yes!

sizhky · Mar 6, 2021 · 1862375 · 1862375
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diff --git a/README.md b/README.md
@@ -1,5 +1,8 @@
-# Pytorch utilities for simple pytorch needs
+# Utilities for simple needs
 
+
+
+```python
 ## Logging and plotting metrics
 ```python
 from torch_snippets import Report
@@ -25,7 +28,7 @@ for epoch in range(n_epochs):
     log.report_avgs(epoch+1) # persist the report
 
 ```
-![](assets/demo.gif)
+![](../assets/demo.gif)
 * Auto calculates time remaining
 * No need to preinitialize empty lists
 * Automatically stores metrics as collection of key words
@@ -39,7 +42,7 @@ for epoch in range(n_epochs):
 
 * Auto calculates average of all metrics in an epoch
 * Plot entire training history with one command  
-![](assets/avgs0.png)
+![](../assets/avgs0.png)
 
 
 * selectively plot logged metrics using regex
@@ -74,3 +77,5 @@ and many more...
 import pytorch_snippets
 dir(pytorch_snippets)
 ```
+
+```
diff --git a/docs/_data/sidebars/home_sidebar.yml b/docs/_data/sidebars/home_sidebar.yml
@@ -15,6 +15,9 @@ entries:
     - output: web,pdf
       title: Report
       url: report.html
+    - output: web,pdf
+      title: Show
+      url: show.html
     output: web
     title: torch_snippets
   output: web

diff --git a/docs/charts.html b/docs/charts.html
diff --git a/docs/index.html b/docs/index.html
@@ -1,6 +1,6 @@
 ---
 
-title: Title
+title: Utilities for simple needs
 
 
 keywords: fastai
@@ -36,7 +36,79 @@
 
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">torch_snippets</span> <span class="kn">import</span> <span class="o">*</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="err">```</span><span class="n">python</span>
+<span class="kn">from</span> <span class="nn">torch_snippets</span> <span class="kn">import</span> <span class="n">Report</span>
+
+<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">log</span> <span class="o">=</span> <span class="n">Report</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>
+    <span class="c1"># No need to --&gt; train_epoch_losses, train_epoch_accuracies = [], []</span>
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">trn_dl</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ix</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">trn_dl</span><span class="p">)):</span>
+        <span class="o">...</span>
+        <span class="n">pos</span> <span class="o">=</span> <span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="p">(</span><span class="n">ix</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">N</span><span class="p">)</span> <span class="c1"># a float between 0 - n_epochs</span>
+        <span class="c1"># give any number of kwargs that need to be reported and stored.</span>
+        <span class="c1"># args should be float</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">record</span><span class="p">(</span><span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">train_acc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">is_correct</span><span class="p">),</span> <span class="n">train_loss</span><span class="o">=</span><span class="n">batch_loss</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">&#39;</span><span class="p">)</span> <span class="c1"># impersistent log</span>
+
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_dl</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ix</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">val_dl</span><span class="p">)):</span>
+        <span class="o">...</span>
+        <span class="n">pos</span> <span class="o">=</span> <span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="p">(</span><span class="n">ix</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">N</span><span class="p">)</span> <span class="c1"># a float between 0 - n_epochs</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">record</span><span class="p">(</span><span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">val_loss</span><span class="o">=</span><span class="n">batch_loss</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">&#39;</span><span class="p">)</span> <span class="c1"># impersistent log</span>
+    <span class="n">log</span><span class="o">.</span><span class="n">report_avgs</span><span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># persist the report</span>
+
+<span class="err">```</span>
+<span class="o">![](</span>../assets/demo.gif<span class="o">)</span>
+<span class="o">*</span> <span class="n">Auto</span> <span class="n">calculates</span> <span class="n">time</span> <span class="n">remaining</span>
+<span class="o">*</span> <span class="n">No</span> <span class="n">need</span> <span class="n">to</span> <span class="n">preinitialize</span> <span class="n">empty</span> <span class="n">lists</span>
+<span class="o">*</span> <span class="n">Automatically</span> <span class="n">stores</span> <span class="n">metrics</span> <span class="k">as</span> <span class="n">collection</span> <span class="n">of</span> <span class="n">key</span> <span class="n">words</span>
+<span class="o">*</span> <span class="n">Persistent</span> <span class="n">vs</span> <span class="n">Transient</span> <span class="n">logging</span> <span class="p">(</span><span class="n">use</span> <span class="err">`</span><span class="n">end</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">&#39;</span><span class="err">`</span><span class="p">)</span>  
+<span class="err">```</span><span class="n">python</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="nb">print</span><span class="p">(</span><span class="n">log</span><span class="o">.</span><span class="n">logged</span><span class="p">)</span> <span class="c1"># get logged metric names</span>
+<span class="c1"># [&#39;train_loss&#39;, &#39;train_acc&#39;, &#39;val_loss&#39;, &#39;val_acc&#39;]</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="n">log</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span> <span class="c1"># plot all the logged metrics</span>
+<span class="err">```</span>
+
+
+<span class="o">*</span> <span class="n">Auto</span> <span class="n">calculates</span> <span class="n">average</span> <span class="n">of</span> <span class="nb">all</span> <span class="n">metrics</span> <span class="ow">in</span> <span class="n">an</span> <span class="n">epoch</span>
+<span class="o">*</span> <span class="n">Plot</span> <span class="n">entire</span> <span class="n">training</span> <span class="n">history</span> <span class="k">with</span> <span class="n">one</span> <span class="n">command</span>  
+<span class="o">![](</span>../assets/avgs0.png<span class="o">)</span>
+
+
+<span class="o">*</span> <span class="n">selectively</span> <span class="n">plot</span> <span class="n">logged</span> <span class="n">metrics</span> <span class="n">using</span> <span class="n">regex</span>
+<span class="err">```</span><span class="n">python</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="n">log</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="s1">&#39;*_loss&#39;</span><span class="p">)</span>
+<span class="c1"># or you can list out the metrics that need plotting</span>
+<span class="c1"># &gt;&gt;&gt; log.plot([&#39;train_acc&#39;,&#39;val_acc&#39;])</span>
+<span class="err">```</span>
+<span class="o">![](</span>assets/avgs1.png<span class="o">)</span>
+
+<span class="c1">## Auxiliary Functions</span>
+<span class="n">There</span> <span class="n">are</span> <span class="n">simple</span> <span class="n">functions</span> <span class="n">that</span> <span class="n">are</span> <span class="n">overloaded</span> <span class="n">to</span> <span class="n">take</span> <span class="n">inputs</span> <span class="ow">and</span> <span class="n">perform</span> <span class="n">repetitive</span> <span class="n">tasks</span> <span class="n">that</span> <span class="n">usually</span> <span class="n">take</span> <span class="n">a</span> <span class="n">few</span> <span class="n">lines</span> <span class="n">to</span> <span class="n">write</span>
+<span class="c1">#### Images</span>
+<span class="err">`</span><span class="n">show</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">inspect</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">Glob</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">read</span><span class="err">`</span>
+<span class="c1">#### FilePaths</span>
+<span class="err">`</span><span class="n">stem</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">Glob</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">parent</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">name</span><span class="err">`</span>
+<span class="c1">#### Dump and load python objects</span>
+<span class="err">`</span><span class="n">loaddill</span><span class="err">`</span><span class="p">,</span><span class="err">`</span><span class="n">dumpdill</span><span class="err">`</span>
+<span class="c1">#### Misc </span>
+<span class="err">`</span><span class="n">Tqdm</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">Timer</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">randint</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">unique</span><span class="err">`</span><span class="p">,</span> <span class="err">`</span><span class="n">diff</span><span class="err">`</span>
+<span class="c1">#### Pytorch Modules</span>
+<span class="err">`</span><span class="n">Reshape</span><span class="err">`</span> <span class="ow">and</span> <span class="err">`</span><span class="n">Permute</span><span class="err">`</span> <span class="p">(</span><span class="err">`</span><span class="n">nn</span><span class="o">.</span><span class="n">Modules</span><span class="err">`</span><span class="p">)</span>
+
+
+<span class="ow">and</span> <span class="n">many</span> <span class="n">more</span><span class="o">...</span> 
+
+<span class="c1">## Install</span>
