diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..a32a052
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+backend/app/__pycache__/
\ No newline at end of file
diff --git a/backend/app/__pycache__/__main__.cpython-310.pyc b/backend/app/__pycache__/__main__.cpython-310.pyc
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deleted file mode 100644
index 42f4005..0000000
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diff --git a/backend/app/__pycache__/model.cpython-310.pyc b/backend/app/__pycache__/model.cpython-310.pyc
deleted file mode 100644
index a0243b2..0000000
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diff --git a/backend/app/__pycache__/model.cpython-38.pyc b/backend/app/__pycache__/model.cpython-38.pyc
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diff --git a/backend/app/confirm_button_hack.py b/backend/app/confirm_button_hack.py
index 0c84634..689f589 100644
--- a/backend/app/confirm_button_hack.py
+++ b/backend/app/confirm_button_hack.py
@@ -51,9 +51,9 @@ def evaluate(self):
if st.button(label):
cache_entry.evaluate()
else:
- raise st.script_runner.StopException
+ st.stop()
return cache_entry.return_value
return wrapped_func
- return function_decorator
+ return function_decorator
\ No newline at end of file
diff --git a/backend/app/frontend.py b/backend/app/frontend.py
index f73c01b..b6b8621 100644
--- a/backend/app/frontend.py
+++ b/backend/app/frontend.py
@@ -17,37 +17,84 @@
def main():
- st.title("Mask Classification Model")
- uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+ st.title("Welcome to VTON World :)")
+
+ with st.container():
+ col1, col2, col3 = st.columns([1,1,1])
+
+ with col1:
+ st.header("Human")
+ uploaded_target = st.file_uploader("Choose an target image", type=["jpg", "jpeg", "png"])
+
+ if uploaded_target:
+ target_bytes = uploaded_target.getvalue()
+ target_img = Image.open(io.BytesIO(target_bytes))
- if uploaded_file:
- image_bytes = uploaded_file.getvalue()
- image = Image.open(io.BytesIO(image_bytes))
+ st.image(target_img, caption='Uploaded target Image')
+
+ with col2:
+ st.header("Cloth")
+ uploaded_garment = st.file_uploader("Choose an garment image", type=["jpg", "jpeg", "png"])
- st.image(image, caption='Uploaded Image')
- st.write("Classifying...")
+ if uploaded_garment:
+ # st.spinner("dehazing now...")
+
+ garment_bytes = uploaded_garment.getvalue()
+ garment_img = Image.open(io.BytesIO(garment_bytes))
- # 기존 stremalit 코드
- # _, y_hat = get_prediction(model, image_bytes)
- # label = config['classes'][y_hat.item()]
- files = [
- ('files', (uploaded_file.name, image_bytes,
- uploaded_file.type))
- ]
- response = requests.post("http://localhost:8001/order", files=files)
- label = response.json()["products"][0]["result"]
- st.write(f'label is {label}')
+ st.image(garment_img, caption='Uploaded garment Image')
+
+ with col3:
+ st.header("Result")
+ if uploaded_target and uploaded_garment:
+ files = [
+ ('files', (uploaded_target.name, target_bytes,
+ uploaded_target.type))
+ ,
+ ('files', (uploaded_garment.name, garment_bytes,
+ uploaded_garment.type))
+ ]
+
+ with col3:
+ st.write(' ')
+ empty_slot = st.empty()
+ empty_slot.markdown("\nLoading...
", unsafe_allow_html=True)
+ response = requests.post("http://localhost:8001/order", files=files)
+ empty_slot.empty()
+ empty_slot.markdown("Here it is !
", unsafe_allow_html=True)
-@cache_on_button_press('Authenticate')
-def authenticate(password) -> bool:
- return password == root_password
+ category = 'lower_body'
+ output_ladi_buffer_dir = '/opt/ml/user_db/ladi/buffer'
+ final_result_dir = output_ladi_buffer_dir
+ final_img = Image.open(os.path.join(final_result_dir, f'{category}.png'))
+
+ st.write(' ')
+ st.write(' ')
+ st.write(' ')
+ st.write(' ')
+ st.write(' ')
+ st.write(' ')
+ st.image(final_img, caption='Final Image', use_column_width=True)
+
+ # option = '선택 안 함'
+ # down_btn = st.download_button(
+ # label='Download Image',
+ # data=dehaze_image_bytes,
+ # file_name='dehazed_image.jpg',
+ # mime='image/jpg',
+ # on_click=save_btn_click(option, dehaze_image_bytes)
+ # )
+# @cache_on_button_press('Authenticate')
+# def authenticate(password) -> bool:
+# return password == root_password
+# password = st.text_input('password', type="password")
+# if authenticate(password):
+# st.success('You are authenticated!')
+# main()
+# else:
+# st.error('The password is invalid.')
-password = st.text_input('password', type="password")
-if authenticate(password):
- st.success('You are authenticated!')
- main()
-else:
- st.error('The password is invalid.')
+main()
diff --git a/backend/app/main.py b/backend/app/main.py
index 10bd6a9..c6e2a2b 100644
--- a/backend/app/main.py
+++ b/backend/app/main.py
@@ -7,7 +7,30 @@
from datetime import datetime
from app.model import MyEfficientNet, get_model, get_config, predict_from_image_byte
+from PIL import Image
+import io
+# scp setting
+import sys, os
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/Self_Correction_Human_Parsing/')
+from simple_extractor import main_schp
+
+# openpose
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/pytorch_openpose/')
+from extract_keypoint import main_openpose
+
+# ladi
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton')
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton/src')
+from inference import main_ladi
+
+
+
+# sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model')
+# print('sys.path:', sys.path)
+# from Self_Correction_Human_Parsing.simple_extractor import main_schp
+# from pytorch_openpose.extract_keypoint import main_openpose
+# from ladi_vton.src.inference import main_ladi
app = FastAPI()
orders = []
@@ -71,17 +94,59 @@ def get_order_by_id(order_id: UUID) -> Optional[Order]:
return next((order for order in orders if order.id == order_id), None)
+# post!!
