Pavel Lutskov
76f8d7dcb6
now there is no mentioning python in c code (except for the boilerplate at the beginning, but the rest is nice clean c). all the bridging is being done in cython (where it belongs). and no memory leaks so there's that!
118 lines
2.9 KiB
Cython
118 lines
2.9 KiB
Cython
cimport numpy as np
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import numpy as np
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import mynet as mn
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from libc.stdlib cimport malloc
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from libc.string cimport memcpy
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ctr = []
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X_train, y_train, X_test, y_test = mn.load_mnist()
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opt = mn.SGDOptimizer(lr=0.1)
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cdef extern from "numpy/arrayobject.h":
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void *PyArray_DATA(np.ndarray arr)
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ctypedef public struct Dense:
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long[2] shape
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int ownmem
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float* W
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float* b
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ctypedef public struct Network:
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size_t n_layers;
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Dense* layers;
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cdef public char * greeting():
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return f'The value is {3**3**3}'.encode('utf-8')
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cdef public void debug_print(object o):
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print(o)
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cdef public void predict(
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Network* net,
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float* X,
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size_t batch_size
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):
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pass
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# try:
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# return net(X)
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# except Exception as e:
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# print(e)
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cdef public void step_net(
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Network* c_net,
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float* batch_data,
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size_t batch_size
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):
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net = wrap_c_network(c_net)
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cdef size_t in_dim = net.geometry[0]
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cdef size_t out_dim = net.geometry[-1]
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batch = np.asarray(<float[:batch_size,:in_dim+out_dim]>batch_data)
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net.step(batch[:, :in_dim], batch[:, in_dim:], opt)
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cdef public float eval_net(Network* c_net):
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net = wrap_c_network(c_net)
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return net.evaluate(X_test, y_test, 'cls')
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cdef public void mnist_batch(float* batch, size_t bs):
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idx = np.random.choice(len(X_train), bs, replace=False)
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arr = np.concatenate([X_train[idx], y_train[idx]], axis=1)
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memcpy(batch, <float*>PyArray_DATA(arr), arr.size*sizeof(float))
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cdef public void create_c_network(Network* c_net):
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net = _create_network()
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c_net.n_layers = len(net.layers)
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c_net.layers = <Dense*>malloc(sizeof(Dense) * c_net.n_layers)
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for i, l in enumerate(net.layers):
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d0, d1 = l.W.shape
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c_net.layers[i].shape[0] = d0
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c_net.layers[i].shape[1] = d1
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c_net.layers[i].W = <float*>malloc(sizeof(float) * d0 * d1)
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c_net.layers[i].b = <float*>malloc(sizeof(float) * d1)
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memcpy(c_net.layers[i].W, PyArray_DATA(l.W), sizeof(float) * d0 * d1)
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memcpy(c_net.layers[i].b, PyArray_DATA(l.b), sizeof(float) * d1)
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c_net.layers[i].ownmem = 1
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cdef public void be_like(Network* c_dst, Network* c_src):
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dst = wrap_c_network(c_dst)
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src = wrap_c_network(c_src)
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dst.be_like(src)
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cdef object wrap_c_network(Network* c_net):
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net = _create_network(init=False)
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for i, l in enumerate(net.layers):
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d0, d1 = l.W.shape
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l.W = np.asarray(<float[:d0,:d1]>c_net.layers[i].W)
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l.b = np.asarray(<float[:d1]>c_net.layers[i].b)
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return net
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cdef void inspect_array(
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np.ndarray[np.float32_t, ndim=2, mode='c'] a
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):
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print(a.flags, flush=True)
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print(a.dtype, flush=True)
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print(a.sum(), flush=True)
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def _create_network(init=True):
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return mn.Network((784, 10), mn.relu, mn.sigmoid, mn.bin_x_entropy,
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initialize=init)
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def combo_net(nets):
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return mn.combo_net(nets)
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