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attribute 模型名称错误

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zhouchengrong
2024-03-27 13:17:41 +08:00
parent 5f5617d5a3
commit d4be7b8053
14 changed files with 897 additions and 13 deletions
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16
app/core/config.py
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@@ -27,16 +27,16 @@ MINIO_SECURE = False
MINIO_ACCESS = "e8zc55mzDOh4IzRrZ9Oa"
MINIO_SECRET = "uHfqJ7UkwA1PTDGfnA44Hp9ux5YkZTkzZLjeOYhE"
OM_TRITON_IP = "10.1.1.150"
OM_TRITON_PORT = "9000"
OM_TRITON_IP = "10.1.1.240"
OM_TRITON_PORT = "10010"
ATT_TRITON_IP = "10.1.1.240"
ATT_TRITON_PORT = "10020"
# service env
# LOGS_PATH = "app/logs/errors.log"
# FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json"
# FASHION_CATEGORIES_MAPPING = "app/service/outfit_matcher/config/fashion_category_mapping.json"
LOGS_PATH = "app/logs/errors.log"
FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json"
FASHION_CATEGORIES_MAPPING = "app/service/outfit_matcher/config/fashion_category_mapping.json"
# pycharm debug
# LOGS_PATH = "logs/errors.log"
@@ -44,6 +44,6 @@ ATT_TRITON_PORT = "10020"
# FASHION_CATEGORIES_MAPPING = "service/outfit_matcher/config/fashion_category_mapping.json"
LOGS_PATH = "app/logs/errors.log"
FASHION_CATEGORIES = "./config/fashion_categories.json"
FASHION_CATEGORIES_MAPPING = "./config/fashion_category_mapping.json"
# LOGS_PATH = "app/logs/errors.log"
# FASHION_CATEGORIES = "./config/fashion_categories.json"
# FASHION_CATEGORIES_MAPPING = "./config/fashion_category_mapping.json"