@app.post("/order", description="주문을 요청합니다")
async def make_order(files: List[UploadFile] = File(...),
# def make_order(files: List[UploadFile] = File(...),
model: MyEfficientNet = Depends(get_model),
config: Dict[str, Any] = Depends(get_config)):
products = []
- for file in files:
- image_bytes = await file.read()
- inference_result = predict_from_image_byte(model=model, image_bytes=image_bytes, config=config)
- product = InferenceImageProduct(result=inference_result)
- products.append(product)
+
+ # target:files[0], garment:files[1]
+
+ target_bytes = await files[0].read()
+ garment_bytes = await files[1].read()
+
+ # TODO image byte
+ target_image = Image.open(io.BytesIO(target_bytes))
+ target_image = target_image.convert("RGB")
+
+ garment_image = Image.open(io.BytesIO(garment_bytes))
+ garment_image = garment_image.convert("RGB")
+
+ input_dir = '/opt/ml/user_db/input/'
+
+ os.makedirs(f'{input_dir}/buffer', exist_ok=True)
+
+ target_image.save(f'{input_dir}/target.jpg')
+ target_image.save(f'{input_dir}/buffer/target/target.jpg')
+
+ garment_image.save(f'{input_dir}/garment.jpg')
+ garment_image.save(f'{input_dir}/buffer/garment/garment.jpg')
+
+ # schp - (1024, 784), (512, 384)
+ target_buffer_dir = f'{input_dir}/buffer/target'
+ main_schp(target_buffer_dir)
+
+
+ # openpose
+ output_openpose_buffer_dir = '/opt/ml/user_db/openpose/buffer'
+ os.makedirs(output_openpose_buffer_dir, exist_ok=True)
+ main_openpose(target_buffer_dir, output_openpose_buffer_dir)
+
+ # ladi-vton
+ output_ladi_buffer_dir = '/opt/ml/user_db/ladi/buffer'
+ db_dir = '/opt/ml/user_db'
+ os.makedirs(output_ladi_buffer_dir, exist_ok=True)
+ main_ladi(db_dir, output_ladi_buffer_dir)
+
+ return None
+ ## return값
+ ## output dir
+
+ inference_result = predict_from_image_byte(model=model, image_bytes=image_bytes, config=config)
+ product = InferenceImageProduct(result=inference_result)
+ products.append(product)
new_order = Order(products=products)
orders.append(new_order)
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rename from model/Self-Correction-Human-Parsing/LICENSE
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/README.md b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/README.md
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv_parsing.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv_parsing.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/demo.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/demo.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/parsing_finetune_cihp.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/parsing_finetune_cihp.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/Misc/semantic_R_50_FPN_1x.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/Misc/semantic_R_50_FPN_1x.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/my_Base-RCNN-FPN.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/my_Base-RCNN-FPN.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/quick_schedules/README.md b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/quick_schedules/README.md
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/anchor_generator.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/anchor_generator.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/backbone/build.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/backbone/build.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/box_regression.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/box_regression.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/matcher.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/matcher.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/meta_arch/panoptic_fpn.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/meta_arch/panoptic_fpn.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/meta_arch/retinanet.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/meta_arch/retinanet.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/meta_arch/semantic_seg.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/meta_arch/semantic_seg.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/poolers.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/poolers.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/postprocessing.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/postprocessing.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/proposal_generator/rpn.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/proposal_generator/rpn.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/proposal_generator/rpn_outputs.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/proposal_generator/rpn_outputs.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/proposal_generator/rrpn.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/proposal_generator/rrpn.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/__init__.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/__init__.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/box_head.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/box_head.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/cascade_rcnn.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/cascade_rcnn.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/keypoint_head.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/keypoint_head.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/mask_head.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/mask_head.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/sampling.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/sampling.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/modeling/test_time_augmentation.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/modeling/test_time_augmentation.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/solver/__init__.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/solver/__init__.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/structures/keypoints.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/structures/keypoints.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/structures/masks.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/structures/masks.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/structures/rotated_boxes.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/structures/rotated_boxes.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/utils/README.md b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/utils/README.md
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/utils/collect_env.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/utils/collect_env.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/utils/colormap.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/utils/colormap.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/detectron2/utils/comm.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/detectron2/utils/comm.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tests/test_model_analysis.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tests/test_model_analysis.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tests/test_model_zoo.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tests/test_model_zoo.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tests/test_visualizer.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tests/test_visualizer.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/README.md b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/README.md
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/benchmark.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/benchmark.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/convert-torchvision-to-d2.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/convert-torchvision-to-d2.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/deploy/README.md b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/deploy/README.md
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/deploy/caffe2_converter.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/deploy/caffe2_converter.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/deploy/caffe2_mask_rcnn.cpp b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/deploy/caffe2_mask_rcnn.cpp
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/deploy/torchscript_traced_mask_rcnn.cpp b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/deploy/torchscript_traced_mask_rcnn.cpp
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/finetune_net.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/finetune_net.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/inference.sh b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/inference.sh
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/plain_train_net.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/plain_train_net.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/run.sh b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/run.sh
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/train_net.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/train_net.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/visualize_data.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/visualize_data.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/visualize_json_results.py b/model/Self_Correction_Human_Parsing/mhp_extension/detectron2/tools/visualize_json_results.py
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rename from model/Self-Correction-Human-Parsing/mhp_extension/detectron2/tools/visualize_json_results.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/global_local_datasets.py b/model/Self_Correction_Human_Parsing/mhp_extension/global_local_parsing/global_local_datasets.py
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rename from model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/global_local_datasets.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/global_local_evaluate.py b/model/Self_Correction_Human_Parsing/mhp_extension/global_local_parsing/global_local_evaluate.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/global_local_train.py b/model/Self_Correction_Human_Parsing/mhp_extension/global_local_parsing/global_local_train.py
