triple dense example

2017-06-28 12:37:20 +00:00
parent a86df107be
commit 8bf0b0257e
4 changed files with 202 additions and 33 deletions
@@ -0,0 +1,152 @@
+import keras
+from keras.models import load_model
+import keras.backend.tensorflow_backend as KTF
+import numpy as np
+import argparse
+import tensorflow as tf
+import os
+import msgpack
+import lmdb
+import random
+
+def export_dense(name, weights, bias):
+    print "########    EXPORT", name, "LAYER    ########"
+    print "Original weighs:"
+    print weights
+    print bias, "\n"
+
+    #input, filters
+    I, C = np.shape(weights) 
+    B = np.shape(bias)
+    print "w shape: ", I, C
+    print "b shape: ", B
+
+    wgs = [ [ j[i] for j in weights ]  for i in xrange(C) ]
+    wgs = np.array(wgs, dtype=np.float32)
+    
+    print "REPOSITIONED WEIGHTS:"
+    print wgs
+
+    bias = np.array(bias, dtype=np.float32)
+    wgs.tofile(name + ".bin", format="f")
+    bias.tofile(name + ".bias.bin", format="f")
+    print "WEIGHTS saved\n"
+
+def export_conv2d(name, weights, bias):
+    print "########    EXPORT", name, "LAYER    ########"
+    print "Original weighs:"
+    print weights
+    print bias, "\n"
+  
+    # height, width, input, filters
+    H, W, N, C = np.shape(weights) 
+    B = np.shape(bias)
+    print "w shape: ", N, C, H, W
+    print "b shape: ", B
+
+    wgs = weights.transpose()
+    wgs = wgs.transpose(0, 1, 3, 2)
+    print "Final shape:", np.shape(wgs)    
+    wgs = np.array(wgs.flatten(), dtype=np.float32)
+    
+    print "REPOSITIONED WEIGHTS:"
+    print wgs
+
+    bias = np.array(bias, dtype=np.float32)
+
+    wgs.tofile(name + ".bin", format="f")
+    bias.tofile(name + ".bias.bin", format="f")
+    print "WEIGHTS saved\n"
+
+def export_conv3d(name, weights, bias):
+    print "########    EXPORT", name, "LAYER    ########"
+    print "Original weighs:"
+    print weights
+    print bias, "\n"
+  
+    print np.shape(weights)
+    # height, width, input, thickness, filters
+    H, W, T, N, C = np.shape(weights) 
+    B = np.shape(bias)
+    print "w shape: ", T, C, H, W   #thickness is number of images for cudnn
+    print "b shape: ", B
+
+    wgs = weights.transpose()
+    wgs = wgs.transpose(0, 1, 4, 3, 2)
+    print "Final shape:", np.shape(wgs)    
+    wgs = np.array(wgs.flatten(), dtype=np.float32)
+    
+    print "REPOSITIONED WEIGHTS:"
+    print wgs
+
+    bias = np.array(bias, dtype=np.float32)
+
+    wgs.tofile(name + ".bin", format="f")
+    bias.tofile(name + ".bias.bin", format="f")
+    print "WEIGHTS saved\n"
+
+
+def get_session(gpu_fraction=0.5):
+    gpu_options = tf.GPUOptions(allow_growth=True) 
+        #per_process_gpu_memory_fraction=gpu_fraction)
+    return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
+
+
+#https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
+if __name__ == '__main__':
+    KTF.set_session(get_session())
+
+    parser = argparse.ArgumentParser(description='KERAS WEIGHTS EXPORTER TO CUDNN')
+    parser.add_argument('model',type=str,
+        help='Path to model h5 file. Model should be on the same path.')
+    parser.add_argument('layers', type=str, help="layers list [ dense, conv2d ]", nargs='+')
+    parser.add_argument('--output', type=str, help="output directory", default="layers")
+    parser.add_argument('--test_db', type=str, help="input db to test", default=None)
+
+    args = parser.parse_args()
+    
+    print "DATA FORMAT: ", keras.backend.image_data_format()
+    
+    print "Load model: ", args.model
+    model = load_model(args.model)
+    weights = model.get_weights()
+
+    ws = np.shape(weights)
+    print "Weights shape:", ws
+
+    if not os.path.exists(args.output):
+        os.makedirs(args.output)
+
+    num = 0
+    name_num = 0
+    for i in args.layers:
+        if i == "conv3d":
+            export_conv3d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
+        elif i == "conv2d":
+            export_conv2d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
+        elif i == "dense":
+            export_dense(args.output + "/dense" + str(name_num), weights[num], weights[num+1])
+        else:
+            print "error: ", i, "is not a layer type"
+            break
+        name_num += 1
+        num += 2
+
+    if args.test_db != None:
+        print "Test on db: ", args.test_db
+        db = lmdb.open(args.test_db, subdir=False, readonly=True, lock=False) 
+        txn = db.begin()
+
+        s = random.randint(0, txn.stat()["entries"]-1)
+        print "camp number: ", s
+        s = txn.get(str(s))
+        c = msgpack.unpackb(s)
+    
+        print "Steer, throttle: ", c["actuators"]
+        print "Speed (m/s): ", c["speed"]
+        grid = np.asarray(c["bitmap"], np.float32)
+        i = np.array(grid.flatten(), dtype=np.float32)
+        i.tofile(args.output + "input.bin", format="f")
+        X = grid[None, :, :]
+    
+        print "Prediction: ", model.predict(X)