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task.py
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import logging
from typing import Any, List, Union
from sqlalchemy.orm.session import Session
from sqlalchemy.sql.functions import func
from app.crud.base import CRUDBase
from app.schemas.task import TaskCreate, TaskUpdate, TaskDatasetModels
from app.models import (Task, Dataset, Model, TaskDataset,
TaskDatasetAccuracyType, AccuracyValue,
AccuracyType, Paper,
Cpu, Tpu, Gpu)
class CRUDTask(CRUDBase[Task, TaskCreate, TaskUpdate]):
def get_multi_and_count_benchmarks(
self, db: Session, *, skip: int = 0, limit: int = 100, q
) -> List[Any]:
if q:
return db.query(Task.id, Task.name, Task.identifier)\
.filter(Task.name.ilike("%{}%".format(q)))\
.offset(skip)\
.limit(limit).all()
dataset_count = db.query(
func.count(TaskDataset.id).label('number_of_benchmarks'),
TaskDataset.task_id
)\
.group_by(TaskDataset.task_id).subquery('dataset_count')
tasks = db.query(
Task.id,
Task.name,
Task.image,
Task.identifier,
dataset_count.c.number_of_benchmarks
)\
.join(dataset_count, dataset_count.c.task_id == Task.id)\
.offset(skip).limit(limit)\
.all()
return tasks
def get_multi_task_datasets_sota(
self,
db: Session, *,
skip: int = 0,
limit: int = 100) -> List[TaskDatasetModels]:
accuracy = db.query(
func.max(AccuracyValue.value).label("value"),
Model.task_dataset_id
)\
.select_from(AccuracyValue)\
.join(AccuracyValue.model)\
.join(TaskDatasetAccuracyType,
TaskDatasetAccuracyType.accuracy_type_id ==
AccuracyValue.accuracy_type_id
)\
.join(Model.paper)\
.filter(TaskDatasetAccuracyType.main)\
.filter(Paper.is_public)\
.group_by(Model.task_dataset_id)\
.subquery('accuracy')
bestModel = db.query(
accuracy.c.value,
Model.name,
Model.id,
Model.hardware_burden,
Model.paper_id,
Model.task_dataset_id).select_from(accuracy)\
.join(Model, Model.id == (db.query(
Model.id
).select_from(Model)
.join(AccuracyValue.model)
.join(TaskDatasetAccuracyType,
TaskDatasetAccuracyType.accuracy_type_id ==
AccuracyValue.accuracy_type_id
)
.filter(TaskDatasetAccuracyType.main)
.filter(accuracy.c.task_dataset_id == Model.task_dataset_id)
.filter(AccuracyValue.value == accuracy.c.value)
.limit(1)
)
).subquery('best_model')
tasks = db.query(Task).limit(5).subquery('tasks')
response = db.query(
tasks.c.id.label("task_id"),
tasks.c.name.label("task_name"),
tasks.c.identifier.label("task_identifier"),
Dataset.id.label("dataset_id"),
Dataset.name.label("dataset_name"),
Dataset.identifier.label("dataset_identifier"),
AccuracyType.name.label("accuracy_type_name"),
bestModel.c.task_dataset_id.label("task_dataset_id"),
bestModel.c.id.label("model_id"),
bestModel.c.name.label("model_name"),
bestModel.c.hardware_burden.label("model_hardware_burden"),
bestModel.c.value.label("model_accuracy"),
Paper.link.label("paper_url"),
Paper.title.label("paper_title"),
Paper.publication_date.label('paper_publication_date')
).select_from(tasks)\
.join(TaskDataset, tasks.c.id == TaskDataset.task_id)\
.join(TaskDataset.dataset)\
.join(TaskDataset.accuracy_types)\
.join(TaskDatasetAccuracyType.accuracy_type)\
.join(bestModel, bestModel.c.task_dataset_id == TaskDataset.id)\
.join(Paper, Paper.id == bestModel.c.paper_id)\
.filter(TaskDatasetAccuracyType.main)\
.all()
res = []
for row in response:
current_item = (
next((item for item in res if item["task_id"] == row.task_id), None))
if current_item:
current_item['datasets'].append({
