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How to use check_function method in hypothesis

Best Python code snippet using hypothesis

test_top_level1.py

Source:test_top_level1.py

...5import nnvm.symbol as sym6import nnvm.compiler7from nnvm.testing.config import ctx_list8from nnvm.testing.check_computation import check_function9def test_check_function():10    # test the testing function11    x = sym.Variable("x")12    y = sym.Variable("y")13    # different styles of returning gradients from the backward function14    check_function(x + 2*y, lambda x, y: x + 2*y,15                   lambda x, y, head_grads: [head_grads, 2*head_grads],16                   shape={'x': (1, 2), y: (1, 2)}, dtype='float32')17    check_function(x + 2*y, lambda x, y: x + 2*y,18                   lambda x, y, head_grads: (head_grads, 2*head_grads),19                   shape={'x': (1, 2), y: (1, 2)}, dtype='float32')20    check_function(x + 2*y, lambda x, y: x + 2*y,21                   lambda x, y, head_grads: {'x': head_grads, 'y': 2*head_grads},22                   shape={'x': (1, 2), y: (1, 2)}, dtype='float32')23    check_function(x + 2*y, lambda x, y: x + 2*y,24                   lambda x, y, head_grads: {'y': 2*head_grads},25                   shape={'x': (1, 2), y: (1, 2)}, dtype='float32')26    check_function(x + 2*y, lambda x, y: x + 2*y,27                   lambda x, y, head_grads: [2*head_grads],28                   grad_input_vars=[y],29                   shape={'x': (1, 2), y: (1, 2)}, dtype='float32')30    check_function(x + 2*y, lambda x, y: x + 2*y,31                   lambda x, y, head_grads: 2*head_grads,32                   grad_input_vars=[y],33                   shape={'x': (1, 2), y: (1, 2)}, dtype='float32')34    check_function(x + 2*y, lambda x, y: x + 2*y,35                   lambda x, y, head_grads: 2*head_grads,36                   grad_input_vars=[y],37                   shape={'x': (1, 2), y: (1, 2)}, dtype='float64')38    # test just numerical gradients39    # different styles of shape and dtype passing40    check_function(x + 2*y, shape={'x': (1, 2), y: (1, 2)},41                   numerical_grads=True)42    check_function(x + 2*y, shape={'x': (1, 2), y: (1, 2)}, dtype='float32',43                   numerical_grads=True)44    check_function(x + 2*y, shape={'x': (1, 2), y: (1, 2)}, dtype={x: 'float32', 'y': 'float32'},45                   numerical_grads=True)46    check_function(x + 2*y, shape=(1, 2), dtype='float32',47                   numerical_grads=True)48    # specifying variable attributes on variable creation49    # (in this case type codes must be used)50    x = sym.Variable("x", dtype=0, shape=(1, 2))51    check_function(x + 2*y, shape={y: (1, 2)}, dtype={'y': 'float32'}, numerical_grads=True)52    y = sym.Variable("y", dtype=0, shape=(1, 2))53    # shape overriding54    def _fwd1(x, y):55        assert x.shape == (1, 1)56        assert y.shape == (1, 2)57        return x + 2*y58    check_function(x + 2*y, _fwd1, shape={x: (1, 1)})59    # in_range60    def _fwd2(x, y):61        assert x.shape == (100,)62        assert (x <= 0.9).all()63        assert (x >= 0.8).all()64        return x + 2*y65    check_function(x + 2*y, _fwd2, shape=(100,), in_range=(0.8, 0.9), numerical_grads=False)66    check_function(x + 2*y, _fwd2, shape=(100,), in_range={'x': (0.8, 0.9)}, numerical_grads=False)67    check_function(x + 2*y, backward=lambda x, y, head_grads: [1.0, 2.0],68                   in_range={'head_grads_0': (1.0, 1.0)})69    # explicit passing of values70    check_function(x + 2*y, backward=lambda x, y, head_grads: [1.0, 2.0],71                   values={'head_grads_0': np.full((1, 2), 1.0)})72    # check that the function reports errors73    def _check_function_must_fail(*args, **kwargs):74        error = AssertionError75        if 'error' in kwargs:76            error = kwargs['error']77            del kwargs['error']78        try:79            check_function(*args, quiet=True, **kwargs)80        except error:81            pass82        else:83            raise AssertionError("check_function didn't raise an exception")84    _check_function_must_fail(x + 2*y, error=ValueError)85    _check_function_must_fail(x + 2*y, lambda x, y: x + y)86    _check_function_must_fail(x + 2*y, backward=lambda x, y, head_grads: [1.0, 2.0])87    _check_function_must_fail(sym.block_grad(x + 2*y), numerical_grads=True)88    _check_function_must_fail(x*x, numerical_grads=True,89                              numerical_grads_params={'atol': 0.0, 'rtol': 0.0})90    _check_function_must_fail(sym.log(-x*x), numerical_grads=True, error=ValueError)91    # different styles of returning results from the forward function92    check_function(x + 2*y, lambda x, y: [x + 2*y], numerical_grads=False)93    _check_function_must_fail(x + 2*y, lambda x, y: [x + 2*y, x], numerical_grads=False,94                              error=ValueError)95    _check_function_must_fail(x + 2*y, lambda x, y: [], numerical_grads=False,96                              error=ValueError)97    # multiple outputs98    z = sym.Group([2*x + y, x + 2*y])99    check_function(z, lambda x, y: [2*x + y, x + 2*y])100    check_function(z, lambda x, y: (2*x + y, x + 2*y))101    check_function(z, backward=lambda x, y, head_grads: [2*head_grads[0] + head_grads[1],102                                                         head_grads[0] + 2*head_grads[1]])103    _check_function_must_fail(z, backward=lambda x, y, head_grads: [2*head_grads[0],104                                                                    2*head_grads[1]])105    check_function(z, backward=lambda x, y, head_grads: [head_grads[1], 2*head_grads[1]],106                   in_range={'head_grads_0': (0, 0)})107    check_function(z, numerical_grads=True)108    z = sym.Group([sym.block_grad(2*x + y), x + 2*y])109    check_function(z, lambda x, y: [2*x + y, x + 2*y], numerical_grads=False)110    _check_function_must_fail(z, lambda x, y: [2*x + y, x + 2*y])111    _check_function_must_fail(z, numerical_grads=True)112    z = sym.Group([2*x + y, sym.block_grad(x + 2*y)])113    _check_function_must_fail(z, numerical_grads=True)114    z = sym.Group([2*x + y, x + 2*y, x, y, sym.sum(x)])115    check_function(z, lambda x, y: [2*x + y, x + 2*y, x, y, np.sum(x)])116    # passing additional parameters to forward and backward117    def _fwd3(x, p):118        assert p == 'v'119        return x + 1120    def _bwd3(x, p, head_grads):121        assert p == 'v'122        return head_grads123    check_function(x + 1, _fwd3, _bwd3, additional_params={'p': 'v'})124    # implicitly created variables and shape/dtype inference for inputs125    x = sym.Variable("x", shape=(2, 3), dtype=0)126    b = sym.Variable("b")127    y = sym.dense(data=x, bias=b, units=4)128    # Don't check gradients on cuda because is doesn't yet support ewise after reduce129    check_function(y, exclude_targets={'cuda'}, numerical_grads=True)130    check_function(y, shape={'x': (3, 4)}, exclude_targets={'cuda'}, numerical_grads=True)131    check_function(y, dtype={'x': 'float64'}, exclude_targets={'cuda'}, numerical_grads=True)132    x = sym.Variable("x")133    b = sym.Variable("b")134    w = sym.Variable("w")135    y = sym.dense(data=x, bias=b, weight=w, units=4)136    def _fwd_dense(x, w, b):137        return np.dot(x, w.T) + b138    check_function(y, _fwd_dense, shape={'x': (1,2)}, dtype={'x': 'float32'}, numerical_grads=False)139    check_function(y, _fwd_dense, shape={'x': (1,2)}, dtype={'w': 'float64'}, numerical_grads=False)140    _check_function_must_fail(y, _fwd_dense, shape={'x': (1,2)},141                              dtype={'w': 'float64', 'b': 'float32'},142                              numerical_grads=False,143                              