D/L 기반 Defect 분류 실습
개요
- Cat vs Dog 이미지를 통해 클래스를 분류하여 D/L기반 분류 실습 진행합니다.
학습/검증 데이터 준비
- 샘플 파일 다운로드
- https://www.kaggle.com/datasets/tongpython/cat-and-dog
- 강아지, 고양이 사진 파일이 각각 5000개씩 존재합니다.
- 샘플 파일 구성
- 강아지 및 고양이 이미지가 분류 안되어 있다고 가정합니다.
- 검증을 위한 이미지를 임의로 분리합니다. (학습 8000장, 테스트 2000장)
- 데이터 셋 등록
- Client에 데이터를 등록합니다.
- Client에 데이터를 등록합니다.
- ClassCodeSet 등록
CAT_DOGClassCodeSet 등록CAT,DOG의 ClassCode 등록
학습 데이터 labling 작업
- 학습 데이터셋에서 추가한 데이터셋을
복제 - Labeling 진행
- 라벨링에 대한 더 자세한 내용은 이미지 분류 메뉴얼을 참조하시기 바랍니다.
- 라벨링 완료
- 라벨링 완료 화면을 확인합니다.
- 라벨링 완료 화면을 확인합니다.
Classification 학습 진행 및 모델 배포 작업
- 학습 레시피 등록
- 학습 레시피 화면 접속
- 학습 레시피 등록
- 학습 레시피 등록 확인
- 학습 레시피 화면 접속
- 🔗 학습 레서피 다운로드
- 🔗 추론 레서피 다운로드
📜 inception_builder_handler.py 예시 코드 보기
import itertools
import shutil
from urllib.parse import urlparse
import uuid
import random
import os
import io
import logging
import time
import json
import grpc
import cv2
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import torch
import torchvision
from torchvision import transforms
import torch.nn as nn
from torch.utils.data import Dataset
import torch.optim as optim
from google.protobuf.wrappers_pb2 import StringValue
import mpp
from mpp.daq import protos
from PIL import Image
##!--{"Name":"hyperparameter","Type":"epoch","Key":"epoch","Value":"","Category":""}
##!--{"Name":"hyperparameter","Type":"batch_size","Key":"batch_size","Value":"","Category":""}
##!--{"Name":"result","Type":"result","Key":"id","Value":"","Category":""}
##!--{"Name":"authentication","Type":"system_address","Key":"operation_service_address","Value":"","Category":""}
##!--{"Name":"authentication","Type":"access_token","Key":"access_token","Value":"","Category":""}
##!--{"Name":"gt_dataset","Type":"gt_dataset","Key":"gt_dataset_id","Value":"","Category":""}
##$--
parameters = '''{
"hyperparameter":{
"epoch" : 20,
"save_epoch" : 1,
"batch_size" : 16,
"lr" : 1e-3,
"optimizer_name" : "Adam",
"input_size" : 299,
"normalize_mean" : 0.5,
"normalize_stdev" : 0.5,
"using_gpu" : true,
"using_amp" : true,
"train_ratio" : 0.8,
"validation_save_random" : false
},
"authentication": {
"operation_service_address": "",
"access_token" : ""
},
"result":{
"id":"",
"volume_id":"default"
},
"gt_dataset":{
"gt_dataset_id" : ""
},
"chunk_size" : 100000
}'''
##$--
logger = globals().get('JOB_LOGGER', logging.getLogger())
logger.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO, format='%(message)s')
def RecipeRun(**kwargs):
operation_builder = Operation_Builder(**kwargs)
operation_config = operation_builder \
.initialize() \
.build()
hyperparameter_builder = HyperparameterBuilder(operation_config['hyperparameter']) \
.initialize() \
.build()
dataset_builder = Classification_Dataset_Builder(operation_config['gt_dataset'])
result = dataset_builder \
.initialize() \
.init_label_data(
operation_channel= operation_builder.get_operation_channel(),
access_token= operation_builder.get_access_token()
) \
.init_dataset_gts(
operation_channel= operation_builder.get_operation_channel(),
access_token= operation_builder.get_access_token()
) \
.create_train_dataset(
operation_channel= operation_builder.get_operation_channel(),
access_token= operation_builder.get_access_token(),
train_ratio= hyperparameter_builder.get_train_ratio(),
transform= hyperparameter_builder.get_transform(),
batch_size= hyperparameter_builder.get_batch_size(),
validation_save_random= hyperparameter_builder.get_validation_save_random()
) \
.build()
if result is False:
logger.error(f"Dataset Build Failed")
dataset_builder.temp_folder_delete()
return None
try:
save_builder = Save_Builder() \
.initialize(
operation_builder,
num_classes=dataset_builder.get_num_classes(),
label_info=dataset_builder.get_label_info()
) \
.init_inference_info(
input_size=hyperparameter_builder.get_input_size()
) \
.build()
model_builder = Torch_Model_Builder()
model_builder \
.initialize(
epoch_total=hyperparameter_builder.get_epoch(),
save_epoch=hyperparameter_builder.get_save_epoch(),
save_builder=save_builder
) \
.init_model(
num_classes=dataset_builder.get_num_classes(),
device=operation_builder.get_device()
) \
.init_optimizer(
optimizer_name=hyperparameter_builder.get_optimizer(),
lr=hyperparameter_builder.get_learning_rate()
) \
.init_criterion(
criterion_name=hyperparameter_builder.get_criterion()
) \
.build()
model_builder.train(
dataset_builder.get_train_loader(),
dataset_builder.get_validation_loader(),
operation_builder.get_device(),
hyperparameter_builder.get_using_amp()
)
except Exception as e:
logger.error(f"Error Message : {e}")
raise Exception(f"Train Failed, Error Message : {e}")
finally:
dataset_builder.temp_folder_delete()
class Operation_Builder:
def __init__(self, **keyword_arguments):
self.__keyword_arguments = keyword_arguments
self.__operation_channel = None
self.__access_token = None
self.__bucket_url = None
self.__chunk_size = None
self.__device = None
def initialize(self):
self.__init_device()
self.__init_access_token()
self.__init_operation_channel()
self.__init_bucket_url()
self.__init_chunk_size()
return self
def __init_device(self):
device = self.__keyword_arguments['hyperparameter']['using_gpu']
if device and not torch.cuda.is_available():
raise Exception("GPU is not available")
self.__device = 'cuda' if device else 'cpu'
def __init_operation_channel(self):
address = self.__keyword_arguments['authentication']['operation_service_address']
selected_address = random.choice(address.split(","))
if not address or not selected_address:
raise Exception("Address is required")
self.__operation_channel = grpc.insecure_channel(selected_address)
def __init_access_token(self):
access_token = self.__keyword_arguments['authentication']['access_token']
if not access_token:
raise Exception("Access token is required")
self.__access_token = access_token
def __init_bucket_url(self):
bucket_id = self.__keyword_arguments['result']['id']
volume_id = self.__keyword_arguments['result']['volume_id']
if not bucket_id:
raise Exception("Bucket URL is required")
if urlparse(bucket_id).scheme == '':
self.__create_bucket(bucket_id, bucket_id+"_title", volume_id)
self.__bucket_url = f"object:///{bucket_id}"
else:
self.__bucket_url = bucket_id
def __create_bucket(self, bucket_id, bucket_title, bucket_volume_id, properties=None):
if properties:
properties = StringValue(value=json.dumps(properties))
stub = protos.daq_object_object_api_v1_pb2_grpc.ObjectServiceStub(self.__operation_channel)
stub.CreateBucket(request=protos.daq_object_object_api_v1_pb2.CreateBucketRequest(
id=bucket_id, title=bucket_title, properties=properties, description=None, volume_id=bucket_volume_id
),metadata=[('authorization', f'Bearer {self.__access_token}')])
def __init_chunk_size(self):
chunk_size = self.__keyword_arguments['chunk_size']
if not chunk_size:
raise Exception("Chunk size is required")
if chunk_size <= 0:
raise Exception("Chunk size must be greater than 0")
self.__chunk_size = chunk_size
def get_operation_channel(self):
return self.__operation_channel
def get_access_token(self):
return self.__access_token
def get_device(self):
return self.__device
def get_bucket_url(self):
return self.__bucket_url
def get_chunk_size(self):
return self.__chunk_size
def build(self):
return self.__keyword_arguments
class HyperparameterBuilder:
def __init__(self, keyword_arguments):
self.__hyperparams = keyword_arguments
def initialize(self):
self.__hyperparams['epoch'] = int(self.__hyperparams['epoch'])
self.__hyperparams['batch_size'] = int(self.__hyperparams['batch_size'])
return self
def get_train_ratio(self):
return self.__hyperparams['train_ratio']
def get_transform(self):
input_size = self.__hyperparams['input_size']
mean = self.__hyperparams['normalize_mean']
stdev = self.__hyperparams['normalize_stdev']
transform = transforms.Compose([
transforms.Lambda(lambda img: Image.fromarray(img).convert("RGB")),
transforms.ToTensor(),
transforms.Resize((input_size, input_size)),
transforms.Normalize((mean, mean, mean), (stdev, stdev, stdev))
])
return transform
def get_batch_size(self):
return self.__hyperparams['batch_size']
def get_validation_save_random(self):
return self.__hyperparams.get('validation_save_random', False)
def get_optimizer(self):
keys = ['optimizer', 'optimizer_name']
for key in keys:
if key in self.__hyperparams:
return self.__hyperparams[key]
raise KeyError(f"Optimizer not found. Tried keys: {', '.join(keys)}")
def get_learning_rate(self):
keys = ['lr', 'learningRate', 'LearningRate', 'Learningrate']
for key in keys:
if key in self.__hyperparams:
return self.__hyperparams[key]
raise KeyError(f"Learning rate not found. Tried keys: {', '.join(keys)}")