358
app/logs/errors.log
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@@ -1,3 +1,267 @@
2024-03-25 17:26:03,804 decorator.py [line:11] INFO function：【load_image】,runtime：【0.13544344902038574】s
2024-03-25 17:26:03,804 decorator.py [line:11] INFO function：【load_image】,runtime：【0.13544344902038574】s
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2024-03-25 17:26:03,901 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07622218132019043】s
2024-03-25 17:26:04,003 decorator.py [line:11] INFO function：【load_image】,runtime：【0.085418701171875】s
2024-03-25 17:26:04,003 decorator.py [line:11] INFO function：【load_image】,runtime：【0.085418701171875】s
2024-03-25 17:26:04,112 decorator.py [line:11] INFO function：【load_image】,runtime：【0.09087872505187988】s
2024-03-25 17:26:04,112 decorator.py [line:11] INFO function：【load_image】,runtime：【0.09087872505187988】s
2024-03-25 17:26:04,213 decorator.py [line:11] INFO function：【load_image】,runtime：【0.08513045310974121】s
2024-03-25 17:26:04,213 decorator.py [line:11] INFO function：【load_image】,runtime：【0.08513045310974121】s
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2024-03-25 17:26:29,302 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07897257804870605】s
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2024-03-25 17:26:29,533 decorator.py [line:11] INFO function：【load_image】,runtime：【0.05791950225830078】s
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2024-03-21 13:51:37,818 decorator.py [line:11] INFO function：【load_image】,runtime：【1.2232704162597656】s
2024-03-21 13:51:37,818 decorator.py [line:11] INFO function：【load_image】,runtime：【1.2232704162597656】s
2024-03-21 13:51:38,052 decorator.py [line:11] INFO function：【load_image】,runtime：【0.19752931594848633】s
@@ -138,3 +402,97 @@
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2024-03-21 13:52:09,813 decorator.py [line:11] INFO function：【load_image】,runtime：【0.25645899772644043】s
2024-03-21 13:52:09,813 decorator.py [line:11] INFO function：【load_image】,runtime：【0.25645899772644043】s
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2024-03-27 11:28:51,561 decorator.py [line:11] INFO function：【load_image】,runtime：【0.09883594512939453】s
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2024-03-27 11:28:54,385 decorator.py [line:11] INFO function：【get_result】,runtime：【3.172989845275879】s
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2024-03-27 11:28:54,627 decorator.py [line:11] INFO function：【load_image】,runtime：【0.10200977325439453】s
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2024-03-27 11:28:57,010 decorator.py [line:11] INFO function：【get_result】,runtime：【2.6036272048950195】s
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2024-03-27 11:28:57,567 decorator.py [line:11] INFO function：【load_image】,runtime：【0.11722540855407715】s
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2024-03-27 11:28:57,678 decorator.py [line:11] INFO function：【load_image】,runtime：【0.08625340461730957】s
2024-03-27 11:28:57,824 decorator.py [line:11] INFO function：【get_result】,runtime：【0.8130843639373779】s
2024-03-27 11:28:57,824 decorator.py [line:11] INFO function：【get_result】,runtime：【0.8130843639373779】s
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2024-03-27 11:28:57,895 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06562995910644531】s
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2024-03-27 11:28:58,000 decorator.py [line:11] INFO function：【load_image】,runtime：【0.08478975296020508】s
2024-03-27 11:28:58,034 decorator.py [line:11] INFO function：【get_result】,runtime：【0.2043590545654297】s
2024-03-27 11:28:58,034 decorator.py [line:11] INFO function：【get_result】,runtime：【0.2043590545654297】s
2024-03-27 11:28:58,101 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06698036193847656】s
2024-03-27 11:28:58,101 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06698036193847656】s
2024-03-27 11:28:58,242 decorator.py [line:11] INFO function：【load_image】,runtime：【0.11905717849731445】s
2024-03-27 11:28:58,242 decorator.py [line:11] INFO function：【load_image】,runtime：【0.11905717849731445】s
2024-03-27 11:28:58,285 decorator.py [line:11] INFO function：【get_result】,runtime：【0.2512340545654297】s
2024-03-27 11:28:58,285 decorator.py [line:11] INFO function：【get_result】,runtime：【0.2512340545654297】s
2024-03-27 11:28:58,367 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07999205589294434】s
2024-03-27 11:28:58,367 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07999205589294434】s
2024-03-27 11:28:58,463 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07760047912597656】s
2024-03-27 11:28:58,463 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07760047912597656】s
2024-03-27 11:28:58,543 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06173586845397949】s
2024-03-27 11:28:58,543 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06173586845397949】s
2024-03-27 11:28:58,636 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07255721092224121】s
2024-03-27 11:28:58,636 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07255721092224121】s
2024-03-27 11:28:58,723 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0690464973449707】s
2024-03-27 11:28:58,723 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0690464973449707】s
2024-03-27 11:28:58,813 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06989359855651855】s
2024-03-27 11:28:58,813 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06989359855651855】s
2024-03-27 11:28:58,907 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07435822486877441】s
2024-03-27 11:28:58,907 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07435822486877441】s
2024-03-27 11:28:58,997 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06914925575256348】s
2024-03-27 11:28:58,997 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06914925575256348】s
2024-03-27 11:28:59,089 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07468628883361816】s
2024-03-27 11:28:59,089 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07468628883361816】s
2024-03-27 11:28:59,342 decorator.py [line:11] INFO function：【get_result】,runtime：【1.055443286895752】s
2024-03-27 11:28:59,342 decorator.py [line:11] INFO function：【get_result】,runtime：【1.055443286895752】s
2024-03-27 11:28:59,419 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07607388496398926】s
2024-03-27 11:28:59,419 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07607388496398926】s
2024-03-27 11:28:59,523 decorator.py [line:11] INFO function：【load_image】,runtime：【0.08356571197509766】s
2024-03-27 11:28:59,523 decorator.py [line:11] INFO function：【load_image】,runtime：【0.08356571197509766】s
2024-03-27 11:28:59,611 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07082462310791016】s
2024-03-27 11:28:59,611 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07082462310791016】s
2024-03-27 11:28:59,695 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06639719009399414】s
2024-03-27 11:28:59,695 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06639719009399414】s
2024-03-27 11:28:59,782 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06848025321960449】s
2024-03-27 11:28:59,782 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06848025321960449】s
2024-03-27 11:28:59,861 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06321930885314941】s
2024-03-27 11:28:59,861 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06321930885314941】s
2024-03-27 11:28:59,949 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06985092163085938】s
2024-03-27 11:28:59,949 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06985092163085938】s
2024-03-27 11:29:00,036 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06791377067565918】s
2024-03-27 11:29:00,036 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06791377067565918】s
2024-03-27 11:29:00,124 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0706472396850586】s
2024-03-27 11:29:00,124 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0706472396850586】s
2024-03-27 11:29:00,171 decorator.py [line:11] INFO function：【get_result】,runtime：【0.8278758525848389】s
2024-03-27 11:29:00,171 decorator.py [line:11] INFO function：【get_result】,runtime：【0.8278758525848389】s
2024-03-27 11:29:00,235 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06318235397338867】s
2024-03-27 11:29:00,235 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06318235397338867】s
2024-03-27 11:29:00,322 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06962013244628906】s
2024-03-27 11:29:00,322 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06962013244628906】s
2024-03-27 11:29:00,405 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06734132766723633】s
2024-03-27 11:29:00,405 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06734132766723633】s
2024-03-27 11:29:00,481 decorator.py [line:11] INFO function：【load_image】,runtime：【0.05873560905456543】s
2024-03-27 11:29:00,481 decorator.py [line:11] INFO function：【load_image】,runtime：【0.05873560905456543】s
2024-03-27 11:29:00,560 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06250882148742676】s
2024-03-27 11:29:00,560 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06250882148742676】s
2024-03-27 11:29:00,638 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0628812313079834】s
2024-03-27 11:29:00,638 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0628812313079834】s
2024-03-27 11:29:00,679 decorator.py [line:11] INFO function：【get_result】,runtime：【0.5070171356201172】s
2024-03-27 11:29:00,679 decorator.py [line:11] INFO function：【get_result】,runtime：【0.5070171356201172】s
2024-03-27 11:29:00,680 api_outfit_matcher.py [line:43] INFO run time is : 9.467643737792969
2024-03-27 11:29:00,680 api_outfit_matcher.py [line:43] INFO run time is : 9.467643737792969

