Improve perfs: global model and use of gevent to support more requests

Author: Eugen Cepoi

Dockerfile

@@ -62,4 +62,4 @@ WORKDIR /opt/web
 
 # Run the app.  CMD is required to run on Heroku
 # $PORT is set by Heroku
-CMD gunicorn --timeout 360 --bind 0.0.0.0:$PORT app:app
+CMD gunicorn --timeout 360 --bind 0.0.0.0:$PORT -k gevent --worker-connections 32 app:app

requirements.txt

@@ -2,3 +2,4 @@ gunicorn
 Flask
 Jinja2
 Werkzeug
+gevent

web/app.py

@@ -7,6 +7,24 @@
 import classify_nsfw
 
 
+def make_transformer(nsfw_net):
+    # Load transformer
+    # Note that the parameters are hard-coded for best results
+    transformer = caffe.io.Transformer({'data': nsfw_net.blobs['data'].data.shape})
+    transformer.set_transpose('data', (2, 0, 1))  # move image channels to outermost
+    transformer.set_mean('data', np.array([104, 117, 123]))  # subtract the dataset-mean value in each channel
+    transformer.set_raw_scale('data', 255)  # rescale from [0, 1] to [0, 255]
+    transformer.set_channel_swap('data', (2, 1, 0))  # swap channels from RGB to BGR
+
+    return transformer
+
+
+nsfw_net = caffe.Net(
+    "/opt/open_nsfw/nsfw_model/deploy.prototxt",
+    "/opt/open_nsfw/nsfw_model/resnet_50_1by2_nsfw.caffemodel",
+    caffe.TEST
+)
+caffe_transformer = make_transformer(nsfw_net)
 app = Flask(__name__)
 
 
@@ -48,12 +66,6 @@ def single_classify():
 
 
 def classify_from_urls(image_entries):
-    nsfw_net = caffe.Net(
-        "/opt/open_nsfw/nsfw_model/deploy.prototxt",
-        "/opt/open_nsfw/nsfw_model/resnet_50_1by2_nsfw.caffemodel",
-        caffe.TEST
-    )
-
     for e in image_entries:
         yield classify_from_url(e, nsfw_net)
 
@@ -80,14 +92,6 @@ def classify_from_url(image_entry, nsfw_net):
 
 
 def classify(image_data, nsfw_net):
-    # Load transformer
-    # Note that the parameters are hard-coded for best results
-    caffe_transformer = caffe.io.Transformer({'data': nsfw_net.blobs['data'].data.shape})
-    caffe_transformer.set_transpose('data', (2, 0, 1))  # move image channels to outermost
-    caffe_transformer.set_mean('data', np.array([104, 117, 123]))  # subtract the dataset-mean value in each channel
-    caffe_transformer.set_raw_scale('data', 255)  # rescale from [0, 1] to [0, 255]
-    caffe_transformer.set_channel_swap('data', (2, 1, 0))  # swap channels from RGB to BGR
-
     # Classify.
     scores = classify_nsfw.caffe_preprocess_and_compute(
         image_data,