def get_input_size(self):
return self.__hyperparams['input_size']
def get_epoch(self):
return self.__hyperparams['epoch']
def get_save_epoch(self):
return self.__hyperparams['save_epoch']
def get_using_amp(self):
return self.__hyperparams.get('using_amp', True)
def get_early_stop_patience(self):
keys = ['earlyStopPatience']
for key in keys:
if key in self.__hyperparams:
return self.__hyperparams[key]
return 0
def get_reduce_learning_rate_patience(self):
keys = ['reduceLRPatience']
for key in keys:
if key in self.__hyperparams:
return self.__hyperparams[key]
return 0
def get_criterion(self):
keys = ['criterion', 'loss']
for key in keys:
if key in self.__hyperparams:
return self.__hyperparams[key]
return 'CrossEntropyLoss'
def build(self):
return self
class Classification_Dataset_Builder:
def __init__(self, keyword_arguments):
self.__gt_dataset_id = keyword_arguments['gt_dataset_id']
self.__local_download_path = None
self.__train_data_loader = None
self.__validation_data_loader = None
self.__classification_gts = None
self.__label_info = None
self.__class_code_info = None
self.__num_classes = None
def initialize(self):
self.__local_download_path = os.path.join(f"/temp/{uuid.uuid4()}")
return self
def init_label_data(self, operation_channel, access_token):
if operation_channel:
stub = protos.daq_dataset_classification_gt_dataset_api_v1_pb2_grpc.ClassificationGtDatasetServiceStub(operation_channel)
classification_gt_dataset = stub.GetClassificationGtDataset(request=protos.daq_dataset_classification_gt_dataset_api_v1_pb2.GetClassificationGtDatasetRequest(
id=self.__gt_dataset_id), metadata=[('authorization', f'Bearer {access_token}')])
class_code_set_id = classification_gt_dataset.class_code_set_id
else:
class_code_set_id = self.__gt_dataset_id
if operation_channel:
stub = protos.daq_dataset_class_code_api_v1_pb2_grpc.ClassCodeServiceStub(operation_channel)
query_parameter = protos.daq_common_pb2.QueryParameter(
page_index=0,
page_size=-1,
where=StringValue(value=f"Id=\"{class_code_set_id}\""),
order_by=None)
response = stub.ListClassCodeSets(request=protos.daq_dataset_class_code_api_v1_pb2.ListClassCodeSetsRequest(
query_parameter=query_parameter), metadata=[('authorization', f'Bearer {access_token}')])
class_info = response.class_code_sets[0].class_codes
num_classes = len(class_info)
label_info = {"label_count" : num_classes}
class_code_info = dict()
for i, class_code in enumerate(class_info):
label_info[f'label_{i}'] = {"code" : class_code.code, "name" : class_code.name}
class_code_info[class_code.code] = i
else:
class_info = os.listdir(class_code_set_id)
num_classes = len(class_info)
label_info = {"label_count" : num_classes}
class_code_info = dict()
for i, class_code in enumerate(class_info):
label_info[f'label_{i}'] = {"code" : i, "name" : class_code}
class_code_info[i] = i
self.__label_info = label_info
self.__class_code_info = class_code_info
self.__num_classes = num_classes
return self
def init_dataset_gts(self, operation_channel, access_token):
if operation_channel:
stub = protos.daq_dataset_classification_gt_dataset_api_v1_pb2_grpc.ClassificationGtDatasetServiceStub(operation_channel)
query_parameter = protos.daq_common_pb2.QueryParameter(
page_index=0,
page_size=-1,
where=StringValue(value=f"GtDatasetId=\"{self.__gt_dataset_id}\""),
order_by=None)
response = stub.ListClassificationGts(request=protos.daq_dataset_classification_gt_dataset_api_v1_pb2.ListClassificationGtsRequest(
query_parameter=query_parameter, with_image=False), metadata=[('authorization', f'Bearer {access_token}')])
self.__classification_gts = response.classification_gts
else:
self.__classification_gts = self.__gt_dataset_id
return self
def create_train_dataset(self, operation_channel, access_token, train_ratio, transform, batch_size, validation_save_random):
logger.info("Create Train Dataset")
try:
train_data_info, validation_data_info = self.data_download(train_ratio,
self.__local_download_path,
self.__classification_gts,
self.__class_code_info,
operation_channel,
access_token)
train_dataset = ClassificationDataset(train_data_info, transform)
train_data_loader = torch.utils.data.DataLoader(train_dataset,
batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
valid_flag = False
if len(validation_data_info[0]) > 0:
valid_flag = True
validation_dataset = ClassificationDataset(validation_data_info, transform)
validation_data_loader = torch.utils.data.DataLoader(validation_dataset,
batch_size=1,
shuffle=validation_save_random,
num_workers=0,
drop_last=False)
self.__train_data_loader = train_data_loader
self.__validation_data_loader = validation_data_loader if valid_flag else None
except Exception as e:
logger.error(f"Error Message : {e}")
raise Exception(f"Create Train Dataset Failed, Error Message : {e}")
return self
def get_label_info(self):
return self.__label_info
def get_class_code_info(self):
return self.__class_code_info
def get_num_classes(self):
return self.__num_classes
def data_download(self, train_ratio, local_download_path, classification_gts, class_code_info, operation_channel, access_token):
train_ratio = min(1, train_ratio)
train_uri_list = list()
train_label_list = list()
validation_uri_list = list()
validation_label_list = list()
if operation_channel:
validation_len = int(len(classification_gts) * (1-train_ratio))
for i in range(len(classification_gts)):
image_id = classification_gts[i].image_id
class_code = classification_gts[i].class_code.value
uri = f"dataset:///?image_id={image_id}"
image = mpp.daq.intel64.load(uri, False, channel=operation_channel, access_token=access_token)
download_path = os.path.join(local_download_path, f"{image_id}.png")
mpp.intel64.save(image, download_path)
train_uri_list.append(download_path)
train_label_list.append(class_code_info[class_code])
else:
validation_len = int(len(train_uri_list) * (1-train_ratio))
class_code_list = os.listdir(classification_gts)
for index in range(len(class_code_list)):
file_list = os.listdir(os.path.join(classification_gts, class_code_list[index]))
for filename in file_list:
image_path = os.path.join(classification_gts, class_code_list[index], filename)
train_uri_list.append(image_path)
train_label_list.append(class_code_info[index])
for _ in range(validation_len):
random_index = random.randrange(len(train_uri_list))
valid_uri = train_uri_list.pop(random_index)
valid_label = train_label_list.pop(random_index)
validation_uri_list.append(valid_uri)
validation_label_list.append(valid_label)
return (train_uri_list, train_label_list), (validation_uri_list, validation_label_list)
def get_train_loader(self):
return self.__train_data_loader
def get_validation_loader(self):
return self.__validation_data_loader
def temp_folder_delete(self):
if os.path.exists(self.__local_download_path):
shutil.rmtree(self.__local_download_path)
logger.info("Temp Folder Delete")
def build(self):
return self.__train_data_loader is not None and self.__validation_data_loader is not None
class Torch_Model_Builder:
def __init__(self):
self.__model = None
self.__optimizer = None
self.__criterion = None
self.__iteration_start_time = None
self.__epoch_start_time = None
self.__epoch_total = None
self.__save_epoch = None
self.__save_builder =None
def initialize(self, epoch_total, save_epoch, save_builder):
self.__epoch_total = epoch_total
self.__save_epoch = save_epoch
self.__save_builder = save_builder
return self
def init_model(self, num_classes, device):
model = torchvision.models.inception_v3(num_classes=num_classes, init_weights=False)
model.to(device)
self.__model = model
return self
def init_optimizer(self, optimizer_name, lr):
if optimizer_name.lower() == "adam":
self.__optimizer = optim.Adam(self.__model.parameters(), lr=lr)
elif optimizer_name.lower() == "sgd":
self.__optimizer = optim.SGD(self.__model.parameters(), lr=lr)
elif optimizer_name.lower() == "adagrad":
self.__optimizer = optim.Adagrad(self.__model.parameters(), lr=lr)
else:
raise Exception("Invalid Optimizer Option")
return self
def init_criterion(self, criterion_name):
if criterion_name.lower() == "crossentropyloss":
criterion = nn.CrossEntropyLoss()
elif criterion_name.lower() == "bcewithlogitsloss":
criterion = nn.BCEWithLogitsLoss()
elif criterion_name.lower() == "focalloss":
criterion = nn.BCEWithLogitsLoss()
elif criterion_name.lower() == "mse":
criterion = nn.MSELoss()
self.__criterion = criterion
return self
def train(self, train_data_loader, valid_data_loader, device, using_amp):
total_iteration = len(train_data_loader)
if total_iteration == 0:
raise RuntimeError("No training data: train_data_loader is empty.")