error=nnvm._base.NNVMError)144    # fails because no shape145    _check_function_must_fail(y, _fwd_dense, numerical_grads=False, error=ValueError)146    # ok because type is float32 by default147    check_function(y, _fwd_dense, shape={'x': (1,2)}, numerical_grads=False)148def test_relu():149    x = sym.Variable("x")150    y = sym.relu(sym.leaky_relu(x, alpha=0.3) - 0.2)151    def forward(x):152        x = (x < 0) * x * 0.3 + (x > 0) * x - 0.2153        return (x > 0) * x154    def backward(head_grads, x):155        sub = (x < 0) * x * 0.3 + (x > 0) * x - 0.2156        return [(sub > 0).astype("float") * \157                ((x > 0).astype("float") + 0.3 * (x < 0).astype("float")) * head_grads]158    shape = {'x': (1, 3, 32, 32)}159    check_function(y, forward, backward, shape=shape)160def test_prelu_nchw():161    x = sym.Variable("x")162    a = sym.Variable("a")163    y = sym.prelu(data=x, alpha=a)164    def forward(x, a):165        return (x < 0) * (x * a.reshape(3, 1, 1)) + (x>=0) * x166    shape = {'x': (1, 3, 32, 32), 'a': (3,)}167    check_function(y, forward, shape=shape)168def test_prelu_nhwc():169    x = sym.Variable("x")170    a = sym.Variable("a")171    y = sym.prelu(data=x, alpha=a, axis=3)172    def forward(x, a):173        return (x < 0) * (x * a.reshape(1, 1, 3)) + (x>=0) * x174    shape = {'x': (1, 32, 32, 3), 'a': (3,)}175    check_function(y, forward, shape=shape)176def test_sym_scalar_pow():177    scalar = 3178    x = sym.Variable("x")179    y = x**scalar180    def forward(x):181        return x**scalar182    def backward(head_grads, x):183        return [scalar * x**(scalar -  1) * head_grads]184    shape = {'x': (1, 3, 32, 32)}185    check_function(y, forward, backward, shape=shape)186def test_scalar_sym_pow():187    scalar = 3188    x = sym.Variable("x")189    y = scalar**x190    def forward(x):191        return scalar**x192    def backward(head_grads, x):193        return [np.log(scalar) * scalar**x * head_grads]194    shape = {'x': (1, 3, 32, 32)}195    check_function(y, forward, backward, shape=shape)196def test_exp():197    x = sym.Variable("x")198    y = sym.exp(x)199    def forward(x):200        return np.exp(x)201    def backward(head_grads, x):202        return [np.exp(x) * head_grads]203    shape = {'x': (1, 3, 32, 32)}204    check_function(y, forward, backward, shape=shape)205def test_log():206    x = sym.Variable("x")207    y = sym.log(x)208    def forward(x):209        return np.log(x)210    def backward(head_grads, x):211        return [1. / x * head_grads]212    shape = {'x': (1, 3, 32, 32)}213    check_function(y, forward, backward, in_range=(0.002, 2.0), shape=shape)214def test_tanh():215    x = sym.Variable("x")216    y = sym.tanh(x)217    def forward(x):218        return np.sinh(x) / np.cosh(x)219    def backward(head_grads, x):220        y_np = forward(x)221        return [(1 - y_np**2) * head_grads]222    shape = {'x': (1, 3, 32, 32)}223    check_function(y, forward, backward, shape=shape)224def test_sigmoid():225    x = sym.Variable("x")226    y = sym.sigmoid(x)227    def forward(x):228        return 1.0 / (1.0 + np.exp(-x))229    def backward(head_grads, x):230        y_np = forward(x)231        return [y_np *(1 - y_np) * head_grads]232    shape = {'x': (1, 3, 32, 32)}233    check_function(y, forward, backward, shape=shape)234def test_softmax():235    x = sym.Variable("x")236    y = sym.softmax(x)237    def forward(x):238        return topi.testing.softmax_python(x)239    def backward(head_grads, x):240        y = topi.testing.softmax_python(x)241        grad = y * (head_grads - np.sum(y * head_grads, axis=1, keepdims=True))242        return [grad]243    check_function(y, forward, backward,244                   shape={'x': (10, 1000)}, numerical_grads=False)245    check_function(y, forward, backward,246                   shape={'x': (2, 