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rename from model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/global_local_train.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/make_id_list.py b/model/Self_Correction_Human_Parsing/mhp_extension/global_local_parsing/make_id_list.py
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rename from model/Self-Correction-Human-Parsing/mhp_extension/global_local_parsing/make_id_list.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/logits_fusion.py b/model/Self_Correction_Human_Parsing/mhp_extension/logits_fusion.py
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rename from model/Self-Correction-Human-Parsing/mhp_extension/logits_fusion.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/make_crop_and_mask_w_mask_nms.py b/model/Self_Correction_Human_Parsing/mhp_extension/make_crop_and_mask_w_mask_nms.py
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/scripts/make_coco_style_annotation.sh b/model/Self_Correction_Human_Parsing/mhp_extension/scripts/make_coco_style_annotation.sh
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rename from model/Self-Correction-Human-Parsing/mhp_extension/scripts/make_coco_style_annotation.sh
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/scripts/make_crop.sh b/model/Self_Correction_Human_Parsing/mhp_extension/scripts/make_crop.sh
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rename from model/Self-Correction-Human-Parsing/mhp_extension/scripts/make_crop.sh
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diff --git a/model/Self-Correction-Human-Parsing/mhp_extension/scripts/parsing_fusion.sh b/model/Self_Correction_Human_Parsing/mhp_extension/scripts/parsing_fusion.sh
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diff --git a/model/Self-Correction-Human-Parsing/modules/__init__.py b/model/Self_Correction_Human_Parsing/modules/__init__.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/bn.py b/model/Self_Correction_Human_Parsing/modules/bn.py
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rename from model/Self-Correction-Human-Parsing/modules/bn.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/deeplab.py b/model/Self_Correction_Human_Parsing/modules/deeplab.py
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rename from model/Self-Correction-Human-Parsing/modules/deeplab.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/dense.py b/model/Self_Correction_Human_Parsing/modules/dense.py
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rename from model/Self-Correction-Human-Parsing/modules/dense.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/functions.py b/model/Self_Correction_Human_Parsing/modules/functions.py
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rename from model/Self-Correction-Human-Parsing/modules/functions.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/misc.py b/model/Self_Correction_Human_Parsing/modules/misc.py
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rename from model/Self-Correction-Human-Parsing/modules/misc.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/residual.py b/model/Self_Correction_Human_Parsing/modules/residual.py
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rename from model/Self-Correction-Human-Parsing/modules/residual.py
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/checks.h b/model/Self_Correction_Human_Parsing/modules/src/checks.h
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rename from model/Self-Correction-Human-Parsing/modules/src/checks.h
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/inplace_abn.cpp b/model/Self_Correction_Human_Parsing/modules/src/inplace_abn.cpp
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rename from model/Self-Correction-Human-Parsing/modules/src/inplace_abn.cpp
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/inplace_abn.h b/model/Self_Correction_Human_Parsing/modules/src/inplace_abn.h
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rename from model/Self-Correction-Human-Parsing/modules/src/inplace_abn.h
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/inplace_abn_cpu.cpp b/model/Self_Correction_Human_Parsing/modules/src/inplace_abn_cpu.cpp
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rename from model/Self-Correction-Human-Parsing/modules/src/inplace_abn_cpu.cpp
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/inplace_abn_cuda.cu b/model/Self_Correction_Human_Parsing/modules/src/inplace_abn_cuda.cu
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rename from model/Self-Correction-Human-Parsing/modules/src/inplace_abn_cuda.cu
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/inplace_abn_cuda_half.cu b/model/Self_Correction_Human_Parsing/modules/src/inplace_abn_cuda_half.cu
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rename from model/Self-Correction-Human-Parsing/modules/src/inplace_abn_cuda_half.cu
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diff --git a/model/Self-Correction-Human-Parsing/modules/src/utils/checks.h b/model/Self_Correction_Human_Parsing/modules/src/utils/checks.h
similarity index 100%
rename from model/Self-Correction-Human-Parsing/modules/src/utils/checks.h
rename to model/Self_Correction_Human_Parsing/modules/src/utils/checks.h
diff --git a/model/Self-Correction-Human-Parsing/modules/src/utils/common.h b/model/Self_Correction_Human_Parsing/modules/src/utils/common.h
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rename from model/Self-Correction-Human-Parsing/modules/src/utils/common.h
rename to model/Self_Correction_Human_Parsing/modules/src/utils/common.h
diff --git a/model/Self-Correction-Human-Parsing/modules/src/utils/cuda.cuh b/model/Self_Correction_Human_Parsing/modules/src/utils/cuda.cuh
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rename from model/Self-Correction-Human-Parsing/modules/src/utils/cuda.cuh
rename to model/Self_Correction_Human_Parsing/modules/src/utils/cuda.cuh
diff --git a/model/Self-Correction-Human-Parsing/networks/AugmentCE2P.py b/model/Self_Correction_Human_Parsing/networks/AugmentCE2P.py
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rename from model/Self-Correction-Human-Parsing/networks/AugmentCE2P.py
rename to model/Self_Correction_Human_Parsing/networks/AugmentCE2P.py
diff --git a/model/Self-Correction-Human-Parsing/networks/__init__.py b/model/Self_Correction_Human_Parsing/networks/__init__.py
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rename from model/Self-Correction-Human-Parsing/networks/__init__.py
rename to model/Self_Correction_Human_Parsing/networks/__init__.py
diff --git a/model/Self-Correction-Human-Parsing/networks/backbone/mobilenetv2.py b/model/Self_Correction_Human_Parsing/networks/backbone/mobilenetv2.py
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rename from model/Self-Correction-Human-Parsing/networks/backbone/mobilenetv2.py
rename to model/Self_Correction_Human_Parsing/networks/backbone/mobilenetv2.py
diff --git a/model/Self-Correction-Human-Parsing/networks/backbone/resnet.py b/model/Self_Correction_Human_Parsing/networks/backbone/resnet.py
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rename from model/Self-Correction-Human-Parsing/networks/backbone/resnet.py
rename to model/Self_Correction_Human_Parsing/networks/backbone/resnet.py
diff --git a/model/Self-Correction-Human-Parsing/networks/backbone/resnext.py b/model/Self_Correction_Human_Parsing/networks/backbone/resnext.py
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rename from model/Self-Correction-Human-Parsing/networks/backbone/resnext.py
rename to model/Self_Correction_Human_Parsing/networks/backbone/resnext.py
diff --git a/model/Self-Correction-Human-Parsing/networks/context_encoding/aspp.py b/model/Self_Correction_Human_Parsing/networks/context_encoding/aspp.py
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rename from model/Self-Correction-Human-Parsing/networks/context_encoding/aspp.py
rename to model/Self_Correction_Human_Parsing/networks/context_encoding/aspp.py
diff --git a/model/Self-Correction-Human-Parsing/networks/context_encoding/ocnet.py b/model/Self_Correction_Human_Parsing/networks/context_encoding/ocnet.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/networks/context_encoding/ocnet.py
rename to model/Self_Correction_Human_Parsing/networks/context_encoding/ocnet.py
diff --git a/model/Self-Correction-Human-Parsing/networks/context_encoding/psp.py b/model/Self_Correction_Human_Parsing/networks/context_encoding/psp.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/networks/context_encoding/psp.py
rename to model/Self_Correction_Human_Parsing/networks/context_encoding/psp.py
diff --git a/model/Self-Correction-Human-Parsing/requirements.txt b/model/Self_Correction_Human_Parsing/requirements.txt
similarity index 100%
rename from model/Self-Correction-Human-Parsing/requirements.txt
rename to model/Self_Correction_Human_Parsing/requirements.txt
diff --git a/model/Self-Correction-Human-Parsing/simple_extractor.py b/model/Self_Correction_Human_Parsing/simple_extractor.py
similarity index 91%
rename from model/Self-Correction-Human-Parsing/simple_extractor.py
rename to model/Self_Correction_Human_Parsing/simple_extractor.py
index ae7e424..436a4e2 100644
--- a/model/Self-Correction-Human-Parsing/simple_extractor.py
+++ b/model/Self_Correction_Human_Parsing/simple_extractor.py
@@ -54,11 +54,11 @@ def get_arguments():
"""
parser = argparse.ArgumentParser(description="Self Correction for Human Parsing")
- parser.add_argument("--dataset", type=str, default='lip', choices=['lip', 'atr', 'pascal'])
- parser.add_argument("--model-restore", type=str, default='', help="restore pretrained model parameters.")
+ parser.add_argument("--dataset", type=str, default='atr', choices=['lip', 'atr', 'pascal'])
+ parser.add_argument("--model-restore", type=str, default='/opt/ml/checkpoints/exp-schp-201908301523-atr.pth', help="restore pretrained model parameters.")
parser.add_argument("--gpu", type=str, default='0', help="choose gpu device.")
parser.add_argument("--input-dir", type=str, default='', help="path of input image folder.")
- parser.add_argument("--output-dir", type=str, default='', help="path of output image folder.")
+ parser.add_argument("--output-dir", type=str, default='/opt/ml/user_db/schp/buffer', help="path of output image folder.")
parser.add_argument("--logits", action='store_true', default=False, help="whether to save the logits.")
