Your message dated Sun, 03 May 2020 19:03:33 +0000 with message-id <e1jvjtl-000djy...@fasolo.debian.org> and subject line Bug#959137: fixed in lasagne 0.1+git20200419.5d3c63c+ds-1 has caused the Debian Bug report #959137, regarding lasagne: (autopkgtest) needs update for new version of numpy: 'numpy.float64' object cannot be interpreted as an integer to be marked as done.
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--- Begin Message ---Source: lasagne Version: 0.1+git20181019.a61b76f-2 Severity: serious X-Debbugs-CC: debian...@lists.debian.org, nu...@packages.debian.org Tags: sid bullseye User: debian...@lists.debian.org Usertags: needs-update Control: affects -1 src:numpy Dear maintainer(s), With a recent upload of numpy the autopkgtest of lasagne fails in testing when that autopkgtest is run with the binary packages of numpy from unstable. It passes when run with only packages from testing. In tabular form: pass fail numpy from testing 1:1.18.3-1 lasagne from testing 0.1+git20181019.a61b76f-2 all others from testing from testing I copied some of the output at the bottom of this report. Currently this regression is blocking the migration of numpy to testing [1]. Of course, numpy shouldn't just break your autopkgtest (or even worse, your package), but it seems to me that the change in numpy was intended and your package needs to update to the new situation. If this is a real problem in your package (and not only in your autopkgtest), the right binary package(s) from numpy should really add a versioned Breaks on the unfixed version of (one of your) package(s). Note: the Breaks is nice even if the issue is only in the autopkgtest as it helps the migration software to figure out the right versions to combine in the tests. More information about this bug and the reason for filing it can be found on https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation Paul [1] https://qa.debian.org/excuses.php?package=numpy https://ci.debian.net/data/autopkgtest/testing/amd64/l/lasagne/5197094/log.gz =================================== FAILURES =================================== ____ TestTPSTransformLayer.test_transform_thin_plate_spline_variable_input _____ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: > num = operator.index(num) E TypeError: 'numpy.float64' object cannot be interpreted as an integer /usr/lib/python3/dist-packages/numpy/core/function_base.py:117: TypeError During handling of the above exception, another exception occurred: self = <test_special.TestTPSTransformLayer object at 0x7f878739e850> def test_transform_thin_plate_spline_variable_input(self): import lasagne from lasagne.utils import floatX from theano.tensor import constant x = np.random.random((10, 3, 28, 28)).astype('float32') x_sym = theano.tensor.tensor4() l_in = lasagne.layers.InputLayer((None, 3, None, 28)) l_loc = lasagne.layers.DenseLayer( lasagne.layers.ReshapeLayer(l_in, ([0], 3*28*28)), num_units=32) > l_trans = lasagne.layers.TPSTransformerLayer( l_in, l_loc, precompute_grid='auto') layers/test_special.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../layers/special.py:702: in __init__ self.out_width = _initialize_tps( ../layers/special.py:865: in _initialize_tps np.linspace(-1, 1, grid_size), <__array_function__ internals>:5: in linspace ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: num = operator.index(num) except TypeError: > raise TypeError( "object of type {} cannot be safely interpreted as an integer." .format(type(num))) E TypeError: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer. /usr/lib/python3/dist-packages/numpy/core/function_base.py:119: TypeError ______ TestTPSTransformLayer.test_transform_thin_plate_spline_downsample _______ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: > num = operator.index(num) E TypeError: 'numpy.float64' object cannot be interpreted as an integer /usr/lib/python3/dist-packages/numpy/core/function_base.py:117: TypeError During handling of the above exception, another exception occurred: self = <test_special.TestTPSTransformLayer object at 0x7f8786b1b940> def test_transform_thin_plate_spline_downsample(self): import lasagne downsample = (0.7, 2.3) x = np.random.random((10, 3, 28, 28)).astype('float32') x_sym = theano.tensor.tensor4() # create