Latest Update

.gitattributes

@@ -33,6 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-ckpt/** filter=lfs diff=lfs merge=lfs -text
 assets/teaser.png filter=lfs diff=lfs merge=lfs -text
-assets/teaser2.png filter=lfs diff=lfs merge=lfs -text
+assets/teaser2.png filter=lfs diff=lfs merge=lfs -text

README-2.md

@@ -1,162 +0,0 @@
-
-<div align="center">
-<h1>IDM-VTON: Improving Diffusion Models for Authentic Virtual Try-on in the Wild</h1>
-
-<a href='https://idm-vton.github.io'><img src='https://img.shields.io/badge/Project-Page-green'></a>
-<a href='https://arxiv.org/abs/2403.05139'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
-<a href='https://huggingface.co/spaces/yisol/IDM-VTON'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
-<a href='https://huggingface.co/yisol/IDM-VTON'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
-
-
-</div>
-
-This is the official implementation of the paper ["Improving Diffusion Models for Authentic Virtual Try-on in the Wild"](https://arxiv.org/abs/2403.05139).
-
-Star ⭐ us if you like it!
-
----
-
-
-<!-- ![teaser2](assets/teaser2.png)&nbsp;
-![teaser](assets/teaser.png)&nbsp; -->
-
-
-## TODO LIST
-
-
-- [x] demo model
-- [x] inference code
-- [ ] training code
-
-
-
-## Requirements
-
-```
-git clone https://github.com/yisol/IDM-VTON.git
-cd IDM-VTON
-
-conda env create -f environment.yaml
-conda activate idm
-```
-
-## Data preparation
-
-### VITON-HD
-You can download VITON-HD dataset from [VITON-HD](https://github.com/shadow2496/VITON-HD).
-
-After download VITON-HD dataset, move vitonhd_test_tagged.json into the test folder.
-
-Structure of the Dataset directory should be as follows.
-
-```
-
-train
-|-- ...
-
-test
-|-- image
-|-- image-densepose
-|-- agnostic-mask
-|-- cloth
-|-- vitonhd_test_tagged.json
-
-```
-
-### DressCode
-You can download DressCode dataset from [DressCode](https://github.com/aimagelab/dress-code).
-
-We provide pre-computed densepose images and captions for garments [here](https://kaistackr-my.sharepoint.com/:u:/g/personal/cpis7_kaist_ac_kr/EaIPRG-aiRRIopz9i002FOwBDa-0-BHUKVZ7Ia5yAVVG3A?e=YxkAip).
-
-We used [detectron2](https://github.com/facebookresearch/detectron2) for obtaining densepose images, refer [here](https://github.com/sangyun884/HR-VITON/issues/45) for more details.
-
-After download the DressCode dataset, place image-densepose directories and caption text files as follows.
-
-```
-DressCode
-|-- dresses
-    |-- images
-    |-- image-densepose
-    |-- dc_caption.txt
-    |-- ...
-|-- lower_body
-    |-- images
-    |-- image-densepose
-    |-- dc_caption.txt
-    |-- ...
-|-- upper_body
-    |-- images
-    |-- image-densepose
-    |-- dc_caption.txt
-    |-- ...
-```
-
-
-## Inference
-
-
-### VITON-HD
-
-Inference using python file with arguments,
-
-```
-accelerate launch inference.py \
-    --width 768 --height 1024 --num_inference_steps 30 \
-    --output_dir "result" \
-    --unpaired \
-    --data_dir "DATA_DIR" \
-    --seed 42 \
-    --test_batch_size 2 \
-    --guidance_scale 2.0
-```
-
-or, you can simply run with the script file.
-
-```
-sh inference.sh
-```
-
-### DressCode
-
-For DressCode dataset, put the category you want to generate images via category argument,
-```
-accelerate launch inference_dc.py \
-    --width 768 --height 1024 --num_inference_steps 30 \
-    --output_dir "result" \
-    --unpaired \
-    --data_dir "DATA_DIR" \
-    --seed 42 
-    --test_batch_size 2
-    --guidance_scale 2.0
-    --category "upper_body" 
-```
-
-or, you can simply run with the script file.
-```
-sh inference.sh
-```
-
-
-## Acknowledgements
-
-For the [demo](https://huggingface.co/spaces/yisol/IDM-VTON), GPUs are supported from [ZeroGPU](https://huggingface.co/zero-gpu-explorers), and masking generation codes are based on [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On).
-
-Parts of our code are based on [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter).
-
-
-
-## Citation
-```
-@article{choi2024improving,
-  title={Improving Diffusion Models for Virtual Try-on},
-  author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
-  journal={arXiv preprint arXiv:2403.05139},
-  year={2024}
-}
-```
-
-## License
-The codes and checkpoints in this repository are under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
-
-
-

README.md

@@ -1,8 +1,53 @@
+---
 base_model: stable-diffusion-xl-1.0-inpainting-0.1
 tags:
   - stable-diffusion-xl
   - inpainting
   - virtual try-on
 license: cc-by-nc-sa-4.0
+---
 
-AILUSION V1 DEMO Virtual Try ON
+
+
+# Check out more codes on our [github repository](https://github.com/yisol/IDM-VTON)!
+
+# IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
+This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'
+- [paper](https://arxiv.org/abs/2403.05139) 
+- [project page](https://idm-vton.github.io/) 
+
+🤗 Try our huggingface [Demo](https://huggingface.co/spaces/yisol/IDM-VTON)
+
+![teaser](assets/teaser.png) 
+![teaser2](assets/teaser2.png) 
+
+
+## TODO LIST
+
+
+- [x] demo model
+- [x] inference code
+- [ ] training code
+
+
+
+
+## Acknowledgements
+
+For the demo, GPUs are supported from [zerogpu](https://huggingface.co/zero-gpu-explorers), and auto masking generation codes are based on [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On).  
+Parts of the code are based on [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter).
+
+
+
+## Citation
+```
+@article{choi2024improving,
+  title={Improving Diffusion Models for Virtual Try-on},
+  author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
+  journal={arXiv preprint arXiv:2403.05139},
+  year={2024}
+}
+```
+
+## License
+The codes and checkpoints in this repository are under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

