triple dense example
This commit is contained in:
Francesco Gatti
@@ -2,3 +2,4 @@
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build/
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.vscode/
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*.bin
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*.pyc
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+21
-21
@@ -8,37 +8,37 @@ from keras.layers.pooling import MaxPooling2D, MaxPooling3D
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from keras.models import Sequential, Model
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from keras.layers import Cropping2D
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import keras.backend.tensorflow_backend as KTF
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from weights_exporter import *
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def dense_model(inp, out):
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def dense_model():
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model = Sequential()
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model.add(Dense(out, input_shape=(1, inp)))
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model.add(Dense(256, input_shape=(1, 512)))
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model.add(ELU())
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model.add(Dense(32))
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model.add(ELU())
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model.add(Dense(2))
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sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
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model.compile(optimizer=sgd, loss="mse")
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return model
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if __name__ == '__main__':
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model = dense_model(8, 2)
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model = dense_model()
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wg = model.get_weights()
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w = np.squeeze(wg[0])
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w = np.array([ i[0] for i in w ] + [ i[1] for i in w ], dtype=np.float32)
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b = np.squeeze(wg[1])
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print "weigths: ", w
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print "bias: ", b
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w.tofile("dense.bin", format="f")
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b.tofile("dense.bias.bin", format="f")
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export_dense("dense0", wg[0], wg[1])
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export_dense("dense1", wg[2], wg[3])
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export_dense("dense2", wg[4], wg[5])
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model.set_weights(wg)
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X = np.array([[[0,1,2,3,4,5,6,7]]], dtype=np.float32)
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i = np.squeeze(X[0][0])
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print "input: ", i
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X = np.random.rand(1, 512)
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i = np.array(X, dtype=np.float32)
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i.tofile("input.bin", format="f")
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r = model.predict( X, batch_size=1)
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print "Input: ", i
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r = model.predict( X[None, :], batch_size=1)
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print "Result: ", r
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print "Result shape: ", np.shape(r)
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print "Result shape: ", np.shape(r)
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+28
-12
@@ -1,24 +1,40 @@
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#include<iostream>
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#include "Layer.h"
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const char *input_bin = "../tests/input.bin";
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const char *d0_bin = "../tests/dense0.bin";
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const char *d0_bias_bin = "../tests/dense0.bias.bin";
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const char *d1_bin = "../tests/dense1.bin";
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const char *d1_bias_bin = "../tests/dense1.bias.bin";
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const char *d2_bin = "../tests/dense2.bin";
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const char *d2_bias_bin = "../tests/dense2.bias.bin";
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int main() {
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// Network layout
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tkDNN::Network net;
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tkDNN::dataDim_t dim(1, 8, 1, 1);
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tkDNN::Dense d(&net, dim, 2, "../tests/dense.bin", "../tests/dense.bias.bin");
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tkDNN::Activation a(&net, d.output_dim, tkDNN::ACTIVATION_ELU);
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tkDNN::dataDim_t dim(1, 512, 1, 1);
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tkDNN::Dense d0 (&net, dim, 256, d0_bin, d0_bias_bin);
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tkDNN::Activation a0 (&net, d0.output_dim, tkDNN::ACTIVATION_ELU);
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tkDNN::Dense d1 (&net, a0.output_dim, 32, d1_bin, d1_bias_bin);
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tkDNN::Activation a1 (&net, d1.output_dim, tkDNN::ACTIVATION_ELU);
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tkDNN::Dense d2 (&net, a1.output_dim, 2, d2_bin, d2_bias_bin);
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// Load input
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value_type *data;
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value_type *input_h;
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readBinaryFile("../tests/input.bin", 8, &input_h, &data);
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dim.print();
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data = d.infer(dim, data);
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dim.print();
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data = a.infer(dim, data);
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dim.print();
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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dim.print(); //print initial dimension
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// Inference
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data = d0.infer(dim, data); dim.print();
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data = a0.infer(dim, data); dim.print();
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data = d1.infer(dim, data); dim.print();
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data = a1.infer(dim, data); dim.print();
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data = d2.infer(dim, data); dim.print();
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// Print result
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printDeviceVector(dim.tot(), data);
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return 0;
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}
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@@ -0,0 +1,152 @@
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import keras
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from keras.models import load_model
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import keras.backend.tensorflow_backend as KTF
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import numpy as np
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import argparse
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import tensorflow as tf
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import os
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import msgpack
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import lmdb
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import random
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def export_dense(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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#input, filters
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I, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", I, C
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print "b shape: ", B
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wgs = [ [ j[i] for j in weights ] for i in xrange(C) ]
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wgs = np.array(wgs, dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def export_conv2d(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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# height, width, input, filters
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H, W, N, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", N, C, H, W
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print "b shape: ", B
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wgs = weights.transpose()
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wgs = wgs.transpose(0, 1, 3, 2)
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print "Final shape:", np.shape(wgs)
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wgs = np.array(wgs.flatten(), dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def export_conv3d(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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print np.shape(weights)
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# height, width, input, thickness, filters
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H, W, T, N, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", T, C, H, W #thickness is number of images for cudnn
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print "b shape: ", B
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wgs = weights.transpose()
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wgs = wgs.transpose(0, 1, 4, 3, 2)
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print "Final shape:", np.shape(wgs)
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wgs = np.array(wgs.flatten(), dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def get_session(gpu_fraction=0.5):
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gpu_options = tf.GPUOptions(allow_growth=True)
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#per_process_gpu_memory_fraction=gpu_fraction)
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return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
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#https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
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if __name__ == '__main__':
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KTF.set_session(get_session())
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parser = argparse.ArgumentParser(description='KERAS WEIGHTS EXPORTER TO CUDNN')
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parser.add_argument('model',type=str,
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help='Path to model h5 file. Model should be on the same path.')
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parser.add_argument('layers', type=str, help="layers list [ dense, conv2d ]", nargs='+')
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parser.add_argument('--output', type=str, help="output directory", default="layers")
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parser.add_argument('--test_db', type=str, help="input db to test", default=None)
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args = parser.parse_args()
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print "DATA FORMAT: ", keras.backend.image_data_format()
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print "Load model: ", args.model
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model = load_model(args.model)
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weights = model.get_weights()
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ws = np.shape(weights)
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print "Weights shape:", ws
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if not os.path.exists(args.output):
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os.makedirs(args.output)
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num = 0
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name_num = 0
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for i in args.layers:
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if i == "conv3d":
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export_conv3d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
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elif i == "conv2d":
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export_conv2d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
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elif i == "dense":
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export_dense(args.output + "/dense" + str(name_num), weights[num], weights[num+1])
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else:
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print "error: ", i, "is not a layer type"
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break
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name_num += 1
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num += 2
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if args.test_db != None:
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print "Test on db: ", args.test_db
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db = lmdb.open(args.test_db, subdir=False, readonly=True, lock=False)
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txn = db.begin()
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s = random.randint(0, txn.stat()["entries"]-1)
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print "camp number: ", s
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s = txn.get(str(s))
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c = msgpack.unpackb(s)
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print "Steer, throttle: ", c["actuators"]
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print "Speed (m/s): ", c["speed"]
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grid = np.asarray(c["bitmap"], np.float32)
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i = np.array(grid.flatten(), dtype=np.float32)
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i.tofile(args.output + "input.bin", format="f")
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X = grid[None, :, :]
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print "Prediction: ", model.predict(X)
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