transformer with fixed input size l_in = lasagne.layers.InputLayer((None, 3, 28, 28)) l_loc = lasagne.layers.DenseLayer(l_in, num_units=32) > l_trans = lasagne.layers.TPSTransformerLayer( l_in, l_loc, downsample_factor=downsample, precompute_grid=False ) layers/test_special.py:547: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../layers/special.py:702: in __init__ self.out_width = _initialize_tps( ../layers/special.py:865: in _initialize_tps np.linspace(-1, 1, grid_size), <__array_function__ internals>:5: in linspace ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: num = operator.index(num) except TypeError: > raise TypeError( "object of type {} cannot be safely interpreted as an integer." .format(type(num))) E TypeError: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer. /usr/lib/python3/dist-packages/numpy/core/function_base.py:119: TypeError _______ TestTPSTransformLayer.test_transform_thin_plate_spline_identity ________ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: > num = operator.index(num) E TypeError: 'numpy.float64' object cannot be interpreted as an integer /usr/lib/python3/dist-packages/numpy/core/function_base.py:117: TypeError During handling of the above exception, another exception occurred: self = <test_special.TestTPSTransformLayer object at 0x7f87875bc070> def test_transform_thin_plate_spline_identity(self): from lasagne.layers import InputLayer, TPSTransformerLayer from lasagne.utils import floatX from theano.tensor import constant batchsize = 5 num_control_points = 16 dest_offset = np.zeros(shape=(batchsize, 2*num_control_points)) l_in = InputLayer((batchsize, 3, 28, 28)) l_loc = InputLayer((batchsize, 2*num_control_points)) > layer = TPSTransformerLayer( l_in, l_loc, control_points=num_control_points ) layers/test_special.py:593: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../layers/special.py:702: in __init__ self.out_width = _initialize_tps( ../layers/special.py:865: in _initialize_tps np.linspace(-1, 1, grid_size), <__array_function__ internals>:5: in linspace ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: num = operator.index(num) except TypeError: > raise TypeError( "object of type {} cannot be safely interpreted as an integer." .format(type(num))) E TypeError: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer. /usr/lib/python3/dist-packages/numpy/core/function_base.py:119: TypeError _________ TestTPSTransformLayer.test_transform_thin_plate_spline_shift _________ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: > num = operator.index(num) E TypeError: 'numpy.float64' object cannot be interpreted as an integer /usr/lib/python3/dist-packages/numpy/core/function_base.py:117: TypeError During handling of the above exception, another exception occurred: self = <test_special.TestTPSTransformLayer object at 0x7f878754d2e0> def test_transform_thin_plate_spline_shift(self): from lasagne.layers import InputLayer, TPSTransformerLayer from theano.tensor import constant batchsize = 5 num_control_points = 16 dest_offset = np.ones(shape=(batchsize, 2*num_control_points)) l_in = InputLayer((batchsize, 3, 28, 28)) l_loc = InputLayer((batchsize, 2*num_control_points)) > layer = TPSTransformerLayer( l_in, l_loc, control_points=num_control_points ) layers/test_special.py:609: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../layers/special.py:702: in __init__ self.out_width = _initialize_tps( ../layers/special.py:865: in _initialize_tps np.linspace(-1, 1, grid_size), <__array_function__ internals>:5: in linspace ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ start = -1, stop = 1, num = 4.0, endpoint = True, retstep = False, dtype = None axis = 0 @array_function_dispatch(_linspace_dispatcher) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): """ Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float, optional Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). geomspace : Similar to `linspace`, but with numbers spaced evenly on a log scale (a geometric progression). logspace : Similar to `geomspace`, but with the end points specified as logarithms. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() """ try: num = operator.index(num) except TypeError: > raise TypeError( "object of type {} cannot be safely interpreted as an integer." .format(type(num))) E TypeError: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer. /usr/lib/python3/dist-packages/numpy/core/function_base.py:119: TypeErrorsignature.asc
