refactor pfp code

client/starwhale/api/_impl/metric.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import typing as t
+from enum import Enum, unique
 from functools import wraps
 
 from sklearn.metrics import (  # type: ignore
@@ -17,7 +18,8 @@
 from .model import PipelineHandler
 
 
-class MetricKind:
+@unique
+class MetricKind(Enum):
     MultiClassification = "multi_classification"
 
 
@@ -40,7 +42,7 @@ def _wrapper(*args: t.Any, **kwargs: t.Any) -> t.Dict[str, t.Any]:
             else:
                 y_true, y_pred = _rt
 
-            _r: t.Dict[str, t.Any] = {"kind": MetricKind.MultiClassification}
+            _r: t.Dict[str, t.Any] = {"kind": MetricKind.MultiClassification.value}
             cr = classification_report(
                 y_true, y_pred, output_dict=True, labels=all_labels
             )

client/starwhale/api/_impl/model.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import io
-import os
 import sys
 import math
 import base64
@@ -22,7 +21,6 @@
 from starwhale.utils.fs import ensure_dir, ensure_file
 from starwhale.base.type import URIType, RunSubDirType
 from starwhale.utils.log import StreamWrapper
-from starwhale.consts.env import SWEnv
 from starwhale.utils.error import FieldTypeOrValueError
 from starwhale.api._impl.job import Context
 from starwhale.core.job.model import STATUS
@@ -93,7 +91,9 @@ def __init__(
         # TODO: split status/result files
         self._timeline_writer = _jl_writer(self.status_dir / "timeline")
 
-        self.evaluation = self._init_datastore()
+        self.evaluation = Evaluation(
+            eval_id=self.context.version, project=self.context.project
+        )
         self._monkey_patch()
 
     def _init_dir(self) -> None:
@@ -108,11 +108,6 @@ def _init_dir(self) -> None:
         ensure_dir(self.status_dir)
         ensure_dir(self.log_dir)
 
-    def _init_datastore(self) -> Evaluation:
-        os.environ[SWEnv.project] = self.context.project
-        os.environ[SWEnv.eval_version] = self.context.version
-        return Evaluation()
-
     def _init_logger(self) -> t.Tuple[loguru.Logger, loguru.Logger]:
         # TODO: remove logger first?
         # TODO: add custom log format, include daemonset pod name
@@ -181,11 +176,11 @@ def ppl(self, data: t.Any, **kw: t.Any) -> t.Any:
     def cmp(self, ppl_result: PPLResultIterator) -> t.Any:
         raise NotImplementedError
 
-    def _builtin_serialize(self, *data: t.Any) -> bytes:
+    def _builtin_serialize(self, data: t.Any) -> bytes:
         return dill.dumps(data)  # type: ignore
 
-    def ppl_result_serialize(self, *data: t.Any) -> bytes:
-        return self._builtin_serialize(*data)
+    def ppl_result_serialize(self, data: t.Any) -> bytes:
+        return self._builtin_serialize(data)
 
     def ppl_result_deserialize(self, data: bytes) -> t.Any:
         return dill.loads(base64.b64decode(data))
@@ -194,7 +189,7 @@ def annotations_serialize(self, data: t.Any) -> bytes:
         return self._builtin_serialize(data)
 
     def annotations_deserialize(self, data: bytes) -> bytes:
-        return dill.loads(base64.b64decode(data))[0]  # type: ignore
+        return dill.loads(base64.b64decode(data))  # type: ignore
 
     def deserialize(self, data: t.Dict[str, t.Any]) -> t.Any:
         data["result"] = self.ppl_result_deserialize(data["result"])
@@ -275,14 +270,14 @@ def _starwhale_internal_run_ppl(self) -> None:
             else:
                 exception = None
 
-            self._do_record(_idx, _annotations, exception, *pred)
+            self._do_record(_idx, _annotations, exception, pred)
 
     def _do_record(
         self,
         idx: int,
         annotations: t.Dict,
         exception: t.Optional[Exception],
-        *args: t.Any,
+        pred: t.Any,
     ) -> None:
         _timeline = {
             "time": now_str(),
@@ -296,7 +291,7 @@ def _do_record(
         _b64: t.Callable[[bytes], str] = lambda x: base64.b64encode(x).decode("ascii")
         self.evaluation.log_result(
             data_id=idx,
-            result=_b64(self.ppl_result_serialize(*args)),
+            result=_b64(self.ppl_result_serialize(pred)),
             annotations=_b64(self.annotations_serialize(annotations)),
         )
         self._update_status(STATUS.RUNNING)

client/starwhale/api/_impl/wrapper.py

@@ -47,19 +47,20 @@ def _log(self, table_name: str, record: Dict[str, Any]) -> None:
 
 
 class Evaluation(Logger):
-    def __init__(self, eval_id: Optional[str] = None):
-        if eval_id is None:
-            eval_id = os.getenv(SWEnv.eval_version, None)
-        if eval_id is None:
+    def __init__(self, eval_id: str = "", project: str = ""):
+        eval_id = eval_id or os.getenv(SWEnv.eval_version, "")
+        if not eval_id:
             raise RuntimeError("eval id should not be None")
         if re.match(r"^[A-Za-z0-9-_]+$", eval_id) is None:
             raise RuntimeError(
                 f"invalid eval id {eval_id}, only letters(A-Z, a-z), digits(0-9), hyphen('-'), and underscore('_') are allowed"
             )
         self.eval_id = eval_id
-        self.project = os.getenv(SWEnv.project)
-        if self.project is None:
+
+        self.project = project or os.getenv(SWEnv.project, "")
+        if not self.project:
             raise RuntimeError(f"{SWEnv.project} is not set")
+
         self._results_table_name = self._get_datastore_table_name("results")
         self._summary_table_name = f"project/{self.project}/eval/summary"
         self._init_writers([self._results_table_name, self._summary_table_name])

client/starwhale/core/dataset/type.py

@@ -2,6 +2,7 @@
 
 import io
 import os
+import base64
 import typing as t
 from abc import ABCMeta, abstractmethod
 from enum import Enum, unique
@@ -166,6 +167,9 @@ class ArtifactType(Enum):
     Text = "text"
 
 
+_TBAType = t.TypeVar("_TBAType", bound="BaseArtifact")
+
+
 class BaseArtifact(ASDictMixin, metaclass=ABCMeta):
     def __init__(
         self,
@@ -224,6 +228,10 @@ def to_bytes(self) -> bytes:
         else:
             raise NoSupportError(f"read raw for type:{type(self.fp)}")
 
+    def carry_raw_data(self: _TBAType) -> _TBAType:
+        self._raw_base64_data = base64.b64encode(self.to_bytes()).decode()
+        return self
+
     def astype(self) -> t.Dict[str, t.Any]:
         return {
             "type": self.type,
@@ -379,7 +387,7 @@ def __init__(
         image_id: int,
         category_id: int,
         segmentation: t.Union[t.List, t.Dict],
-        area: float,
+        area: t.Union[float, int],
         bbox: t.Union[BoundingBox, t.List[float]],
         iscrowd: int,
     ) -> None:

client/starwhale/core/eval/model.py

@@ -1,28 +1,25 @@
 from __future__ import annotations
 
-import os
 import json
 import typing as t
 import subprocess
 from abc import ABCMeta, abstractmethod
 from http import HTTPStatus
 from collections import defaultdict
 
-from loguru import logger
-
 from starwhale.utils import load_yaml
 from starwhale.consts import HTTPMethod, DEFAULT_PAGE_IDX, DEFAULT_PAGE_SIZE
 from starwhale.base.uri import URI
 from starwhale.utils.fs import move_dir
 from starwhale.api._impl import wrapper
 from starwhale.base.type import InstanceType, JobOperationType
 from starwhale.base.cloud import CloudRequestMixed
-from starwhale.consts.env import SWEnv
 from starwhale.utils.http import ignore_error
 from starwhale.utils.error import NotFoundError, NoSupportError
 from starwhale.utils.config import SWCliConfigMixed
 from starwhale.utils.process import check_call
 from starwhale.core.eval.store import EvaluationStorage
+from starwhale.api._impl.metric import MetricKind
 from starwhale.core.eval.executor import EvalExecutor
 from starwhale.core.runtime.process import Process as RuntimeProcess
 
@@ -95,22 +92,26 @@ def _get_version(self) -> str:
         raise NotImplementedError
 
     def _get_report(self) -> t.Dict[str, t.Any]:
-        # use datastore
-        os.environ[SWEnv.project] = self.uri.project
-        os.environ[SWEnv.eval_version] = self._get_version()
-        logger.debug(
-            f"eval instance:{self.uri.instance}, project:{self.uri.project}, eval_id:{self._get_version()}"
-        )
-        _evaluation = wrapper.Evaluation()
-        _summary = _evaluation.get_metrics()
-        return dict(
-            summary=_summary,
-            labels={str(i): l for i, l in enumerate(list(_evaluation.get("labels")))},
-            confusion_matrix=dict(
-                binarylabel=list(_evaluation.get("confusion_matrix/binarylabel"))
-            ),
-            kind=_summary["kind"],
+        evaluation = wrapper.Evaluation(
+            eval_id=self._get_version(), project=self.uri.project
         )
+        summary = evaluation.get_metrics()
+        kind = summary.get("kind", "")
+
+        ret = {
+            "kind": kind,
+            "summary": summary,
+        }
+
+        if kind == MetricKind.MultiClassification.value:
+            ret["labels"] = {
+                str(i): l for i, l in enumerate(list(evaluation.get("labels")))
+            }
+            ret["confusion_matrix"] = {
+                "binarylabel": list(evaluation.get("confusion_matrix/binarylabel"))
+            }
+
+        return ret
 
