Removed dependency on pandas, instead use generic csv

pytorch_lightning/core/lightning.py

@@ -4,12 +4,11 @@
 import warnings
 from abc import ABC, abstractmethod
 from argparse import Namespace
+import csv
 
-
-import pandas as pd
 import torch
 import torch.distributed as dist
-#
+
 from pytorch_lightning.core.decorators import data_loader
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
@@ -1217,10 +1216,12 @@ def load_hparams_from_tags_csv(tags_csv):
         logging.warning(f'Missing Tags: {tags_csv}.')
         return Namespace()
 
-    tags_df = pd.read_csv(tags_csv)
-    dic = tags_df.to_dict(orient='records')
-    ns_dict = {row['key']: convert(row['value']) for row in dic}
-    ns = Namespace(**ns_dict)
+    tags = {}
+    with open(tags_csv) as f:
+        csv_reader = csv.reader(f, delimiter=',')
+        for row in list(csv_reader)[1:]:
+            tags[row[0]] = convert(row[1])
+    ns = Namespace(**tags)
     return ns
 
 

pytorch_lightning/core/memory.py

@@ -9,7 +9,6 @@
 from subprocess import PIPE
 
 import numpy as np
-import pandas as pd
 import torch
 
 
@@ -146,24 +145,14 @@ def make_summary(self):
 
         Layer Name, Layer Type, Input Size, Output Size, Number of Parameters
         '''
-
-        cols = ['Name', 'Type', 'Params']
-        if self.model.example_input_array is not None:
-            cols.extend(['In_sizes', 'Out_sizes'])
-
-        df = pd.DataFrame(np.zeros((len(self.layer_names), len(cols))))
-        df.columns = cols
-
-        df['Name'] = self.layer_names
-        df['Type'] = self.layer_types
-        df['Params'] = self.param_nums
-        df['Params'] = df['Params'].map(get_human_readable_count)
-
+        arrays = [['Name', self.layer_names],
+                  ['Type', self.layer_types],
+                  ['Params', list(map(get_human_readable_count, self.param_nums))]]
         if self.model.example_input_array is not None:
-            df['In_sizes'] = self.in_sizes
-            df['Out_sizes'] = self.out_sizes
+            arrays.append(['In sizes', self.in_sizes])
+            arrays.append(['Out sizes', self.out_sizes])
 
-        self.summary = df
+        self.summary = _format_summary_table(*arrays)
         return
 
     def summarize(self):
@@ -176,6 +165,51 @@ def summarize(self):
         self.make_summary()
 
 
+def _format_summary_table(*cols):
+    '''
+    Takes in a number of arrays, each specifying a column in
+    the summary table, and combines them all into one big
+    string defining the summary table that are nicely formatted.
+    '''
+    n_rows = len(cols[0][1])
+    n_cols = 1 + len(cols)
+
+    # Layer counter
+    counter = list(map(str, list(range(n_rows))))
+    counter_len = max([len(c) for c in counter])
+
+    # Get formatting length of each column
+    length = []
+    for c in cols:
+        str_l = len(c[0])  # default length is header length
+        for a in c[1]:
+            if isinstance(a, np.ndarray):
+                array_string = '[' + ', '.join([str(j) for j in a]) + ']'
+                str_l = max(len(array_string), str_l)
+            else:
+                str_l = max(len(a), str_l)
+        length.append(str_l)
+
+    # Formatting
+    s = '{:<{}}'
+    full_length = sum(length) + 3 * n_cols
+    header = [s.format(' ', counter_len)] + [s.format(c[0], l) for c, l in zip(cols, length)]
+
+    # Summary = header + divider + Rest of table
+    summary = ' | '.join(header) + '\n' + '-' * full_length
+    for i in range(n_rows):
+        line = s.format(counter[i], counter_len)
+        for c, l in zip(cols, length):
+            if isinstance(c[1][i], np.ndarray):
+                array_string = '[' + ', '.join([str(j) for j in c[1][i]]) + ']'
+                line += ' | ' + array_string + ' ' * (l - len(array_string))
+            else:
+                line += ' | ' + s.format(c[1][i], l)
+        summary += '\n' + line
+
+    return summary
+
+
 def print_mem_stack():  # pragma: no cover
     for obj in gc.get_objects():
         try:

pytorch_lightning/logging/tensorboard.py

@@ -4,7 +4,7 @@
 from pkg_resources import parse_version
 
 import torch
-import pandas as pd
+import csv
 from torch.utils.tensorboard import SummaryWriter
 
 from .base import LightningLoggerBase, rank_zero_only
@@ -108,12 +108,17 @@ def save(self):
         dir_path = os.path.join(self.save_dir, self.name, 'version_%s' % self.version)
         if not os.path.isdir(dir_path):
             dir_path = self.save_dir
+
         # prepare the file path
         meta_tags_path = os.path.join(dir_path, self.NAME_CSV_TAGS)
+
         # save the metatags file
-        df = pd.DataFrame({'key': list(self.tags.keys()),
-                           'value': list(self.tags.values())})
-        df.to_csv(meta_tags_path, index=False)
+        with open(meta_tags_path, 'w', newline='') as csvfile:
+            fieldnames = ['key', 'value']
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writerow({'key': 'key', 'value': 'value'})
+            for k, v in self.tags.items():
+                writer.writerow({'key': k, 'value': v})
 
     @rank_zero_only
     def finalize(self, status):

requirements.txt

@@ -3,6 +3,6 @@ tqdm>=4.35.0
 numpy>=1.16.4
 torch>=1.1
 torchvision>=0.4.0, < 0.5  # the 0.5. has some issues with torch JIT
-pandas>=0.24  # lower version do not support py3.7
 tensorboard>=1.14
-future>=0.17.1  # required for builtins in setup.py
+future>=0.17.1  # required for builtins in setup.py
+