self.__iteration_start_time = time.time()
for epoch in range(1, self.__epoch_total + 1):
self.__epoch_start_time = time.time()
logger.info(f"Epoch : {epoch:4d}/{self.__epoch_total:4d}")
train_epoch_loss = 0.0
for n_epoch, batch in enumerate(train_data_loader, 1):
train_inputs, loss = self.train_step(batch, device, using_amp)
train_epoch_loss += loss.item()
if n_epoch % max(1, total_iteration // 10) == 0 or n_epoch == total_iteration:
iteration_elapsed_time = time.time() - self.__iteration_start_time
logger.info(f"Epoch : {epoch:4d}, Iterations : {n_epoch:4d}/{total_iteration:4d}, Loss : {loss : 4.4f}, Time : {iteration_elapsed_time : 4.4f}")
self.__iteration_start_time = time.time()
self.__save_builder.append_train_loss(train_epoch_loss / n_epoch)
self.__save_builder.append_train_acc(100.0 - (train_epoch_loss / n_epoch))
self.__save_builder.set_hierarchy_root(f"epoch_{epoch}")
valid_epoch_loss = 0.0
if valid_data_loader:
predict_list = np.array([])
label_list = np.array([])
with torch.no_grad():
self.__model.eval()
for _, valid_batch in enumerate(valid_data_loader, 1):
valid_inputs = valid_batch[0].to(device)
valid_labels = valid_batch[1].to(device)
with torch.cuda.amp.autocast(enabled=using_amp):
valid_outputs = self.__model(valid_inputs)
valid_loss = self.__criterion(valid_outputs, valid_labels)
valid_epoch_loss += valid_loss.item()
predict_list = np.concatenate([predict_list, valid_outputs.argmax(dim=1).cpu().numpy()], 0)
label_list = np.concatenate([label_list, valid_labels.cpu().numpy()], 0)
self.__model.train()
self.__save_builder.append_valid_loss(valid_epoch_loss / n_epoch)
self.__save_builder.append_valid_acc(100.0 - (valid_epoch_loss / n_epoch))
self.__save_builder.save_validateion(epoch, label_list, predict_list)
epoch_valid_loss_mean = valid_epoch_loss/len(valid_data_loader) if valid_data_loader else 0
self.__save_builder.save_training(self.__model, epoch, self.__save_epoch, train_inputs)
self.__save_builder.save_train_valid_csv()
train_loss = train_epoch_loss/total_iteration
self.monitoring(epoch, epoch_valid_loss_mean, train_loss)
return self
def train_step(self, batch, device, using_amp):
inputs = batch[0].to(device)
labels = batch[1].to(device)
with torch.cuda.amp.autocast(enabled=using_amp):
output, _ = self.__model(inputs)
loss = self.__criterion(output, labels)
self.__optimizer.zero_grad()
loss.backward()
self.__optimizer.step()
return inputs, loss
def monitoring(self, epoch, valid_loss, train_loss):
epoch_elapsed_time = time.time() - self.__epoch_start_time
remaining_epochs = self.__epoch_total - epoch
estimated_time_per_epoch = epoch_elapsed_time if epoch > 1 else 0 # 첫 번째 epoch의 경우 시간을 0으로 설정
estimated_remaining_time = remaining_epochs * estimated_time_per_epoch
logger.info("MonitoringData:"
f"Epoch:[{epoch:4d}/{self.__epoch_total:4d}], "
f"Train Loss: {train_loss:4.4f}, "
f"Valid Loss : {valid_loss:4.4f}, "
f"Time: {epoch_elapsed_time:4.2f}s, "
f"Estimated Remaining Time: {estimated_remaining_time / 60:.2f} minutes")
def build(self):
return self.__model
class Save_Builder:
def __init__(self):
self.__train_loss_list = list()
self.__valid_loss_list = list()
self.__train_acc_list = list()
self.__valid_acc_list = list()
self.__inference_info = None
self.__hierarchy_root = None
self.__label_info = None
self.__operation_builder = None
self.__num_classes = None
def initialize(self, operation_builder, num_classes, label_info):
self.__operation_builder = operation_builder
self.__num_classes = num_classes
self.__label_info = label_info
return self
def init_inference_info(self, input_size):
self.__inference_info = {'inference_info' : json.dumps({"input_size": input_size, "label_info": self.__label_info})}
return self
def append_train_loss(self, loss):
self.__train_loss_list.append(loss)
return self
def append_valid_loss(self, loss):
self.__valid_loss_list.append(loss)
return self
def append_train_acc(self, loss):
self.__train_acc_list.append(loss)
return self
def append_valid_acc(self, loss):
self.__valid_acc_list.append(loss)
return self
def set_hierarchy_root(self, hierarchy_root):
self.__hierarchy_root = hierarchy_root
return self
def save_training(self, model, epoch, save_epoch, inputs):
train_loss_image = self.score_list_graph_image(epoch, self.__train_loss_list, self.__train_loss_list[0], "Train Loss Graph", 'r')
self.save_file(train_loss_image, "train_loss/train_loss_image.png")
train_loss_csv = self.score_list_csv(self.__train_loss_list)
self.save_csv(train_loss_csv, "train_loss/train_loss_csv.csv")
if epoch % save_epoch == 0 or epoch == epoch:
self.upload_model(model, "model/model.pth", inputs)
def save_validateion(self, epoch, label_list, predict_list):
valid_loss_image = self.score_list_graph_image(epoch, self.__valid_loss_list, self.__valid_loss_list[0], "Valid Loss Graph", 'g')
self.save_file(valid_loss_image, "valid_loss/valid_loss_graph.png")
valid_loss_csv = self.score_list_csv(self.__valid_loss_list)
self.save_csv(valid_loss_csv, "valid_loss/valid_loss_csv.csv")
confusion_matrix = self.confusion_matrix_image(label_list, predict_list, labels=[self.__label_info[f'label_{i}']['name'] for i in range(self.__num_classes)])
self.save_file(confusion_matrix, "confusion_matrix/confusion_matrix.png")
def save_train_valid_csv(self):
data = self.create_csv()
self.save_csv(data, "train_loss_csv/train_loss_csv.csv")
def create_csv(self):
has_valid = self.__valid_loss_list is not None
if has_valid:
result = [['Epoch', 'Train Loss', 'Valid Loss', 'Train Accuracy', 'Valid Accuracy']]
max_length = max(len(self.__train_loss_list), len(self.__valid_loss_list))
else:
result = [['Epoch', 'Train Loss', 'Train Accuracy']]
max_length = len(self.__train_loss_list)
for epoch in range(max_length):
train_loss = self.__train_loss_list[epoch] if epoch < len(self.__train_loss_list) else None
train_acc = self.__train_acc_list[epoch] if epoch < len(self.__train_acc_list) else None
if has_valid:
valid_loss = self.__valid_loss_list[epoch] if epoch < len(self.__valid_loss_list) else None
valid_acc = self.__valid_acc_list[epoch] if epoch < len(self.__valid_acc_list) else None
result.append([epoch + 1, train_loss, valid_loss, train_acc, valid_acc])
else:
result.append([epoch + 1, train_loss, train_acc])
return result
def save_csv(self, csv, path):
save_uri = f"{self.__operation_builder.get_bucket_url()}/{self.__hierarchy_root}/{path}"
