SDK では、実験名と親実行 ID を使用して、最適な子実行を選択できます (プライマリ メトリックごと)。 次に、labels.json および model.onnx ファイルをダウンロードできます。
次のコードでは、該当するプライマリ メトリックに基づいて、最適な子実行が返されます。
from azure.identity import DefaultAzureCredential
from azure.ai.ml import MLClient
mlflow_client = MlflowClient()
credential = DefaultAzureCredential()
ml_client = None
try:
ml_client = MLClient.from_config(credential)
except Exception as ex:
print(ex)
# Enter details of your Azure Machine Learning workspace
subscription_id = ''
resource_group = ''
workspace_name = ''
ml_client = MLClient(credential, subscription_id, resource_group, workspace_name)
import mlflow
from mlflow.tracking.client import MlflowClient
# Obtain the tracking URL from MLClient
MLFLOW_TRACKING_URI = ml_client.workspaces.get(
name=ml_client.workspace_name
).mlflow_tracking_uri
mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
# Specify the job name
job_name = ''
# Get the parent run
mlflow_parent_run = mlflow_client.get_run(job_name)
best_child_run_id = mlflow_parent_run.data.tags['automl_best_child_run_id']
# get the best child run
best_run = mlflow_client.get_run(best_child_run_id)
inputs = {'model_name': 'fasterrcnn_resnet34_fpn', # enter the faster rcnn or retinanet model name
'batch_size': 8, # enter the batch size of your choice
'height_onnx': 600, # enter the height of input to ONNX model
'width_onnx': 800, # enter the width of input to ONNX model
'job_name': job_name,
'task_type': 'image-object-detection',
'min_size': 600, # minimum size of the image to be rescaled before feeding it to the backbone
'max_size': 1333, # maximum size of the image to be rescaled before feeding it to the backbone
'box_score_thresh': 0.3, # threshold to return proposals with a classification score > box_score_thresh
'box_nms_thresh': 0.5, # NMS threshold for the prediction head
'box_detections_per_img': 100 # maximum number of detections per image, for all classes
}
inputs = {'model_name': 'yolov5', # enter the yolo model name
'batch_size': 8, # enter the batch size of your choice
'height_onnx': 640, # enter the height of input to ONNX model
'width_onnx': 640, # enter the width of input to ONNX model
'job_name': job_name,
'task_type': 'image-object-detection',
'img_size': 640, # image size for inference
'model_size': 'small', # size of the yolo model
'box_score_thresh': 0.1, # threshold to return proposals with a classification score > box_score_thresh
'box_iou_thresh': 0.5
}
inputs = {'model_name': 'maskrcnn_resnet50_fpn', # enter the maskrcnn model name
'batch_size': 8, # enter the batch size of your choice
'height_onnx': 600, # enter the height of input to ONNX model
'width_onnx': 800, # enter the width of input to ONNX model
'job_name': job_name,
'task_type': 'image-instance-segmentation',
'min_size': 600, # minimum size of the image to be rescaled before feeding it to the backbone
'max_size': 1333, # maximum size of the image to be rescaled before feeding it to the backbone
'box_score_thresh': 0.3, # threshold to return proposals with a classification score > box_score_thresh
'box_nms_thresh': 0.5, # NMS threshold for the prediction head
'box_detections_per_img': 100 # maximum number of detections per image, for all classes
}
batch_size = 8 # use the batch size used to generate the model
returned_job_run = mlflow_client.get_run(returned_job.name)
# Download run's artifacts/outputs
onnx_model_path = mlflow_client.download_artifacts(
returned_job_run.info.run_id, 'outputs/model_'+str(batch_size)+'.onnx', local_dir
)
import glob
import numpy as np
from PIL import Image
def preprocess(image, resize_size, crop_size_onnx):
"""Perform pre-processing on raw input image
:param image: raw input image
:type image: PIL image
:param resize_size: value to resize the image
:type image: Int
:param crop_size_onnx: expected height of an input image in onnx model
:type crop_size_onnx: Int
:return: pre-processed image in numpy format
:rtype: ndarray 1xCxHxW
"""
image = image.convert('RGB')
# resize
image = image.resize((resize_size, resize_size))
# center crop
left = (resize_size - crop_size_onnx)/2
top = (resize_size - crop_size_onnx)/2
right = (resize_size + crop_size_onnx)/2
bottom = (resize_size + crop_size_onnx)/2
image = image.crop((left, top, right, bottom))
np_image = np.array(image)
# HWC -> CHW
np_image = np_image.transpose(2, 0, 1) # CxHxW
# normalize the image
mean_vec = np.array([0.485, 0.456, 0.406])
std_vec = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(np_image.shape).astype('float32')
for i in range(np_image.shape[0]):
norm_img_data[i,:,:] = (np_image[i,:,:]/255 - mean_vec[i])/std_vec[i]
np_image = np.expand_dims(norm_img_data, axis=0) # 1xCxHxW
return np_image
# following code loads only batch_size number of images for demonstrating ONNX inference
# make sure that the data directory has at least batch_size number of images
test_images_path = "automl_models_multi_cls/test_images_dir/*" # replace with path to images
# Select batch size needed
batch_size = 8
# you can modify resize_size based on your trained model
resize_size = 256
# height and width will be the same for classification
crop_size_onnx = height_onnx_crop_size
image_files = glob.glob(test_images_path)
img_processed_list = []
for i in range(batch_size):
img = Image.open(image_files[i])
img_processed_list.append(preprocess(img, resize_size, crop_size_onnx))
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
PyTorch を使用する
import glob
import torch
import numpy as np
from PIL import Image
from torchvision import transforms
def _make_3d_tensor(x) -> torch.Tensor:
"""This function is for images that have less channels.