'dataset_id': row.dataset_id,
'dataset_name': row.dataset_name,
'dataset_identifier': row.dataset_identifier,
'task_dataset_id': row.task_dataset_id,
'accuracy_name': row.accuracy_type_name,
'sota_id': row.model_id,
'sota_name': row.model_name,
'sota_accuracy_value': row.model_accuracy,
'sota_hardware_burden': row.model_hardware_burden,
'sota_paper_link': row.paper_url,
'sota_paper_publication_date': row.paper_publication_date,
'sota_paper_title': row.paper_title
})
else:
new_item = {
'task_id': row.task_id,
'task_identifier': row.task_identifier,
'task_name': row.task_name,
'datasets': [{
'dataset_id': row.dataset_id,
'dataset_name': row.dataset_name,
'dataset_identifier': row.dataset_identifier,
'task_dataset_id': row.task_dataset_id,
'accuracy_name': row.accuracy_type_name,
'sota_id': row.model_id,
'sota_name': row.model_name,
'sota_accuracy_value': row.model_accuracy,
'sota_hardware_burden': row.model_hardware_burden,
'sota_paper_link': row.paper_url,
'sota_paper_publication_date': row.paper_publication_date,
'sota_paper_title': row.paper_title
}],
}
res.append(new_item)
return res
def get_task_datasets_sota(
self,
db: Session, *,
task_id: Union[int, str],
) -> TaskDatasetModels:
accuracy = db.query(
func.max(AccuracyValue.value).label("value"),
Model.task_dataset_id
)\
.select_from(AccuracyValue)\
.join(AccuracyValue.model)\
.join(TaskDatasetAccuracyType,
TaskDatasetAccuracyType.accuracy_type_id ==
AccuracyValue.accuracy_type_id
)\
.join(Model.paper)\
.filter(TaskDatasetAccuracyType.main)\
.filter(Paper.is_public)\
.group_by(Model.task_dataset_id)\
.subquery('accuracy')
bestModel = db.query(
accuracy.c.value,
Model.name,
Model.id,
Model.hardware_burden,
Model.paper_id,
Model.task_dataset_id).select_from(accuracy)\
.join(Model, Model.id == (db.query(
Model.id
).select_from(Model)
.join(AccuracyValue.model)
.join(TaskDatasetAccuracyType,
TaskDatasetAccuracyType.accuracy_type_id ==
AccuracyValue.accuracy_type_id
)
.filter(TaskDatasetAccuracyType.main)
.filter(accuracy.c.task_dataset_id == Model.task_dataset_id)
.filter(AccuracyValue.value == accuracy.c.value)
.limit(1)
)
).subquery('best_model')
filterTask = (Task.id == task_id) if isinstance(
task_id, int) else (Task.identifier == task_id)
tasks = db.query(Task).filter(filterTask).limit(5).subquery('tasks')
response = db.query(
tasks.c.id.label("task_id"),
tasks.c.identifier.label("task_identifier"),
tasks.c.name.label("task_name"),
tasks.c.image.label("task_image"),
tasks.c.description.label("task_description"),
Dataset.id.label("dataset_id"),
Dataset.identifier.label("dataset_identifier"),
Dataset.name.label("dataset_name"),
AccuracyType.name.label("accuracy_type_name"),
bestModel.c.task_dataset_id.label("task_dataset_id"),
bestModel.c.id.label("model_id"),
bestModel.c.name.label("model_name"),
bestModel.c.hardware_burden.label("model_hardware_burden"),
bestModel.c.value.label("model_accuracy"),
Paper.title.label("paper_title"),
Paper.link.label("paper_url"),
Paper.publication_date.label('paper_publication_date')
).select_from(tasks)\
.join(TaskDataset, tasks.c.id == TaskDataset.task_id)\
.join(TaskDataset.dataset)\
.join(TaskDataset.accuracy_types)\
.join(TaskDatasetAccuracyType.accuracy_type)\
.join(bestModel, bestModel.c.task_dataset_id == TaskDataset.id)\
.join(Paper, Paper.id == bestModel.c.paper_id)\
.filter(TaskDatasetAccuracyType.main)\
.all()
res = None
if len(response):