app.py

@@ -1,313 +0,0 @@
-import gradio as gr
-from PIL import Image
-from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
-from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
-from src.unet_hacked_tryon import UNet2DConditionModel
-from transformers import (
-    CLIPImageProcessor,
-    CLIPVisionModelWithProjection,
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-)
-from diffusers import DDPMScheduler,AutoencoderKL
-from typing import List
-
-import torch
-import os
-from transformers import AutoTokenizer
-import spaces
-import numpy as np
-from utils_mask import get_mask_location
-from torchvision import transforms
-import apply_net
-from preprocess.humanparsing.run_parsing import Parsing
-from preprocess.openpose.run_openpose import OpenPose
-from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
-from torchvision.transforms.functional import to_pil_image
-
-
-def pil_to_binary_mask(pil_image, threshold=0):
-    np_image = np.array(pil_image)
-    grayscale_image = Image.fromarray(np_image).convert("L")
-    binary_mask = np.array(grayscale_image) > threshold
-    mask = np.zeros(binary_mask.shape, dtype=np.uint8)
-    for i in range(binary_mask.shape[0]):
-        for j in range(binary_mask.shape[1]):
-            if binary_mask[i,j] == True :
-                mask[i,j] = 1
-    mask = (mask*255).astype(np.uint8)
-    output_mask = Image.fromarray(mask)
-    return output_mask
-
-
-base_path = 'Roopansh/Ailusion-VTON-DEMO-v1'
-example_path = os.path.join(os.path.dirname(__file__), 'example')
-
-unet = UNet2DConditionModel.from_pretrained(
-    base_path,
-    subfolder="unet",
-    torch_dtype=torch.float16,
-)
-unet.requires_grad_(False)
-tokenizer_one = AutoTokenizer.from_pretrained(
-    base_path,
-    subfolder="tokenizer",
-    revision=None,
-    use_fast=False,
-)
-tokenizer_two = AutoTokenizer.from_pretrained(
-    base_path,
-    subfolder="tokenizer_2",
-    revision=None,
-    use_fast=False,
-)
-noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
-
-text_encoder_one = CLIPTextModel.from_pretrained(
-    base_path,
-    subfolder="text_encoder",
-    torch_dtype=torch.float16,
-)
-text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
-    base_path,
-    subfolder="text_encoder_2",
-    torch_dtype=torch.float16,
-)
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-    base_path,
-    subfolder="image_encoder",
-    torch_dtype=torch.float16,
-    )
-vae = AutoencoderKL.from_pretrained(base_path,
-                                    subfolder="vae",
-                                    torch_dtype=torch.float16,
-)
-
-# "stabilityai/stable-diffusion-xl-base-1.0",
-UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
-    base_path,
-    subfolder="unet_encoder",
-    torch_dtype=torch.float16,
-)
-
-parsing_model = Parsing(0)
-openpose_model = OpenPose(0)
-
-UNet_Encoder.requires_grad_(False)
-image_encoder.requires_grad_(False)
-vae.requires_grad_(False)
-unet.requires_grad_(False)
-text_encoder_one.requires_grad_(False)
-text_encoder_two.requires_grad_(False)
-tensor_transfrom = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-    )
-
-pipe = TryonPipeline.from_pretrained(
-        base_path,
-        unet=unet,
-        vae=vae,
-        feature_extractor= CLIPImageProcessor(),
-        text_encoder = text_encoder_one,
-        text_encoder_2 = text_encoder_two,
-        tokenizer = tokenizer_one,
-        tokenizer_2 = tokenizer_two,
-        scheduler = noise_scheduler,
-        image_encoder=image_encoder,
-        torch_dtype=torch.float16,
-)
-pipe.unet_encoder = UNet_Encoder
-
-@spaces.GPU
-def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed):
-    device = "cuda"
-    
-    openpose_model.preprocessor.body_estimation.model.to(device)
-    pipe.to(device)
-    pipe.unet_encoder.to(device)
-
-    garm_img= garm_img.convert("RGB").resize((768,1024))
-    human_img_orig = dict["background"].convert("RGB")    
-    
-    if is_checked_crop:
-        width, height = human_img_orig.size
-        target_width = int(min(width, height * (3 / 4)))
-        target_height = int(min(height, width * (4 / 3)))
-        left = (width - target_width) / 2
-        top = (height - target_height) / 2
-        right = (width + target_width) / 2
-        bottom = (height + target_height) / 2
-        cropped_img = human_img_orig.crop((left, top, right, bottom))
-        crop_size = cropped_img.size
-        human_img = cropped_img.resize((768,1024))
-    else:
-        human_img = human_img_orig.resize((768,1024))
-
-
-    if is_checked:
-        keypoints = openpose_model(human_img.resize((384,512)))
-        model_parse, _ = parsing_model(human_img.resize((384,512)))
-        mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
-        mask = mask.resize((768,1024))
-    else:
-        mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
-        # mask = transforms.ToTensor()(mask)
-        # mask = mask.unsqueeze(0)
-    mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
-    mask_gray = to_pil_image((mask_gray+1.0)/2.0)
-
-
-    human_img_arg = _apply_exif_orientation(human_img.resize((384,512)))
-    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
-     
-    
-
-    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
-    # verbosity = getattr(args, "verbosity", None)
-    pose_img = args.func(args,human_img_arg)    
-    pose_img = pose_img[:,:,::-1]    
-    pose_img = Image.fromarray(pose_img).resize((768,1024))
-    
-    with torch.no_grad():
-        # Extract the images
-        with torch.cuda.amp.autocast():
-            with torch.no_grad():
-                prompt = "model is wearing " + garment_des
-                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-                with torch.inference_mode():
-                    (
-                        prompt_embeds,
-                        negative_prompt_embeds,
-                        pooled_prompt_embeds,
-                        negative_pooled_prompt_embeds,
-                    ) = pipe.encode_prompt(
-                        prompt,
-                        num_images_per_prompt=1,
-                        do_classifier_free_guidance=True,
-                        negative_prompt=negative_prompt,
-                    )
-                                    
-                    prompt = "a photo of " + garment_des
-                    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-                    if not isinstance(prompt, List):
-                        prompt = [prompt] * 1
-                    if not isinstance(negative_prompt, List):
-                        negative_prompt = [negative_prompt] * 1
-                    with torch.inference_mode():
-                        (
-                            prompt_embeds_c,
-                            _,
-                            _,
-                            _,
-                        ) = pipe.encode_prompt(
-                            prompt,
-                            num_images_per_prompt=1,
-                            do_classifier_free_guidance=False,
-                            negative_prompt=negative_prompt,
-                        )
-
-
-
-                    pose_img =  tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16)
-                    garm_tensor =  tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16)
-                    generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
-                    images = pipe(
-                        prompt_embeds=prompt_embeds.to(device,torch.float16),
-                        negative_prompt_embeds=negative_prompt_embeds.to(device,torch.float16),
-                        pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.float16),
-                        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,torch.float16),
-                        num_inference_steps=denoise_steps,
-                        generator=generator,
-                        strength = 1.0,
-                        pose_img = pose_img.to(device,torch.float16),
-                        text_embeds_cloth=prompt_embeds_c.to(device,torch.float16),
-                        cloth = garm_tensor.to(device,torch.float16),
-                        mask_image=mask,
-                        image=human_img, 
-                        height=1024,
-                        width=768,
-                        ip_adapter_image = garm_img.resize((768,1024)),
-                        guidance_scale=2.0,
-                    )[0]
-
-    if is_checked_crop:
-        out_img = images[0].resize(crop_size)        
-        human_img_orig.paste(out_img, (int(left), int(top)))    
-        return human_img_orig, mask_gray
-    else:
-        return images[0], mask_gray
-    # return images[0], mask_gray
-
-garm_list = os.listdir(os.path.join(example_path,"cloth"))
-garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
-
-human_list = os.listdir(os.path.join(example_path,"human"))
-human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
-
-human_ex_list = []
-for ex_human in human_list_path:
-    ex_dict= {}
-    ex_dict['background'] = ex_human
-    ex_dict['layers'] = None
-    ex_dict['composite'] = None
-    human_ex_list.append(ex_dict)
-
-##default human
-
-
-image_blocks = gr.Blocks().queue()
-with image_blocks as demo:
-    gr.Markdown("## AILUSION VTON 👕👔👚")
-    gr.Markdown("Virtual Try-on with your image and garment image. Check out the [source codes](https://github.com/yisol/IDM-VTON) and the [model](https://huggingface.co/yisol/IDM-VTON)")
-    with gr.Row():
-        with gr.Column():
-            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
-            with gr.Row():
-                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
-            with gr.Row():
-                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
-
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=10,
-                examples=human_ex_list
-            )
-
-        with gr.Column():
-            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
-            with gr.Row(elem_id="prompt-container"):
-                with gr.Row():
-                    prompt = gr.Textbox(placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", show_label=False, elem_id="prompt")
-            example = gr.Examples(
-                inputs=garm_img,
-                examples_per_page=8,
-                examples=garm_list_path)
-        with gr.Column():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False)
-        with gr.Column():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False)
-
-
-
-
-    with gr.Column():
-        try_button = gr.Button(value="Try-on")
-        with gr.Accordion(label="Advanced Settings", open=False):
-            with gr.Row():
-                denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1)
-                seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)
-
-
-
-    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out,masked_img], api_name='tryon')
-
-            
-
-
-image_blocks.launch()
-

