det2

app.py

@@ -2,23 +2,25 @@
 import os
 import sys
 import time
-from pathlib import Path
-from omegaconf import OmegaConf
 from glob import glob
 from os.path import join as opj
+from pathlib import Path
 
+import apply_net
 import gradio as gr
-from PIL import Image
 import torch
+from omegaconf import OmegaConf
+from PIL import Image
 
-from utils_stableviton import get_mask_location, get_batch, tensor2img
 from cldm.model import create_model
 from cldm.plms_hacked import PLMSSampler
+from detectron2.data.detection_utils import _apply_exif_orientation, convert_PIL_to_numpy
+from utils_stableviton import get_batch, get_mask_location, tensor2img
 
 PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute()
 sys.path.insert(0, str(PROJECT_ROOT))
 
-from preprocess.detectron2.projects.DensePose.apply_net_gradio import DensePose4Gradio
+# from detectron2.projects.DensePose.apply_net_gradio import DensePose4Gradio
 from preprocess.humanparsing.run_parsing import Parsing
 from preprocess.openpose.run_openpose import OpenPose
 
@@ -30,10 +32,10 @@ IMG_W = 384
 openpose_model_hd = OpenPose(0)
 openpose_model_hd.preprocessor.body_estimation.model.to('cuda')
 parsing_model_hd = Parsing(0)
-densepose_model_hd = DensePose4Gradio(
-    cfg='preprocess/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml',
-    model='https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl',
-)
+# densepose_model_hd = DensePose4Gradio(
+#     cfg='preprocess/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml',
+#     model='https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl',
+# )
 
 category_dict = ['upperbody', 'lowerbody', 'dress']
 category_dict_utils = ['upper_body', 'lower_body', 'dresses']
@@ -50,6 +52,8 @@ model = model.cuda()
 model.eval()
 sampler = PLMSSampler(model)
 # #### model init <<<<
+
+
 def stable_viton_model_hd(
         batch,
         n_steps,
@@ -69,25 +73,27 @@ def stable_viton_model_hd(
     sampler.model.batch = batch
 
     ts = torch.full((1,), 999, device=z.device, dtype=torch.long)
-    start_code = model.q_sample(z, ts)     
+    start_code = model.q_sample(z, ts)
 
     output, _, _ = sampler.sample(
         n_steps,
         bs,
-        (4, IMG_H//8, IMG_W//8),
+        (4, IMG_H // 8, IMG_W // 8),
         cond,
-        x_T=start_code, 
+        x_T=start_code,
         verbose=False,
         eta=0.0,
-        unconditional_conditioning=uc_full,       
+        unconditional_conditioning=uc_full,
     )
 
     output = model.decode_first_stage(output)
     output = tensor2img(output)
     pil_output = Image.fromarray(output)
     return pil_output
-    
+
 # @spaces.GPU  # TODO: turn on when final upload
+
+
 @torch.no_grad()
 def process_hd(vton_img, garm_img, n_steps):
     model_type = 'hd'
@@ -112,19 +118,28 @@ def process_hd(vton_img, garm_img, n_steps):
     stt = time.time()
     print('get densepose... ', end='')
     vton_img = vton_img.resize((IMG_W, IMG_H))  # size for densepose
-    densepose = densepose_model_hd.execute(vton_img)  # densepose
+    # densepose = densepose_model_hd.execute(vton_img)  # densepose
+
+    human_img_arg = _apply_exif_orientation(vton_img.resize((IMG_W, IMG_H)))
+    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((IMG_W, IMG_H))
+
     print('%.2fs' % (time.time() - stt))
 
     batch = get_batch(
-        vton_img, 
-        garm_img, 
-        densepose, 
-        masked_vton_img, 
-        mask, 
-        IMG_H, 
+        vton_img,
+        garm_img,
+        densepose,
+        masked_vton_img,
+        mask,
+        IMG_H,
         IMG_W
     )
-    
+
     sample = stable_viton_model_hd(
         batch,
         n_steps

preprocess/detectron2/projects/DensePose/apply_net.py → apply_net.py

@@ -7,29 +7,40 @@ import logging
 import os
 import sys
 from typing import Any, ClassVar, Dict, List
-
-import cv2
-import numpy as np
 import torch
-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)
+
 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 PIL import Image
+
+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
 """
@@ -40,9 +51,9 @@ logger = logging.getLogger(LOGGER_NAME)
 _ACTION_REGISTRY: Dict[str, "Action"] = {}
 
