mmr/tkDNN
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Merge with master works

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Signed-off-by:  Micaela Verucchi <micaelaverucchi@gmail.com>
		Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-04-08 14:59:42 +02:00
parent 6ec417d89f 62c8f528a4
commit 0951f4644f
16 changed files with 948 additions and 190 deletions
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tests/simple/test_model.py
+38 -27
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@@ -1,43 +1,54 @@
import keras
import numpy as np
from keras.models import Sequential
from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda
from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda, Conv1D
from keras.layers import Bidirectional, CuDNNLSTM
from keras.layers.convolutional import Convolution2D, Convolution3D
from keras.layers.pooling import MaxPooling2D, MaxPooling3D, AveragePooling3D
from keras.models import Sequential, Model
from keras.layers import Cropping2D
import keras.backend.tensorflow_backend as KTF
import struct
from keras.models import Sequential, Model
def dense_model():
model = Sequential()
def bin_write(f, data):
data = data.flatten()
fmt = 'f'*len(data)
bin = struct.pack(fmt, *data)
f.write(bin)
def create_model():
x1 = Input((3, 8), name='x1')
conv = Conv1D(4, 2)(x1)
lstm = Bidirectional(CuDNNLSTM(5, return_sequences=True))(conv)
lstm2 = Bidirectional(CuDNNLSTM(5, return_sequences=False))(lstm)
model = Model([x1], [lstm2])
model.summary()
model.add(Reshape((10, 10, 1), input_shape=(10, 10)))
model.add(Convolution2D(2, (4, 4), subsample=(2, 2),
bias_initializer='random_uniform', activation="relu"))
model.add(Convolution2D(4, (2, 2), subsample=(1, 1),
bias_initializer='random_uniform', activation="relu"))
model.add(Flatten())
model.add(Dense(4, bias_initializer='random_uniform', activation="relu"))
sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
model.compile(optimizer=sgd, loss="mse")
return model
if __name__ == '__main__':
print "DATA FORMAT: ", keras.backend.image_data_format()
print ("DATA FORMAT: ", keras.backend.image_data_format())
model = dense_model()
model.save("net.h5")
model = create_model()
model.save("net.hdf5")
grid = np.random.rand(10,10)
X = grid[None,:,:]
i = np.array(grid.flatten(), dtype=np.float32)
print i
i.tofile("input.bin", format="f")
print "Input: ", X
np.random.seed(2)
x = np.random.rand(1,1,3,8)
r = model.predict( x[0], batch_size=1)
r = np.array([r])
x = x.transpose(0, 3, 1, 2)
#r = r.transpose(0, 3, 1, 2)
print("in: ", np.shape(x))
print("out: ", np.shape(r))
print("output: ", r.tolist())
x = np.array(x.flatten(), dtype=np.float32)
f = open("input.bin", mode='wb')
bin_write(f, x)
r = np.array(r.flatten(), dtype=np.float32)
f = open("output.bin", mode='wb')
bin_write(f, r)
r = model.predict( X, batch_size=1)
print np.shape(r)
print "Result: ", r
print "Result shape: ", np.shape(r)
r.tofile("output.bin", format="f")
tests/simple/test_simple.cpp
+9 -10
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@@ -2,22 +2,21 @@
#include "tkdnn.h"
const char *input_bin = "../tests/simple/input.bin";
const char *c0_bin = "../tests/simple/layers/c0.bin";
const char *c1_bin = "../tests/simple/layers/c1.bin";
const char *d2_bin = "../tests/simple/layers/d2.bin";
const char *c0_bin = "../tests/simple/layers/conv1d_1.bin";
const char *l1_bin = "../tests/simple/layers/bidirectional_1.bin";
const char *l2_bin = "../tests/simple/layers/bidirectional_2.bin";
const char *output_bin = "../tests/simple/output.bin";
int main() {
// Network layout
tk::dnn::dataDim_t dim(1, 1, 10, 10, 1);
tk::dnn::dataDim_t dim(1, 8, 1, 3);
tk::dnn::Network net(dim);
tk::dnn::Conv2d l0(&net, 2, 4, 4, 2, 2, 0, 0, c0_bin);
tk::dnn::Activation l1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d l2(&net, 4, 2, 2, 1, 1, 0, 0, c1_bin);
tk::dnn::Activation l3(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Dense l5(&net, 4, d2_bin);
tk::dnn::Activation l6(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d l0(&net, 4, 1, 2, 1, 1, 0, 0, c0_bin);
tk::dnn::LSTM l1(&net, 5, true, l1_bin);
tk::dnn::LSTM l2(&net, 5, false, l2_bin);
net.print();
net.print();