return parser.parse_args()
@@ -88,9 +88,11 @@ def get_palette(num_cls):
return palette
-def main():
+def main_schp(target_buffer_dir):
+
args = get_arguments()
-
+ args.input_dir = target_buffer_dir
+
gpus = [int(i) for i in args.gpu.split(',')]
assert len(gpus) == 1
if not args.gpu == 'None':
@@ -151,5 +153,5 @@ def main():
return
-if __name__ == '__main__':
- main()
+# if __name__ == '__main__':
+# main(target_buffer_dir)
diff --git a/model/Self-Correction-Human-Parsing/train.py b/model/Self_Correction_Human_Parsing/train.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/train.py
rename to model/Self_Correction_Human_Parsing/train.py
diff --git a/model/Self-Correction-Human-Parsing/utils/__init__.py b/model/Self_Correction_Human_Parsing/utils/__init__.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/__init__.py
rename to model/Self_Correction_Human_Parsing/utils/__init__.py
diff --git a/model/Self-Correction-Human-Parsing/utils/consistency_loss.py b/model/Self_Correction_Human_Parsing/utils/consistency_loss.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/consistency_loss.py
rename to model/Self_Correction_Human_Parsing/utils/consistency_loss.py
diff --git a/model/Self-Correction-Human-Parsing/utils/criterion.py b/model/Self_Correction_Human_Parsing/utils/criterion.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/criterion.py
rename to model/Self_Correction_Human_Parsing/utils/criterion.py
diff --git a/model/Self-Correction-Human-Parsing/utils/encoding.py b/model/Self_Correction_Human_Parsing/utils/encoding.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/encoding.py
rename to model/Self_Correction_Human_Parsing/utils/encoding.py
diff --git a/model/Self-Correction-Human-Parsing/utils/kl_loss.py b/model/Self_Correction_Human_Parsing/utils/kl_loss.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/kl_loss.py
rename to model/Self_Correction_Human_Parsing/utils/kl_loss.py
diff --git a/model/Self-Correction-Human-Parsing/utils/lovasz_softmax.py b/model/Self_Correction_Human_Parsing/utils/lovasz_softmax.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/lovasz_softmax.py
rename to model/Self_Correction_Human_Parsing/utils/lovasz_softmax.py
diff --git a/model/Self-Correction-Human-Parsing/utils/miou.py b/model/Self_Correction_Human_Parsing/utils/miou.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/miou.py
rename to model/Self_Correction_Human_Parsing/utils/miou.py
diff --git a/model/Self-Correction-Human-Parsing/utils/schp.py b/model/Self_Correction_Human_Parsing/utils/schp.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/schp.py
rename to model/Self_Correction_Human_Parsing/utils/schp.py
diff --git a/model/Self-Correction-Human-Parsing/utils/soft_dice_loss.py b/model/Self_Correction_Human_Parsing/utils/soft_dice_loss.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/soft_dice_loss.py
rename to model/Self_Correction_Human_Parsing/utils/soft_dice_loss.py
diff --git a/model/Self-Correction-Human-Parsing/utils/transforms.py b/model/Self_Correction_Human_Parsing/utils/transforms.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/transforms.py
rename to model/Self_Correction_Human_Parsing/utils/transforms.py
diff --git a/model/Self-Correction-Human-Parsing/utils/warmup_scheduler.py b/model/Self_Correction_Human_Parsing/utils/warmup_scheduler.py
similarity index 100%
rename from model/Self-Correction-Human-Parsing/utils/warmup_scheduler.py
rename to model/Self_Correction_Human_Parsing/utils/warmup_scheduler.py
diff --git a/model/ladi-vton/src/dataset/__pycache__/dresscode.cpython-310.pyc b/model/ladi-vton/src/dataset/__pycache__/dresscode.cpython-310.pyc
deleted file mode 100644
index 1ed3579..0000000
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diff --git a/model/ladi-vton/src/utils/__pycache__/val_metrics.cpython-310.pyc b/model/ladi-vton/src/utils/__pycache__/val_metrics.cpython-310.pyc
deleted file mode 100644
index a871a51..0000000
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diff --git a/model/ladi-vton/LICENSE b/model/ladi_vton/LICENSE
similarity index 100%
rename from model/ladi-vton/LICENSE
rename to model/ladi_vton/LICENSE
diff --git a/model/ladi-vton/README.md b/model/ladi_vton/README.md
similarity index 100%
rename from model/ladi-vton/README.md
rename to model/ladi_vton/README.md
diff --git a/model/ladi-vton/assets/teaser.png b/model/ladi_vton/assets/teaser.png
similarity index 100%
rename from model/ladi-vton/assets/teaser.png
rename to model/ladi_vton/assets/teaser.png
diff --git a/model/ladi-vton/data/noun_chunks/dresscode.json b/model/ladi_vton/data/noun_chunks/dresscode.json
similarity index 100%
rename from model/ladi-vton/data/noun_chunks/dresscode.json
rename to model/ladi_vton/data/noun_chunks/dresscode.json
diff --git a/model/ladi-vton/data/noun_chunks/vitonhd.json b/model/ladi_vton/data/noun_chunks/vitonhd.json
similarity index 100%
rename from model/ladi-vton/data/noun_chunks/vitonhd.json
rename to model/ladi_vton/data/noun_chunks/vitonhd.json
diff --git a/model/ladi-vton/environment.yml b/model/ladi_vton/environment.yml
similarity index 100%
rename from model/ladi-vton/environment.yml
rename to model/ladi_vton/environment.yml
diff --git a/model/ladi-vton/hubconf.py b/model/ladi_vton/hubconf.py
similarity index 100%
rename from model/ladi-vton/hubconf.py
rename to model/ladi_vton/hubconf.py
diff --git a/model/ladi-vton/src/inference.py b/model/ladi_vton/inference.py
similarity index 90%
rename from model/ladi-vton/src/inference.py
rename to model/ladi_vton/inference.py
index 93a7256..287130f 100644
--- a/model/ladi-vton/src/inference.py
+++ b/model/ladi_vton/inference.py
@@ -17,10 +17,14 @@
from dataset.dresscode import DressCodeDataset
from dataset.vitonhd import VitonHDDataset
from models.AutoencoderKL import AutoencoderKL
-from src.utils.encode_text_word_embedding import encode_text_word_embedding
-from utils.set_seeds import set_seed
-from utils.val_metrics import compute_metrics
-from vto_pipelines.tryon_pipe import StableDiffusionTryOnePipeline
+
+import sys
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton/src/utils')
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton/src/vto_pipelines')
+from encode_text_word_embedding import encode_text_word_embedding ###수정
+from set_seeds import set_seed
+from val_metrics import compute_metrics
+from tryon_pipe import StableDiffusionTryOnePipeline
PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute()
@@ -41,7 +45,8 @@ def parse_args():
parser.add_argument(
"--output_dir",
type=str,
- required=True,
+ default="/",
+ #required=True, ##수정
help="Path to the output directory",
)
@@ -71,10 +76,12 @@ def parse_args():
help="The name of the repository to keep in sync with the local `output_dir`.")