1
app/schemas/similar_match.py
View File

@@ -3,3 +3,4 @@ from pydantic import BaseModel
class SimilarMatchMItem(BaseModel):
image_path: str
result_number: int

2
app/service/outfit_matcher/outfit_evaluator.py
View File

@@ -8,7 +8,7 @@ from matplotlib import pyplot as plt, image as mpimg
from minio import Minio
from torchvision import transforms
from app.core.config import MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, MINIO_PORT, OM_TRITON_PORT, OM_TRITON_IP
from app.core.config import *
from app.service.outfit_matcher.foco import extract_main_colors
from app.service.utils.decorator import RunTime

19
app/service/outfit_matcher/test_param/test.json Normal file
View File

@@ -0,0 +1,19 @@
{
"topk": 1,
"max_outfits": 5,
"is_best": true,
"query": [
{
"image_path": "mi-tu/26/BOTTOM/PANTS/MKTS27000_0BLK.jpg/3f4676db-98a1-44d4-947f-9d1f59828629.jpg",
"item_name": "MKTS27000",
"semantic_category": "BOTTOM/PANTS"
}
],
"database": [
{
"image_path": "mi-tu/26/TOP/BLOUSE/MKTS27002_0WHT.jpg/131cc29e-8f70-4134-a0e8-82f826b00058.jpg",
"item_name": "MKTS27002",
"semantic_category": "TOP/BLOUSE"
}
]
}

