command line flow of image classification for caffe model

docker_run.sh

@@ -30,5 +30,5 @@ docker run \
   $docker_devices \
   -v $HERE/share:/opt/ml-suite/share \
   -w /opt/ml-suite \
-  xilinx-ml-suite-ubuntu-16.04-xrt-2018.2-caffe-mls-1.4:latest \
+  durgabhavaniv/xilinx-ml_suite:latest \
   bash

docs/image_classifier_terminal_caffe.md

@@ -0,0 +1,86 @@
+## Running an image classifier with caffe model in docker containers.
+
+1. Clone ML Suite
+
+  ```
+  git clone https://github.com/Xilinx/ml-suite.git
+  ```
+
+2. Download ml-suite container
+
+  ```
+  https://www.xilinx.com/member/forms/download/eula-xef.html?filename=xilinx-ml-suite-ubuntu-16.04-xrt-2018.2-caffe-mls-1.4.tar.gz
+  ```
+
+3. Load container
+
+  ```
+  sudo docker load < xilinx-ml-suite-ubuntu-16.04-xrt-2018.2-caffe-mls-1.4.tar.gz
+  ```
+
+4. Run docker container
+
+  ```
+  $ cd ml-suite
+  $ sudo ./docker_run.sh
+  ```
+
+5. One time setup
+
+  ```
+  python -m ck pull repo:ck-env 
+  python -m ck install package:imagenet-2012-val-min
+  python -m ck install package:imagenet-2012-aux
+  head -n 500 $HOME/CK-TOOLS/dataset-imagenet-ilsvrc2012-aux/val.txt > $HOME/CK-TOOLS/dataset-imagenet-ilsvrc2012-val-min/val_map.txt
+  ```
+
+  Resize all the images to a common dimension for Caffe
+  ```
+  python -m pip --no-cache-dir install opencv-python --user 
+  python MLsuite/examples/caffe/resize.py $HOME/CK-TOOLS/dataset-imagenet-ilsvrc2012-val-min 256 256
+  ``` 
+
+  Get necessary models
+  ```
+  cd /opt/ml-suite/examples/caffe
+  python getModels.py
+  ```
+
+6. environment setup
+
+  ```
+  export MLSUITE_ROOT=/opt/ml-suite
+  source $MLSUITE_ROOT/overlaybins/setup.sh
+  ```
+
+7. **Quantize the model** - The quantizer will generate scaling parameters for quantizing floats INT8. This is required, because FPGAs will take advantage of Fixed Point Precision, to achieve more parallelization at lower power
+
+  ```
+  cd /opt/ml-suite/share
+  export DECENT_DEBUG=1
+  /opt/caffe/build/tools/decent_q quantize -model /opt/models/caffe/bvlc_googlenet/bvlc_googlenet_train_val.prototxt -weights /opt/models/caffe/bvlc_googlenet/bvlc_googlenet.caffemodel -auto_test -test_iter 1 --calib_iter 1
+  ```
+
+  This outputs quantize_info.txt, deploy.prototxt, deploy.caffemodel to $HOME/share/quantize_results/ directory
+
+8. **Compile the Model** - In this step, the network Graph (prototxt) and the Weights (caffemodel) are compiled by the compiler
+
+  ```
+  python $MLSUITE_ROOT/xfdnn/tools/compile/bin/xfdnn_compiler_caffe.pyc -b 1 -i 96 -m 9 -d 256 -mix --pipelineconvmaxpool --usedeephi --quant_cfgfile quantize_results/quantize_info.txt -n quantize_results/deploy.prototxt -w quantize_results/deploy.caffemodel -g work/compiler -qz work/quantizer -C
+  ```  
+
+  This outputs compiler.json, quantizer.json and deploy.caffemodel_data.h5 to $HOME/share/work/ directory
+
+9. **Subgraph Cutting** - In this step, the original graph is cut, and a custom FPGA accelerated python layer is inserted to be used for Inference.
+
+  ```
+  python $MLSUITE_ROOT/xfdnn/rt/scripts/framework/caffe/xfdnn_subgraph.py --inproto quantize_results/deploy.prototxt --trainproto /opt/models/caffe/bvlc_googlenet/bvlc_googlenet_train_val.prototxt --outproto xfdnn_auto_cut_deploy.prototxt --cutAfter data --xclbin $MLSUITE_ROOT/overlaybins/$MLSUITE_PLATFORM/overlay_4.xclbin --netcfg work/compiler.json --quantizecfg work/quantizer.json --weights work/deploy.caffemodel_data.h5 --profile True
+  ```  
+
+  This output xfdnn_auto_cut_deploy.prototxt to $HOME/share/ directory
+
+10. Running image classification for caffe model.
+
+  ```
+  python $MLSUITE_ROOT/examples/caffe/caffe_run.py --caffemodel $MLSUITE_ROOT/share/quantize_results/deploy.caffemodel --prototxt $MLSUITE_ROOT/share/xfdnn_auto_cut_deploy.prototxt --synset_words $MLSUITE_ROOT/examples/deployment_modes/synset_words.txt  --image $MLSUITE_ROOT/examples/deployment_modes/dog.jpg
+  ```