 
-class Action(object):
+class Action:
     @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
+    def add_arguments(cls: type, parser: argparse.ArgumentParser):
         parser.add_argument(
             "-v",
             "--verbosity",
@@ -51,7 +62,7 @@ class Action(object):
         )
 
 
-def register_action(cls):
+def register_action(cls: type):
     """
     Decorator for action classes to automate action registration
     """
@@ -62,11 +73,10 @@ def register_action(cls):
 
 class InferenceAction(Action):
     @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
+    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("input", metavar="<input>", help="Input data")
         parser.add_argument(
             "--opts",
             help="Modify config options using the command-line 'KEY VALUE' pairs",
@@ -75,28 +85,29 @@ class InferenceAction(Action):
         )
 
     @classmethod
-    def execute(cls, args: argparse.Namespace):
+    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
+        # 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(img)["instances"]
-                cls.execute_on_outputs(context, {"file_name": file_name, "image": img}, outputs)
+        # 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, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
+        cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
     ):
         cfg = get_cfg()
         add_densepose_config(cfg)
@@ -109,7 +120,7 @@ class InferenceAction(Action):
         return cfg
 
     @classmethod
-    def _get_input_file_list(cls, input_spec: str):
+    def _get_input_file_list(cls: type, input_spec: str):
         if os.path.isdir(input_spec):
             file_list = [
                 os.path.join(input_spec, fname)
@@ -132,13 +143,13 @@ class DumpAction(InferenceAction):
     COMMAND: ClassVar[str] = "dump"
 
     @classmethod
-    def add_parser(cls, subparsers: argparse._SubParsersAction):
+    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, parser: argparse.ArgumentParser):
+    def add_arguments(cls: type, parser: argparse.ArgumentParser):
         super(DumpAction, cls).add_arguments(parser)
         parser.add_argument(
             "--output",
@@ -149,7 +160,7 @@ class DumpAction(InferenceAction):
 
     @classmethod
     def execute_on_outputs(
-        cls, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
+        cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
     ):
         image_fpath = entry["file_name"]
         logger.info(f"Processing {image_fpath}")
@@ -167,12 +178,12 @@ class DumpAction(InferenceAction):
         context["results"].append(result)
 
     @classmethod
-    def create_context(cls, args: argparse.Namespace, cfg: CfgNode):
+    def create_context(cls: type, args: argparse.Namespace, cfg: CfgNode):
         context = {"results": [], "out_fname": args.output}
         return context
 
     @classmethod
-    def postexecute(cls, context: Dict[str, Any]):
+    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):
@@ -201,13 +212,13 @@ class ShowAction(InferenceAction):
     }
 
     @classmethod
-    def add_parser(cls, subparsers: argparse._SubParsersAction):
+    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, parser: argparse.ArgumentParser):
+    def add_arguments(cls: type, parser: argparse.ArgumentParser):
         super(ShowAction, cls).add_arguments(parser)
         parser.add_argument(
             "visualizations",
@@ -246,7 +257,7 @@ class ShowAction(InferenceAction):
 
     @classmethod
     def setup_config(
-        cls, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
+        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))
@@ -258,21 +269,23 @@ class ShowAction(InferenceAction):
 
     @classmethod
     def execute_on_outputs(
-        cls, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
+        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_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 = cls._get_out_fname(entry_idx, context["out_fname"])
+        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)
@@ -281,16 +294,17 @@ class ShowAction(InferenceAction):
         context["entry_idx"] += 1
 
     @classmethod
-    def postexecute(cls, context: Dict[str, Any]):
+    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, entry_idx: int, fname_base: str):
+    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, args: argparse.Namespace, cfg: CfgNode) -> Dict[str, Any]:
+    def create_context(cls: type, args: argparse.Namespace, cfg: CfgNode) -> Dict[str, Any]:
         vis_specs = args.visualizations.split(",")
         visualizers = []
         extractors = []
@@ -316,80 +330,6 @@ class ShowAction(InferenceAction):
         return context
 