res = {
'task_id': response[0].task_id,
'task_identifier': response[0].task_identifier,
'task_name': response[0].task_name,
'task_image': response[0].task_image,
'task_description': response[0].task_description,
'datasets': [],
}
for row in response:
res['datasets'].append({
'dataset_id': row.dataset_id,
'dataset_name': row.dataset_name,
'dataset_identifier': row.dataset_identifier,
'task_dataset_id': row.task_dataset_id,
'accuracy_name': row.accuracy_type_name,
'sota_id': row.model_id,
'sota_name': row.model_name,
'sota_accuracy_value': row.model_accuracy,
'sota_hardware_burden': row.model_hardware_burden,
'sota_paper_link': row.paper_url,
'sota_paper_publication_date': row.paper_publication_date,
'sota_paper_title': row.paper_title,
})
return res
def get_models(
self,
db: Session, *,
task_id: Union[int, str],
dataset_id: Union[int, str],
) -> TaskDatasetModels:
filterTask = (Task.id == task_id) if isinstance(
task_id, int) else (Task.identifier == task_id)
filterDataset = (Dataset.id == dataset_id) if isinstance(
dataset_id, int) else (Dataset.identifier == dataset_id)
task_dataset = db.query(
Task.id.label('task_id'),
Task.identifier.label('task_identifier'),
Task.name.label('task_name'),
Task.image.label('task_image'),
Dataset.id.label('dataset_id'),
Dataset.identifier.label('dataset_identifier'),
Dataset.name.label('dataset_name'),
AccuracyType.name.label('accuracy_type_name'),
AccuracyType.description.label('accuracy_type_description'),
TaskDatasetAccuracyType.main.label('accuracy_type_main'),
).select_from(TaskDataset)\
.join(TaskDataset.task)\
.join(TaskDataset.dataset)\
.join(TaskDataset.accuracy_types)\
.join(TaskDatasetAccuracyType.accuracy_type)\
.filter(
filterTask,
filterDataset,
)\
.all()
task_dataset_res = {}
if len(task_dataset):
task_dataset_res = {
'task_id': task_dataset[0].task_id,
'task_name': task_dataset[0].task_name,
'task_identifier': task_dataset[0].task_identifier,
'task_image': task_dataset[0].task_image,
'dataset_id': task_dataset[0].dataset_id,
'dataset_identifier': task_dataset[0].dataset_identifier,
'dataset_name': task_dataset[0].dataset_name,
'accuracy_types': [
{
'name': acc.accuracy_type_name,
'description': acc.accuracy_type_description,
'main': acc.accuracy_type_main
} for acc in task_dataset
]
}
models = db.query(
Model.id.label('model_id'),
Model.name.label('model_name'),
Model.gflops.label('model_gflops'),
Model.number_of_parameters.label('model_number_of_parameters'),
Model.multiply_adds.label('model_multiply_adds'),
Model.hardware_burden.label('model_hardware_burden'),
AccuracyValue.value.label('accuracy_value'),
AccuracyType.name.label('accuracy_type'),
Paper.title.label('paper_title'),
Paper.link.label('paper_link'),
Paper.code_link.label('paper_code_link'),
Paper.publication_date.label('paper_publication_date'),
).join(TaskDataset.models)\
.join(Model.accuracy_values)\
.join(AccuracyValue.accuracy_type)\
.join(Model.paper)\
.join(TaskDataset.task)\
.join(TaskDataset.dataset)\
.filter(
filterTask,
filterDataset,
Paper.is_public
)\
.all()
modes_res = []
for row in models:
current_item = (
next((item for item in modes_res if item["id"] == row.model_id), None))
if current_item:
current_item[row.accuracy_type] = row.accuracy_value
else:
new_model = {
'id': row.model_id,
'name': row.model_name,
'gflops': row.model_gflops,
'number_of_parameters': row.model_number_of_parameters,
'multiply_adds': row.model_multiply_adds,