11
app/service/similar_match/service.py
View File

@@ -10,6 +10,8 @@ from pymilvus import MilvusClient
from app.core.config import *
from torchvision import transforms
from app.service.utils.decorator import RunTime
class SimilarMatch:
def __init__(self):
@@ -77,19 +79,20 @@ class SimilarMatch:
features = results.as_numpy("output__1") # Shape (N, 64)
return features
@RunTime
def match_features(self, features):
# 连接milvus
# 连接milvus
client = MilvusClient(uri="http://10.1.1.240:19530", db_name="mixi")
try:
res = client.search(
search_response = client.search(
collection_name="mixi_outfit", # Replace with the actual name of your collection
# Replace with your query vector
data=[features[0]],
limit=5, # Max. number of search results to return
output_fields=["id", "image_path"], # Search parameters
)
return res
return search_response
finally:
client.close()
@@ -97,6 +100,6 @@ class SimilarMatch:
if __name__ == '__main__':
service = SimilarMatch()
features = service.get_features(img_path="test/2024 SS/MKTS27000.jpg")
res = service.match_features(features)
search_response = service.match_features(features)
print(json.dumps(res, indent=4))
print(json.dumps(search_response, indent=4))

1
app/service/utils/decorator.py
View File

@@ -9,6 +9,7 @@ def RunTime(func):
t2 = time.time()
if t2 - t1 > 0.05:
logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
print(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
return res
return wrapper

BIN
requirements.txt
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Binary file not shown.

110
test/attribute/infer_test.py Normal file
View File

@@ -0,0 +1,110 @@
import cv2
import mmcv
import numpy as np
import tritonclient.http as httpclient
import torch
def preprocess(img):
img = mmcv.imread(img)
ori_shape = img.shape[:2]
img_scale = (224, 224)
scale_factor = []
img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
scale_factor.append(x)
scale_factor.append(y)
img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
return preprocessed_img, ori_shape
def get_attribute(model_save_name, sample):
triton_client = httpclient.InferenceServerClient(url=f"10.1.1.240:10020")
inputs = [
httpclient.InferInput("input__0", sample.shape, datatype="FP32")
]
inputs[0].set_data_from_numpy(sample, binary_data=True)
results = triton_client.infer(model_name=model_save_name, inputs=inputs)
inference_output = torch.from_numpy(results.as_numpy(f"output__0"))
scores = inference_output.detach().numpy()
print(f"{model} is ok")
image, shape = preprocess(cv2.imread("8365b04b-69d0-4f6d-a61b-a8cc136256263422b10ff45d4ae4e10be3fc25a9c36b.jpg"))
# get_attribute("attr_retrieve_D_sleeve_shape", image)
model_list = ['top_length',
'top_type',
'top_Sleeve_length',
'top_Sleeve_shape',
'top_Sleeve_shoulder',
'top_Neckline',
'top_print',
'top_material',
'top_Softness',
'top_Design',
'top_optype',
'top_Silhouette',
'top_Collar',
'bottom_sub-Type',
'bottom_length',
'bottom_print',
'bottom_material',
'bottom_Softness_B',
'bottom_Silhouette_B',
'bottom_OPType_B',
'bottom_design',
'outwear_outer_length',
# 'outwear_2_outer_type',
'outwear_outer_sleeve_length',
'outwear_outer_sleeve_shape',
'outwear_outer_sleeve_shoulder',
'outwear_outer_collar',
'outwear_print',
'outwear_material',
'outwear_outer_softness',
'outwear_outer_design',
# 'outwear_11_outer_opening',
'outwear_outer_optype',
'outwear_outer_silhouette',
'jumpsuit_length',
'jumpsuit_sleeve_length',
'jumpsuit_sleeve_shape',
'jumpsuit_sleeve_shoulder',
'jumpsuit_neckline',
'jumpsuit_collar',
'jumpsuit_print',
'jumpsuit_material',
'jumpsuit_softness',
'jumpsuit_design',
'jumpsuit_optype',
'jumpsuit_subtype',
'dress_length',
'dress_sleeve_length',
'dress_sleeve_shape',
'dress_sleeve_shoulder',
'dress_neckline',
'dress_print',
'dress_collar',
'dress_material',
'dress_design',
'dress_softness',
'dress_silohouette12',
# 'dress_'
'dress_type'
]
except_model = []
# 'attr_recong_B_optype', 'attr_recong_D_length', 'attr_recong_O_length', 'attr_recong_O_optype', 'attr_recong_material',
# 'attr_recong_print', 'attr_retrieve_D_collar', 'attr_retrieve_D_design', 'attr_retrieve_D_neckline', 'attr_retrieve_D_silohouette',
# 'attr_retrieve_D_sleeve_shape', 'attr_retrieve_D_sleeve_shoulder', 'attr_retrieve_D_type', 'attr_retrieve_T_length', 'attr_retrieve_T_optype'
# for model in model_list:
# get_attribute(model, image)
for model in model_list:
try:
get_attribute(model, image)
except Exception as e:
print(e)
except_model.append(model)
print(except_model)