 
-@register_action
-class GetAction(InferenceAction):
-    """
-    Get action that outputs results to a pickle file
-    """
-
-    COMMAND: ClassVar[str] = "get"
-
-    @classmethod
-    def add_parser(cls, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Get model outputs to a file.")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
-        super(GetAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "--output",
-            metavar="<dump_file>",
-            default="",
-            help="File name to save images to",
-        )
-
-    @classmethod
-    def execute_on_outputs(
-        cls, 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]
-
-                # decode
-                out_dir = context["out_fname"]
-                H, W, _ = entry['image'].shape
-
-                i = result['pred_densepose'][0].labels.cpu().numpy()
-                i_scale = (i.astype(np.float32) * 255 / 24).astype(np.uint8)
-                i_color = cv2.applyColorMap(i_scale, cv2.COLORMAP_PARULA)
-                i_color = cv2.cvtColor(i_color, cv2.COLOR_RGB2BGR)
-                i_color[i == 0] = [0, 0, 0]
-
-                box = result["pred_boxes_XYXY"][0]
-                box[2] = box[2] - box[0]
-                box[3] = box[3] - box[1]
-                x, y, w, h = [int(v) for v in box]
-
-                bg = np.zeros((H, W, 3))
-                bg[y:y + h, x:x + w, :] = i_color
-
-                bg_img = Image.fromarray(np.uint8(bg), "RGB")
-                os.makedirs(out_dir, exist_ok=True)
-                bg_img.save(os.path.join(out_dir, os.path.splitext(os.path.basename(result['file_name']))[0] + '.jpg'))
-
-    @classmethod
-    def create_context(cls, args: argparse.Namespace, cfg: CfgNode):
-        context = {"results": [], "out_fname": args.output}
-        return context
-
-    @classmethod
-    def postexecute(cls, context: Dict[str, Any]):
-        pass
-
-
 def create_argument_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
         description=DOC,
@@ -405,7 +345,7 @@ def create_argument_parser() -> argparse.ArgumentParser:
 def main():
     parser = create_argument_parser()
     args = parser.parse_args()
-    verbosity = args.verbosity if hasattr(args, "verbosity") else None
+    verbosity = getattr(args, "verbosity", None)
     global logger
     logger = setup_logger(name=LOGGER_NAME)
     logger.setLevel(verbosity_to_level(verbosity))
@@ -414,3 +354,6 @@ def main():
 
 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

densepose

@@ -1 +0,0 @@
-preprocess/detectron2/projects/DensePose/densepose/

{preprocess/detectron2/projects/DensePose/densepose → densepose}/__init__.py

@@ -1,6 +1,4 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
-
-# pyre-unsafe
 from .data.datasets import builtin  # just to register data
 from .converters import builtin as builtin_converters  # register converters
 from .config import (

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/__init__.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .hflip import HFlipConverter
 from .to_mask import ToMaskConverter
 from .to_chart_result import ToChartResultConverter, ToChartResultConverterWithConfidences

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/base.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any, Tuple, Type
 import torch
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/builtin.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from ..structures import DensePoseChartPredictorOutput, DensePoseEmbeddingPredictorOutput
 from . import (
     HFlipConverter,

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/chart_output_hflip.py

@@ -1,6 +1,4 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
-
-# pyre-unsafe
 from dataclasses import fields
 import torch
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/chart_output_to_chart_result.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Dict
 import torch
 from torch.nn import functional as F

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/hflip.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any
 
 from .base import BaseConverter

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/segm_to_mask.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any
 import torch
 from torch.nn import functional as F

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/to_chart_result.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any
 
 from detectron2.structures import Boxes

{preprocess/detectron2/projects/DensePose/densepose → densepose}/converters/to_mask.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any, Tuple
 
 from detectron2.structures import BitMasks, Boxes

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/__init__.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .meshes import builtin
 from .build import (
     build_detection_test_loader,

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/build.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import itertools
 import logging
 import numpy as np

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/combined_loader.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import random
 from collections import deque
 from typing import Any, Collection, Deque, Iterable, Iterator, List, Sequence

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/dataset_mapper.py

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import copy
 import logging
 from typing import Any, Dict, List, Tuple

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/datasets/__init__.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from . import builtin  # ensure the builtin datasets are registered
 
 __all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")]

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/datasets/builtin.py

@@ -1,6 +1,4 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
-
-# pyre-unsafe
 from .chimpnsee import register_dataset as register_chimpnsee_dataset
 from .coco import BASE_DATASETS as BASE_COCO_DATASETS
 from .coco import DATASETS as COCO_DATASETS