parser.add_argument("--num_vstar", default=16, type=int, help="Number of predicted v* images to use")
- parser.add_argument("--test_order", type=str, required=True, choices=["unpaired", "paired"])
- parser.add_argument("--dataset", type=str, required=True, choices=["dresscode", "vitonhd"], help="dataset to use")
- parser.add_argument("--category", type=str, choices=['all', 'lower_body', 'upper_body', 'dresses'], default='all')
- parser.add_argument("--use_png", default=False, action="store_true")
+ ###수정
+ parser.add_argument("--test_order", type=str, default="unpaired", choices=["unpaired", "paired"]) #required=True,
+ ###수정
+ parser.add_argument("--dataset", type=str, default="dresscode", choices=["dresscode", "vitonhd"], help="dataset to use") #required=True,
+ parser.add_argument("--category", type=str, choices=['all', 'lower_body', 'upper_body', 'dresses'], default='lower_body') ###
+ parser.add_argument("--use_png", default=True, action="store_true")
parser.add_argument("--num_inference_steps", default=50, type=int)
parser.add_argument("--guidance_scale", default=7.5, type=float)
parser.add_argument("--compute_metrics", default=False, action="store_true")
@@ -88,8 +95,10 @@ def parse_args():
@torch.inference_mode()
-def main():
+def main_ladi(db_dir, output_buffer_dir):
args = parse_args()
+ args.dresscode_dataroot = db_dir
+ args.output_dir = output_buffer_dir
# Check if the dataset dataroot is provided
if args.dataset == "vitonhd" and args.vitonhd_dataroot is None:
@@ -202,7 +211,8 @@ def main():
# Prepare the dataloader and create the output directory
test_dataloader = accelerator.prepare(test_dataloader)
- save_dir = os.path.join(args.output_dir, args.test_order)
+ save_dir = os.path.join(args.output_dir, args.test_order) ## 수정
+ save_dir = args.output_dir
os.makedirs(save_dir, exist_ok=True)
generator = torch.Generator("cuda").manual_seed(args.seed)
@@ -230,6 +240,7 @@ def main():
torchvision.transforms.InterpolationMode.BILINEAR,
antialias=True)
agnostic = torch.cat([low_im_mask, low_pose_map], 1)
+
low_grid, theta, rx, ry, cx, cy, rg, cg = tps(low_cloth, agnostic)
# We upsample the grid to the original image size and warp the cloth using the predicted TPS parameters
@@ -291,16 +302,16 @@ def main():
# Save images
for gen_image, cat, name in zip(generated_images, category, batch["im_name"]):
- if not os.path.exists(os.path.join(save_dir, cat)):
- os.makedirs(os.path.join(save_dir, cat))
+ # if not os.path.exists(os.path.join(save_dir, cat)):
+ # os.makedirs(os.path.join(save_dir, cat))
if args.use_png:
name = name.replace(".jpg", ".png")
gen_image.save(
- os.path.join(save_dir, cat, name))
+ os.path.join(save_dir, f'{cat}.png'))
else:
gen_image.save(
- os.path.join(save_dir, cat, name), quality=95)
+ os.path.join(save_dir, f'{cat}.jpg'), quality=95)
# Free up memory
del val_pipe
@@ -321,5 +332,5 @@ def main():
json.dump(metrics, f, indent=4)
-if __name__ == "__main__":
- main()
+# if __name__ == "__main__":
+# main()
diff --git a/model/pytorch-openpose/src/__init__.py b/model/ladi_vton/src/__init__.py
similarity index 100%
rename from model/pytorch-openpose/src/__init__.py
rename to model/ladi_vton/src/__init__.py
diff --git a/model/ladi_vton/src/dataset/__pycache__/dresscode.cpython-310.pyc b/model/ladi_vton/src/dataset/__pycache__/dresscode.cpython-310.pyc
new file mode 100644
index 0000000..0cede4d
Binary files /dev/null and b/model/ladi_vton/src/dataset/__pycache__/dresscode.cpython-310.pyc differ
diff --git a/model/ladi-vton/src/dataset/__pycache__/vitonhd.cpython-310.pyc b/model/ladi_vton/src/dataset/__pycache__/vitonhd.cpython-310.pyc
similarity index 83%
rename from model/ladi-vton/src/dataset/__pycache__/vitonhd.cpython-310.pyc
rename to model/ladi_vton/src/dataset/__pycache__/vitonhd.cpython-310.pyc
index 69a6c88..369dcca 100644
Binary files a/model/ladi-vton/src/dataset/__pycache__/vitonhd.cpython-310.pyc and b/model/ladi_vton/src/dataset/__pycache__/vitonhd.cpython-310.pyc differ
diff --git a/model/ladi-vton/src/dataset/dresscode.py b/model/ladi_vton/src/dataset/dresscode.py
similarity index 90%
rename from model/ladi-vton/src/dataset/dresscode.py
rename to model/ladi_vton/src/dataset/dresscode.py
index 779b837..44613d3 100644
--- a/model/ladi-vton/src/dataset/dresscode.py
+++ b/model/ladi_vton/src/dataset/dresscode.py
@@ -19,8 +19,10 @@
from PIL import Image, ImageDraw, ImageOps
from numpy.linalg import lstsq
-from src.utils.labelmap import label_map
-from src.utils.posemap import kpoint_to_heatmap
+import sys
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton/src/utils')
+from labelmap import label_map
+from posemap import kpoint_to_heatmap
class DressCodeDataset(data.Dataset):
@@ -76,11 +78,12 @@ def __init__(self,
for c in category:
assert c in ['dresses', 'upper_body', 'lower_body']
- dataroot = os.path.join(self.dataroot, c)
+ # dataroot = os.path.join(self.dataroot, c)
+ dataroot = self.dataroot ## 수정
if phase == 'train':
- filename = os.path.join(dataroot, f"{phase}_pairs.txt")
+ filename = os.path.join(dataroot, 'input/buffer', f"{phase}_pairs.txt")
else:
- filename = os.path.join(dataroot, f"{phase}_pairs_{order}.txt")
+ filename = os.path.join(dataroot, 'input/buffer', f"{phase}_pairs_{order}.txt")
with open(filename, 'r') as f:
for line in f.readlines():
@@ -107,7 +110,7 @@ def __getitem__(self, index):
c_name = self.c_names[index]
im_name = self.im_names[index]
dataroot = self.dataroot_names[index]
- category = dataroot.split('/')[-1]
+ category = self.category[0] #dataroot.split('/')[-1] ## 수정
if "captions" in self.outputlist: # Captions
captions = self.captions_dict[c_name.split('_')[0]]
@@ -120,19 +123,23 @@ def __getitem__(self, index):
if "clip_cloth_features" in self.outputlist: # Precomputed CLIP in-shop embeddings
clip_cloth_features = self.clip_cloth_features[self.clip_cloth_features_names.index(c_name)].float()
+ #####here
if "cloth" in self.outputlist: # In-shop clothing image
# Clothing image
- cloth = Image.open(os.path.join(dataroot, 'images', c_name))
- mask = Image.open(os.path.join(dataroot, 'masks', c_name.replace(".jpg", ".png")))
+ cloth = Image.open(os.path.join(dataroot, 'input/buffer/garment', c_name))
+
+ #############수정 해야함 !! test로 mask 없앰
+ # mask = Image.open(os.path.join(dataroot, 'masks', c_name.replace(".jpg", ".png")))
# Mask out the background
- cloth = Image.composite(ImageOps.invert(mask.convert('L')), cloth, ImageOps.invert(mask.convert('L')))
+ # cloth = Image.composite(ImageOps.invert(mask.convert('L')), cloth, ImageOps.invert(mask.convert('L')))
cloth = cloth.resize((self.width, self.height))
cloth = self.transform(cloth) # [-1,1]
+ #####here
if "image" in self.outputlist or "im_head" in self.outputlist or "im_cloth" in self.outputlist:
# Person image
- image = Image.open(os.path.join(dataroot, 'images', im_name))
+ image = Image.open(os.path.join(dataroot, 'input/buffer/target', im_name))