mpp.intel64.save_csv(csv, save_uri, channel=self.__operation_builder.get_operation_channel(), access_token=self.__operation_builder.get_access_token(), chunk_size=self.__operation_builder.get_chunk_size())
return self
def save_file(self, file, path):
save_uri = f"{self.__operation_builder.get_bucket_url()}/{self.__hierarchy_root}/{path}"
mpp.intel64.save(file, save_uri, channel=self.__operation_builder.get_operation_channel(), access_token=self.__operation_builder.get_access_token(), chunk_size=self.__operation_builder.get_chunk_size())
return self
def upload_model(self, model, path, inputs):
model_save_uri = f"{self.__operation_builder.get_bucket_url()}/{self.__hierarchy_root}/{path}"
mpp.daq.object_service.upload_model(model, uri=model_save_uri, inference_info=self.__inference_info, example=inputs, channel=self.__operation_builder.get_operation_channel(), access_token=self.__operation_builder.get_access_token(), chunk_size=self.__operation_builder.get_chunk_size())
return self
def score_list_graph_image(self, total_epoch, loss_list, y_max=None, title="", color='r'):
y_section = 100
if 0<=y_max<3: y_section = 0.1
elif 3<=y_max<10 : y_section = 1
elif 10<=y_max<50 : y_section = 5
elif 50<=y_max<100 : y_section = 10
elif 100<=y_max<500 : y_section = 50
elif 500<=y_max<1000 : y_section = 100
elif 1000<=y_max<5000 : y_section = 500
elif 5000<=y_max<10000 : y_section = 1000
elif 10000<=y_max : y_section = 5000
x_section = 10
if 1<=total_epoch<=10: x_section = 1
elif 10<total_epoch<=50: x_section = 5
elif 50<total_epoch<=100: x_section = 10
elif 100<total_epoch<=500 : x_section = 50
elif 500<total_epoch : x_section = 100
axes = plt.axes()
axes.set_xlim([1, total_epoch])
axes.set_ylim([0, y_max])
x_axis = list(range(0, total_epoch+1, x_section))
x_axis[0] = 1
plt.xticks(x_axis)
plt.yticks(list(np.arange(0, y_max, y_section)))
plt.plot(range(1, len(loss_list)+1), loss_list, color, label=title)
plt.ylabel("loss")
plt.xlabel("Epoch")
plt.legend()
loss_image_buffer = io.BytesIO()
plt.savefig(loss_image_buffer, format='png')
img_arr = np.frombuffer(loss_image_buffer.getvalue(), dtype=np.uint8)
img = cv2.imdecode(img_arr, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.clf()
plt.close()
return img
def confusion_matrix_image(self, true_list, pred_list, labels):
matrix = confusion_matrix(true_list, pred_list, labels=[x for x in range(len(labels))])
plt.figure(figsize=(9,9))
plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.get_cmap('Blues'))
plt.title("Confusion Matrix")
plt.colorbar()
marks = np.arange(len(labels))
nlabels = []
for k in range(len(matrix)):
nlabel = f'{labels[k]}'
nlabels.append(nlabel)
plt.xticks(marks, labels, rotation=45)
plt.yticks(marks, nlabels, rotation=45)
for i, j in itertools.product(range(matrix.shape[0]), range(matrix.shape[1])):
plt.text(j, i, matrix[i, j], horizontalalignment="center", color="black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
matrix_buffer = io.BytesIO()
plt.savefig(matrix_buffer, format='png')
img_arr = np.frombuffer(matrix_buffer.getvalue(), dtype=np.uint8)
img = cv2.imdecode(img_arr, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.clf()
plt.close()
return img
def score_list_csv(self, score_list, header='Loss'):
result = [['Epoch', header]] + [[epoch, score] for epoch, score in enumerate(score_list, 1)]
return result
def build(self):
return self
class ClassificationDataset(Dataset):
def __init__(self, train_dataset, transform):
self.__uri_list = train_dataset[0]
self.__label_list = train_dataset[1]
self.__trainform = transform
def __len__(self):
return len(self.__uri_list)
def __getitem__(self, index):
uri = self.__uri_list[index]
image = mpp.intel64.load(uri, False)
image = self.__trainform(image)
label = self.__label_list[index]
return image, label
if __name__ =="__main__":
kwargs = json.loads(parameters)
kwargs['authentication']['operation_service_address'] = ""
kwargs['authentication']['access_token'] = ""
kwargs['gt_dataset']['gt_dataset_id'] = r""
kwargs['result']['id'] = r""
RecipeRun(**kwargs)
- 학습 진행
- 학습 진행시 등록한 학습 레시피를 선택한 뒤 실행합니다.
- 모델 다운로드
- 특정 모델을
*.pth다운로드합니다. 
- 특정 모델을
- 추론 모델 등록
- 다음과 같은 구조로 추론 모델을 등록합니다.
model_handler/
├─ data/
│ ├─ model.pth
│ └─ inference.json
├─ model_handler.py
└─ model.json inference.json-
{
"label": {
"0": "DOG",
"1": "CAT"
}
}
-
model.json-
{
"model_file": "model.pth",
"device": "cuda"
}
-
📜 model_handler.py 예시 코드 보기
import os
import pickle
import numpy as np
import torch
import torchvision.transforms as transforms
import torch.nn as nn
import torchvision.models as models
import cv2
import tensorflow as tf
import json
## Gpu Memory 제한
memory_limit = 3
gpus = tf.config.list_physical_devices('GPU')
if gpus :
try :
for gpu in gpus :
tf.config.experimental.set_virtual_device_configuration(gpu,[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=memory_limit*1024)])
except RuntimeError as e :
print(e)
pass
class ModelHandler:
def __init__(self, data=None, context=None):
base_path = os.path.dirname(os.path.abspath(__file__))
model_path = os.path.join(base_path, "data", "model.pth")
infer_path = os.path.join(base_path, "data", "inference.json")
with open(infer_path, "r", encoding="utf-8") as f:
infer_cfg = json.load(f)
raw_map = infer_cfg.get("label", {})
self.label_map = {int(k): v for k, v in raw_map.items()}
# TorchScript JIT 모델 로드
self.model = torch.jit.load(model_path, map_location='cpu')
self.model.eval()
self.input_size = 299
self.transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((self.input_size, self.input_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
def __call__(self, data, context):
# 데이터 역직렬화 및 전처리
image_np = pickle.loads(data) # H x W x 3
image_tensor = self.transform(image_np).unsqueeze(0) # (1, 3, 299, 299)
# 추론
with torch.no_grad():
outputs = self.model(image_tensor)
preds = torch.softmax(outputs, dim=1)
pred_idx = torch.argmax(preds, dim=1).item()
score = preds[0][pred_idx].item()
result = {
'result_code': self.label_map.get(pred_idx, str(pred_idx)),
'score': score
}
return pickle.dumps(result), context
if __name__ == "__main__":
handler = ModelHandler()
test_image = cv2.imread("test_image.png")
test_image = cv2.resize(test_image, (299, 299))
data = pickle.dumps(test_image)
output, _ = handler(data, context={})
result = pickle.loads(output)
print(result)
- 추론 모델 등록
- 추론 모델에서 모델을 추가합니다.
- 추론 모델 버전을 추가합니다.
- 추론 모델에서 모델을 추가합니다.
- 모델 배포
- 모델 배포 합니다.
- 모델 배포 상태 확인합니다.
- 모델 배포 합니다.