examples/caffe/README.md

@@ -69,7 +69,7 @@ After the setup, run through a sample end to end caffe classification example us
 4. **Inference** - Run a single image on the FPGA
 
   ```
-  python run.py --prototxt /opt/models/caffe/bvlc_googlenet/bvlc_googlenet_train_val.prototxt --caffemodel /opt/models/caffe/bvlc_googlenet/bvlc_googlenet.caffemodel --image ../deployment_modes/dog.jpg
+  python run.py --image ../deployment_modes/dog.jpg
   ```
 
 5. **Benchmark FPGA performance** - evaluate network throughput and/or latency in a streaming deployment scenario (FPGA only)

examples/caffe/REST/app.py

@@ -7,13 +7,13 @@
 import io
 
 app = flask.Flask(__name__)
- 
+
 def LoadImage(prototxt,caffemodel,labels):
   import numpy as np
   import xdnn_io
   global net
   net = caffe.Net(prototxt,caffemodel,caffe.TEST)
-  return net  
+  return net
 
 def InferImage(net,image,labels):
   import numpy as np
@@ -36,50 +36,57 @@ def InferImage(net,image,labels):
       pass
   Labels = xdnn_io.get_labels(labels)
   xdnn_io.printClassification(softmax,[image],Labels)
-  return xdnn_io.getClassification(softmax,[image],Labels) 
+  return xdnn_io.getClassification(softmax,[image],Labels)
+
+#@app.route('/predict', methods=['GET','POST']) # go through link http://172.24.157.249:5000/predict for result
 
-@app.route("/predict", methods=["POST"])
+@app.route('/') # go through link http://172.24.157.249:5000/ for result
 def predict():
   data = {"success": False}
 
   global prototxt
   global model
   global synset_words
+  global image
 
-  if flask.request.method == "POST":
-    image = flask.request.files["image"]
-    response = InferImage(net, image, synset_words)
-    data["success"] = True
-    data["response"] = response
+  response = InferImage(net, image, synset_words)
+  data["success"] = True
+  data["response"] = response
 
   return flask.jsonify(data)
 
 if __name__ == "__main__":
   parser = argparse.ArgumentParser(description='pyXFDNN')
-  parser.add_argument('--caffemodel', default="/opt/models/caffe/bvlc_googlenet/bvlc_googlenet.caffemodel", help='path to caffemodel file eg: /opt/models/caffe/bvlc_googlenet/bvlc_googlenet.caffemodel')
-  parser.add_argument('--prototxt', default="xfdnn_auto_cut_deploy.prototxt", help='path to  prototxt file eg: xfdnn_auto_cut_deploy.prototxt')
-  parser.add_argument('--synset_words', default="$HOME/CK-TOOLS/dataset-imagenet-ilsvrc2012-aux/synset_words.txt", help='path to synset_words eg: $HOME/CK-TOOLS/dataset-imagenet-ilsvrc2012-aux/synset_words.txt')
+  parser.add_argument('--caffemodel', default="/opt/ml-suite/share/quantize_results/deploy.caffemodel", help='path to caffe model file eg: /opt/models/caffe/bvlc_googlenet/bvlc_googlenet.caffemodel')
+  parser.add_argument('--prototxt', default="/opt/ml-suite/share/xfdnn_auto_cut_deploy.prototxt", help='path to  prototxt file eg: xfdnn_auto_cut_deploy.prototxt')
+  parser.add_argument('--synset_words', default="/opt/ml-suite/examples/deployment_modes/synset_words.txt", help='path to synset_words eg: $HOME/CK-TOOLS/dataset-imagenet-ilsvrc2012-aux/synset_words.txt')
   parser.add_argument('--port', default=5000)
-
+  parser.add_argument('--image', default="/opt/ml-suite/examples/deployment_modes/dog.jpg")
+
   args = vars(parser.parse_args())
 