image = image.resize((self.width, self.height))
image = self.transform(image) # [-1,1]
@@ -161,15 +168,18 @@ def __getitem__(self, index):
skeleton = skeleton.resize((self.width, self.height))
skeleton = self.transform(skeleton)
+ #####here
if "im_pose" in self.outputlist or "parser_mask" in self.outputlist or "im_mask" in self.outputlist or \
"parse_mask_total" in self.outputlist or "parse_array" in self.outputlist or "pose_map" in \
self.outputlist or "parse_array" in self.outputlist or "shape" in self.outputlist or "im_head" in self.outputlist:
# Label Map
- parse_name = im_name.replace('_0.jpg', '_4.png')
- im_parse = Image.open(os.path.join(dataroot, 'label_maps', parse_name))
+ # parse_name = im_name.replace('_0.jpg', '_4.png')
+ parse_name = im_name.replace('.jpg', '.png')
+ im_parse = Image.open(os.path.join(dataroot, 'schp/buffer', parse_name))
im_parse = im_parse.resize((self.width, self.height), Image.NEAREST)
parse_array = np.array(im_parse)
+
parse_shape = (parse_array > 0).astype(np.float32)
parse_head = (parse_array == 1).astype(np.float32) + \
@@ -188,8 +198,7 @@ def __getitem__(self, index):
parser_mask_changeable = (parse_array == label_map["background"]).astype(np.float32)
arms = (parse_array == 14).astype(np.float32) + (parse_array == 15).astype(np.float32)
-
- if dataroot.split('/')[-1] == 'dresses':
+ if category == 'dresses':
label_cat = 7
parse_cloth = (parse_array == 7).astype(np.float32)
parse_mask = (parse_array == 7).astype(np.float32) + \
@@ -197,7 +206,7 @@ def __getitem__(self, index):
(parse_array == 13).astype(np.float32)
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
- elif dataroot.split('/')[-1] == 'upper_body':
+ elif category == 'upper_body':
label_cat = 4
parse_cloth = (parse_array == 4).astype(np.float32)
parse_mask = (parse_array == 4).astype(np.float32)
@@ -206,7 +215,7 @@ def __getitem__(self, index):
(parse_array == label_map["pants"]).astype(np.float32)
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
- elif dataroot.split('/')[-1] == 'lower_body':
+ elif category == 'lower_body':
label_cat = 6
parse_cloth = (parse_array == 6).astype(np.float32)
parse_mask = (parse_array == 6).astype(np.float32) + \
@@ -242,8 +251,8 @@ def __getitem__(self, index):
shape = self.transform2D(parse_shape) # [-1,1]
# Load pose points
- pose_name = im_name.replace('_0.jpg', '_2.json')
- with open(os.path.join(dataroot, 'keypoints', pose_name), 'r') as f:
+ pose_name = im_name.replace('.jpg', '.json')
+ with open(os.path.join(dataroot, 'openpose/buffer', pose_name), 'r') as f:
pose_label = json.load(f)
pose_data = pose_label['keypoints']
pose_data = np.array(pose_data)
@@ -286,11 +295,11 @@ def __getitem__(self, index):
# just for visualization
im_pose = self.transform2D(im_pose)
- im_arms = Image.new('L', (self.width, self.height))
+ im_arms = Image.new('L', (self.width, self.height))
arms_draw = ImageDraw.Draw(im_arms)
- if dataroot.split('/')[-1] == 'dresses' or dataroot.split('/')[-1] == 'upper_body' or dataroot.split('/')[
- -1] == 'lower_body':
- with open(os.path.join(dataroot, 'keypoints', pose_name), 'r') as f:
+
+ if category == 'dresses' or category == 'upper_body' or category == 'lower_body':
+ with open(os.path.join(dataroot, 'openpose/buffer', pose_name), 'r') as f:
data = json.load(f)
shoulder_right = np.multiply(tuple(data['keypoints'][2][:2]), self.height / 512.0)
shoulder_left = np.multiply(tuple(data['keypoints'][5][:2]), self.height / 512.0)
@@ -320,16 +329,15 @@ def __getitem__(self, index):
arms_draw.line(np.concatenate(
(wrist_left, elbow_left, shoulder_left, shoulder_right, elbow_right, wrist_right)).astype(
np.uint16).tolist(), 'white', 45, 'curve')
-
hands = np.logical_and(np.logical_not(im_arms), arms)
- if dataroot.split('/')[-1] == 'dresses' or dataroot.split('/')[-1] == 'upper_body':
+ if category == 'dresses' or category == 'upper_body':
parse_mask += im_arms
parser_mask_fixed += hands
# delete neck
parse_head_2 = torch.clone(parse_head)
- if dataroot.split('/')[-1] == 'dresses' or dataroot.split('/')[-1] == 'upper_body':
+ if category == 'dresses' or category == 'upper_body':
with open(os.path.join(dataroot, 'keypoints', pose_name), 'r') as f:
data = json.load(f)
points = []
diff --git a/model/ladi-vton/src/dataset/vitonhd.py b/model/ladi_vton/src/dataset/vitonhd.py
similarity index 99%
rename from model/ladi-vton/src/dataset/vitonhd.py
rename to model/ladi_vton/src/dataset/vitonhd.py
index f9f226a..6e16299 100644
--- a/model/ladi-vton/src/dataset/vitonhd.py
+++ b/model/ladi_vton/src/dataset/vitonhd.py
@@ -18,8 +18,10 @@
import torchvision.transforms as transforms
from PIL import Image, ImageDraw
-from src.utils.posemap import get_coco_body25_mapping
-from src.utils.posemap import kpoint_to_heatmap
+import sys
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton/src/utils')
+from posemap import get_coco_body25_mapping
+from posemap import kpoint_to_heatmap
class VitonHDDataset(data.Dataset):
diff --git a/model/ladi-vton/src/models/AutoencoderKL.py b/model/ladi_vton/src/models/AutoencoderKL.py
similarity index 100%
rename from model/ladi-vton/src/models/AutoencoderKL.py
rename to model/ladi_vton/src/models/AutoencoderKL.py
diff --git a/model/ladi-vton/src/models/ConvNet_TPS.py b/model/ladi_vton/src/models/ConvNet_TPS.py
similarity index 100%
rename from model/ladi-vton/src/models/ConvNet_TPS.py
rename to model/ladi_vton/src/models/ConvNet_TPS.py
diff --git a/model/ladi-vton/src/models/UNet.py b/model/ladi_vton/src/models/UNet.py
similarity index 96%
rename from model/ladi-vton/src/models/UNet.py
rename to model/ladi_vton/src/models/UNet.py
index 3ae22cd..0aefd98 100644
--- a/model/ladi-vton/src/models/UNet.py
+++ b/model/ladi_vton/src/models/UNet.py
@@ -1,4 +1,4 @@
-from .unet_parts import *
+from unet_parts import * ## 수정
class UNetVanilla(nn.Module):
diff --git a/model/ladi-vton/src/models/__pycache__/AutoencoderKL.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/AutoencoderKL.cpython-310.pyc
similarity index 98%
rename from model/ladi-vton/src/models/__pycache__/AutoencoderKL.cpython-310.pyc
rename to model/ladi_vton/src/models/__pycache__/AutoencoderKL.cpython-310.pyc
index 999a9bf..24d6ba0 100644
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diff --git a/model/ladi_vton/src/models/__pycache__/ConvNet_TPS.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/ConvNet_TPS.cpython-310.pyc
new file mode 100644
index 0000000..87dbc27
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diff --git a/model/ladi_vton/src/models/__pycache__/UNet.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/UNet.cpython-310.pyc
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diff --git a/model/ladi_vton/src/models/__pycache__/emasc.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/emasc.cpython-310.pyc
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diff --git a/model/ladi_vton/src/models/__pycache__/inversion_adapter.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/inversion_adapter.cpython-310.pyc