Predict 모듈 사용
📜 inference_sample 레시피 예시 코드 보기
###
### Created By [MPP v25.06.002]/[RC v25.8.1.0]
###
##!--{"Name":"__mainfunction__","Type":"string","Key":"","Value":"resultdata = RecipeRun(gs_semimage1, gs_semimage2, gs_semimage3, gs_semimage4, gs_semimage5, gs_semimage6, gs_semimage7, gs_semimage8, gs_semimage9, gs_semimage10, gs_semimage11, gs_semimage12, gs_semimage13, gs_semimage14, gs_semimage15, gs_semimage16, gs_semimage17, gs_semimage18, gs_semimage19, gs_semimage20, gs_layercount, gs_layerimage1, gs_layerindex1, gs_refimage1, gs_refvector1, gs_layerimage2, gs_layerindex2, gs_refimage2, gs_refvector2, gs_layerimage3, gs_layerindex3, gs_refimage3, gs_refvector3, gs_layerimage4, gs_layerindex4, gs_refimage4, gs_refvector4, gs_layerimage5, gs_layerindex5, gs_refimage5, gs_refvector5, gs_layerimage6, gs_layerindex6, gs_refimage6, gs_refvector6, gs_layerimage7, gs_layerindex7, gs_refimage7, gs_refvector7, gs_layerimage8, gs_layerindex8, gs_refimage8, gs_refvector8, gs_layerimage9, gs_layerindex9, gs_refimage9, gs_refvector9, gs_layerimage10, gs_layerindex10, gs_refimage10, gs_refvector10, gs_layerimage11, gs_layerindex11, gs_refimage11, gs_refvector11, gs_layerimage12, gs_layerindex12, gs_refimage12, gs_refvector12, gs_layerimage13, gs_layerindex13, gs_refimage13, gs_refvector13, gs_layerimage14, gs_layerindex14, gs_refimage14, gs_refvector14, gs_layerimage15, gs_layerindex15, gs_refimage15, gs_refvector15, gs_layerimage16, gs_layerindex16, gs_refimage16, gs_refvector16, gs_layerimage17, gs_layerindex17, gs_refimage17, gs_refvector17, gs_layerimage18, gs_layerindex18, gs_refimage18, gs_refvector18, gs_layerimage19, gs_layerindex19, gs_refimage19, gs_refvector19, gs_layerimage20, gs_layerindex20, gs_refimage20, gs_refvector20, gs_resultimageurl, gs_resultdataurl, gs_resultimageextension, gs_waferid, gs_defectid, gs_xreal, gs_yreal, gs_scale, gs_xindex, gs_yindex, gs_particlecode, gs_angle, gs_clipimagewidth, gs_clipimageheight, gs_imagefov, gs_templateimage1, gs_templateimage2, gs_templateimage3, gs_templateimage4, gs_templateimage5, gs_templateimage6, gs_templateimage7, gs_templateimage8, gs_templateimage9, gs_templateimage10, gs_templateimage11, gs_templateimage12, gs_templateimage13, gs_templateimage14, gs_templateimage15, gs_templateimage16, gs_templateimage17, gs_templateimage18, gs_templateimage19, gs_templateimage20, gs_floor_plan_deploy_id_1, gs_floor_plan_deploy_id_2, gs_floor_plan_deploy_id_3, gs_floor_plan_deploy_id_4, gs_floor_plan_deploy_id_5, gs_floor_plan_deploy_id_6, gs_floor_plan_deploy_id_7, gs_floor_plan_deploy_id_8, gs_floor_plan_deploy_id_9, gs_floor_plan_deploy_id_10, gs_model_deploy_id_1, gs_model_deploy_id_2, gs_model_deploy_id_3, gs_model_deploy_id_4, gs_model_deploy_id_5, gs_model_deploy_id_6, gs_model_deploy_id_7, gs_model_deploy_id_8, gs_model_deploy_id_9, gs_model_deploy_id_10, gs_model_deploy_id_11, gs_model_deploy_id_12, gs_model_deploy_id_13, gs_model_deploy_id_14, gs_model_deploy_id_15, gs_model_deploy_id_16, gs_model_deploy_id_17, gs_model_deploy_id_18, gs_model_deploy_id_19, gs_model_deploy_id_20, gs_access_token, gs_system_address)","Category":"Others","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
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##!--{"Name":"gs_semimage1","Type":"image","Key":"","Value":"D:\\dog.4011.jpg","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage2","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage3","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage4","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage5","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
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##!--{"Name":"gs_semimage7","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage8","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage9","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage10","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage11","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage12","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage13","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage14","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage15","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage16","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_semimage17","Type":"image","Key":"","Value":"","Category":"gs_semimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
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##!--{"Name":"gs_templateimage1","Type":"image","Key":"","Value":"","Category":"gs_templateimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_templateimage2","Type":"image","Key":"","Value":"","Category":"gs_templateimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_templateimage3","Type":"image","Key":"","Value":"","Category":"gs_templateimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##!--{"Name":"gs_templateimage4","Type":"image","Key":"","Value":"","Category":"gs_templateimage","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
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##!--{"Name":"gs_system_address","Type":"system_address","Key":"","Value":"192.168.70.101:5120","Category":"Others","Control":"TextBox","ModulePropertyInfo":{"Name":"","Alias":"","Type":"","Items":[],"MinValue":"","MaxValue":"","ModuleName":"","IsNotUseTrackbar":true},"ParentGroup":"","ChildGroup":"","GroupParameterName":"","Description":"","IsActive":false,"InputVariable":"","DescriptionImage":""}
##[--{"OutputProperty":{"Name":"output","Alias":"Output","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"Load_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"InputProperty":{"Name":"","Alias":"","Type":"","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"5760346357","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"OutputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"Load","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""},"InputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"GrayToRGB","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}}
##[--{"OutputProperty":{"Name":"output","Alias":"Output","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"GrayToRGB_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"InputProperty":{"Name":"","Alias":"","Type":"","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"6725827858","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"OutputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"GrayToRGB","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""},"InputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"Resize","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}}
##[--{"OutputProperty":{"Name":"output","Alias":"Output","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"Resize_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"InputProperty":{"Name":"","Alias":"","Type":"","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"3562637217","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"OutputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"Resize","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""},"InputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"DMDcollTemplate","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}}
##[--{"OutputProperty":{"Name":"","Alias":"","Type":"","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"Resize_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"InputProperty":{"Name":"","Alias":"","Type":"","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"4064238097","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"OutputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"Resize","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""},"InputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"PredictClass","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}}