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--- Begin Message ---Source: lasagne Source-Version: 0.1+git20200419.5d3c63c+ds-1 Done: Stephen Sinclair <radars...@gmail.com> We believe that the bug you reported is fixed in the latest version of lasagne, which is due to be installed in the Debian FTP archive. A summary of the changes between this version and the previous one is attached. Thank you for reporting the bug, which will now be closed. If you have further comments please address them to 959...@bugs.debian.org, and the maintainer will reopen the bug report if appropriate. Debian distribution maintenance software pp. Stephen Sinclair <radars...@gmail.com> (supplier of updated lasagne package) (This message was generated automatically at their request; if you believe that there is a problem with it please contact the archive administrators by mailing ftpmas...@ftp-master.debian.org) -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA512 Format: 1.8 Date: Thu, 30 Apr 2020 12:51:33 +0000 Source: lasagne Architecture: source Version: 0.1+git20200419.5d3c63c+ds-1 Distribution: unstable Urgency: medium Maintainer: Debian Science Maintainers <debian-science-maintain...@lists.alioth.debian.org> Changed-By: Stephen Sinclair <radars...@gmail.com> Closes: 912533 959137 Changes: lasagne (0.1+git20200419.5d3c63c+ds-1) unstable; urgency=medium . [ Stephen Sinclair ] * New upstream version. * Update gitlab-ci.yml for salsa. * Update pytest-pep8 patch. * Patch float64 to int conversion error. (Closes: #912533, #959137) * Fix test pytest-py3, by creating pytest.ini. * Fix a SyntaxWarning: 'is' with a literal. . [ Anton Gladky ] * [750a02d] Apply cme fix dpkg. Set compat-level 13 * [bf374c0] Use secure URI in Homepage field. * [f959d44] Update standards version to 4.5.0, no changes needed. * [8e7e6f1] Set Rules-Requires-Root: no * [70f8ec9] Update d/watch Checksums-Sha1: a00f70def8599f671e0d7acf1728b38bde3461c6 2533 lasagne_0.1+git20200419.5d3c63c+ds-1.dsc a7d38db53ddb2bd85c4e0ccfae6e880a056f7a16 143932 lasagne_0.1+git20200419.5d3c63c+ds.orig.tar.xz 1a499f42fba23b79c3cb6be03417f804cf952905 6280 lasagne_0.1+git20200419.5d3c63c+ds-1.debian.tar.xz f45fcd333338c1d5a7cc85a968185f61194f5459 8112 lasagne_0.1+git20200419.5d3c63c+ds-1_source.buildinfo Checksums-Sha256: 90ea4a7950b6496a0edfd4f2eae33daf3f2523d1e0b15499695c6a694c3189ff 2533 lasagne_0.1+git20200419.5d3c63c+ds-1.dsc 69097f02d67ed4b21483e46caf2a9c1626eeeee545bbaa01f955af7071c063f8 143932 lasagne_0.1+git20200419.5d3c63c+ds.orig.tar.xz b7c27d7b4e4f26202e0f0f44e9e215f26e0f64b0abd48bc5f94c43b33a4494e1 6280 lasagne_0.1+git20200419.5d3c63c+ds-1.debian.tar.xz 6bebcf85f0e3cf06c96ddbd1a08034dbae1ab435f630d91757b3b8abd81f4f67 8112 lasagne_0.1+git20200419.5d3c63c+ds-1_source.buildinfo Files: 2b16d1d74afe3379cc5743d170ab042c 2533 science optional lasagne_0.1+git20200419.5d3c63c+ds-1.dsc 9cc99eab3c31192440dbf5e49aa485ec 143932 science optional lasagne_0.1+git20200419.5d3c63c+ds.orig.tar.xz a80761f1044e08d61a744ea55df6f95c 6280 science optional lasagne_0.1+git20200419.5d3c63c+ds-1.debian.tar.xz 50752d652e391b8068b100e59026d323 8112 science optional lasagne_0.1+git20200419.5d3c63c+ds-1_source.buildinfo -----BEGIN PGP SIGNATURE----- iQIzBAEBCgAdFiEEu71F6oGKuG/2fnKF0+Fzg8+n/wYFAl6vEXwACgkQ0+Fzg8+n /wbqqhAAglF1IuiYbG92cTPsGnEIG1Sb/yNPzBV3YTLSG9sHXFFbVj8aE3C87z7T yOVPTaG8iYVC5Ksu+Bbs/9Qk2eyBeeuodbZ68RL7ktwTJWnVVvN6h73xkbIUleDh v6IGiLGtqsjXoifr9xNP42/WLSjm0FG/b8A7AFEaJX1DsN+IJ2GtLFCVmXFh28z+ Rjg14btdckePRmNdpKUTkk5Q09rwVrhs2JkxMpq7gcIHWjI/k0K8nHuKaUe9HoNi bUhekJI6YMI0ToCaIUPRwQatA4tGXnv54IkdV8LiwTBh8m1I4mM5ujw5s/mKSzLg lq95dy+bCTTD/ABNjqu4cr593y1LW2NmXMPaVPKgmUEcFE7/zdUaetirYpeNERbp KP+IXKeqwCdoVIVPcWgn+e2mAYzqve+QEaHoLWArMf7l705faC4bvANkMwQmKya2 dn7NkizRotYv4P1CEsFJKU+M07kupWkieP0aGHld056QxFRrdT9fofmZJZyBrF+p XmH3ZnrJK2xgQaVzDDW0oL1VvLBLDLLptoaCJa9Kl9iSQdPDeU98ocf5Ky7cE2Ri 6wSy7lDlIXfzMiPqN8ffgAeNWuCx+DDpqFjTduB+5IX2IQVM/gz9kfopzZ75QzQD 5oyJON/unXlCO9H9EBj4a+P3fEYzklWNAl9Mg4vw51Cp/jiakdE= =NQRC -----END PGP SIGNATURE-----
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