:param x: input tensor
:type x: torch.Tensor
:return: return a tensor with the correct number of channels
:rtype: torch.Tensor
"""
return x if x.shape[0] == 3 else x.expand((3, x.shape[1], x.shape[2]))
def preprocess(image, resize_size, crop_size_onnx):
transform = transforms.Compose([
transforms.Resize(resize_size),
transforms.CenterCrop(crop_size_onnx),
transforms.ToTensor(),
transforms.Lambda(_make_3d_tensor),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
img_data = transform(image)
img_data = img_data.numpy()
img_data = np.expand_dims(img_data, axis=0)
return img_data
# following code loads only batch_size number of images for demonstrating ONNX inference
# make sure that the data directory has at least batch_size number of images
test_images_path = "automl_models_multi_cls/test_images_dir/*" # replace with path to images
# Select batch size needed
batch_size = 8
# you can modify resize_size based on your trained model
resize_size = 256
# height and width will be the same for classification
crop_size_onnx = height_onnx_crop_size
image_files = glob.glob(test_images_path)
img_processed_list = []
for i in range(batch_size):
img = Image.open(image_files[i])
img_processed_list.append(preprocess(img, resize_size, crop_size_onnx))
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
import glob
import numpy as np
from PIL import Image
def preprocess(image, resize_size, crop_size_onnx):
"""Perform pre-processing on raw input image
:param image: raw input image
:type image: PIL image
:param resize_size: value to resize the image
:type image: Int
:param crop_size_onnx: expected height of an input image in onnx model
:type crop_size_onnx: Int
:return: pre-processed image in numpy format
:rtype: ndarray 1xCxHxW
"""
image = image.convert('RGB')
# resize
image = image.resize((resize_size, resize_size))
# center crop
left = (resize_size - crop_size_onnx)/2
top = (resize_size - crop_size_onnx)/2
right = (resize_size + crop_size_onnx)/2
bottom = (resize_size + crop_size_onnx)/2
image = image.crop((left, top, right, bottom))
np_image = np.array(image)
# HWC -> CHW
np_image = np_image.transpose(2, 0, 1) # CxHxW
# normalize the image
mean_vec = np.array([0.485, 0.456, 0.406])
std_vec = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(np_image.shape).astype('float32')
for i in range(np_image.shape[0]):
norm_img_data[i,:,:] = (np_image[i,:,:] / 255 - mean_vec[i]) / std_vec[i]
np_image = np.expand_dims(norm_img_data, axis=0) # 1xCxHxW
return np_image
# following code loads only batch_size number of images for demonstrating ONNX inference
# make sure that the data directory has at least batch_size number of images
test_images_path = "automl_models_multi_label/test_images_dir/*" # replace with path to images
# Select batch size needed
batch_size = 8
# you can modify resize_size based on your trained model
resize_size = 256
# height and width will be the same for classification
crop_size_onnx = height_onnx_crop_size
image_files = glob.glob(test_images_path)
img_processed_list = []
for i in range(batch_size):
img = Image.open(image_files[i])
img_processed_list.append(preprocess(img, resize_size, crop_size_onnx))
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
PyTorch を使用する
import glob
import torch
import numpy as np
from PIL import Image
from torchvision import transforms
def _make_3d_tensor(x) -> torch.Tensor:
"""This function is for images that have less channels.