app_VTON.py

@@ -1,328 +0,0 @@
-import gradio as gr
-import argparse, torch, os
-from PIL import Image
-from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
-from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
-from src.unet_hacked_tryon import UNet2DConditionModel
-from transformers import (
-    CLIPImageProcessor,
-    CLIPVisionModelWithProjection,
-)
-from diffusers import AutoencoderKL
-from typing import List
-from util.common import open_folder
-from util.image import pil_to_binary_mask, save_output_image
-from utils_mask import get_mask_location
-from torchvision import transforms
-import apply_net
-from preprocess.humanparsing.run_parsing import Parsing
-from preprocess.openpose.run_openpose import OpenPose
-from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
-from torchvision.transforms.functional import to_pil_image
-from util.pipeline import quantize_4bit, restart_cpu_offload, torch_gc
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--share", type=str, default=False, help="Set to True to share the app publicly.")
-parser.add_argument("--lowvram", action="store_true", help="Enable CPU offload for model operations.")
-parser.add_argument("--load_mode", default=None, type=str, choices=["4bit", "8bit"], help="Quantization mode for optimization memory consumption")
-parser.add_argument("--fixed_vae", action="store_true", default=True,  help="Use fixed vae for FP16.")
-args = parser.parse_args()
-
-load_mode = args.load_mode
-fixed_vae = args.fixed_vae
-
-dtype = torch.float16
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model_id = 'yisol/IDM-VTON'
-vae_model_id = 'madebyollin/sdxl-vae-fp16-fix'
-
-dtypeQuantize = dtype
-
-if(load_mode in ('4bit','8bit')):
-    dtypeQuantize = torch.float8_e4m3fn
-
-ENABLE_CPU_OFFLOAD = args.lowvram
-torch.backends.cudnn.allow_tf32 = False
-torch.backends.cuda.allow_tf32 = False
-need_restart_cpu_offloading = False
-
-unet = None
-pipe = None
-UNet_Encoder = None
-example_path = os.path.join(os.path.dirname(__file__), 'example')
-
-def start_tryon(dict, garm_img, garment_des, category, is_checked, is_checked_crop, denoise_steps, is_randomize_seed, seed, number_of_images):
-    global pipe, unet, UNet_Encoder, need_restart_cpu_offloading
-
-    if pipe == None:
-        unet = UNet2DConditionModel.from_pretrained(
-            model_id,
-            subfolder="unet",
-            torch_dtype=dtypeQuantize,
-        )
-        if load_mode == '4bit':
-            quantize_4bit(unet)
-            
-        unet.requires_grad_(False)
-       
-        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-            model_id,
-            subfolder="image_encoder",
-            torch_dtype=torch.float16,
-            )
-        if load_mode == '4bit':
-            quantize_4bit(image_encoder)
-        
-        if fixed_vae:
-            vae = AutoencoderKL.from_pretrained(vae_model_id, torch_dtype=dtype)
-        else:            
-            vae = AutoencoderKL.from_pretrained(model_id,
-                                                subfolder="vae",
-                                                torch_dtype=dtype,
-            )
-
-        # "stabilityai/stable-diffusion-xl-base-1.0",
-        UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
-            model_id,
-            subfolder="unet_encoder",
-            torch_dtype=dtypeQuantize,
-        )
-     
-        if load_mode == '4bit':
-            quantize_4bit(UNet_Encoder)
-
-        UNet_Encoder.requires_grad_(False)
-        image_encoder.requires_grad_(False)
-        vae.requires_grad_(False)
-        unet.requires_grad_(False)
-              
-        pipe_param = {
-                'pretrained_model_name_or_path': model_id,
-                'unet': unet,     
-                'torch_dtype': dtype,   
-                'vae': vae,
-                'image_encoder': image_encoder,
-                'feature_extractor': CLIPImageProcessor(),
-            }
-        
-        pipe = TryonPipeline.from_pretrained(**pipe_param).to(device)
-        pipe.unet_encoder = UNet_Encoder    
-        pipe.unet_encoder.to(pipe.unet.device)
-
-        if load_mode == '4bit':
-            if pipe.text_encoder is not None:
-                quantize_4bit(pipe.text_encoder)
-            if pipe.text_encoder_2 is not None:
-                quantize_4bit(pipe.text_encoder_2)
-           
-    else:
-        if ENABLE_CPU_OFFLOAD:
-            need_restart_cpu_offloading =True
-    
-    torch_gc() 
-    parsing_model = Parsing(0)
-    openpose_model = OpenPose(0)
-    openpose_model.preprocessor.body_estimation.model.to(device)
-    tensor_transfrom = transforms.Compose(
-                    [
-                        transforms.ToTensor(),
-                        transforms.Normalize([0.5], [0.5]),
-                    ]
-            )
-    
-    if need_restart_cpu_offloading:
-        restart_cpu_offload(pipe, load_mode)
-    elif ENABLE_CPU_OFFLOAD:
-        pipe.enable_model_cpu_offload()
-
-    #if load_mode != '4bit' :
-    #    pipe.enable_xformers_memory_efficient_attention()    
-
-    garm_img= garm_img.convert("RGB").resize((768,1024))
-    human_img_orig = dict["background"].convert("RGB")    
-    
-    if is_checked_crop:
-        width, height = human_img_orig.size
-        target_width = int(min(width, height * (3 / 4)))
-        target_height = int(min(height, width * (4 / 3)))
-        left = (width - target_width) / 2
-        top = (height - target_height) / 2
-        right = (width + target_width) / 2
-        bottom = (height + target_height) / 2
-        cropped_img = human_img_orig.crop((left, top, right, bottom))
-        crop_size = cropped_img.size
-        human_img = cropped_img.resize((768,1024))
-    else:
-        human_img = human_img_orig.resize((768,1024))
-
-    if is_checked:
-        keypoints = openpose_model(human_img.resize((384,512)))
-        model_parse, _ = parsing_model(human_img.resize((384,512)))
-        mask, mask_gray = get_mask_location('hd', category, model_parse, keypoints)
-        mask = mask.resize((768,1024))
-    else:
-        mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
-        # mask = transforms.ToTensor()(mask)
-        # mask = mask.unsqueeze(0)
-    
-    mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
-    mask_gray = to_pil_image((mask_gray+1.0)/2.0)
-
-    human_img_arg = _apply_exif_orientation(human_img.resize((384,512)))
-    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
-
-    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
-    # verbosity = getattr(args, "verbosity", None)
-    pose_img = args.func(args,human_img_arg)    
-    pose_img = pose_img[:,:,::-1]    
-    pose_img = Image.fromarray(pose_img).resize((768,1024))
-    
-    if pipe.text_encoder is not None:        
-        pipe.text_encoder.to(device)
-
-    if pipe.text_encoder_2 is not None:
-        pipe.text_encoder_2.to(device)
-
-    with torch.no_grad():
-        # Extract the images
-        with torch.cuda.amp.autocast(dtype=dtype):
-            with torch.no_grad():
-                prompt = "model is wearing " + garment_des
-                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-                with torch.inference_mode():
