To finally have model running on device w/o opencl supporting.

Author: solderzzc

src/embedding/FaceProcessing.py

@@ -22,6 +22,8 @@
 from scipy.misc import imread, imresize
 import sklearn.preprocessing
 
+global mx
+mx = None
 global globGraph
 globGraph = None
 global mod
@@ -32,6 +34,7 @@
 DEBUG = False
 
 DATA_RUNTIME_FOLDER = os.getenv('DATA_RUNTIME_FOLDER', '/data/runtime')
+HAS_OPENCL = os.getenv('HAS_OPENCL', 'true')
 
 def load_graph(frozen_graph_filename):
     return None
@@ -108,6 +111,21 @@ def get_model(ctx, image_size, model_str, layer):
 def init_embedding_processor():
     global mod2
     global mod3
+
+    if HAS_OPENCL == 'false':
+        global mx
+        import mxnet as mx
+        print('need init mxnet')
+
+        mod2 = None
+        if os.path.isfile(DATA_RUNTIME_FOLDER+'/model-0000.params'):
+            ctx = mx.cpu(0)
+            mod3 = get_model(ctx, [112,112], DATA_RUNTIME_FOLDER+'/model,0', 'fc1')
+            print('backup model loaded')
+            return mod3
+    else:
+        print('has opencl supporting')
+
     if os.path.isfile(DATA_RUNTIME_FOLDER+'/net2'):
         global __t
         global graph_runtime

src/embedding/embedding_client.py

@@ -0,0 +1,18 @@
+# -*- coding: utf-8 -*-
+import requests
+import json
+from faces import save_embedding
+
+addr = 'http://localhost:6000/api/embedding'
+
+def get_remote_embedding(embstr):
+    # prepare headers for http request
+    content_type = 'image/jpeg'
+    headers = {'content-type': content_type}
+    # send http request with image and receive response
+    response = requests.post(addr, data=embstr, headers=headers)
+    print(response)
+    # decode response
+    embedding = json.loads(response.text)['embedding'] 
+    print(embedding)
+    return save_embedding.convert_string_to_embedding(embedding)

src/embedding/embedding_server.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+import os
+from flask import Flask, request, jsonify
+import FaceProcessing
+from faces import save_embedding
+# Initialize the Flask application
+app = Flask(__name__)
+# route http posts to this method
+BASEDIR = os.getenv('RUNTIME_BASEDIR',os.path.abspath(os.path.dirname(__file__)))
+
+@app.route('/api/embedding', methods=['POST'])
+def embedding():
+    embedding = FaceProcessing.FaceProcessingBase64ImageData2(request.data)
+    embedding_str = save_embedding.convert_embedding_to_string(embedding)
+    print(embedding_str)
+    return jsonify({'embedding':embedding_str}), 200
+if __name__ == '__main__':
+    FaceProcessing.init_embedding_processor()
+    print("start to warm up")
+    embedding = FaceProcessing.FaceProcessingImageData2(os.path.join(BASEDIR,"image","Mike_Alden_0001_tmp.png"))
+    print("warmed up")
+    print(embedding)
+    app.run(host="0.0.0.0", port=6000)

src/embedding/upload_api-v2.py

@@ -3,7 +3,7 @@
 from __future__ import division
 from __future__ import print_function
 
-import os, json, time, sys, thread
+import os, json, time, sys, thread, base64
 import argparse
 import unicodedata
 import shutil
@@ -130,6 +130,7 @@
 # run as worker only
 CLUSTER_WORKERONLY = os.getenv('CLUSTER_WORKERONLY', False)
 
+HAS_OPENCL = os.getenv('HAS_OPENCL', 'true')
 SAVE_ORIGINAL_FACE = False
 original_face_img_path = os.path.join(BASEDIR, 'data', 'original_face_img')
 if not os.path.exists(original_face_img_path):
@@ -142,6 +143,10 @@
 
 counter = 0
 
+if HAS_OPENCL == 'false':
+    from embedding_client import get_remote_embedding
+
+
 def featureCalculation(imgpath):
     img = misc.imread(os.path.expanduser(imgpath))
     prewhitened = facenet.prewhiten(img)
@@ -1623,7 +1628,12 @@ def getQueueName():
 
 def featureCalculation2(imgpath):
     embedding=None
-    embedding = FaceProcessing.FaceProcessingImageData2(imgpath)
+    if HAS_OPENCL == 'false':
+        with open(imgpath, "rb") as image_file:
+            encoded_string = base64.b64encode(image_file.read())
+        embedding = get_remote_embedding(encoded_string)
+    else:
+        embedding = FaceProcessing.FaceProcessingImageData2(imgpath)
     return embedding
 
 @worker_process_init.connect()
@@ -1638,11 +1648,11 @@ def setup(sender=None, **kwargs):
         check_groupid_changed()
         init_fs()
 
-
-        mod = FaceProcessing.init_embedding_processor()
-        print("start to warm up")
-        embedding = featureCalculation2(os.path.join(BASEDIR,"image","Mike_Alden_0001_tmp.png"))
-        print("warmed up")
+        if HAS_OPENCL == 'true':
+            mod = FaceProcessing.init_embedding_processor()
+            print("start to warm up")
+            embedding = featureCalculation2(os.path.join(BASEDIR,"image","Mike_Alden_0001_tmp.png"))
+            print("warmed up")
         #if embedding is not None:
         #    print("worker embedding ready")
 
@@ -1672,7 +1682,10 @@ def extract_v2(image):
 
     if current_groupid is None:
         return json.dumps({"embedding_path":"","error":"please join group"})
-    embedding = FaceProcessing.FaceProcessingBase64ImageData2(imgstring)
+    if HAS_OPENCL == 'false':
+        embedding = get_remote_embedding(imgstring)
+    else:
+        embedding = FaceProcessing.FaceProcessingBase64ImageData2(imgstring)
     embedding_path=''
     embedding_str=''
     if embedding is not None: