0.499.34

conda/torch_snippets/meta.yaml

@@ -1,9 +1,9 @@
 package:
   name: torch_snippets
-  version: 0.499.33
+  version: 0.499.34
 source:
-  sha256: 600a4e6ab5d8539f94ceebcc82180eda82dfbce57e081230788a2595a9d0462d
-  url: https://files.pythonhosted.org/packages/3f/bb/c2ccb89e806ea62339f164be9fd9a5d3fa791312b0a0ec559807ceffec8f/torch_snippets-0.499.33.tar.gz
+  sha256: 9fb11abf70db9855972d92d0c2185e29c8cbf08f8bca30fb4aea080f282955e9
+  url: https://files.pythonhosted.org/packages/c5/fd/31535a73899128862b6a9b53b4f1fcb079bbbb7c0026ca27fdb241b6b396/torch_snippets-0.499.34.tar.gz
 about:
   description: "# Utilities for simple needs\n\n\n\n## torch snippets does a lot of\
     \ default importing for you\nWhether it is numpy, pandas, matplotlib or the useful\

nbs/adapters.ipynb

@@ -108,7 +108,7 @@
     "    df[\"@source\"] = \"manual\"\n",
     "    df[\"@z_order\"] = \"0\"\n",
     "    if \"text\" in df.columns:\n",
-    "        df[\"attribute\"] = [[{\"@name\": \"OCR\", \"#text\": text}] for text in df[\"text\"]]\n",
+    "        df[\"attribute\"] = [[{\"@name\": \"OCR\", \"#text\": text if text==text else ''}] for text in df[\"text\"]]\n",
     "    df.drop(\n",
     "        [\n",
     "            c\n",
@@ -269,7 +269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.15 (default, Nov 24 2022, 09:04:07) \n[Clang 14.0.6 ]"
+   "version": "3.8.15"
   },
   "orig_nbformat": 4,
   "vscode": {

settings.ini

@@ -8,7 +8,7 @@ author = Yeshwanth Reddy
 author_email = 1992chinna@gmail.com
 copyright = sizhky
 branch = master
-version = 0.499.33
+version = 0.499.34
 min_python = 3.7
 audience = Developers
 language = English

torch_snippets.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: torch-snippets
-Version: 0.499.33
+Version: 0.499.34
 Summary: One line functions for common tasks
 Home-page: https://github.com/sizhky/torch_snippets/tree/master/
 Author: Yeshwanth Reddy

torch_snippets/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.499.33"
+__version__ = "0.499.34"
 from .loader import *
 from .paths import *
 from .markup import *

torch_snippets/adapters.py

@@ -1,7 +1,15 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/adapters.ipynb.
 
 # %% auto 0
-__all__ = ['np_2_b64', 'b64_2_np', 'b64_2_file', 'bytes_2_file', 'file_2_bytes', 'csvs_2_cvat', 'cvat_2_csvs']
+__all__ = [
+    "np_2_b64",
+    "b64_2_np",
+    "b64_2_file",
+    "bytes_2_file",
+    "file_2_bytes",
+    "csvs_2_cvat",
+    "cvat_2_csvs",
+]
 
 # %% ../nbs/adapters.ipynb 2
 import base64, cv2, numpy as np
@@ -28,9 +36,9 @@ def b64_2_np(input: str) -> np.ndarray:
 
 
 def b64_2_file(
-    input: str, # base64 encoded string
-    fpath: Union[str, P] # Place where you want to save the file
-    ) -> None:
+    input: str,  # base64 encoded string
+    fpath: Union[str, P],  # Place where you want to save the file
+) -> None:
     """Save a file encoded as a base64 `input` at given `fpath`"""
     with open(fpath, "wb") as pdf_file:
         input = bytes(input, "utf-8")
@@ -42,7 +50,7 @@ def b64_2_file(
 def bytes_2_file(
     input: bytes,  # bytes
     fpath: Union[str, P],  # Place where you want to save the file
-    silent: bool = False
+    silent: bool = False,
 ) -> None:
     """Save bytes `input` at given `fpath`"""
     with open(fpath, "wb") as file_to_write:
@@ -56,6 +64,7 @@ def file_2_bytes(fpath):
         output = f.read()
     return output
 
+
 # %% ../nbs/adapters.ipynb 4
 def _process(
     df: pd.DataFrame, label_column="readable_label", default_label="Background"
@@ -69,7 +78,10 @@ def _process(
     df["@source"] = "manual"
     df["@z_order"] = "0"
     if "text" in df.columns:
-        df["attribute"] = [[{"@name": "OCR", "#text": text}] for text in df["text"]]
+        df["attribute"] = [
+            [{"@name": "OCR", "#text": text if text == text else ""}]
+            for text in df["text"]
+        ]
     df.drop(
         [
             c
@@ -104,7 +116,7 @@ def csvs_2_cvat(
     parquet=False,
     relative_df=True,
     default_label="Background",
-    extension='jpg'
+    extension="jpg",
 ):
     images_folder, csvs_folder = [P(_) for _ in [images_folder, csvs_folder]]
     data = AttrDict({"annotations": {"image": []}})
@@ -161,7 +173,7 @@ def _get_attribute_data(item, column_name):
 
 
 def _cvat_ann_2_csv(ann):
-    if 'box' not in ann:
+    if "box" not in ann:
         return pd.DataFrame()
     if isinstance(ann.box, AttrDict):
         rows = [a.to_dict() for a in [ann.box]]
@@ -203,5 +215,3 @@ def cvat_2_csvs(xmlfile, csvs_folder):
             df.to_csv(save_at, index=False)
         except Exception as e:
             Warn(f'{e} @ {item["@name"]}')
-
-

torch_snippets/loader.py

@@ -95,6 +95,10 @@
 
     __all__ += ["torch", "nn", "F", "Dataset", "DataLoader", "optim"]
 except:
+    Warn(
+        "Unable to load torch and dependent libraries from torch-snippets. \n"
+        "Functionalities might be limited. pip install lovely-tensors in case there are torch related errors"
+    )
     ...
 import matplotlib  # ; matplotlib.use('Agg')
 import matplotlib.pyplot as plt
@@ -318,7 +322,6 @@ def show(
     try:
         if isinstance(img, (str, Path)):
             img = read(str(img), 1)
-        if isinstance(img, torch.Tensor):
             img = img.cpu().detach().numpy().copy()
         if isinstance(img, PIL.Image.Image):
             img = np.array(img)