-                    (
-                        prompt_embeds,
-                        negative_prompt_embeds,
-                        pooled_prompt_embeds,
-                        negative_pooled_prompt_embeds,
-                    ) = pipe.encode_prompt(
-                        prompt,
-                        num_images_per_prompt=1,
-                        do_classifier_free_guidance=True,
-                        negative_prompt=negative_prompt,
-                    )
-                                    
-                    prompt = "a photo of " + garment_des
-                    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-                    if not isinstance(prompt, List):
-                        prompt = [prompt] * 1
-                    if not isinstance(negative_prompt, List):
-                        negative_prompt = [negative_prompt] * 1
-                    with torch.inference_mode():
-                        (
-                            prompt_embeds_c,
-                            _,
-                            _,
-                            _,
-                        ) = pipe.encode_prompt(
-                            prompt,
-                            num_images_per_prompt=1,
-                            do_classifier_free_guidance=False,
-                            negative_prompt=negative_prompt,
-                        )
-
-                    pose_img =  tensor_transfrom(pose_img).unsqueeze(0).to(device,dtype)
-                    garm_tensor =  tensor_transfrom(garm_img).unsqueeze(0).to(device,dtype)
-                    results = []
-                    current_seed = seed
-                    for i in range(number_of_images):  
-                        if is_randomize_seed:
-                            current_seed = torch.randint(0, 2**32, size=(1,)).item()                        
-                        generator = torch.Generator(device).manual_seed(current_seed) if seed != -1 else None                     
-                        current_seed = current_seed + i
-
-                        images = pipe(
-                            prompt_embeds=prompt_embeds.to(device,dtype),
-                            negative_prompt_embeds=negative_prompt_embeds.to(device,dtype),
-                            pooled_prompt_embeds=pooled_prompt_embeds.to(device,dtype),
-                            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,dtype),
-                            num_inference_steps=denoise_steps,
-                            generator=generator,
-                            strength = 1.0,
-                            pose_img = pose_img.to(device,dtype),
-                            text_embeds_cloth=prompt_embeds_c.to(device,dtype),
-                            cloth = garm_tensor.to(device,dtype),
-                            mask_image=mask,
-                            image=human_img, 
-                            height=1024,
-                            width=768,
-                            ip_adapter_image = garm_img.resize((768,1024)),
-                            guidance_scale=2.0,
-                            dtype=dtype,
-                            device=device,
-                        )[0]
-                        if is_checked_crop:
-                            out_img = images[0].resize(crop_size)        
-                            human_img_orig.paste(out_img, (int(left), int(top)))   
-                            img_path = save_output_image(human_img_orig, base_path="outputs", base_filename='img', seed=current_seed)
-                            results.append(img_path)
-                        else:
-                            img_path = save_output_image(images[0], base_path="outputs", base_filename='img')
-                            results.append(img_path)
-                    return results, mask_gray
-    
-garm_list = os.listdir(os.path.join(example_path,"cloth"))
-garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
-
-human_list = os.listdir(os.path.join(example_path,"human"))
-human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
-
-human_ex_list = []
-for ex_human in human_list_path:
-    if "Jensen" in ex_human or "sam1 (1)" in ex_human:
-        ex_dict = {}
-        ex_dict['background'] = ex_human
-        ex_dict['layers'] = None
-        ex_dict['composite'] = None
-        human_ex_list.append(ex_dict)
-
-image_blocks = gr.Blocks().queue()
-with image_blocks as demo:
-    gr.Markdown("## V7 - IDM-VTON 👕👔👚 improved by SECourses and DEVAIEXP: 1-Click Installers Latest Version On : https://www.patreon.com/posts/103022942")
-    gr.Markdown("Virtual Try-on with your image and garment image. Check out the [source codes](https://github.com/yisol/IDM-VTON) and the [model](https://huggingface.co/yisol/IDM-VTON)")
-    with gr.Row():
-        with gr.Column():
-            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
-            with gr.Row():
-                category = gr.Radio(choices=["upper_body", "lower_body", "dresses"], label="Select Garment Category", value="upper_body")
-                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
-            with gr.Row():
-                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=True)
-
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=2,
-                examples=human_ex_list
-            )
-
-        with gr.Column():
-            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
-            with gr.Row(elem_id="prompt-container"):
-                with gr.Row():
-                    prompt = gr.Textbox(placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", show_label=False, elem_id="prompt")
-            example = gr.Examples(
-                inputs=garm_img,
-                examples_per_page=8,
-                examples=garm_list_path)
-        with gr.Column():
-            with gr.Row():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-                masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False)
-            with gr.Row():
-                btn_open_outputs = gr.Button("Open Outputs Folder")
-                btn_open_outputs.click(fn=open_folder)
-        with gr.Column():
-            with gr.Row():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-                image_gallery = gr.Gallery(label="Generated Images", show_label=True)
-            with gr.Row():
-                try_button = gr.Button(value="Try-on")
-                denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=120, value=30, step=1)
-                seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=1)
-                is_randomize_seed = gr.Checkbox(label="Randomize seed for each generated image", value=True)  
-                number_of_images = gr.Number(label="Number Of Images To Generate (it will start from your input seed and increment by 1)", minimum=1, maximum=9999, value=1, step=1)
-
-
-    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, category, is_checked, is_checked_crop, denoise_steps, is_randomize_seed, seed, number_of_images], outputs=[image_gallery, masked_img],api_name='tryon')
-
-image_blocks.launch(inbrowser=True,share=args.share)

