[MNT] merge branch main into feature/hetero

learnware/client/package_utils.py

@@ -105,7 +105,13 @@ def filter_nonexist_conda_packages(packages: list) -> Tuple[List[str], List[str]
 
         command = f"conda env create --name env_test --file {test_yaml_file} --dry-run --json"
         result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-        output = json.loads(result.stdout.strip()).get("bad_deps", [])
+        stdout = result.stdout.strip()
+        last_bracket = stdout.rfind("\n{")
+        if last_bracket != -1:
+            stdout = stdout[last_bracket:]
+            pass
+        print(stdout)
+        output = json.loads(stdout).get("bad_deps", [])
 
         if len(output) > 0:
             exist_packages = []

learnware/market/easy/database_ops.py

@@ -167,6 +167,21 @@ class DatabaseOperations(object):
             pass
         pass
 
+    def get_learnware_info(self, id: str):
+        with self.engine.connect() as conn:
+            r = conn.execute(text("SELECT semantic_spec, zip_path, folder_path, use_flag FROM tb_learnware WHERE id=:id;"), dict(id=id))
+            row = r.fetchone()
+            if row is None:
+                return None
+            else:
+                semantic_spec = json.loads(row[0])
+                zip_path = row[1]
+                folder_path = row[2]
+                use_flag = int(row[3])
+                return {'semantic_spec': semantic_spec, 'zip_path': zip_path, 'folder_path': folder_path, 'use_flag': use_flag}
+            pass
+        pass
+
     def load_market(self):
         with self.engine.connect() as conn:
             cursor = conn.execute(text("SELECT id, semantic_spec, zip_path, folder_path, use_flag FROM tb_learnware;"))

learnware/market/easy/organizer.py

@@ -3,7 +3,7 @@ import copy
 import zipfile
 import tempfile
 from shutil import copyfile, rmtree
-from typing import Tuple, List, Union
+from typing import Tuple, List, Union, Dict
 
 from .database_ops import DatabaseOperations
 from ..base import BaseOrganizer, BaseChecker
@@ -392,5 +392,23 @@ class EasyOrganizer(BaseOrganizer):
         self.use_flags[learnware_id] = self.dbops.get_learnware_use_flag(learnware_id)
         pass
 
+    def get_learnware_info_from_storage(self, learnware_id: str) -> Dict:
+        """return learnware zip path and semantic_specification from storage
+
+        Parameters
+        ----------
+        learnware_id : str
+            learnware id
+
+        Returns
+        -------
+        Dict
+            - semantic_spec: semantic_specification
+            - zip_path: zip_path
+            - folder_path: folder_path
+            - use_flag: use_flag
+        """
+        return self.dbops.get_learnware_info(learnware_id)
+
     def __len__(self):
         return len(self.learnware_list)

learnware/specification/regular/image/rkme.py

@@ -18,7 +18,7 @@ from tqdm import tqdm
 
 from . import cnn_gp
 from ..base import RegularStatSpecification
-from ..table.rkme import solve_qp, choose_device, setup_seed
+from ..table.rkme import rkme_solve_qp, choose_device, setup_seed
 
 
 class RKMEImageSpecification(RegularStatSpecification):
@@ -97,6 +97,9 @@ class RKMEImageSpecification(RegularStatSpecification):
         -------
 
         """
+        if len(X.shape) != 4:
+            raise ValueError("X should be in shape of [N, C, H, W]. ")
+
         if (
             X.shape[2] != RKMEImageSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageSpecification.IMAGE_WIDTH
         ) and not resize:
@@ -175,7 +178,7 @@ class RKMEImageSpecification(RegularStatSpecification):
         C = torch.sum(C, dim=1) / x_features.shape[0]
 
         if nonnegative_beta:
-            beta = solve_qp(K.double(), C.double()).to(self.device)
+            beta = rkme_solve_qp(K.double(), C.double())[0].to(self.device)
         else:
             beta = torch.linalg.inv(K + torch.eye(K.shape[0]).to(self.device) * 1e-5) @ C