10)})247def test_log_softmax():248    x = sym.Variable("x")249    y = sym.log_softmax(x)250    def forward(x):251        return topi.testing.log_softmax_python(x)252    def backward(head_grads, x):253        y = topi.testing.log_softmax_python(x)254        grad = head_grads - np.exp(y) * np.sum(head_grads, axis=1, keepdims=True)255        return [grad]256    check_function(y, forward, backward,257                   shape={'x': (10, 1000)}, numerical_grads=False)258    check_function(y, forward, backward,259                   shape={'x': (2, 10)})260def test_dense():261    x = sym.Variable("x", shape=(10, 100))262    w = sym.Variable("dense_weight", shape=(3, 100))263    b = sym.Variable("dense_bias", shape=(3,))264    y = sym.dense(x, w, b, use_bias=True, units=3, name="dense")265    y = sym.flatten(y)266    def forward(x, dense_weight, dense_bias):267        return np.dot(x, dense_weight.T) + dense_bias268    shape = {269        'x': (10, 100),270        'w': (3, 100),271        'b': (3,)272    }273    # Don't check gradients on cuda because is doesn't yet support ewise after reduce274    check_function(y, forward, shape=shape,275                   exclude_targets={'cuda'}, numerical_grads=True)276    check_function(y, forward, shape=shape,277                   only_targets={'cuda'}, numerical_grads=False)278def test_batchnorm():279    x = sym.Variable("x")280    beta = sym.Variable("beta")281    gamma = sym.Variable("gamma")282    moving_var = sym.Variable("moving_var")283    moving_mean = sym.Variable("moving_mean")284    eps = 1e-5285    y = sym.batch_norm(286        x, gamma, beta, moving_mean, moving_var, epsilon=eps)287    def forward(x, gamma, beta, moving_mean, moving_var):288        return (x - moving_mean) / np.sqrt(moving_var + eps) * gamma + beta289    shape = {290        'x': (10, 20),291        'gamma': (20,),292        'beta': (20,),293        'moving_mean': (20,),294        'moving_var': (20,)295    }296    check_function(y, forward, in_range=(0.001, 1.0), shape=shape)297def verify_concatenate(ishape, axis):298    x = [sym.Variable("x%d" % i, shape=ishape[i]) for i in range(len(ishape))]299    y = sym.concatenate(*x, axis=axis) + 1300    def forward(**kwargs):301        return np.concatenate(list(kwargs.values()), axis=axis) + 1302    check_function(y, forward)303def test_concatenate():304    verify_concatenate([(2, 3, 4), (1, 3, 4)], axis=0)305    verify_concatenate([(2, 4), (2, 7)], axis=1)306def verify_split(ishape, indices_or_sections, axis):307    x = sym.Variable("x", shape=ishape)308    y = sym.split(x, indices_or_sections=indices_or_sections, axis=axis)309    def forward(x):310        return np.split(x, indices_or_sections, axis=axis)311    check_function(y, forward)312def test_split():313    verify_split((2, 3), 2, axis=0)314    verify_split((5, 3), [3], axis=0)315    verify_split((5, 9, 3), [3, 4], axis=1)316def verify_strided_slice(ishape, begin, end, strideinp=None):317    stride = strideinp if strideinp else [1, 1, 1]318    x = sym.Variable("x", shape=ishape)319    if strideinp:320        y = sym.strided_slice(x, begin = begin, end = end, stride = stride) + 1321    else:322        y = sym.strided_slice(x, begin = begin, end = end) + 1323    for i in range(len(begin), 3):324        begin.append(0)325    for i in range(len(end), 3):326        end.append(ishape[i])327    def test_forward(x):328        return x[begin[0]:end[0]:stride[0],329                    begin[1]:end[1]:stride[1], begin[2]:end[2]:stride[2]] + 1330    check_function(y, test_forward)331def test_strided_slice():332    verify_strided_slice((3, 4, 3), [0, 0, 0], [4, -5, 4], [1, -1, 2])333    verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1])334    verify_strided_slice((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1])335    verify_strided_slice((3, 4, 3), [1, 0, 0], [2, 2, 3], [1, 1, 2])336    