+<span class="err">`</span><span class="n">pip</span> <span class="n">install</span> <span class="n">torch_snippets</span><span class="err">`</span>
+
+<span class="c1">## Usage</span>
+<span class="err">```</span><span class="n">python</span>
+<span class="kn">import</span> <span class="nn">pytorch_snippets</span>
+<span class="nb">dir</span><span class="p">(</span><span class="n">pytorch_snippets</span><span class="p">)</span>
+<span class="err">```</span>
 </pre></div>
 
     </div>

diff --git a/docs/show.html b/docs/show.html
diff --git a/docs/sidebar.json b/docs/sidebar.json
@@ -2,6 +2,7 @@
   "torch_snippets": {
     "Overview": "/",
     "Charts": "charts.html",
-    "Report": "report.html"
+    "Report": "report.html",
+    "Show": "show.html"
   }
 }
diff --git a/nbs/charts.ipynb b/nbs/charts.ipynb
diff --git a/nbs/index.ipynb b/nbs/index.ipynb
@@ -1,20 +1,93 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from torch_snippets import *"
+    "# Utilities for simple needs"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "## Logging and plotting metrics\n",
+    "```python\n",
+    "from torch_snippets import Report\n",
+    "\n",
+    "n_epochs = 5\n",
+    "log = Report(n_epochs)\n",
+    "\n",
+    "for epoch in range(n_epochs):\n",
+    "    # No need to --> train_epoch_losses, train_epoch_accuracies = [], []\n",
+    "    N = len(trn_dl)\n",
+    "    for ix, batch in enumerate(iter(trn_dl)):\n",
+    "        ...\n",
+    "        pos = (epoch + (ix+1)/N) # a float between 0 - n_epochs\n",
+    "        # give any number of kwargs that need to be reported and stored.\n",
+    "        # args should be float\n",
+    "        log.record(pos=pos, train_acc=np.mean(is_correct), train_loss=batch_loss, end='\\r') # impersistent log\n",
+    "\n",
+    "    N = len(val_dl)\n",
+    "    for ix, batch in enumerate(iter(val_dl)):\n",
+    "        ...\n",
+    "        pos = (epoch + (ix+1)/N) # a float between 0 - n_epochs\n",
+    "        log.record(pos=pos, val_loss=batch_loss, end='\\r') # impersistent log\n",
+    "    log.report_avgs(epoch+1) # persist the report\n",
+    "\n",
+    "```\n",
+    "![](../assets/demo.gif)\n",
+    "* Auto calculates time remaining\n",
+    "* No need to preinitialize empty lists\n",
+    "* Automatically stores metrics as collection of key words\n",
+    "* Persistent vs Transient logging (use `end='\\r'`)  \n",
+    "```python\n",
+    ">>> print(log.logged) # get logged metric names\n",
+    "# ['train_loss', 'train_acc', 'val_loss', 'val_acc']\n",
+    ">>> log.plot() # plot all the logged metrics\n",
+    "```\n",
+    "\n",
+    "\n",
+    "* Auto calculates average of all metrics in an epoch\n",
+    "* Plot entire training history with one command  \n",
+    "![](../assets/avgs0.png)\n",
+    "\n",
+    "\n",
+    "* selectively plot logged metrics using regex\n",
+    "```python\n",
+    ">>> log.plot('*_loss')\n",
+    "# or you can list out the metrics that need plotting\n",
+    "# >>> log.plot(['train_acc','val_acc'])\n",
+    "```\n",
+    "![](assets/avgs1.png)\n",
+    "\n",
+    "## Auxiliary Functions\n",
+    "There are simple functions that are overloaded to take inputs and perform repetitive tasks that usually take a few lines to write\n",
+    "#### Images\n",
+    "`show`, `inspect`, `Glob`, `read`\n",
+    "#### FilePaths\n",
+    "`stem`, `Glob`, `parent`, `name`\n",
+    "#### Dump and load python objects\n",
+    "`loaddill`,`dumpdill`\n",
+    "#### Misc \n",
+    "`Tqdm`, `Timer`, `randint`, `unique`, `diff`\n",
+    "#### Pytorch Modules\n",
+    "`Reshape` and `Permute` (`nn.Modules`)\n",
+    "\n",
+    "\n",
+    "and many more... \n",
+    " \n",
+    "## Install\n",
+    "`pip install torch_snippets`\n",
+    "\n",
+    "## Usage\n",
+    "```python\n",
+    "import pytorch_snippets\n",
+    "dir(pytorch_snippets)\n",
+    "```\n"
+   ]
   }
  ],
  "metadata": {

diff --git a/nbs/show.ipynb b/nbs/show.ipynb
diff --git a/settings.ini b/settings.ini
@@ -13,7 +13,7 @@ author = Yeshwanth Reddy
 author_email = [email protected]
 copyright = sizhky
 branch = master
-version = 0.321
+version = 0.322
 min_python = 3.6
 audience = Developers
 language = English

diff --git a/torch_snippets.egg-info/PKG-INFO b/torch_snippets.egg-info/PKG-INFO
@@ -1,13 +1,16 @@
 Metadata-Version: 2.1
 Name: torch-snippets
-Version: 0.320
+Version: 0.322
 Summary: One line functions for common tasks
 Home-page: https://github.com/sizhky/torch_snippets/tree/master/
 Author: Yeshwanth Reddy
 Author-email: [email protected]
 License: Apache Software License 2.0
-Description: # Pytorch utilities for simple pytorch needs
+Description: # Utilities for simple needs
 
+
+
+        ```python
         ## Logging and plotting metrics
         ```python
         from torch_snippets import Report
@@ -33,7 +36,7 @@ Description: # Pytorch utilities for simple pytorch needs
             log.report_avgs(epoch+1) # persist the report
 