apply_net.py

@@ -1,359 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import argparse
-import glob
-import logging
-import os
-import sys
-from typing import Any, ClassVar, Dict, List
-import torch
-
-from detectron2.config import CfgNode, get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.engine.defaults import DefaultPredictor
-from detectron2.structures.instances import Instances
-from detectron2.utils.logger import setup_logger
-
-from densepose import add_densepose_config
-from densepose.structures import DensePoseChartPredictorOutput, DensePoseEmbeddingPredictorOutput
-from densepose.utils.logger import verbosity_to_level
-from densepose.vis.base import CompoundVisualizer
-from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer
-from densepose.vis.densepose_outputs_vertex import (
-    DensePoseOutputsTextureVisualizer,
-    DensePoseOutputsVertexVisualizer,
-    get_texture_atlases,
-)
-from densepose.vis.densepose_results import (
-    DensePoseResultsContourVisualizer,
-    DensePoseResultsFineSegmentationVisualizer,
-    DensePoseResultsUVisualizer,
-    DensePoseResultsVVisualizer,
-)
-from densepose.vis.densepose_results_textures import (
-    DensePoseResultsVisualizerWithTexture,
-    get_texture_atlas,
-)
-from densepose.vis.extractor import (
-    CompoundExtractor,
-    DensePoseOutputsExtractor,
-    DensePoseResultExtractor,
-    create_extractor,
-)
-
-DOC = """Apply Net - a tool to print / visualize DensePose results
-"""
-
-LOGGER_NAME = "apply_net"
-logger = logging.getLogger(LOGGER_NAME)
-
-_ACTION_REGISTRY: Dict[str, "Action"] = {}
-
-
-class Action:
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        parser.add_argument(
-            "-v",
-            "--verbosity",
-            action="count",
-            help="Verbose mode. Multiple -v options increase the verbosity.",
-        )
-
-
-def register_action(cls: type):
-    """
-    Decorator for action classes to automate action registration
-    """
-    global _ACTION_REGISTRY
-    _ACTION_REGISTRY[cls.COMMAND] = cls
-    return cls
-
-
-class InferenceAction(Action):
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(InferenceAction, cls).add_arguments(parser)
-        parser.add_argument("cfg", metavar="<config>", help="Config file")
-        parser.add_argument("model", metavar="<model>", help="Model file")
-        parser.add_argument(
-            "--opts",
-            help="Modify config options using the command-line 'KEY VALUE' pairs",
-            default=[],
-            nargs=argparse.REMAINDER,
-        )
-
-    @classmethod
-    def execute(cls: type, args: argparse.Namespace, human_img):
-        logger.info(f"Loading config from {args.cfg}")
-        opts = []
-        cfg = cls.setup_config(args.cfg, args.model, args, opts)
-        logger.info(f"Loading model from {args.model}")
-        predictor = DefaultPredictor(cfg)
-        # logger.info(f"Loading data from {args.input}")
-        # file_list = cls._get_input_file_list(args.input)
-        # if len(file_list) == 0:
-        #     logger.warning(f"No input images for {args.input}")
-        #     return
-        context = cls.create_context(args, cfg)
-        # for file_name in file_list:
-        #     img = read_image(file_name, format="BGR")  # predictor expects BGR image.
-        with torch.no_grad():
-            outputs = predictor(human_img)["instances"]
-            out_pose = cls.execute_on_outputs(context, {"image": human_img}, outputs)
-        cls.postexecute(context)
-        return out_pose
-
-    @classmethod
-    def setup_config(
-        cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
-    ):
-        cfg = get_cfg()
-        add_densepose_config(cfg)
-        cfg.merge_from_file(config_fpath)
-        cfg.merge_from_list(args.opts)
-        if opts:
-            cfg.merge_from_list(opts)
-        cfg.MODEL.WEIGHTS = model_fpath
-        cfg.freeze()
-        return cfg
-
-    @classmethod
-    def _get_input_file_list(cls: type, input_spec: str):
-        if os.path.isdir(input_spec):
-            file_list = [
-                os.path.join(input_spec, fname)
-                for fname in os.listdir(input_spec)
-                if os.path.isfile(os.path.join(input_spec, fname))
-            ]
-        elif os.path.isfile(input_spec):
-            file_list = [input_spec]
-        else:
-            file_list = glob.glob(input_spec)
-        return file_list
-
-
-@register_action
-class DumpAction(InferenceAction):
-    """
-    Dump action that outputs results to a pickle file
-    """
-
-    COMMAND: ClassVar[str] = "dump"
-
-    @classmethod
-    def add_parser(cls: type, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Dump model outputs to a file.")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(DumpAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "--output",
-            metavar="<dump_file>",
-            default="results.pkl",
-            help="File name to save dump to",
-        )
-
-    @classmethod
-    def execute_on_outputs(
-        cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
-    ):
-        image_fpath = entry["file_name"]
-        logger.info(f"Processing {image_fpath}")
-        result = {"file_name": image_fpath}
-        if outputs.has("scores"):
-            result["scores"] = outputs.get("scores").cpu()
-        if outputs.has("pred_boxes"):
-            result["pred_boxes_XYXY"] = outputs.get("pred_boxes").tensor.cpu()
-            if outputs.has("pred_densepose"):
-                if isinstance(outputs.pred_densepose, DensePoseChartPredictorOutput):
-                    extractor = DensePoseResultExtractor()
-                elif isinstance(outputs.pred_densepose, DensePoseEmbeddingPredictorOutput):
-                    extractor = DensePoseOutputsExtractor()
-                result["pred_densepose"] = extractor(outputs)[0]
-        context["results"].append(result)
-
-    @classmethod
-    def create_context(cls: type, args: argparse.Namespace, cfg: CfgNode):
-        context = {"results": [], "out_fname": args.output}
-        return context
-
-    @classmethod
-    def postexecute(cls: type, context: Dict[str, Any]):
-        out_fname = context["out_fname"]
-        out_dir = os.path.dirname(out_fname)
-        if len(out_dir) > 0 and not os.path.exists(out_dir):
-            os.makedirs(out_dir)
-        with open(out_fname, "wb") as hFile:
-            torch.save(context["results"], hFile)
-            logger.info(f"Output saved to {out_fname}")
-
-
-@register_action
-class ShowAction(InferenceAction):
-    """
-    Show action that visualizes selected entries on an image
-    """
-
-    COMMAND: ClassVar[str] = "show"
-    VISUALIZERS: ClassVar[Dict[str, object]] = {
-        "dp_contour": DensePoseResultsContourVisualizer,
-        "dp_segm": DensePoseResultsFineSegmentationVisualizer,
-        "dp_u": DensePoseResultsUVisualizer,
-        "dp_v": DensePoseResultsVVisualizer,
-        "dp_iuv_texture": DensePoseResultsVisualizerWithTexture,
-        "dp_cse_texture": DensePoseOutputsTextureVisualizer,
-        "dp_vertex": DensePoseOutputsVertexVisualizer,
-        "bbox": ScoredBoundingBoxVisualizer,
-    }
-
-    @classmethod
-    def add_parser(cls: type, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Visualize selected entries")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(ShowAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "visualizations",
-            metavar="<visualizations>",
-            help="Comma separated list of visualizations, possible values: "
-            "[{}]".format(",".join(sorted(cls.VISUALIZERS.keys()))),
-        )
-        parser.add_argument(
-            "--min_score",
-            metavar="<score>",
-            default=0.8,
-            type=float,
-            help="Minimum detection score to visualize",
-        )
-        parser.add_argument(
-            "--nms_thresh", metavar="<threshold>", default=None, type=float, help="NMS threshold"
-        )
-        parser.add_argument(
-            "--texture_atlas",
-            metavar="<texture_atlas>",
-            default=None,
-            help="Texture atlas file (for IUV texture transfer)",
-        )
-        parser.add_argument(
-            "--texture_atlases_map",
-            metavar="<texture_atlases_map>",
-            default=None,
-            help="JSON string of a dict containing texture atlas files for each mesh",
-        )
-        parser.add_argument(
-            "--output",
-            metavar="<image_file>",
-            default="outputres.png",
-            help="File name to save output to",
-        )
-
-    @classmethod
-    def setup_config(
-        cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
-    ):
-        opts.append("MODEL.ROI_HEADS.SCORE_THRESH_TEST")
-        opts.append(str(args.min_score))
-        if args.nms_thresh is not None:
-            opts.append("MODEL.ROI_HEADS.NMS_THRESH_TEST")
-            opts.append(str(args.nms_thresh))
-        cfg = super(ShowAction, cls).setup_config(config_fpath, model_fpath, args, opts)
-        return cfg
-
-    @classmethod
-    def execute_on_outputs(
-        cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
-    ):
-        import cv2
-        import numpy as np
-        visualizer = context["visualizer"]
-        extractor = context["extractor"]
-        # image_fpath = entry["file_name"]
-        # logger.info(f"Processing {image_fpath}")
-        image = cv2.cvtColor(entry["image"], cv2.COLOR_BGR2GRAY)
-        image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
-        data = extractor(outputs)
-        image_vis = visualizer.visualize(image, data)
-
-        return image_vis
-        entry_idx = context["entry_idx"] + 1
-        out_fname = './image-densepose/' + image_fpath.split('/')[-1]
-        out_dir = './image-densepose'
-        out_dir = os.path.dirname(out_fname)
-        if len(out_dir) > 0 and not os.path.exists(out_dir):
-            os.makedirs(out_dir)
-        cv2.imwrite(out_fname, image_vis)
-        logger.info(f"Output saved to {out_fname}")
-        context["entry_idx"] += 1
-
-    @classmethod
-    def postexecute(cls: type, context: Dict[str, Any]):
-        pass
-# python ./apply_net.py show ./configs/densepose_rcnn_R_50_FPN_s1x.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl /home/alin0222/DressCode/upper_body/images dp_segm -v --opts MODEL.DEVICE cpu
-
-    @classmethod
-    def _get_out_fname(cls: type, entry_idx: int, fname_base: str):
-        base, ext = os.path.splitext(fname_base)
-        return base + ".{0:04d}".format(entry_idx) + ext
-
-    @classmethod
-    def create_context(cls: type, args: argparse.Namespace, cfg: CfgNode) -> Dict[str, Any]:
-        vis_specs = args.visualizations.split(",")
-        visualizers = []
-        extractors = []
-        for vis_spec in vis_specs:
-            texture_atlas = get_texture_atlas(args.texture_atlas)
-            texture_atlases_dict = get_texture_atlases(args.texture_atlases_map)
-            vis = cls.VISUALIZERS[vis_spec](
-                cfg=cfg,
-                texture_atlas=texture_atlas,
-                texture_atlases_dict=texture_atlases_dict,
-            )
-            visualizers.append(vis)
-            extractor = create_extractor(vis)
-            extractors.append(extractor)
-        visualizer = CompoundVisualizer(visualizers)
-        extractor = CompoundExtractor(extractors)
-        context = {
-            "extractor": extractor,
-            "visualizer": visualizer,
-            "out_fname": args.output,
-            "entry_idx": 0,
-        }
-        return context
-
-
-def create_argument_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description=DOC,
-        formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=120),
-    )
-    parser.set_defaults(func=lambda _: parser.print_help(sys.stdout))
-    subparsers = parser.add_subparsers(title="Actions")
-    for _, action in _ACTION_REGISTRY.items():
-        action.add_parser(subparsers)
-    return parser
-
-
-def main():
-    parser = create_argument_parser()
-    args = parser.parse_args()
-    verbosity = getattr(args, "verbosity", None)
-    global logger
-    logger = setup_logger(name=LOGGER_NAME)
-    logger.setLevel(verbosity_to_level(verbosity))
-    args.func(args)
-
-
-if __name__ == "__main__":
-    main()
-
-
-# python ./apply_net.py show ./configs/densepose_rcnn_R_50_FPN_s1x.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl /home/alin0222/Dresscode/dresses/humanonly dp_segm -v --opts MODEL.DEVICE cuda