56
test/attribute/model.py Normal file
View File

@@ -0,0 +1,56 @@
var = ["bottom_OPType_B",
"bottom_Silhouette_B",
"bottom_Softness_B",
"bottom_design",
"bottom_length",
"bottom_material",
"bottom_print",
"bottom_sub-Type",
"category",
"dress_collar",
"dress_design",
"dress_length",
"dress_material",
"dress_neckline",
"dress_print",
"dress_silohouette2",
"dress_sleeve_length",
"dress_sleeve_shape",
"dress_sleeve_shoulder",
"dress_softness",
"dress_type",
"jumpsuit_collar",
"jumpsuit_design",
"jumpsuit_length",
"jumpsuit_material",
"jumpsuit_optype",
"jumpsuit_print",
"jumpsuit_sleeve_length",
"jumpsuit_sleeve_shape",
"jumpsuit_sleeve_shoulder",
"jumpsuit_softness",
"jumpsuit_subtype",
"outwear_material",
"outwear_outear_length",
"outwear_outer_collar",
"outwear_outer_design",
"outwear_outer_optype",
"outwear_outer_silhouette",
"outwear_outer_sleeve_length",
"outwear_outer_sleeve_shape",
"outwear_outer_sleeve_shoulder",
"outwear_outer_softness",
"outwear_print",
"top_Collar",
"top_Design",
"top_Neckline",
"top_Silhouette",
"top_Sleeve_length",
"top_Sleeve_shape",
"top_Sleeve_shoulder",
"top_Softness",
"top_length",
"top_material",
"top_optype",
"top_print",
"top_type", ]

51
test/attribute_recognition_model_infer/attribute_recognition_model_infer.py Normal file
View File