verify_strided_slice((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1])337    verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3])338    verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 1000, 3])339    verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4])340    verify_strided_slice((3, 4, 3), [1, 1], [4, 4, 3])341def verify_take(src_shape, indices_src, axis=None):342    src_dtype = "float32"343    indices_dtype = "int32"344    indices_src = np.array(indices_src, dtype=indices_dtype)345    a = sym.Variable("a", shape=src_shape)346    indices = sym.Variable("indices", shape=indices_src.shape)347    y = sym.take(a, indices, axis=axis)348    def forward(a, indices):349        return np.take(a, indices=indices, axis=axis)350    a_src = np.arange(np.prod(src_shape), dtype=src_dtype).reshape(src_shape)351    check_function(y, forward,352                   dtype={'a': src_dtype, 'indices': indices_dtype},353                   values={'a': a_src, 'indices': indices_src})354def test_take():355    verify_take((4,), [1])356    verify_take((4,), [[0,1,2,3]])357    verify_take((3,3,3), [[11,25]])358    verify_take((4,), [[0,1],[2,3]])359    verify_take((4,), [1], 0)360    verify_take((2,2), [[[1,0],[0,1]]], 0)361    verify_take((2,2), [[[1,0],[0,1]]], 1)362    verify_take((4,3,5,6), [[2,1,0,0]], -2)363def verify_squeeze(shape, axis):364    x = sym.Variable("x")365    if axis is not None:366        y = sym.squeeze(x, axis=axis)367    else:368        y = sym.squeeze(x)369    y = y + 1370    def forward(x):371        return np.squeeze(x, axis=axis) + 1372    def backward(head_grads, x):373        return [np.reshape(head_grads, x.shape)]374    check_function(y, forward, backward, shape=shape)375def test_squeeze():376    verify_squeeze((1, 3, 2, 5), None)377    verify_squeeze((1, 3, 1), axis=0)378    verify_squeeze((1, 3, 2, 5, 1), axis=-1)379def test_pad():380    x = sym.Variable("x")381    y = sym.pad(x, pad_width=((0, 0), (0, 0), (0, 1), (2, 3)), pad_value=1.)382    def forward(x):383        return np.pad(x,384                      pad_width=((0, 0), (0, 0), (0, 1), (2, 3)),385                      mode='constant', constant_values=1.)386    shape = {'x': (1, 3, 28, 28)}387    check_function(y, forward, shape=shape)388def verify_lrn(ishape, size, axis, bias, alpha, beta):389    x = sym.Variable("x", shape=ishape)390    y = sym.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)391    def forward1(x):392        return topi.testing.lrn_python(x, size, axis, bias, alpha, beta)393    check_function(y, forward1)394    def forward2(x):395        y = forward1(x)396        return (y > 0)*y397    #Checking LRN op followed by elementwise op relu398    check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})399def verify_l2_normalize(ishape, eps, axis):400    x = sym.Variable("x", shape=ishape)401    y = sym.l2_normalize(x, eps=eps, axis=axis)402    def forward1(x):403        return topi.testing.l2_normalize_python(x, eps, axis)404    check_function(y, forward1)405    def forward2(x):406        y = forward1(x)407        return (y > 0)*y408    #Checking L2 normalization op followed by elementwise op relu409    check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})410def test_lrn():411    verify_lrn((1, 3, 20, 20), 3, 1, 1.0, 1.0, 0.5)412    verify_lrn((1, 3, 20, 20), 3, 1, 2.0, 1.0, 0.75)413def test_l2_normalize():414    verify_l2_normalize((1, 3, 20, 20), 0.001, (1,))415    verify_l2_normalize((1, 3, 20, 20), 0.001, (1, 2))416if __name__ == "__main__":417    test_check_function()418    test_split()419    test_concatenate()420    test_log_softmax()421    test_batchnorm()422    test_dense()423    test_relu()424    test_prelu_nchw()425    test_prelu_nhwc()426    test_sym_scalar_pow()427    test_scalar_sym_pow()428    test_exp()429    test_log()430    test_tanh()431    test_sigmoid()...