ckpt/densepose/model_final_162be9.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b8a7382001b16e453bad95ca9dbc68ae8f2b839b304cf90eaf5c27fbdb4dae91
-size 255757821

ckpt/humanparsing/parsing_atr.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:04c7d1d070d0e0ae943d86b18cb5aaaea9e278d97462e9cfb270cbbe4cd977f4
-size 266859305

ckpt/humanparsing/parsing_lip.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8436e1dae96e2601c373d1ace29c8f0978b16357d9038c17a8ba756cca376dbc
-size 266863411

ckpt/openpose/.DS_Store

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e953475b1378e1d0566f8ad8de20077ce8610ae23fb2b5f8bfe57104aca8e911
-size 6148

ckpt/openpose/ckpts/body_pose_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:25a948c16078b0f08e236bda51a385d855ef4c153598947c28c0d47ed94bb746
-size 209267595

configs/Base-DensePose-RCNN-FPN.yaml

@@ -1,48 +0,0 @@
-VERSION: 2
-MODEL:
-  META_ARCHITECTURE: "GeneralizedRCNN"
-  BACKBONE:
-    NAME: "build_resnet_fpn_backbone"
-  RESNETS:
-    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
-  FPN:
-    IN_FEATURES: ["res2", "res3", "res4", "res5"]
-  ANCHOR_GENERATOR:
-    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
-    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
-  RPN:
-    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
-    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
-    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
-    # Detectron1 uses 2000 proposals per-batch,
-    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
-    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
-    POST_NMS_TOPK_TRAIN: 1000
-    POST_NMS_TOPK_TEST: 1000
-
-  DENSEPOSE_ON: True
-  ROI_HEADS:
-    NAME: "DensePoseROIHeads"
-    IN_FEATURES: ["p2", "p3", "p4", "p5"]
-    NUM_CLASSES: 1
-  ROI_BOX_HEAD:
-    NAME: "FastRCNNConvFCHead"
-    NUM_FC: 2
-    POOLER_RESOLUTION: 7
-    POOLER_SAMPLING_RATIO: 2
-    POOLER_TYPE: "ROIAlign"
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    POOLER_TYPE: "ROIAlign"
-    NUM_COARSE_SEGM_CHANNELS: 2
-DATASETS:
-  TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival")
-  TEST: ("densepose_coco_2014_minival",)
-SOLVER:
-  IMS_PER_BATCH: 16
-  BASE_LR: 0.01
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-  WARMUP_FACTOR: 0.1
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)

configs/HRNet/densepose_rcnn_HRFPN_HRNet_w32_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://1drv.ms/u/s!Aus8VCZ_C_33dYBMemi9xOUFR0w"
-  BACKBONE:
-    NAME: "build_hrfpn_backbone"
-  RPN:
-    IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5']
-  ROI_HEADS:
-    IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5']
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  CLIP_GRADIENTS:
-    ENABLED: True
-    CLIP_TYPE: "norm"
-  BASE_LR: 0.03

configs/HRNet/densepose_rcnn_HRFPN_HRNet_w40_s1x.yaml

@@ -1,23 +0,0 @@
-_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://1drv.ms/u/s!Aus8VCZ_C_33ck0gvo5jfoWBOPo"
-  BACKBONE:
-    NAME: "build_hrfpn_backbone"
-  RPN:
-    IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5']
-  ROI_HEADS:
-    IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5']
-  HRNET:
-    STAGE2:
-      NUM_CHANNELS: [40, 80]
-    STAGE3:
-      NUM_CHANNELS: [40, 80, 160]
-    STAGE4:
-      NUM_CHANNELS: [40, 80, 160, 320]
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  CLIP_GRADIENTS:
-    ENABLED: True
-    CLIP_TYPE: "norm"
-  BASE_LR: 0.03

configs/HRNet/densepose_rcnn_HRFPN_HRNet_w48_s1x.yaml

@@ -1,23 +0,0 @@
-_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://1drv.ms/u/s!Aus8VCZ_C_33dKvqI6pBZlifgJk"
-  BACKBONE:
-    NAME: "build_hrfpn_backbone"
-  RPN:
-    IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5']
-  ROI_HEADS:
-    IN_FEATURES: ['p1', 'p2', 'p3', 'p4', 'p5']
-  HRNET:
-    STAGE2:
-      NUM_CHANNELS: [48, 96]
-    STAGE3:
-      NUM_CHANNELS: [48, 96, 192]
-    STAGE4:
-      NUM_CHANNELS: [48, 96, 192, 384]
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  CLIP_GRADIENTS:
-    ENABLED: True
-    CLIP_TYPE: "norm"
-  BASE_LR: 0.03

configs/cse/Base-DensePose-RCNN-FPN-Human.yaml

@@ -1,20 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  ROI_DENSEPOSE_HEAD:
-    CSE:
-      EMBEDDERS:
-        "smpl_27554":
-          TYPE: vertex_feature
-          NUM_VERTICES: 27554
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_smpl_27554_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_coco_2014_train_cse"
-    - "densepose_coco_2014_valminusminival_cse"
-  TEST:
-    - "densepose_coco_2014_minival_cse"
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "smpl_27554"