   if args["caffemodel"]:
     model=args["caffemodel"]
-  
+
   if args["prototxt"]:
     prototxt=args["prototxt"]
 
   if args["synset_words"]:
     synset_words=args["synset_words"]
-
+
+  if args["image"]:
+    image=args["image"]
+
   if args["port"]:
     port=args["port"]
   else:
-    port=9000 
+    port=9000
 
   print("Loading FPGA with image...")
   net = LoadImage(prototxt, model, synset_words)
-
-  print("Starting Flask Server...")
-  app.run(port=port)
 
+  #print("infering the image")
+  #InferImage(net,image,synset_words)
+
+  print("Starting Flask Server...")
+  app.run(port=port,host='0.0.0.0')

examples/caffe/REST/run.sh

@@ -14,6 +14,4 @@ PORT="5000"
 
 python ../run.py --prototxt ${MODEL_PROTOTXT} --caffemodel ${MODEL_WEIGHTS} --prepare
 
-python app.py --caffemodel ${MODEL_WEIGHTS} --prototxt xfdnn_auto_cut_deploy.prototxt --synset_words ${LABELS} --port ${PORT} 
-
-
+python app.py

examples/caffe/caffe_run.py

@@ -0,0 +1,39 @@
+from __future__ import print_function
+
+import os,sys,argparse
+import caffe
+import io
+import numpy as np
+import xdnn_io
+
+# Use this routine to classify a single image
+def Classify(prototxt,caffemodel,image,labels):
+  classifier = caffe.Classifier(prototxt,caffemodel,
+    image_dims=[256,256], mean=np.array([104,117,123]),
+    raw_scale=255, channel_swap=[2,1,0])
+  predictions = classifier.predict([caffe.io.load_image(image)]).flatten()
+  labels = np.loadtxt(labels, str, delimiter='\t')
+  top_k = predictions.argsort()[-1:-6:-1]
+  for l,p in zip(labels[top_k],predictions[top_k]):
+    print (l," : ",p)
+  return classifier
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(description='pyXFDNN')
+  parser.add_argument('--caffemodel', default="/opt/ml-suite/share/quantize_results/deploy.caffemodel", help='path to caffe model eg: /opt/ml-suite/share/quantize_results/deploy.caffemodel')
+  parser.add_argument('--prototxt', default="/opt/ml-suite/share/xfdnn_auto_cut_deploy.prototxt", help='path to  prototxt file eg: /opt/ml-suite/share/xfdnn_auto_cut_deploy.prototxt')
+  parser.add_argument('--synset_words', default="/opt/ml-suite/examples/deployment_modes/synset_words.txt", help='path to synset_words eg: /opt/ml-suite/examples/deployment_modes/synset_words.txt')
+  parser.add_argument('--image', default="/opt/ml-suite/examples/deployment_modes/dog.jpg")
+  args = vars(parser.parse_args())
+
+  if args["caffemodel"]:
+    model=args["caffemodel"]
+  if args["prototxt"]:
+    prototxt=args["prototxt"]
+  if args["synset_words"]:
+    synset_words=args["synset_words"]
+  if args["image"]:
+    image=args["image"]
+
+  print("Loading FPGA with image and classify...")
+  Classify(prototxt, model, image, synset_words)