##[--{"OutputProperty":{"Name":"output","Alias":"Output","Type":"List","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"PredictClass_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"InputProperty":{"Name":"","Alias":"","Type":"","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"3562637218","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},"OutputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"PredictClass","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""},"InputModule":{"Name":"","Description":"","Inputs":[],"Outputs":[],"Category":"","Module":"","Key":"DMDcollTemplate","X":0,"Y":0,"Width":0,"Height":0,"Code":"","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}}
##{--
#region import
import os
import sys
import mpp
import cv2
import numpy as np
import Core
import DAQ
import iDAQ
from Core._branch_node import branch_node_context
#endregion
def DMDcollTemplate(input1, input2, input3, input4, input5):
import mpp
import os
result_image = input1
result_data = input2
print(input2)
global_items = input5
defect_id = global_items['gs_defectid']
#### Result Uri
summary_file_uri = os.path.join(global_items['gs_resultdataurl'].strip(), f'{defect_id}_Summary.csv')
#### Summary Data
summary_data = list()
summary_data.append(['DEFECTID', 'Label', 'Score'])
summary_data.append([defect_id, result_data['result_code'], result_data['score'] ])
#### Save Result
mpp.intel64.save_csv(summary_data, summary_file_uri)
return None, None, None, None, None
##}--
def RecipeRun(gs_semimage1=None, gs_semimage2=None, gs_semimage3=None, gs_semimage4=None, gs_semimage5=None, gs_semimage6=None, gs_semimage7=None, gs_semimage8=None, gs_semimage9=None, gs_semimage10=None, gs_semimage11=None, gs_semimage12=None, gs_semimage13=None, gs_semimage14=None, gs_semimage15=None, gs_semimage16=None, gs_semimage17=None, gs_semimage18=None, gs_semimage19=None, gs_semimage20=None, gs_layercount=None, gs_layerimage1=None, gs_layerindex1=None, gs_refimage1=None, gs_refvector1=None, gs_layerimage2=None, gs_layerindex2=None, gs_refimage2=None, gs_refvector2=None, gs_layerimage3=None, gs_layerindex3=None, gs_refimage3=None, gs_refvector3=None, gs_layerimage4=None, gs_layerindex4=None, gs_refimage4=None, gs_refvector4=None, gs_layerimage5=None, gs_layerindex5=None, gs_refimage5=None, gs_refvector5=None, gs_layerimage6=None, gs_layerindex6=None, gs_refimage6=None, gs_refvector6=None, gs_layerimage7=None, gs_layerindex7=None, gs_refimage7=None, gs_refvector7=None, gs_layerimage8=None, gs_layerindex8=None, gs_refimage8=None, gs_refvector8=None, gs_layerimage9=None, gs_layerindex9=None, gs_refimage9=None, gs_refvector9=None, gs_layerimage10=None, gs_layerindex10=None, gs_refimage10=None, gs_refvector10=None, gs_layerimage11=None, gs_layerindex11=None, gs_refimage11=None, gs_refvector11=None, gs_layerimage12=None, gs_layerindex12=None, gs_refimage12=None, gs_refvector12=None, gs_layerimage13=None, gs_layerindex13=None, gs_refimage13=None, gs_refvector13=None, gs_layerimage14=None, gs_layerindex14=None, gs_refimage14=None, gs_refvector14=None, gs_layerimage15=None, gs_layerindex15=None, gs_refimage15=None, gs_refvector15=None, gs_layerimage16=None, gs_layerindex16=None, gs_refimage16=None, gs_refvector16=None, gs_layerimage17=None, gs_layerindex17=None, gs_refimage17=None, gs_refvector17=None, gs_layerimage18=None, gs_layerindex18=None, gs_refimage18=None, gs_refvector18=None, gs_layerimage19=None, gs_layerindex19=None, gs_refimage19=None, gs_refvector19=None, gs_layerimage20=None, gs_layerindex20=None, gs_refimage20=None, gs_refvector20=None, gs_resultimageurl=None, gs_resultdataurl=None, gs_resultimageextension=None, gs_waferid=None, gs_defectid=None, gs_xreal=None, gs_yreal=None, gs_scale=None, gs_xindex=None, gs_yindex=None, gs_particlecode=None, gs_angle=None, gs_clipimagewidth=None, gs_clipimageheight=None, gs_imagefov=None, gs_templateimage1=None, gs_templateimage2=None, gs_templateimage3=None, gs_templateimage4=None, gs_templateimage5=None, gs_templateimage6=None, gs_templateimage7=None, gs_templateimage8=None, gs_templateimage9=None, gs_templateimage10=None, gs_templateimage11=None, gs_templateimage12=None, gs_templateimage13=None, gs_templateimage14=None, gs_templateimage15=None, gs_templateimage16=None, gs_templateimage17=None, gs_templateimage18=None, gs_templateimage19=None, gs_templateimage20=None, gs_floor_plan_deploy_id_1=None, gs_floor_plan_deploy_id_2=None, gs_floor_plan_deploy_id_3=None, gs_floor_plan_deploy_id_4=None, gs_floor_plan_deploy_id_5=None, gs_floor_plan_deploy_id_6=None, gs_floor_plan_deploy_id_7=None, gs_floor_plan_deploy_id_8=None, gs_floor_plan_deploy_id_9=None, gs_floor_plan_deploy_id_10=None, gs_model_deploy_id_1=None, gs_model_deploy_id_2=None, gs_model_deploy_id_3=None, gs_model_deploy_id_4=None, gs_model_deploy_id_5=None, gs_model_deploy_id_6=None, gs_model_deploy_id_7=None, gs_model_deploy_id_8=None, gs_model_deploy_id_9=None, gs_model_deploy_id_10=None, gs_model_deploy_id_11=None, gs_model_deploy_id_12=None, gs_model_deploy_id_13=None, gs_model_deploy_id_14=None, gs_model_deploy_id_15=None, gs_model_deploy_id_16=None, gs_model_deploy_id_17=None, gs_model_deploy_id_18=None, gs_model_deploy_id_19=None, gs_model_deploy_id_20=None, gs_access_token=None, gs_system_address=None):
resultdata = None
import Core
import DAQ
##<--{"Name":"Load","Description":"","Inputs":[{"Name":"path","Alias":"Path","Type":"Global","Value":"gs_semimage1","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"3985706443","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"gray_mode","Alias":"Gray Mode","Type":"Bool","Value":"True","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"3985706444","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Outputs":[{"Name":"output","Alias":"Output","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"Load_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Category":"Core","Module":"BasicOperation","Key":"Load","X":8994,"Y":4786,"Width":0,"Height":0,"Code":"def Load(path, gray_mode):\r\n \"\"\"\r\n 입력 경로의 파일에서 이미지 데이터를 읽어온다.\r\n\r\n 이미지는 1채널 이미지와 3채널(R,G,B) 이미지로 읽어올 수 있다.\r\n 읽어오는 방식은 인자 `gray_mode`의 값에 따라 아래와 같이 처리된다.\r\n\r\n - ``True`` : 1채널 이미지로 읽어온다.\r\n - ``False`` : 3채널(R,G,B) 이미지로 읽어온다.\r\n\r\n Parameters\r\n ----------\r\n path : str\r\n 이미지 파일 경로\r\n gray_mode : bool\r\n True 이면 이미지를 1채널 이미지로 읽어온다.(False는 원본 이미지 그대로 읽음)\r\n\r\n Returns\r\n -------\r\n ndarray\r\n 출력 이미지\r\n\r\n See Also\r\n --------\r\n - ``mpp.intel64.load``\r\n \"\"\" \r\n output = mpp.intel64.load(path, gray_mode)\r\n \r\n if output is None:\r\n raise NameError('API Execution Failed')\r\n\r\n return output\r\n","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}
Load_output = Core.BasicOperation.Load(gs_semimage1, True)
##<--{"Name":"GrayToRGB","Description":null,"Inputs":[{"Name":"input","Alias":"Input","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"5760346357","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Outputs":[{"Name":"output","Alias":"Output","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"GrayToRGB_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Category":"Core","Module":"BasicOperation","Key":"GrayToRGB","X":8989,"Y":4885,"Width":0,"Height":0,"Code":"def GrayToRGB(input):\r\n \"\"\"\r\n 1채널 이미지를 3채널(R,G,B) 이미지로 변환한다.\r\n\r\n Parameters\r\n ----------\r\n input : ndarray\r\n 1채널 입력 이미지\r\n\r\n Returns\r\n -------\r\n ndarray\r\n 3채널(R,G,B) 출력 이미지\r\n\r\n See Also\r\n --------\r\n - ``mpp.intel64.gray_to_rgb``\r\n \"\"\" \r\n output = mpp.intel64.gray_to_rgb(input)\r\n\r\n return output\r\n","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}