configs/cse/Base-DensePose-RCNN-FPN.yaml

@@ -1,60 +0,0 @@
-VERSION: 2
-MODEL:
-  META_ARCHITECTURE: "GeneralizedRCNN"
-  BACKBONE:
-    NAME: "build_resnet_fpn_backbone"
-  RESNETS:
-    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
-  FPN:
-    IN_FEATURES: ["res2", "res3", "res4", "res5"]
-  ANCHOR_GENERATOR:
-    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
-    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
-  RPN:
-    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
-    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
-    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
-    # Detectron1 uses 2000 proposals per-batch,
-    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
-    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
-    POST_NMS_TOPK_TRAIN: 1000
-    POST_NMS_TOPK_TEST: 1000
-
-  DENSEPOSE_ON: True
-  ROI_HEADS:
-    NAME: "DensePoseROIHeads"
-    IN_FEATURES: ["p2", "p3", "p4", "p5"]
-    NUM_CLASSES: 1
-  ROI_BOX_HEAD:
-    NAME: "FastRCNNConvFCHead"
-    NUM_FC: 2
-    POOLER_RESOLUTION: 7
-    POOLER_SAMPLING_RATIO: 2
-    POOLER_TYPE: "ROIAlign"
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    POOLER_TYPE: "ROIAlign"
-    NUM_COARSE_SEGM_CHANNELS: 2
-    PREDICTOR_NAME: "DensePoseEmbeddingPredictor"
-    LOSS_NAME: "DensePoseCseLoss"
-    CSE:
-      # embedding loss, possible values:
-      # - "EmbeddingLoss"
-      # - "SoftEmbeddingLoss"
-      EMBED_LOSS_NAME: "EmbeddingLoss"
-SOLVER:
-  IMS_PER_BATCH: 16
-  BASE_LR: 0.01
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-  WARMUP_FACTOR: 0.1
-  CLIP_GRADIENTS:
-    CLIP_TYPE: norm
-    CLIP_VALUE: 1.0
-    ENABLED: true
-    NORM_TYPE: 2.0
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
-DENSEPOSE_EVALUATION:
-  TYPE: cse
-  STORAGE: file

configs/cse/densepose_rcnn_R_101_FPN_DL_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    CSE:
-      EMBED_LOSS_NAME: "EmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_101_FPN_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    CSE:
-      EMBED_LOSS_NAME: "EmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_101_FPN_soft_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_50_FPN_DL_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    CSE:
-      EMBED_LOSS_NAME: "EmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_50_FPN_DL_soft_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_50_FPN_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    CSE:
-      EMBED_LOSS_NAME: "EmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_16k.yaml

@@ -1,133 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 1
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "cat_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_7466_256.pkl"
-        "dog_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_7466_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds2_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds2_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds2_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds2_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CATEGORY_MAPS:
-    "densepose_lvis_v1_ds2_train_v1":
-      "1202": 943 # zebra -> sheep
-      "569": 943  # horse -> sheep
-      "496": 943  # giraffe -> sheep
-      "422": 943  # elephant -> sheep
-      "80": 943   # cow -> sheep
-      "76": 943   # bear -> sheep
-      "225": 943  # cat -> sheep
-      "378": 943  # dog -> sheep
-    "densepose_lvis_v1_ds2_val_v1":
-      "1202": 943 # zebra -> sheep
-      "569": 943  # horse -> sheep
-      "496": 943  # giraffe -> sheep
-      "422": 943  # elephant -> sheep
-      "80": 943   # cow -> sheep
-      "76": 943   # bear -> sheep
-      "225": 943  # cat -> sheep
-      "378": 943  # dog -> sheep
-  CLASS_TO_MESH_NAME_MAPPING:
-    # Note: different classes are mapped to a single class
-    # mesh is chosen based on GT data, so this is just some
-    # value which has no particular meaning
-    "0": "sheep_5004"
-SOLVER:
-  MAX_ITER: 16000
-  STEPS: (12000, 14000)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_CA_finetune_4k.yaml

@@ -1,133 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 1
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "cat_5001":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5001
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_5001_256.pkl"
-        "dog_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_5002_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds1_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds1_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds1_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds1_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CATEGORY_MAPS:
-    "densepose_lvis_v1_ds1_train_v1":
-      "1202": 943 # zebra -> sheep
-      "569": 943  # horse -> sheep
-      "496": 943  # giraffe -> sheep
-      "422": 943  # elephant -> sheep
-      "80": 943   # cow -> sheep
-      "76": 943   # bear -> sheep
-      "225": 943  # cat -> sheep
-      "378": 943  # dog -> sheep
-    "densepose_lvis_v1_ds1_val_v1":
-      "1202": 943 # zebra -> sheep
-      "569": 943  # horse -> sheep
-      "496": 943  # giraffe -> sheep
-      "422": 943  # elephant -> sheep
-      "80": 943   # cow -> sheep
-      "76": 943   # bear -> sheep
-      "225": 943  # cat -> sheep
-      "378": 943  # dog -> sheep
-  CLASS_TO_MESH_NAME_MAPPING:
-    # Note: different classes are mapped to a single class
-    # mesh is chosen based on GT data, so this is just some
-    # value which has no particular meaning
-    "0": "sheep_5004"
-SOLVER:
-  MAX_ITER: 4000
-  STEPS: (3000, 3500)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_16k.yaml

@@ -1,119 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_maskonly_24k/270668502/model_final_21b1d2.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 9
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "cat_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_7466_256.pkl"
-        "dog_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_7466_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds2_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds2_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds2_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds2_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "bear_4936"
-    "1": "cow_5002"
-    "2": "cat_7466"
-    "3": "dog_7466"
-    "4": "elephant_5002"
-    "5": "giraffe_5002"
-    "6": "horse_5004"
-    "7": "sheep_5004"
-    "8": "zebra_5002"
-SOLVER:
-  MAX_ITER: 16000
-  STEPS: (12000, 14000)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_i2m_16k.yaml

@@ -1,121 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_maskonly_24k/270668502/model_final_21b1d2.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 9
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      PIX_TO_SHAPE_CYCLE_LOSS:
-        ENABLED: True
-      EMBEDDERS:
-        "cat_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_7466_256.pkl"
-        "dog_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_7466_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds2_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds2_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds2_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds2_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "bear_4936"
-    "1": "cow_5002"
-    "2": "cat_7466"
-    "3": "dog_7466"
-    "4": "elephant_5002"
-    "5": "giraffe_5002"
-    "6": "horse_5004"
-    "7": "sheep_5004"
-    "8": "zebra_5002"
-SOLVER:
-  MAX_ITER: 16000
-  STEPS: (12000, 14000)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_I0_finetune_m2m_16k.yaml