@@ -0,0 +1,51 @@
from pprint import pprint
import cv2
import mmcv
import numpy as np
import tritonclient.http as httpclient
import torch
from app.core.config import ATT_TRITON_PORT, ATT_TRITON_IP
model_list = ['bottom_design', 'bottom_length', 'bottom_material', 'bottom_OPType_B', 'bottom_print', 'bottom_Silhouette_B', 'bottom_Softness_B', 'bottom_sub-Type', 'category', 'dress_collar', 'dress_design', 'dress_length', 'dress_material', 'dress_neckline', 'dress_print', 'dress_silohouette12', 'dress_sleeve_length', 'dress_sleeve_shape', 'dress_sleeve_shoulder', 'dress_softness', 'dress_type', 'jumpsuit_collar', 'jumpsuit_design', 'jumpsuit_length', 'jumpsuit_material', 'jumpsuit_optype',
'jumpsuit_print', 'jumpsuit_sleeve_length', 'jumpsuit_sleeve_shape', 'jumpsuit_sleeve_shoulder', 'jumpsuit_softness', 'jumpsuit_subtype', 'outwear_material', 'outwear_outear_length', 'outwear_outer_collar', 'outwear_outer_design', 'outwear_outer_optype', 'outwear_outer_silhouette', 'outwear_outer_sleeve_length', 'outwear_outer_sleeve_shape', 'outwear_outer_sleeve_shoulder', 'outwear_outer_softness', 'outwear_print', 'top_Collar', 'top_Design', 'top_length', 'top_material',
'top_Neckline', 'top_optype', 'top_print', 'top_Silhouette', 'top_Sleeve_length', 'top_Sleeve_shape', 'top_Sleeve_shoulder', 'top_Softness', 'top_type']
def preprocess(img):
img = mmcv.imread(img)
ori_shape = img.shape[:2]
img_scale = (224, 224)
scale_factor = []
img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
scale_factor.append(x)
scale_factor.append(y)
img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
return preprocessed_img, ori_shape
def get_attribute(model_save_name, sample):
triton_client = httpclient.InferenceServerClient(url=f"{ATT_TRITON_IP}:{ATT_TRITON_PORT}")
inputs = [
httpclient.InferInput("input__0", sample.shape, datatype="FP32")
]
inputs[0].set_data_from_numpy(sample, binary_data=True)
results = triton_client.infer(model_name=model_save_name, inputs=inputs)
inference_output = torch.from_numpy(results.as_numpy(f"output__0"))
scores = inference_output.detach().numpy()
pprint(scores)
print(f"{model_save_name} is ok")
image, shape = preprocess(cv2.imread("test_top1.jpg"))
except_model = []
for model in model_list:
try:
get_attribute(model, image)
except Exception as e:
print(e)
except_model.append(model)
print(except_model)