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index 0000000..f34d305
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diff --git a/model/ladi_vton/src/models/__pycache__/unet_parts.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/unet_parts.cpython-310.pyc
new file mode 100644
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diff --git a/model/ladi-vton/src/models/__pycache__/vae.cpython-310.pyc b/model/ladi_vton/src/models/__pycache__/vae.cpython-310.pyc
similarity index 98%
rename from model/ladi-vton/src/models/__pycache__/vae.cpython-310.pyc
rename to model/ladi_vton/src/models/__pycache__/vae.cpython-310.pyc
index 338a7c0..916601a 100644
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similarity index 100%
rename from model/ladi-vton/src/models/emasc.py
rename to model/ladi_vton/src/models/emasc.py
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similarity index 100%
rename from model/ladi-vton/src/models/inversion_adapter.py
rename to model/ladi_vton/src/models/inversion_adapter.py
diff --git a/model/ladi-vton/src/models/unet_parts.py b/model/ladi_vton/src/models/unet_parts.py
similarity index 100%
rename from model/ladi-vton/src/models/unet_parts.py
rename to model/ladi_vton/src/models/unet_parts.py
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similarity index 100%
rename from model/ladi-vton/src/models/vae.py
rename to model/ladi_vton/src/models/vae.py
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similarity index 100%
rename from model/pytorch-openpose/model/.gitkeep
rename to model/ladi_vton/src/utils/__init__.py
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diff --git a/model/ladi-vton/src/utils/compute_cloth_clip_features.py b/model/ladi_vton/src/utils/compute_cloth_clip_features.py
similarity index 100%
rename from model/ladi-vton/src/utils/compute_cloth_clip_features.py
rename to model/ladi_vton/src/utils/compute_cloth_clip_features.py
diff --git a/model/ladi-vton/src/utils/data_utils.py b/model/ladi_vton/src/utils/data_utils.py
similarity index 100%
rename from model/ladi-vton/src/utils/data_utils.py
rename to model/ladi_vton/src/utils/data_utils.py
diff --git a/model/ladi-vton/src/utils/encode_text_word_embedding.py b/model/ladi_vton/src/utils/encode_text_word_embedding.py
similarity index 100%
rename from model/ladi-vton/src/utils/encode_text_word_embedding.py
rename to model/ladi_vton/src/utils/encode_text_word_embedding.py
diff --git a/model/ladi-vton/src/utils/generate_fid_stats.py b/model/ladi_vton/src/utils/generate_fid_stats.py
similarity index 100%
rename from model/ladi-vton/src/utils/generate_fid_stats.py
rename to model/ladi_vton/src/utils/generate_fid_stats.py
diff --git a/model/ladi-vton/src/utils/image_from_pipe.py b/model/ladi_vton/src/utils/image_from_pipe.py
similarity index 100%
rename from model/ladi-vton/src/utils/image_from_pipe.py
rename to model/ladi_vton/src/utils/image_from_pipe.py
diff --git a/model/ladi-vton/src/utils/labelmap.py b/model/ladi_vton/src/utils/labelmap.py
similarity index 100%
rename from model/ladi-vton/src/utils/labelmap.py
rename to model/ladi_vton/src/utils/labelmap.py
diff --git a/model/ladi-vton/src/utils/posemap.py b/model/ladi_vton/src/utils/posemap.py
similarity index 100%
rename from model/ladi-vton/src/utils/posemap.py
rename to model/ladi_vton/src/utils/posemap.py
diff --git a/model/ladi-vton/src/utils/set_seeds.py b/model/ladi_vton/src/utils/set_seeds.py
similarity index 100%
rename from model/ladi-vton/src/utils/set_seeds.py
rename to model/ladi_vton/src/utils/set_seeds.py
diff --git a/model/ladi-vton/src/utils/val_metrics.py b/model/ladi_vton/src/utils/val_metrics.py
similarity index 99%
rename from model/ladi-vton/src/utils/val_metrics.py
rename to model/ladi_vton/src/utils/val_metrics.py
index b7ba72d..63724f6 100644
--- a/model/ladi-vton/src/utils/val_metrics.py
+++ b/model/ladi_vton/src/utils/val_metrics.py
@@ -13,7 +13,7 @@
from torchvision import transforms
from tqdm import tqdm
-from .generate_fid_stats import make_custom_stats
+from generate_fid_stats import make_custom_stats ##수정
class GTTestDataset(torch.utils.data.Dataset):
diff --git a/model/ladi-vton/src/utils/vgg_loss.py b/model/ladi_vton/src/utils/vgg_loss.py
similarity index 100%
rename from model/ladi-vton/src/utils/vgg_loss.py
rename to model/ladi_vton/src/utils/vgg_loss.py
diff --git a/model/ladi-vton/src/vto_pipelines/__pycache__/tryon_pipe.cpython-310.pyc b/model/ladi_vton/src/vto_pipelines/__pycache__/tryon_pipe.cpython-310.pyc
similarity index 95%
rename from model/ladi-vton/src/vto_pipelines/__pycache__/tryon_pipe.cpython-310.pyc
rename to model/ladi_vton/src/vto_pipelines/__pycache__/tryon_pipe.cpython-310.pyc
index 44d89fa..ec0b30a 100644
Binary files a/model/ladi-vton/src/vto_pipelines/__pycache__/tryon_pipe.cpython-310.pyc and b/model/ladi_vton/src/vto_pipelines/__pycache__/tryon_pipe.cpython-310.pyc differ
diff --git a/model/ladi-vton/src/vto_pipelines/tryon_pipe.py b/model/ladi_vton/src/vto_pipelines/tryon_pipe.py
similarity index 99%
rename from model/ladi-vton/src/vto_pipelines/tryon_pipe.py
rename to model/ladi_vton/src/vto_pipelines/tryon_pipe.py
index f230756..1675775 100644
--- a/model/ladi-vton/src/vto_pipelines/tryon_pipe.py
+++ b/model/ladi_vton/src/vto_pipelines/tryon_pipe.py
@@ -21,7 +21,8 @@
from diffusers.utils import is_accelerate_available, randn_tensor
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
-from src.utils.data_utils import mask_features
+# from src.utils.data_utils import mask_features ## 수정
+from data_utils import mask_features ## 수정
class StableDiffusionTryOnePipeline(DiffusionPipeline):
diff --git a/model/pytorch-openpose/extract_keypoint.py b/model/pytorch-openpose/extract_keypoint.py
deleted file mode 100644
index eb2bb9e..0000000
--- a/model/pytorch-openpose/extract_keypoint.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import cv2
-import matplotlib.pyplot as plt
-import copy
-import numpy as np
-
-from src import model
-from src import util
-from src.body import Body
-from src.hand import Hand
-
-import json
-
-TEST_IMAGE = '/opt/ml/dataset/ganddddi/sw2_0.jpg'
-OUTPUT_JSON = '/opt/ml/pytorch-openpose/output/sw2_0_hi.json'
-
-
-# Body, Hand model load
-body_estimation = Body('model/body_pose_model.pth')
-hand_estimation = Hand('model/hand_pose_model.pth')
-
-# image read
-test_image = TEST_IMAGE
-oriImg = cv2.imread(test_image) # B,G,R order
-
-# body_estimation foreward
-candidate, subset = body_estimation(oriImg)
-
-json_dict = {
- 'keypoints' : candidate.tolist()
-}
-
-# json 파일 저장
-with open(OUTPUT_JSON, 'w') as f:
- json.dump(json_dict, f, indent=4)
-
-
-# print(len(candidate)) # 14*4
-# print(candidate)
-# print(len(subset[0])) # [[20개]]
-# exit()
-# canvas = copy.deepcopy(oriImg)
-# canvas = util.draw_bodypose(canvas, candidate, subset)
-
-# # hand를 detect 후 hands_list에 넣는다.