     @classmethod
     def _get_job_cls(

client/starwhale/core/eval/view.py

@@ -3,9 +3,10 @@
 
 from rich import box
 from loguru import logger
-from rich.tree import Tree
+from rich.panel import Panel
 from rich.table import Table
 from rich.pretty import Pretty
+from rich.columns import Columns
 
 from starwhale.utils import Order, console, sort_obj_list
 from starwhale.consts import (
@@ -18,6 +19,7 @@
 from starwhale.base.type import URIType, InstanceType, JobOperationType
 from starwhale.base.view import BaseTermView
 from starwhale.core.eval.model import EvaluationJob
+from starwhale.api._impl.metric import MetricKind
 
 
 class JobTermView(BaseTermView):
@@ -130,7 +132,21 @@ def info(self, page: int = DEFAULT_PAGE_IDX, size: int = DEFAULT_PAGE_SIZE) -> N
             self._print_tasks(_rt["tasks"][0])
 
         if "report" in _rt:
-            self._render_job_report(_rt["report"])
+            _report = _rt["report"]
+            _kind = _rt["report"].get("kind", "")
+
+            if "summary" in _report:
+                self._render_summary_report(_report["summary"], _kind)
+
+            if _kind == MetricKind.MultiClassification.value:
+                self._render_multi_classification_job_report(_rt["report"])
+
+    def _render_summary_report(self, summary: t.Dict[str, t.Any], kind: str) -> None:
+        console.rule(f"[bold green]{kind.upper()} Summary")
+        contents = [
+            Panel(f"[b]{k}[/b]\n[yellow]{v}", expand=True) for k, v in summary.items()
+        ]
+        console.print(Columns(contents))
 
     def _print_tasks(self, tasks: t.List[t.Dict[str, t.Any]]) -> None:
         table = Table(box=box.SIMPLE)
@@ -159,39 +175,17 @@ def _print_tasks(self, tasks: t.List[t.Dict[str, t.Any]]) -> None:
         )
         console.print(table)
 
-    # TODO: use new result format
-    def _render_job_report(self, report: t.Dict[str, t.Any]) -> None:
+    def _render_multi_classification_job_report(
+        self, report: t.Dict[str, t.Any]
+    ) -> None:
         if not report:
             console.print(":turtle: no report")
             return
 
         labels: t.Dict[str, t.Any] = report.get("labels", {})
         sort_label_names = sorted(list(labels.keys()))
 
-        def _print_report() -> None:
-            # TODO: add other kind report
-            def _r(_tree: t.Any, _obj: t.Any) -> None:
-                if not isinstance(_obj, dict):
-                    _tree.add(str(_obj))
-
-                for _k, _v in _obj.items():
-                    if _k == "id":
-                        continue
-                    if isinstance(_v, (list, tuple)):
-                        _k = f"{_k}: [green]{'|'.join(_v)}"
-                    elif isinstance(_v, dict):
-                        _k = _k
-                    elif isinstance(_v, str):
-                        _k = f"{_k}:{_v}"
-                    else:
-                        _k = f"{_k}: [green]{_v:.4f}"
-
-                    _ntree = _tree.add(_k)
-                    if isinstance(_v, dict):
-                        _r(_ntree, _v)
-
-            tree = Tree("Summary")
-            _r(tree, report["summary"])
+        def _print_labels() -> None:
             if len(labels) == 0:
                 return
 
@@ -209,7 +203,7 @@ def _r(_tree: t.Any, _obj: t.Any) -> None:
                 )
 
             console.rule(f"[bold green]{report['kind'].upper()} Report")
-            console.print(self.comparison(tree, table))
+            console.print(table)
 
         def _print_confusion_matrix() -> None:
             cm = report.get("confusion_matrix", {})
@@ -236,7 +230,7 @@ def _print_confusion_matrix() -> None:
             console.rule(f"[bold green]{report['kind'].upper()} Confusion Matrix")
             console.print(self.comparison(mtable, btable))
 
-        _print_report()
+        _print_labels()
         _print_confusion_matrix()
 
     @classmethod

client/tests/core/test_model.py

@@ -210,6 +210,7 @@ def some(self):
             Context(
                 workdir=Path(_model_data_dir),
                 version="rwerwe9",
+                project="self",
             ),
             "some",
         )

example/PennFudanPed/.gitignore

@@ -0,0 +1,2 @@
+data/
+model/

example/PennFudanPed/Makefile

@@ -0,0 +1,12 @@
+.POHNY: train
+train:
+	mkdir -p models
+	python3 pfp/train.py
+
+.POHNY: download-data
+download-data:
+	rm -rf data
+	mkdir -p data
+	curl -o data/pfp.zip https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip
+	unzip data/pfp.zip -d data
+	rm -rf data/pfp.zip