273
test/json/outfit_response.py Normal file
View File

@@ -0,0 +1,273 @@
a = [{'best_outfits': [({'image_path': 'test/2024 SS/MKTS27047.jpg',
'item_name': 'MKTS27047',
'mapped_cate': 'all-body',
'semantic_category': 'ONE PIECE/DRESS'},
{'image_path': 'test/2024 SS/MLSS27156.jpg',
'item_name': 'MLSS27156',
'mapped_cate': 'outerwear',
'semantic_category': 'TOP/VEST'}),
({'image_path': 'test/2024 SS/MKTS27047.jpg',
'item_name': 'MKTS27047',
'mapped_cate': 'all-body',
'semantic_category': 'ONE PIECE/DRESS'},
{'image_path': 'test/2024 SS/MLSS27157.jpg',
'item_name': 'MLSS27157',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/WINDBREAKER'}),
({'image_path': 'test/2024 SS/MKTS27047.jpg',
'item_name': 'MKTS27047',
'mapped_cate': 'all-body',
'semantic_category': 'ONE PIECE/DRESS'},
{'image_path': 'test/2024 SS/MLSS27150.jpg',
'item_name': 'MLSS27150',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/JACKET'}),
({'image_path': 'test/2024 SS/MKTS27047.jpg',
'item_name': 'MKTS27047',
'mapped_cate': 'all-body',
'semantic_category': 'ONE PIECE/DRESS'},
{'image_path': 'test/2024 SS/MSE_56720.jpg',
'item_name': 'MSE_56720',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'}),
({'image_path': 'test/2024 SS/MKTS27047.jpg',
'item_name': 'MKTS27047',
'mapped_cate': 'all-body',
'semantic_category': 'ONE PIECE/DRESS'},
{'image_path': 'test/2024 SS/MLSS27109.jpg',
'item_name': 'MLSS27109',
'mapped_cate': 'outerwear',
'semantic_category': 'JEANS/JEANS JACKET'})],
'best_scores': [-0.111208215,
-0.120505564,
-0.1213615,
-0.12190802,
-0.12419219]},
{'best_outfits': [({'image_path': 'test/2024 SS/MKTS27028.jpg',
'item_name': 'MKTS27028',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/JACKET'},
{'image_path': 'test/2024 SS/MSE_58107.jpg',
'item_name': 'MSE_58107',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27133.jpg',
'item_name': 'MLSS27133',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'}),
({'image_path': 'test/2024 SS/MKTS27028.jpg',
'item_name': 'MKTS27028',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/JACKET'},
{'image_path': 'test/2024 SS/MLSS27132.jpg',
'item_name': 'MLSS27132',
'mapped_cate': 'tops',
'semantic_category': 'TOP/TOP'},
{'image_path': 'test/2024 SS/MLSS27159.jpg',
'item_name': 'MLSS27159',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'}),
({'image_path': 'test/2024 SS/MKTS27028.jpg',
'item_name': 'MKTS27028',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/JACKET'},
{'image_path': 'test/2024 SS/MLSS27132.jpg',
'item_name': 'MLSS27132',
'mapped_cate': 'tops',
'semantic_category': 'TOP/TOP'},
{'image_path': 'test/2024 SS/MLSS27133.jpg',
'item_name': 'MLSS27133',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'}),
({'image_path': 'test/2024 SS/MKTS27028.jpg',
'item_name': 'MKTS27028',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/JACKET'},
{'image_path': 'test/2024 SS/MKTS27039.jpg',
'item_name': 'MKTS27039',
'mapped_cate': 'tops',
'semantic_category': 'TOP/TOP'},
{'image_path': 'test/2024 SS/MLSS27226.jpg',
'item_name': 'MLSS27226',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'}),
({'image_path': 'test/2024 SS/MKTS27028.jpg',
'item_name': 'MKTS27028',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/JACKET'},
{'image_path': 'test/2024 SS/MLSS27146.jpg',
'item_name': 'MLSS27146',
'mapped_cate': 'tops',
'semantic_category': 'TOP/TOP'},
{'image_path': 'test/2024 SS/MSE_58518.jpg',
'item_name': 'MSE_58518',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'})],
'best_scores': [-0.111208215,
-0.11449928,
-0.11642833,
-0.120505564,
-0.1213615]},
{'best_outfits': [({'image_path': 'test/2024 SS/MSE_58057.jpg',
'item_name': 'MSE_58057',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'},
{'image_path': 'test/2024 SS/MKTS27046.jpg',
'item_name': 'MKTS27046',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MSE_58518.jpg',
'item_name': 'MSE_58518',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'}),
({'image_path': 'test/2024 SS/MSE_58057.jpg',
'item_name': 'MSE_58057',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'},
{'image_path': 'test/2024 SS/MSE_58473.jpg',
'item_name': 'MSE_58473',
'mapped_cate': 'tops',
'semantic_category': 'KNIT/KNIT TOP'},
{'image_path': 'test/2024 SS/MKTS27031.jpg',
'item_name': 'MKTS27031',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'}),
({'image_path': 'test/2024 SS/MSE_58057.jpg',