'operation_per_network_pass': (
row.model_gflops
if row.model_gflops else row.model_multiply_adds
),
'hardware_burden': row.model_hardware_burden,
'paper_title': row.paper_title,
'paper_code_link': row.paper_code_link,
'paper_link': row.paper_link,
'paper_publication_date': row.paper_publication_date,
}
new_model[row.accuracy_type] = row.accuracy_value
modes_res.append(new_model)
task_dataset_res['models'] = modes_res
return task_dataset_res
def get_models_csv(
self,
db: Session, *,
task_id: Union[int, str],
dataset_id: Union[int, str],
) -> TaskDatasetModels:
filterTask = (Task.id == task_id) if isinstance(
task_id, int) else (Task.identifier == task_id)
filterDataset = (Dataset.id == dataset_id) if isinstance(
dataset_id, int) else (Dataset.identifier == dataset_id)
models = db.query(
Task.name.label('task_name'),
Dataset.name.label('dataset_name'),
Model.id.label('model_id'),
Model.name.label('model_name'),
Model.gflops.label('model_gflops'),
Model.number_of_parameters.label('model_number_of_parameters'),
Model.multiply_adds.label('model_multiply_adds'),
Model.epochs.label('model_epochs'),
Model.extra_training_time.label('model_extra_training_time'),
Model.training_time.label('model_training_time'),
Model.number_of_cpus.label('model_number_of_cpus'),
Model.hardware_burden.label('model_hardware_burden'),
Cpu.name.label('model_cpu'),
Model.number_of_gpus.label('model_number_of_gpus'),
Gpu.name.label('model_gpu'),
Model.number_of_tpus.label('model_number_of_tpus'),
Tpu.name.label('model_tpu'),
AccuracyValue.value.label('accuracy_value'),
AccuracyType.name.label('accuracy_type'),
Paper.title.label('paper_title'),
Paper.code_link.label('paper_code_link'),
Paper.publication_date.label('paper_publication_date'),
Paper.link.label('paper_link'),
).join(TaskDataset.models)\
.join(Model.accuracy_values)\
.join(AccuracyValue.accuracy_type)\
.join(Model.paper)\
.join(Model.cpu, isouter=True)\
.join(Model.tpu, isouter=True)\
.join(Model.gpu, isouter=True)\
.join(TaskDataset.task)\
.join(TaskDataset.dataset)\
.filter(
filterTask,
filterDataset,
Paper.is_public
)\
.all()
logging.error(models)
models_res = []
for row in models:
current_item = next(
(item for item in models_res if item["model_id"] == row.model_id), None)
if current_item:
current_item[row.accuracy_type] = row.accuracy_value
else:
new_model = {
'task_name': row.task_name,
'dataset_name': row.dataset_name,
'model_id': row.model_id,
'paper_publication_date': row.paper_publication_date.year,
'paper_title': row.paper_title,
'paper_link': row.paper_link,
'paper_code_link': row.paper_code_link,
'model_name': row.model_name,
row.accuracy_type: row.accuracy_value,
'model_gflops': row.model_gflops,
'model_multiply_adds': row.model_multiply_adds,
'model_operation_per_network_pass': (
row.model_gflops
if row.model_gflops else row.model_multiply_adds
),
'model_extra_training_time': row.model_extra_training_time,
'model_number_of_cpus': row.model_number_of_cpus,
'model_cpu': row.model_cpu,
'model_number_of_gpus': row.model_number_of_gpus,
'model_gpu': row.model_gpu,
'model_number_of_tpus': row.model_number_of_tpus,
'model_tpu': row.model_tpu,
'model_training_time': row.model_training_time,
'model_hardware_burden': row.model_hardware_burden,
'model_number_of_parameters': row.model_number_of_parameters,
'model_epochs': row.model_epochs,
}
models_res.append(new_model)
return models_res
task = CRUDTask(Task)