-# # detect된 hand 들은 hands_list에 들어 있을 듯하다. ex) [hand1, hand2]
-# hands_list = util.handDetect(candidate, subset, oriImg)
-
-# all_hand_peaks = []
-# for x, y, w, is_left in hands_list:
-# # cv2.rectangle(canvas, (x, y), (x+w, y+w), (0, 255, 0), 2, lineType=cv2.LINE_AA)
-# # cv2.putText(canvas, 'left' if is_left else 'right', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-# # if is_left:
-# # plt.imshow(oriImg[y:y+w, x:x+w, :][:, :, [2, 1, 0]])
-# # plt.show()
-# peaks = hand_estimation(oriImg[y:y+w, x:x+w, :])
-# peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], peaks[:, 0]+x)
-# peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
-# # else:
-# # peaks = hand_estimation(cv2.flip(oriImg[y:y+w, x:x+w, :], 1))
-# # peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], w-peaks[:, 0]-1+x)
-# # peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
-# # print(peaks)
-# all_hand_peaks.append(peaks)
-
-# canvas = util.draw_handpose(canvas, all_hand_peaks)
-
-# plt.imshow(canvas[:, :, [2, 1, 0]])
-# plt.axis('off')
-
-# plt.savefig(OUTPUT)
-
-# # plt.show()?
diff --git a/model/pytorch-openpose/.gitignore b/model/pytorch_openpose/.gitignore
similarity index 100%
rename from model/pytorch-openpose/.gitignore
rename to model/pytorch_openpose/.gitignore
diff --git a/model/pytorch-openpose/README.md b/model/pytorch_openpose/README.md
similarity index 100%
rename from model/pytorch-openpose/README.md
rename to model/pytorch_openpose/README.md
diff --git a/model/pytorch-openpose/demo.py b/model/pytorch_openpose/demo.py
similarity index 96%
rename from model/pytorch-openpose/demo.py
rename to model/pytorch_openpose/demo.py
index 2891d11..8cd7b72 100644
--- a/model/pytorch-openpose/demo.py
+++ b/model/pytorch_openpose/demo.py
@@ -13,7 +13,7 @@
# Body, Hand model load
-body_estimation = Body('model/body_pose_model.pth')
+body_estimation = Body('/opt/ml/checkpoints/body_pose_model.pth')
hand_estimation = Hand('model/hand_pose_model.pth')
# image read
diff --git a/model/pytorch_openpose/extract_keypoint.py b/model/pytorch_openpose/extract_keypoint.py
new file mode 100644
index 0000000..69b21bb
--- /dev/null
+++ b/model/pytorch_openpose/extract_keypoint.py
@@ -0,0 +1,44 @@
+import cv2
+import matplotlib.pyplot as plt
+import copy
+import numpy as np
+
+from src import model
+from src import util
+from src.body import Body
+from src.hand import Hand
+
+import os
+
+import json
+
+# TEST_IMAGE = '/opt/ml/dataset/ganddddi/sw2_0.jpg'
+# OUTPUT_JSON = '/opt/ml/pytorch-openpose/output/sw2_0_hi.json'
+
+# target_image 512, 384
+def main_openpose(target_buffer_dir, output_buffer_dir):
+
+ img_name = 'target.jpg'
+ test_image = os.path.join(target_buffer_dir , img_name)
+
+ # Body, Hand model load
+ body_estimation = Body('/opt/ml/checkpoints/body_pose_model.pth')
+ # hand_estimation = Hand('model/hand_pose_model.pth')
+
+
+ # image read
+ # test_image = test_image
+ oriImg = cv2.imread(test_image) # B,G,R order
+ oriImg = cv2.resize(oriImg, (384, 512)) # resize
+
+ # body_estimation foreward
+ candidate, subset = body_estimation(oriImg)
+
+ json_dict = {
+ 'keypoints' : candidate.tolist()
+ }
+
+ # json 파일 저장
+ with open(f'{output_buffer_dir}/{img_name[:-4]}.json', 'w') as f:
+ json.dump(json_dict, f, indent=4)
+
diff --git a/model/pytorch_openpose/model/.gitkeep b/model/pytorch_openpose/model/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/model/pytorch-openpose/notebooks/detectHand.ipynb b/model/pytorch_openpose/notebooks/detectHand.ipynb
similarity index 99%
rename from model/pytorch-openpose/notebooks/detectHand.ipynb
rename to model/pytorch_openpose/notebooks/detectHand.ipynb
index 609cf0b..f245109 100644
--- a/model/pytorch-openpose/notebooks/detectHand.ipynb
+++ b/model/pytorch_openpose/notebooks/detectHand.ipynb
@@ -18,7 +18,7 @@
"import numpy as np\n",
"import math\n",
"\n",
- "body_estimation = Body('../model/body_pose_model.pth')\n",
+ "body_estimation = Body('/opt/ml/checkpoints/body_pose_model.pth')\n",
"\n",
"test_image = '../images/demo.jpg'\n",
"oriImg = cv2.imread(test_image) # B,G,R order\n",
@@ -177,7 +177,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
""
]
diff --git a/model/pytorch-openpose/notebooks/hand.ipynb b/model/pytorch_openpose/notebooks/hand.ipynb
similarity index 100%
rename from model/pytorch-openpose/notebooks/hand.ipynb
rename to model/pytorch_openpose/notebooks/hand.ipynb
diff --git a/model/pytorch-openpose/notebooks/network_graph.ipynb b/model/pytorch_openpose/notebooks/network_graph.ipynb
similarity index 100%
rename from model/pytorch-openpose/notebooks/network_graph.ipynb
rename to model/pytorch_openpose/notebooks/network_graph.ipynb
diff --git a/model/pytorch-openpose/pytorch-openpose.yaml b/model/pytorch_openpose/pytorch-openpose.yaml
similarity index 100%
rename from model/pytorch-openpose/pytorch-openpose.yaml
rename to model/pytorch_openpose/pytorch-openpose.yaml
diff --git a/model/pytorch-openpose/requirements.txt b/model/pytorch_openpose/requirements.txt
similarity index 100%
rename from model/pytorch-openpose/requirements.txt
rename to model/pytorch_openpose/requirements.txt
diff --git a/model/pytorch_openpose/src/__init__.py b/model/pytorch_openpose/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/model/pytorch-openpose/src/body.py b/model/pytorch_openpose/src/body.py
similarity index 99%
rename from model/pytorch-openpose/src/body.py
rename to model/pytorch_openpose/src/body.py
index ecf0693..0fbce8a 100644
--- a/model/pytorch-openpose/src/body.py
+++ b/model/pytorch_openpose/src/body.py
@@ -208,7 +208,7 @@ def __call__(self, oriImg):
return candidate, subset
if __name__ == "__main__":
- body_estimation = Body('../model/body_pose_model.pth')
+ body_estimation = Body('/opt/ml/checkpoints/body_pose_model.pth')
test_image = '../images/ski.jpg'
oriImg = cv2.imread(test_image) # B,G,R order
diff --git a/model/pytorch-openpose/src/hand.py b/model/pytorch_openpose/src/hand.py
similarity index 100%
rename from model/pytorch-openpose/src/hand.py
rename to model/pytorch_openpose/src/hand.py
diff --git a/model/pytorch-openpose/src/hand_model_output_size.json b/model/pytorch_openpose/src/hand_model_output_size.json
similarity index 100%
rename from model/pytorch-openpose/src/hand_model_output_size.json
rename to model/pytorch_openpose/src/hand_model_output_size.json
diff --git a/model/pytorch-openpose/src/hand_model_outputsize.py b/model/pytorch_openpose/src/hand_model_outputsize.py
similarity index 100%
rename from model/pytorch-openpose/src/hand_model_outputsize.py
rename to model/pytorch_openpose/src/hand_model_outputsize.py
diff --git a/model/pytorch-openpose/src/model.py b/model/pytorch_openpose/src/model.py
similarity index 100%
rename from model/pytorch-openpose/src/model.py
rename to model/pytorch_openpose/src/model.py
diff --git a/model/pytorch-openpose/src/util.py b/model/pytorch_openpose/src/util.py
similarity index 100%
rename from model/pytorch-openpose/src/util.py
rename to model/pytorch_openpose/src/util.py