'item_name': 'MSE_58057',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'},
{'image_path': 'test/2024 SS/MPO_SP7687.jpg',
'item_name': 'MPO_SP7687',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27107.jpg',
'item_name': 'MLSS27107',
'mapped_cate': 'bottoms',
'semantic_category': 'JEANS/JEANS'}),
({'image_path': 'test/2024 SS/MSE_58057.jpg',
'item_name': 'MSE_58057',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'},
{'image_path': 'test/2024 SS/MLSS27142.jpg',
'item_name': 'MLSS27142',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27159.jpg',
'item_name': 'MLSS27159',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'}),
({'image_path': 'test/2024 SS/MSE_58057.jpg',
'item_name': 'MSE_58057',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'},
{'image_path': 'test/2024 SS/MLSS27142.jpg',
'item_name': 'MLSS27142',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27226.jpg',
'item_name': 'MLSS27226',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SKIRT'})],
'best_scores': [-0.111208215,
-0.11449928,
-0.11642833,
-0.120505564,
-0.1213615]},
{'best_outfits': [({'image_path': 'test/2024 SS/MSE_58107.jpg',
'item_name': 'MSE_58107',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27159.jpg',
'item_name': 'MLSS27159',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'},
{'image_path': 'test/2024 SS/MSE_58289.jpg',
'item_name': 'MSE_58289',
'mapped_cate': 'outerwear',
'semantic_category': 'OUTERWEAR/BLAZER'}),
({'image_path': 'test/2024 SS/MSE_58107.jpg',
'item_name': 'MSE_58107',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27111.jpg',
'item_name': 'MLSS27111',
'mapped_cate': 'bottoms',
'semantic_category': 'JEANS/JEANS PANTS'}),
({'image_path': 'test/2024 SS/MSE_58107.jpg',
'item_name': 'MSE_58107',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27104.jpg',
'item_name': 'MLSS27104',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/SHORTS'}),
({'image_path': 'test/2024 SS/MSE_58107.jpg',
'item_name': 'MSE_58107',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27159.jpg',
'item_name': 'MLSS27159',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'}),
({'image_path': 'test/2024 SS/MSE_58107.jpg',
'item_name': 'MSE_58107',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27160.jpg',
'item_name': 'MLSS27160',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'})],
'best_scores': [-0.111208215,
-0.11449928,
-0.120505564,
-0.1213615,
-0.12190802]},
{'best_outfits': [({'image_path': 'test/2024 SS/MSE_58495.jpg',
'item_name': 'MSE_58495',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27159.jpg',
'item_name': 'MLSS27159',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'}),
({'image_path': 'test/2024 SS/MSE_58495.jpg',
'item_name': 'MSE_58495',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27160.jpg',
'item_name': 'MLSS27160',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'}),
({'image_path': 'test/2024 SS/MSE_58495.jpg',
'item_name': 'MSE_58495',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27159.jpg',
'item_name': 'MLSS27159',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'},
{'image_path': 'test/2024 SS/MLSS27109.jpg',
'item_name': 'MLSS27109',
'mapped_cate': 'outerwear',
'semantic_category': 'JEANS/JEANS JACKET'}),
({'image_path': 'test/2024 SS/MSE_58495.jpg',
'item_name': 'MSE_58495',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MLSS27119.jpg',
'item_name': 'MLSS27119',
'mapped_cate': 'bottoms',
'semantic_category': 'JEANS/JEANS SKIRT'}),
({'image_path': 'test/2024 SS/MSE_58495.jpg',
'item_name': 'MSE_58495',
'mapped_cate': 'tops',
'semantic_category': 'TOP/SHIRT'},
{'image_path': 'test/2024 SS/MKTS27000.jpg',
'item_name': 'MKTS27000',
'mapped_cate': 'bottoms',
'semantic_category': 'BOTTOM/PANTS'})],
'best_scores': [-0.111208215,
-0.11449928,
-0.120505564,
-0.1213615,
-0.12190802]}]

12
test/minio_client_test/minio_client.py Normal file
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@@ -0,0 +1,12 @@
from minio import Minio
from app.core.config import MINIO_PORT, MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE
minio_client = Minio(
f"{MINIO_IP}:{MINIO_PORT}",
access_key=MINIO_ACCESS,
secret_key=MINIO_SECRET,
secure=MINIO_SECURE)
for i in minio_client.list_objects('test'):
print(i.object_name)

0
test/outfit_matcher/triton_infer.py Normal file
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