GrayToRGB_output = Core.BasicOperation.GrayToRGB(Load_output)
##<--{"Name":"Resize","Description":"","Inputs":[{"Name":"input","Alias":"Input","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"6725827858","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"width","Alias":"Width","Type":"Int","Value":"384","MinValue":"1","MaxValue":"1000000","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"6725827859","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"height","Alias":"Height","Type":"Int","Value":"384","MinValue":"1","MaxValue":"1000000","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"6725827860","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"interpolation","Alias":"Interpolation","Type":"List","Value":"\"linear\"","MinValue":"","MaxValue":"","Items":["\"cubic\"","\"nearest\"","\"linear\"","\"lanczos\"","\"supersampling\""],"DefaultValue":"","IsUsercontrol":true,"Key":"6725827861","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"antialias","Alias":"Antialias","Type":"Bool","Value":"True","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"6725827862","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Outputs":[{"Name":"output","Alias":"Output","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"Resize_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Category":"Core","Module":"BasicOperation","Key":"Resize","X":8985,"Y":4995,"Width":0,"Height":0,"Code":"def Resize(input, width, height, interpolation, antialias):\r\n \"\"\"\r\n 입력 이미지의 크기를 입력한 폭과 넓이로 변경한다.\r\n\r\n 보간하는 방식은 인자 `interplation` 인자의 값에 따라 아래와 같이 처리된다.\r\n\r\n - ``cubic`` : 3차회선 보간법(4×4 이웃 픽셀 참조)\r\n - ``lanczos`` : Lanczos 보간법(8×8 이웃 픽셀 참조)\r\n - ``linear`` : 양선형 보간법(2×2 이웃 픽셀 참조)\r\n - ``nearest`` : 최근방 이웃 보간법\r\n - ``supersampling`` : 슈퍼 샘플링 보간법(xFactor<1, yFactor<1)\r\n\r\n Parameters\r\n ----------\r\n input : ndarray\r\n 입력 이미지\r\n width : int\r\n 변경할 이미지의 폭( > 0)\r\n height : int\r\n 변경할 이미지의 넓이( > 0)\r\n interpolation : str \r\n 문자열 값 : `\"cubic\"`, `\"lanczos\"`, `\"linear\"`, `\"nearest\"`, `\"supersampling\"` 중 하나\r\n antialias : bool\r\n 안티엘리어싱 옵션\r\n\r\n Returns\r\n -------\r\n ndarray\r\n 결과 이미지\r\n\r\n See Also\r\n --------\r\n - ``mpp.intel64.resize``\r\n \"\"\" \r\n if input is None:\r\n raise NameError('Source is None')\r\n\r\n output = mpp.intel64.resize(input, width, height, interpolation, antialias)\r\n if output is None:\r\n raise NameError('API Execution Failed')\r\n\r\n return output\r\n","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}
Resize_output = Core.BasicOperation.Resize(GrayToRGB_output, 384, 384, "linear", True)
##<--{"Name":"PredictClass","Description":"","Inputs":[{"Name":"data","Alias":"Data","Type":"NdArray","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"4064238097","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"context","Alias":"Context","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"4064238098","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"deploy_id","Alias":"Deploy Id","Type":"Global","Value":"gs_model_deploy_id_1","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"4064238099","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Outputs":[{"Name":"output","Alias":"Output","Type":"List","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"PredictClass_output","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"context","Alias":"Context","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"PredictClass_context","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Category":"DAQ","Module":"Inference","Key":"PredictClass","X":9045,"Y":5109,"Width":0,"Height":0,"Code":"def PredictClass(data, context, deploy_id):\r\n \"\"\"\r\n `모델 관리` 화면에 등록된 이미지 기반 Classification 모델 중, 배포된 모델에 대하여 Predict를 수행한다.\r\n \r\n Parameters\r\n ----------\r\n data : ndarray\r\n Predict 수행할 이미지\r\n context : Any\r\n Predict 시 필요한 추가 정보\r\n deploy_id : str\r\n 배포된 모델의 Deploy ID\r\n\r\n Returns\r\n -------\r\n Tuple[list, Any]\r\n - 결과 데이터(모델의 model_handler가 반환하는 Classification 정보. 예: class score 별 정렬된 label/score 값 정보 목록)\r\n - Predict 결과 추가 정보(모델의 model_handler가 반환하는 추가 정보)\r\n \"\"\" \r\n output, context = mpp.daq.inference_service.predict(deploy_id=deploy_id, data=data, context=context)\r\n return output, context\r\n","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}
PredictClass_output, PredictClass_context = DAQ.Inference.PredictClass(Resize_output, None, gs_model_deploy_id_1)
##<--{"Name":"DMDcollTemplate","Description":"","Inputs":[{"Name":"input1","Alias":"input1","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"3562637217","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"input2","Alias":"input2","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"3562637218","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"input3","Alias":"input3","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"3562637219","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"input4","Alias":"input4","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"3562637220","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"input5","Alias":"input5","Type":"String","Value":"locals()","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":true,"Key":"3562637221","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Outputs":[{"Name":"output1","Alias":"output1","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"DMDcollTemplate_output1","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"output2","Alias":"output2","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"DMDcollTemplate_output2","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"output3","Alias":"output3","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"DMDcollTemplate_output3","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"output4","Alias":"output4","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"DMDcollTemplate_output4","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false},{"Name":"output5","Alias":"output5","Type":"Object","Value":"","MinValue":"","MaxValue":"","Items":[],"DefaultValue":"","IsUsercontrol":false,"Key":"DMDcollTemplate_output5","DependentPropertyName":"","DependentPropertyValue":"","IsNotUseTrackbar":true,"IsModuleVariable":false}],"Category":"DAQ","Module":"UserFunction","Key":"DMDcollTemplate","X":8985,"Y":5222,"Width":0,"Height":0,"Code":"def DMDcollTemplate(input1, input2, input3, input4, input5):\r\n\timport mpp\r\n\timport os\r\n\t\r\n\tresult_image = input1\r\n\tresult_data = input2\r\n\tglobal_items = input5\r\n\tdefect_id = global_items['gs_defectid']\r\n\tinput_image_file_name_without_extension = mpp.daq.utilities.get_image_file_name_without_extension(global_items['gs_semimage1'])\r\n\t\r\n\t#### Result Uri\r\n\timage_file_name = f'{defect_id}_result.jpg' if input_image_file_name_without_extension is None else f'{input_image_file_name_without_extension}_result.jpg'\r\n\tresult_image_file_uri = os.path.join(global_items['gs_resultimageurl'].strip(), image_file_name)\r\n\tsummary_file_uri = os.path.join(global_items['gs_resultdataurl'].strip(), f'{defect_id}_Summary.csv')\r\n\tdefect_file_uri = os.path.join(global_items['gs_resultdataurl'].strip(), f'{defect_id}_Defect.csv')\r\n\tdcoll_file_uri = os.path.join(global_items['gs_resultdataurl'].strip(), f'{defect_id}_DColl.csv')\r\n\r\n\t#### Summary Data\r\n\tsummary_data = list()\r\n\tsummary_data.append(['WAFERID','DEFECTID','DIEX','DIEY','XREAL','YREAL','IMAGEWIDTH','IMAGEHEIGHT','ALIGNMENT','MEASUREMENT','DEFECT','EXTENSIONFUNC','RELATIVEPATH','PATH','GDSX','GDSY'])\r\n\tsummary_data.append([global_items['gs_waferid'],global_items['gs_defectid'],global_items['gs_xindex'],global_items['gs_yindex'],global_items['gs_xreal'],global_items['gs_yreal'],'0','0','OK','OK','OK','OK',image_file_name,result_image_file_uri,'0','0'])\r\n\r\n\t#### DCOLL Data\r\n\tdcoll_data = list()\r\n\tdcoll_data.append(['ID', 'TYPE', 'NAME', 'VALUE', 'DESCRIPTION'])\r\n\tdcoll_data.append([defect_id, 'IMG', 'PATH', result_image_file_uri, 'Result Image'])\r\n\tdcoll_data.append([defect_id, 'SMF', 'DEFECTID', defect_id, 'DefectID'])\r\n\r\n\t#### TODO : Change Following Items\r\n\t#### MEASURE -> MESURE : Due to Typo in the SMF File.