mysql import script

1 files changed, 37 insertions, 21 deletions

diff --git a/megapixels/commands/faiss/build.py b/megapixels/commands/faiss/build.py
index e95619af..e525542a 100644
--- a/megapixels/commands/faiss/build.py
+++ b/megapixels/commands/faiss/build.py
@@ -3,44 +3,60 @@ Index all of the FAISS datasets
 """
 
 import os
+import glob
 import click
+import faiss
+import time
+import numpy as np
 
-from app.utils.file_utils import load_recipe, load_csv
+from app.utils.file_utils import load_recipe, load_csv_safe
 from app.settings import app_cfg as cfg
 
+engine = create_engine('sqlite:///:memory:')
+
+class DefaultRecipe:
+  def __init__(self):
+    self.dim = 128
+    self.factory_type = 'Flat'
+
 @click.command()
 @click.pass_context
 def cli(ctx):
-  """train the FAISS index"""
-  
-  recipe = {
-    "dim": 128,
-    "factory_type": "Flat"
-  }
-
+  """build the FAISS index.
+    - looks for all datasets in faiss/metadata/
+    - uses the recipe above by default
+    - however you can override this by adding a new recipe in faiss/recipes/{name}.json
+  """
   datasets = []
-  for fn in glob.iglob(os.path.join(cfg.DIR_FAISS_DATASETS, "*")):
+  for fn in glob.iglob(os.path.join(cfg.DIR_FAISS_METADATA, "*")):
     name = os.path.basename(fn)
     recipe_fn = os.path.join(cfg.DIR_FAISS_RECIPES, name + ".json")
     if os.path.exists(recipe_fn):
-      train(name, load_recipe(recipe_fn))
+      build_faiss(name, load_recipe(recipe_fn))
     else:
-      train(name, recipe)
+      build_faiss(name, DefaultRecipe())
+    # index identities
+    # certain CSV files should be loaded into mysql
+    # User.__table__.drop()
+  SQLemployees.create(engine)
 
-def train(name, recipe):
-  vec_fn = os.path.join(cfg.DIR_FAISS_DATASETS, name, "vecs.csv")
+def build_faiss(name, recipe):
+  vec_fn = os.path.join(cfg.DIR_FAISS_METADATA, name, "vecs.csv")
   index_fn = os.path.join(cfg.DIR_FAISS_INDEXES, name + ".index")
 
-  index = faiss.index_factory(recipe.dimension, recipe.factory)
+  index = faiss.index_factory(recipe.dim, recipe.factory_type)
 
-  keys, rows = file_utils.load_csv_safe(vec_fn)
-  feats = np.array([ float(x[1].split(",")) for x in rows]).astype('float32')
+  keys, rows = load_csv_safe(vec_fn)
+  feats = np.array([ list(map(float, row[3].split(","))) for row in rows ]).astype('float32')
   n, d = feats.shape
 
-  train_start = time.time()
-  index.train(feats)
-  train_end = time.time()
-  train_time = train_end - train_start
-  print("{} train time: {:.1f}s".format(name, train_time))
+  print("{}: training {} x {} dim vectors".format(name, n, d))
+  print(recipe.factory_type)
+
+  add_start = time.time()
+  index.add(feats)
+  add_end = time.time()
+  add_time = add_end - add_start
+  print("{}: add time: {:.1f}s".format(name, add_time))
 
   faiss.write_index(index, index_fn)