HW6.py

Source:HW6.py

1from Functions import *23H = [[1, 1, 1, 0, 0, 0, 0, 0],4     [0, 0, 0, 1, 1, 1, 0, 0],5     [1, 0, 0, 1, 0, 0, 1, 0],6     [0, 1, 0, 0, 1, 0, 0, 1]]78N = int(len(H[0]))9K = int(len(H))10M = N - K11delta = 0.5  # actually is delta**212c = [0.2, 0.2, -0.9, 0.6, 0.5, -1.1, -0.4, -1.2]1314check_function = [[] for _ in range(K)]15bit_function = [[] for _ in range(N)]16for i in range(K):17    check_function[i] = [i for i, x in enumerate(H[i]) if x == 1]18    for j in range(N):19        if H[i][j] == 1:20            bit_function[j].append(i)21print('check node connections: ', check_function)22print('bit node connections: ', bit_function, '\n')2324L_Xi = []25for item in c:26    L_Xi.append(2 * item / delta)27# print(L_Xi)2829L_qij = [[] for _ in range(K)]30for j in range(K):31    for k in range(len(check_function[j])):32        L_qij[j].append(L_Xi[check_function[j][k]])33        # L_qij[j].append(LLR_qij(q(1, c[check_function[j][k]], delta), q(-1, c[check_function[j][k]], delta)))34# print(L_qij, len(L_qij))3536iter_num = 837for m in range(iter_num):38    # print(m, L_qij)39    rji = [[] for _ in range(N)]40    for p in range(len(L_qij)):41        for i in range(len(L_qij[p])):42            compute_data = L_qij[p].copy()43            compute_data.remove(L_qij[p][i])44            rji[check_function[p][i]].append(LLR_rji(compute_data))45    # print(m, rji)46    L_qij_update = [[] for _ in range(K)]47    for b in range(len(rji)):48        for i in range(len(rji[b])):49            data = rji[b].copy()50            data.remove(rji[b][i])51            l_qij = L_Xi[b] + sum(data)52            L_qij_update[bit_function[b][rji[b].index(rji[b][i])]].append(round(l_qij, 4))53    # print(m, L_qij_update)54    L_Q = []55    for a in range(len(rji)):56        L_Q.append(LLR_q_update((2 * c[a]) / delta, rji[a]))57    print(f'LQ after {m+1} iterations', L_Q)58    L_qij = L_qij_update5960    temp_decision = []61    for item in L_Q:62        if item < 0:63            temp_decision.append(1)64        elif item > 0:65            temp_decision.append(0)
...

test_worker.py

Source:test_worker.py

1#2# Licensed under the Apache License, Version 2.0 (the "License");3# you may not use this file except in compliance with the License.4# You may obtain a copy of the License at5#6#    http://www.apache.org/licenses/LICENSE-2.07#8# Unless required by applicable law or agreed to in writing, software9# distributed under the License is distributed on an "AS IS" BASIS,10# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or11# implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import mock15from neutron.common import utils16from neutron.tests import base17from neutron import worker as neutron_worker18class PeriodicWorkerTestCase(base.BaseTestCase):19    def test_periodic_worker_lifecycle(self):20        check_function = mock.Mock()21        worker = neutron_worker.PeriodicWorker(22            check_function, interval=1, initial_delay=1)23        self.addCleanup(worker.stop)24        worker.wait()25        self.assertFalse(check_function.called)26        worker.start()27        utils.wait_until_true(28            lambda: check_function.called,29            timeout=5,30            exception=RuntimeError("check_function not called"))31        worker.stop()32        check_function.reset_mock()33        worker.wait()34        self.assertFalse(check_function.called)35        worker.reset()36        utils.wait_until_true(37            lambda: check_function.called,38            timeout=5,...

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