\r\n\tdcoll_data.append([defect_id, 'SMF', 'MESURE1', float(result_data[0]), 'Measure1'])\r\n\tdcoll_data.append([defect_id, 'SMF', 'MESURE2', float(result_data[1]), 'Measure2'])\r\n\tdcoll_data.append([defect_id, 'SMF', 'MESURE3', float(result_data[2]), 'Measure3'])\r\n\tdcoll_data.append([defect_id, 'SMF', 'MESURE4', float(result_data[3]), 'Measure4'])\r\n\tdcoll_data.append([defect_id, 'SMF', 'MESURE5', float(result_data[4]), 'Measure5'])\r\n\r\n\t#### Save Result\r\n\tmpp.intel64.save(result_image, result_image_file_uri)\r\n\tmpp.intel64.save_csv(summary_data, summary_file_uri)\r\n\tmpp.intel64.save_csv(summary_data, defect_file_uri)\r\n\tmpp.intel64.save_csv(dcoll_data, dcoll_file_uri)\r\n\t\r\n\toutput1 = None\r\n\toutput2 = None\r\n\toutput3 = None\r\n\toutput4 = None\r\n\toutput5 = None\r\n\r\n\treturn output1, output2, output3, output4, output5\r\n","ModuleGroup":null,"Type":"Normal","TargetModuleDataString":""}
DMDcollTemplate_output1, DMDcollTemplate_output2, DMDcollTemplate_output3, DMDcollTemplate_output4, DMDcollTemplate_output5 = DMDcollTemplate(Resize_output, PredictClass_output, None, None, locals())
return resultdata ### _main_return_
if __name__ == "__main__" :
resultdata = RecipeRun(gs_semimage1=gs_semimage1, gs_semimage2=gs_semimage2, gs_semimage3=gs_semimage3, gs_semimage4=gs_semimage4, gs_semimage5=gs_semimage5, gs_semimage6=gs_semimage6, gs_semimage7=gs_semimage7, gs_semimage8=gs_semimage8, gs_semimage9=gs_semimage9, gs_semimage10=gs_semimage10, gs_semimage11=gs_semimage11, gs_semimage12=gs_semimage12, gs_semimage13=gs_semimage13, gs_semimage14=gs_semimage14, gs_semimage15=gs_semimage15, gs_semimage16=gs_semimage16, gs_semimage17=gs_semimage17, gs_semimage18=gs_semimage18, gs_semimage19=gs_semimage19, gs_semimage20=gs_semimage20, gs_layercount=gs_layercount, gs_layerimage1=gs_layerimage1, gs_layerindex1=gs_layerindex1, gs_refimage1=gs_refimage1, gs_refvector1=gs_refvector1, gs_layerimage2=gs_layerimage2, gs_layerindex2=gs_layerindex2, gs_refimage2=gs_refimage2, gs_refvector2=gs_refvector2, gs_layerimage3=gs_layerimage3, gs_layerindex3=gs_layerindex3, gs_refimage3=gs_refimage3, gs_refvector3=gs_refvector3, gs_layerimage4=gs_layerimage4, gs_layerindex4=gs_layerindex4, gs_refimage4=gs_refimage4, gs_refvector4=gs_refvector4, gs_layerimage5=gs_layerimage5, gs_layerindex5=gs_layerindex5, gs_refimage5=gs_refimage5, gs_refvector5=gs_refvector5, gs_layerimage6=gs_layerimage6, gs_layerindex6=gs_layerindex6, gs_refimage6=gs_refimage6, gs_refvector6=gs_refvector6, gs_layerimage7=gs_layerimage7, gs_layerindex7=gs_layerindex7, gs_refimage7=gs_refimage7, gs_refvector7=gs_refvector7, gs_layerimage8=gs_layerimage8, gs_layerindex8=gs_layerindex8, gs_refimage8=gs_refimage8, gs_refvector8=gs_refvector8, gs_layerimage9=gs_layerimage9, gs_layerindex9=gs_layerindex9, gs_refimage9=gs_refimage9, gs_refvector9=gs_refvector9, gs_layerimage10=gs_layerimage10, gs_layerindex10=gs_layerindex10, gs_refimage10=gs_refimage10, gs_refvector10=gs_refvector10, gs_layerimage11=gs_layerimage11, gs_layerindex11=gs_layerindex11, gs_refimage11=gs_refimage11, gs_refvector11=gs_refvector11, gs_layerimage12=gs_layerimage12, gs_layerindex12=gs_layerindex12, gs_refimage12=gs_refimage12, gs_refvector12=gs_refvector12, gs_layerimage13=gs_layerimage13, gs_layerindex13=gs_layerindex13, gs_refimage13=gs_refimage13, gs_refvector13=gs_refvector13, gs_layerimage14=gs_layerimage14, gs_layerindex14=gs_layerindex14, gs_refimage14=gs_refimage14, gs_refvector14=gs_refvector14, gs_layerimage15=gs_layerimage15, gs_layerindex15=gs_layerindex15, gs_refimage15=gs_refimage15, gs_refvector15=gs_refvector15, gs_layerimage16=gs_layerimage16, gs_layerindex16=gs_layerindex16, gs_refimage16=gs_refimage16, gs_refvector16=gs_refvector16, gs_layerimage17=gs_layerimage17, gs_layerindex17=gs_layerindex17, gs_refimage17=gs_refimage17, gs_refvector17=gs_refvector17, gs_layerimage18=gs_layerimage18, gs_layerindex18=gs_layerindex18, gs_refimage18=gs_refimage18, gs_refvector18=gs_refvector18, gs_layerimage19=gs_layerimage19, gs_layerindex19=gs_layerindex19, gs_refimage19=gs_refimage19, gs_refvector19=gs_refvector19, gs_layerimage20=gs_layerimage20, gs_layerindex20=gs_layerindex20, gs_refimage20=gs_refimage20, gs_refvector20=gs_refvector20, gs_resultimageurl=gs_resultimageurl, gs_resultdataurl=gs_resultdataurl, gs_resultimageextension=gs_resultimageextension, gs_waferid=gs_waferid, gs_defectid=gs_defectid, gs_xreal=gs_xreal, gs_yreal=gs_yreal, gs_scale=gs_scale, gs_xindex=gs_xindex, gs_yindex=gs_yindex, gs_particlecode=gs_particlecode, gs_angle=gs_angle, gs_clipimagewidth=gs_clipimagewidth, gs_clipimageheight=gs_clipimageheight, gs_imagefov=gs_imagefov, gs_templateimage1=gs_templateimage1, gs_templateimage2=gs_templateimage2, gs_templateimage3=gs_templateimage3, gs_templateimage4=gs_templateimage4, gs_templateimage5=gs_templateimage5, gs_templateimage6=gs_templateimage6, gs_templateimage7=gs_templateimage7, gs_templateimage8=gs_templateimage8, gs_templateimage9=gs_templateimage9, gs_templateimage10=gs_templateimage10, gs_templateimage11=gs_templateimage11, gs_templateimage12=gs_templateimage12, gs_templateimage13=gs_templateimage13, gs_templateimage14=gs_templateimage14, gs_templateimage15=gs_templateimage15, gs_templateimage16=gs_templateimage16, gs_templateimage17=gs_templateimage17, gs_templateimage18=gs_templateimage18, gs_templateimage19=gs_templateimage19, gs_templateimage20=gs_templateimage20, gs_floor_plan_deploy_id_1=gs_floor_plan_deploy_id_1, gs_floor_plan_deploy_id_2=gs_floor_plan_deploy_id_2, gs_floor_plan_deploy_id_3=gs_floor_plan_deploy_id_3, gs_floor_plan_deploy_id_4=gs_floor_plan_deploy_id_4, gs_floor_plan_deploy_id_5=gs_floor_plan_deploy_id_5, gs_floor_plan_deploy_id_6=gs_floor_plan_deploy_id_6, gs_floor_plan_deploy_id_7=gs_floor_plan_deploy_id_7, gs_floor_plan_deploy_id_8=gs_floor_plan_deploy_id_8, gs_floor_plan_deploy_id_9=gs_floor_plan_deploy_id_9, gs_floor_plan_deploy_id_10=gs_floor_plan_deploy_id_10, gs_model_deploy_id_1=gs_model_deploy_id_1, gs_model_deploy_id_2=gs_model_deploy_id_2, gs_model_deploy_id_3=gs_model_deploy_id_3, gs_model_deploy_id_4=gs_model_deploy_id_4, gs_model_deploy_id_5=gs_model_deploy_id_5, gs_model_deploy_id_6=gs_model_deploy_id_6, gs_model_deploy_id_7=gs_model_deploy_id_7, gs_model_deploy_id_8=gs_model_deploy_id_8, gs_model_deploy_id_9=gs_model_deploy_id_9, gs_model_deploy_id_10=gs_model_deploy_id_10, gs_model_deploy_id_11=gs_model_deploy_id_11, gs_model_deploy_id_12=gs_model_deploy_id_12, gs_model_deploy_id_13=gs_model_deploy_id_13, gs_model_deploy_id_14=gs_model_deploy_id_14, gs_model_deploy_id_15=gs_model_deploy_id_15, gs_model_deploy_id_16=gs_model_deploy_id_16, gs_model_deploy_id_17=gs_model_deploy_id_17, gs_model_deploy_id_18=gs_model_deploy_id_18, gs_model_deploy_id_19=gs_model_deploy_id_19, gs_model_deploy_id_20=gs_model_deploy_id_20, gs_access_token=gs_access_token, gs_system_address=gs_system_address)
D/L 결과 시각화 및 정확도 평가
- valid data를 등록합니다. (dog, cat 각각)
- 레시피 코드를 불러온 뒤 서버에 레시피 등록합니다.
- 레시피 목록에서 시뮬레이션 한 뒤 결과 데이터를 확인합니다.
