[:,:,1] != 0), roi_normal[:,:,2] != 0).astype(np.float32)
+    mask[min_y:max_y+1, min_x:max_x+1] = roi_mask
+    mask = mask[:, :, None]
+    mask = mask > 0.5
+
+    return normal, mask
+
+
+def get_sample(base_data_dir, sample_path, info):
+    # e.g. sample_path = "val/100277_DT_img.png"
+    scene_name = sample_path.split('/')[0]
+    img_name, img_ext = sample_path.split('/')[-1].split('_img')
+
+    dataset_path = os.path.join(base_data_dir, 'dsine_eval', 'oasis')
+    img_path = '%s/%s' % (dataset_path, sample_path)
+    normal_path = img_path.replace('_img'+img_ext, '_normal.pkl')
+    intrins_path = img_path.replace('_img'+img_ext, '_intrins.npy')
+    assert os.path.exists(img_path)
+    assert os.path.exists(normal_path)
+    assert os.path.exists(intrins_path)
+
+    # read image (H, W, 3)
+    img = cv2.cvtColor(cv2.imread(img_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    img = img.astype(np.float32) / 255.0
+
+    # read normal (H, W, 3)
+    h = img.shape[0]
+    w = img.shape[1]
+    normal, normal_mask = read_normal(normal_path, h, w)
+
+    # read intrins (3, 3)
+    intrins = np.load(intrins_path)
+
+    sample = Sample(
+        img=img,
+        normal=normal,
+        normal_mask=normal_mask,
+        intrins=intrins,
+
+        dataset_name='oasis',
+        scene_name=scene_name,
+        img_name=img_name,
+        info=info
+    )
+
+    return sample

Lotus-2/evaluation/dataset_normal/scannet/__init__.py

@@ -0,0 +1,48 @@
+""" Get samples from ScanNet (https://github.com/ScanNet/ScanNet)
+    NOTE: GT surface normals and data split are from FrameNet (ICCV 2019) - https://github.com/hjwdzh/FrameNet
+"""
+import os
+import cv2
+import numpy as np
+
+from evaluation.dataset_normal import Sample
+
+
+def get_sample(base_data_dir, sample_path, info):
+    # e.g. sample_path = "scene0532_00/000000_img.png"
+    scene_name = sample_path.split('/')[0]
+    img_name, img_ext = sample_path.split('/')[1].split('_img')
+
+    dataset_path = os.path.join(base_data_dir, 'dsine_eval', 'scannet')
+    img_path = '%s/%s' % (dataset_path, sample_path)
+    normal_path = img_path.replace('_img'+img_ext, '_normal.png')
+    intrins_path = img_path.replace('_img'+img_ext, '_intrins.npy')
+    assert os.path.exists(img_path)
+    assert os.path.exists(normal_path)
+    assert os.path.exists(intrins_path)
+
+    # read image (H, W, 3)
+    img = cv2.cvtColor(cv2.imread(img_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    img = img.astype(np.float32) / 255.0
+
+    # read normal (H, W, 3)
+    normal = cv2.cvtColor(cv2.imread(normal_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    normal_mask = np.sum(normal, axis=2, keepdims=True) > 0
+    normal = (normal.astype(np.float32) / 255.0) * 2.0 - 1.0
+
+    # read intrins (3, 3)
+    intrins = np.load(intrins_path)
+
+    sample = Sample(
+        img=img,
+        normal=normal,
+        normal_mask=normal_mask,
+        intrins=intrins,
+
+        dataset_name='scannet',
+        scene_name=scene_name,
+        img_name=img_name,
+        info=info
+    )
+
+    return sample

Lotus-2/evaluation/dataset_normal/sintel/__init__.py

@@ -0,0 +1,49 @@
+""" Get samples from Sintel (http://sintel.is.tue.mpg.de/)
+    NOTE: We computed the GT surface normals by doing discontinuity-aware plane fitting
+"""
+import os
+import cv2
+import numpy as np
+os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
+
+from evaluation.dataset_normal import Sample
+
+
+
+def get_sample(base_data_dir, sample_path, info):
+    # e.g. sample_path = "alley_1/frame_0001_img.png"
+    scene_name = sample_path.split('/')[0]
+    img_name, img_ext = sample_path.split('/')[1].split('_img')
+
+    dataset_path = os.path.join(base_data_dir, 'dsine_eval', 'sintel')
+    img_path = '%s/%s' % (dataset_path, sample_path)
+    normal_path = img_path.replace('_img'+img_ext, '_normal.exr')
+    intrins_path = img_path.replace('_img'+img_ext, '_intrins.npy')
+    assert os.path.exists(img_path)
+    assert os.path.exists(normal_path)
+    assert os.path.exists(intrins_path)
+
+    # read image (H, W, 3)
+    img = cv2.cvtColor(cv2.imread(img_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    img = img.astype(np.float32) / 255.0
+
+    # read normal (H, W, 3)
+    normal = cv2.cvtColor(cv2.imread(normal_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)
+    normal_mask = np.linalg.norm(normal, axis=2, keepdims=True) > 0.5
+
+    # read intrins (3, 3)
+    intrins = np.load(intrins_path)
+
+    sample = Sample(
+        img=img,
+        normal=normal,
+        normal_mask=normal_mask,
+        intrins=intrins,
+
+        dataset_name='sintel',
+        scene_name=scene_name,
+        img_name=img_name,
+        info=info
+    )
+
+    return sample