@@ -1,138 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_maskonly_24k/267687159/model_final_354e61.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 9
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      SHAPE_TO_SHAPE_CYCLE_LOSS:
-        ENABLED: True
-      EMBEDDERS:
-        "cat_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_7466_256.pkl"
-        "dog_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_7466_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-        "smpl_27554":
-          TYPE: vertex_feature
-          NUM_VERTICES: 27554
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_smpl_27554_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds2_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds2_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds2_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds2_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "bear_4936"
-    "1": "cow_5002"
-    "2": "cat_7466"
-    "3": "dog_7466"
-    "4": "elephant_5002"
-    "5": "giraffe_5002"
-    "6": "horse_5004"
-    "7": "sheep_5004"
-    "8": "zebra_5002"
-SOLVER:
-  MAX_ITER: 16000
-  STEPS: (12000, 14000)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True
-  MESH_ALIGNMENT_MESH_NAMES:
-    - bear_4936
-    - cow_5002
-    - cat_7466
-    - dog_7466
-    - elephant_5002
-    - giraffe_5002
-    - horse_5004
-    - sheep_5004
-    - zebra_5002

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_16k.yaml

@@ -1,119 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 9
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "cat_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_7466_256.pkl"
-        "dog_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_7466_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds2_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds2_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds2_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds2_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "bear_4936"
-    "1": "cow_5002"
-    "2": "cat_7466"
-    "3": "dog_7466"
-    "4": "elephant_5002"
-    "5": "giraffe_5002"
-    "6": "horse_5004"
-    "7": "sheep_5004"
-    "8": "zebra_5002"
-SOLVER:
-  MAX_ITER: 16000
-  STEPS: (12000, 14000)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_4k.yaml

@@ -1,119 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 9
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "cat_5001":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5001
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_5001_256.pkl"
-        "dog_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_5002_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds1_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds1_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds1_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds1_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "bear_4936"
-    "1": "cow_5002"
-    "2": "cat_5001"
-    "3": "dog_5002"
-    "4": "elephant_5002"
-    "5": "giraffe_5002"
-    "6": "horse_5004"
-    "7": "sheep_5004"
-    "8": "zebra_5002"
-SOLVER:
-  MAX_ITER: 4000
-  STEPS: (3000, 3500)
-DENSEPOSE_EVALUATION:
-  EVALUATE_MESH_ALIGNMENT: True

configs/cse/densepose_rcnn_R_50_FPN_soft_animals_finetune_maskonly_24k.yaml

@@ -1,118 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_HEADS:
-    NUM_CLASSES: 9
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    COARSE_SEGM_TRAINED_BY_MASKS: True
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBED_LOSS_WEIGHT: 0.0
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "cat_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cat_7466_256.pkl"
-        "dog_7466":
-          TYPE: vertex_feature
-          NUM_VERTICES: 7466
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_dog_7466_256.pkl"
-        "sheep_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_sheep_5004_256.pkl"
-        "horse_5004":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5004
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_horse_5004_256.pkl"
-        "zebra_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_zebra_5002_256.pkl"
-        "giraffe_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_giraffe_5002_256.pkl"
-        "elephant_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_elephant_5002_256.pkl"
-        "cow_5002":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5002
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_cow_5002_256.pkl"
-        "bear_4936":
-          TYPE: vertex_feature
-          NUM_VERTICES: 4936
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_bear_4936_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_lvis_v1_ds2_train_v1"
-  TEST:
-    - "densepose_lvis_v1_ds2_val_v1"
-  WHITELISTED_CATEGORIES:
-    "densepose_lvis_v1_ds2_train_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-    "densepose_lvis_v1_ds2_val_v1":
-      - 943  # sheep
-      - 1202 # zebra
-      - 569  # horse
-      - 496  # giraffe
-      - 422  # elephant
-      - 80   # cow
-      - 76   # bear
-      - 225  # cat
-      - 378  # dog
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "bear_4936"
-    "1": "cow_5002"
-    "2": "cat_7466"
-    "3": "dog_7466"
-    "4": "elephant_5002"
-    "5": "giraffe_5002"
-    "6": "horse_5004"
-    "7": "sheep_5004"
-    "8": "zebra_5002"
-SOLVER:
-  MAX_ITER: 24000
-  STEPS: (20000, 22000)

configs/cse/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k.yaml

@@ -1,29 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_s1x/250533982/model_final_2c4512.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-      EMBEDDING_DIST_GAUSS_SIGMA: 0.1
-      GEODESIC_DIST_GAUSS_SIGMA: 0.1
-      EMBEDDERS:
-        "chimp_5029":
-          TYPE: vertex_feature
-          NUM_VERTICES: 5029
-          FEATURE_DIM: 256
-          FEATURES_TRAINABLE: False
-          IS_TRAINABLE: True
-          INIT_FILE: "https://dl.fbaipublicfiles.com/densepose/data/cse/lbo/phi_chimp_5029_256.pkl"
-DATASETS:
-  TRAIN:
-    - "densepose_chimps_cse_train"
-  TEST:
-    - "densepose_chimps_cse_val"
-  CLASS_TO_MESH_NAME_MAPPING:
-    "0": "chimp_5029"
-SOLVER:
-  MAX_ITER: 4000
-  STEPS: (3000, 3500)

configs/cse/densepose_rcnn_R_50_FPN_soft_s1x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN-Human.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseV1ConvXHead"
-    CSE:
-      EMBED_LOSS_NAME: "SoftEmbeddingLoss"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_DL_WC1M_s1x.yaml

@@ -1,18 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "iid_iso"
-    SEGM_CONFIDENCE:
-      ENABLED: True
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "iid_iso"
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_DL_WC2M_s1x.yaml

@@ -1,18 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "indep_aniso"
-    SEGM_CONFIDENCE:
-      ENABLED: True
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "indep_aniso"
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml

@@ -1,10 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_WC1M_s1x.yaml

@@ -1,18 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "iid_iso"
-    SEGM_CONFIDENCE:
-      ENABLED: True
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  WARMUP_FACTOR: 0.025

configs/densepose_rcnn_R_101_FPN_WC1_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "iid_iso"
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  WARMUP_FACTOR: 0.025

configs/densepose_rcnn_R_101_FPN_WC2M_s1x.yaml

@@ -1,18 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "indep_aniso"
-    SEGM_CONFIDENCE:
-      ENABLED: True
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  WARMUP_FACTOR: 0.025

configs/densepose_rcnn_R_101_FPN_WC2_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "indep_aniso"
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)
-  WARMUP_FACTOR: 0.025

configs/densepose_rcnn_R_101_FPN_s1x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml

@@ -1,17 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-  ROI_DENSEPOSE_HEAD:
-    NUM_COARSE_SEGM_CHANNELS: 15
-    POOLER_RESOLUTION: 14
-    HEATMAP_SIZE: 56
-    INDEX_WEIGHTS: 2.0
-    PART_WEIGHTS: 0.3
-    POINT_REGRESSION_WEIGHTS: 0.1
-    DECODER_ON: False
-SOLVER:
-  BASE_LR: 0.002
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_50_FPN_DL_WC1M_s1x.yaml

@@ -1,18 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "iid_iso"
-    SEGM_CONFIDENCE:
-      ENABLED: True
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "iid_iso"
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_50_FPN_DL_WC2M_s1x.yaml

@@ -1,18 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "indep_aniso"
-    SEGM_CONFIDENCE:
-      ENABLED: True
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml

@@ -1,16 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-    UV_CONFIDENCE:
-      ENABLED: True
-      TYPE: "indep_aniso"
-    POINT_REGRESSION_WEIGHTS: 0.0005
-SOLVER:
-  CLIP_GRADIENTS:
-    ENABLED: True
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)

configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml

@@ -1,10 +0,0 @@
-_BASE_: "Base-DensePose-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-  ROI_DENSEPOSE_HEAD:
-    NAME: "DensePoseDeepLabHead"
-SOLVER:
-  MAX_ITER: 130000
-  STEPS: (100000, 120000)