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/datasets/chimpnsee.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Optional
 
 from detectron2.data import DatasetCatalog, MetadataCatalog

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/datasets/coco.py

@@ -1,6 +1,4 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
-
-# pyre-unsafe
 import contextlib
 import io
 import logging

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/datasets/dataset_type.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from enum import Enum
 
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/datasets/lvis.py

@@ -1,6 +1,4 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
-
-# pyre-unsafe
 import logging
 import os
 from typing import Any, Dict, Iterable, List, Optional

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/image_list_dataset.py

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import logging
 import numpy as np
 from typing import Any, Callable, Dict, List, Optional, Union

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/inference_based_loader.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import random
 from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple
 import torch

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/meshes/__init__.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 
-# pyre-unsafe
-
 from . import builtin
 
 __all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")]

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/meshes/builtin.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 
-# pyre-unsafe
-
 from .catalog import MeshInfo, register_meshes
 
 DENSEPOSE_MESHES_DIR = "https://dl.fbaipublicfiles.com/densepose/meshes/"

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/meshes/catalog.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 
-# pyre-unsafe
-
 import logging
 from collections import UserDict
 from dataclasses import dataclass

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/__init__.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .densepose_uniform import DensePoseUniformSampler
 from .densepose_confidence_based import DensePoseConfidenceBasedSampler
 from .densepose_cse_uniform import DensePoseCSEUniformSampler

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/densepose_base.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any, Dict, List, Tuple
 import torch
 from torch.nn import functional as F

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/densepose_confidence_based.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import random
 from typing import Optional, Tuple
 import torch

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/densepose_cse_base.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from typing import Any, Dict, List, Tuple
 import torch
 from torch.nn import functional as F

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/densepose_cse_confidence_based.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import random
 from typing import Optional, Tuple
 import torch

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/densepose_cse_uniform.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .densepose_cse_base import DensePoseCSEBaseSampler
 from .densepose_uniform import DensePoseUniformSampler
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/densepose_uniform.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import random
 import torch
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/mask_from_densepose.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from detectron2.structures import BitMasks, Instances
 
 from densepose.converters import ToMaskConverter

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/samplers/prediction_to_gt.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Optional
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/transform/__init__.py

@@ -1,5 +1,3 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .image import ImageResizeTransform

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/transform/image.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import torch
 
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/utils.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import os
 from typing import Dict, Optional
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/video/__init__.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .frame_selector import (
     FrameSelectionStrategy,
     RandomKFramesSelector,

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/video/frame_selector.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import random
 from collections.abc import Callable
 from enum import Enum

{preprocess/detectron2/projects/DensePose/densepose → densepose}/data/video/video_keyframe_dataset.py

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import csv
 import logging
 import numpy as np

{preprocess/detectron2/projects/DensePose/densepose → densepose}/engine/__init__.py

@@ -1,5 +1,3 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .trainer import Trainer

{preprocess/detectron2/projects/DensePose/densepose → densepose}/engine/trainer.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 
-# pyre-unsafe
-
 import logging
 import os
 from collections import OrderedDict

{preprocess/detectron2/projects/DensePose/densepose → densepose}/evaluation/__init__.py

@@ -1,5 +1,3 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from .evaluator import DensePoseCOCOEvaluator

{preprocess/detectron2/projects/DensePose/densepose → densepose}/evaluation/d2_evaluator_adapter.py

@@ -1,7 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 from detectron2.data.catalog import Metadata
 from detectron2.evaluation import COCOEvaluator
 

{preprocess/detectron2/projects/DensePose/densepose → densepose}/evaluation/densepose_coco_evaluation.py

@@ -5,8 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 # This is a modified version of cocoeval.py where we also have the densepose evaluation.
 
-# pyre-unsafe
-
 __author__ = "tsungyi"
 
 import copy
@@ -18,7 +16,6 @@ import time
 from collections import defaultdict
 from enum import Enum
 from typing import Any, Dict, Tuple
-# pyre-fixme[21]: Could not find module `scipy.spatial.distance`.
 import scipy.spatial.distance as ssd
 import torch
 import torch.nn.functional as F

{preprocess/detectron2/projects/DensePose/densepose → densepose}/evaluation/evaluator.py

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
 # Copyright (c) Facebook, Inc. and its affiliates.
 
-# pyre-unsafe
-
 import contextlib
 import copy
 import io