:param x: input tensor
:type x: torch.Tensor
:return: return a tensor with the correct number of channels
:rtype: torch.Tensor
"""
return x if x.shape[0] == 3 else x.expand((3, x.shape[1], x.shape[2]))
def preprocess(image, resize_size, crop_size_onnx):
"""Perform pre-processing on raw input image
:param image: raw input image
:type image: PIL image
:param resize_size: value to resize the image
:type image: Int
:param crop_size_onnx: expected height of an input image in onnx model
:type crop_size_onnx: Int
:return: pre-processed image in numpy format
:rtype: ndarray 1xCxHxW
"""
transform = transforms.Compose([
transforms.Resize(resize_size),
transforms.CenterCrop(crop_size_onnx),
transforms.ToTensor(),
transforms.Lambda(_make_3d_tensor),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
img_data = transform(image)
img_data = img_data.numpy()
img_data = np.expand_dims(img_data, axis=0)
return img_data
# following code loads only batch_size number of images for demonstrating ONNX inference
# make sure that the data directory has at least batch_size number of images
test_images_path = "automl_models_multi_label/test_images_dir/*" # replace with path to images
# Select batch size needed
batch_size = 8
# you can modify resize_size based on your trained model
resize_size = 256
# height and width will be the same for classification
crop_size_onnx = height_onnx_crop_size
image_files = glob.glob(test_images_path)
img_processed_list = []
for i in range(batch_size):
img = Image.open(image_files[i])
img_processed_list.append(preprocess(img, resize_size, crop_size_onnx))
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
import glob
import numpy as np
from PIL import Image
def preprocess(image, height_onnx, width_onnx):
"""Perform pre-processing on raw input image
:param image: raw input image
:type image: PIL image
:param height_onnx: expected height of an input image in onnx model
:type height_onnx: Int
:param width_onnx: expected width of an input image in onnx model
:type width_onnx: Int
:return: pre-processed image in numpy format
:rtype: ndarray 1xCxHxW
"""
image = image.convert('RGB')
image = image.resize((width_onnx, height_onnx))
np_image = np.array(image)
# HWC -> CHW
np_image = np_image.transpose(2, 0, 1) # CxHxW
# normalize the image
mean_vec = np.array([0.485, 0.456, 0.406])
std_vec = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(np_image.shape).astype('float32')
for i in range(np_image.shape[0]):
norm_img_data[i,:,:] = (np_image[i,:,:] / 255 - mean_vec[i]) / std_vec[i]
np_image = np.expand_dims(norm_img_data, axis=0) # 1xCxHxW
return np_image
# following code loads only batch_size number of images for demonstrating ONNX inference
# make sure that the data directory has at least batch_size number of images
test_images_path = "automl_models_od/test_images_dir/*" # replace with path to images
image_files = glob.glob(test_images_path)
img_processed_list = []
for i in range(batch_size):
img = Image.open(image_files[i])
img_processed_list.append(preprocess(img, height_onnx, width_onnx))
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
import glob
import numpy as np
from yolo_onnx_preprocessing_utils import preprocess
# use height and width based on the generated model
test_images_path = "automl_models_od_yolo/test_images_dir/*" # replace with path to images
image_files = glob.glob(test_images_path)
img_processed_list = []
pad_list = []
for i in range(batch_size):
img_processed, pad = preprocess(image_files[i])
img_processed_list.append(img_processed)
pad_list.append(pad)
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
import glob
import numpy as np
from PIL import Image
def preprocess(image, resize_height, resize_width):
"""Perform pre-processing on raw input image
:param image: raw input image
:type image: PIL image
:param resize_height: resize height of an input image
:type resize_height: Int
:param resize_width: resize width of an input image
:type resize_width: Int
:return: pre-processed image in numpy format
:rtype: ndarray of shape 1xCxHxW
"""
image = image.convert('RGB')
image = image.resize((resize_width, resize_height))
np_image = np.array(image)
# HWC -> CHW
np_image = np_image.transpose(2, 0, 1) # CxHxW
# normalize the image
mean_vec = np.array([0.485, 0.456, 0.406])
std_vec = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(np_image.shape).astype('float32')
for i in range(np_image.shape[0]):
norm_img_data[i,:,:] = (np_image[i,:,:]/255 - mean_vec[i])/std_vec[i]
np_image = np.expand_dims(norm_img_data, axis=0) # 1xCxHxW
return np_image
# following code loads only batch_size number of images for demonstrating ONNX inference
# make sure that the data directory has at least batch_size number of images
# use height and width based on the trained model
# use height and width based on the generated model
test_images_path = "automl_models_is/test_images_dir/*" # replace with path to images
image_files = glob.glob(test_images_path)
img_processed_list = []
for i in range(batch_size):
img = Image.open(image_files[i])
img_processed_list.append(preprocess(img, height_onnx, width_onnx))
if len(img_processed_list) > 1:
img_data = np.concatenate(img_processed_list)
elif len(img_processed_list) == 1:
img_data = img_processed_list[0]
else:
img_data = None
assert batch_size == img_data.shape[0]
Python SDK azure-ai-ml v2 (現行)