         ```
-        ![](assets/demo.gif)
+        ![](../assets/demo.gif)
         * Auto calculates time remaining
         * No need to preinitialize empty lists
         * Automatically stores metrics as collection of key words
@@ -47,7 +50,7 @@ Description: # Pytorch utilities for simple pytorch needs
 
         * Auto calculates average of all metrics in an epoch
         * Plot entire training history with one command  
-        ![](assets/avgs0.png)
+        ![](../assets/avgs0.png)
 
 
         * selectively plot logged metrics using regex
@@ -83,6 +86,8 @@ Description: # Pytorch utilities for simple pytorch needs
         dir(pytorch_snippets)
         ```
 
+        ```
+
 Keywords: snippets,torch
 Platform: UNKNOWN
 Classifier: Development Status :: 3 - Alpha

diff --git a/torch_snippets/loader.py b/torch_snippets/loader.py
@@ -14,10 +14,12 @@
     'readlines','writelines',
     'zip_files','unzip_file',
     'BB','bbfy','xywh2xyXY','df2bbs',
-    'Info','Warn','Debug','Excep'
+    'Info','Warn','Debug','Excep',
+    'display'
 ]
 
 import glob, numpy as np, pandas as pd, tqdm, os, sys, re
+from IPython.display import display
 import PIL
 from PIL import Image
 try:
@@ -274,6 +276,10 @@ def show(img=None, ax=None, title=None, sz=None, bbs=None, confs=None,
         if isinstance(img, torch.Tensor): img = img.cpu().detach().numpy().copy()
         if isinstance(img, PIL.Image.Image): img = np.array(img)
     except: ...
+    if not isinstance(img, np.ndarray):
+        display(img)
+        return
+
     if len(img.shape) == 3 and len(img) == 3:
         # this is likely a torch tensor
         img = img.transpose(1,2,0)