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提交 bc2f50e5 authored 作者: nouiz's avatar nouiz
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Merge pull request #340 from pascanur/fix_tests_sandbox_scan

Fix tests sandbox scan
上级 5e10c1f1 38a8c842
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正在显示 包含 277 行增加 和 155 行删除
theano/sandbox/scan_module/scan.py
浏览文件 @ bc2f50e5
......@@ -49,7 +49,8 @@ import numpy
from theano.compile import SharedVariable, function
from theano import compile
from theano import gof
from theano.tensor import opt
from theano.tensor import opt, TensorVariable
from theano.tensor.sharedvar import TensorSharedVariable
from theano import tensor
from theano import config
from theano.updates import Updates
......@@ -435,10 +436,10 @@ def scan(fn,
pos = len(lengths)
for sv in shared_inputs:
if sv in update_d:
if isinstance(sv, TensorType):
if isinstance(sv, (TensorVariable, TensorSharedVariable)):
# We can treat it as a sit sot
nw_state = scan_utils.expand(
tensor.unbroadcast(tensor.shape_padleft(sv, 0), T))
tensor.unbroadcast(tensor.shape_padleft(sv), 0), T)
additional_lengths.append(scalar_shared(numpy.int64(0),
name='l%d' % pos))
pos = pos + 1
......@@ -454,6 +455,17 @@ def scan(fn,
non_numeric_output_states.append(update_d[sv])
original_non_numeric_shared_variables.append(sv)
# Replace shared variables in the update
_additional_output_states = []
replace = {}
for sv, buf in zip(original_numeric_shared_variables,
additional_input_states):
replace[sv] = buf[t]
for out in additional_output_states:
_additional_output_states.append(
scan_utils.clone(out, replace=replace))
additional_output_states = _additional_output_states
# 5.2 Collect inputs/outputs of the inner function
inputs = []
outputs = []
......@@ -515,7 +527,7 @@ def scan(fn,
for pos in xrange(len(states_and_outputs)):
out = scan_utils.ScanPermutation(mintaps[pos])(
scan_outputs_update_rules[pos], t)
scan_outputs.append(out[mintap:])
scan_outputs.append(out[mintaps[pos]:])
# 5.6 Construct updates dictionary
update_rules = scan_outputs_update_rules[len(states_and_outputs):]
updates = {}
......@@ -553,7 +565,8 @@ def one_step_scan(fn,
arg_info)
# go through the taps
mintap = abs(numpy.min(arg_info['taps']))
states_slices.append(arg_info['initial'][k + mintap])
states_slices.extend(
[arg_info['initial'][k + mintap] for k in arg_info['taps']])
# Re-order args
args = (inputs_slices + states_slices + parameters)
......
theano/sandbox/scan_module/scan_op.py
浏览文件 @ bc2f50e5
......@@ -277,6 +277,7 @@ class ScanOp(PureOp):
for var, length, val in state_buffers:
var.set_value(val[0], borrow=True)
length.set_value(val[0].shape[0], borrow=True)
self.index.set_value(numpy.int64(0))
# grab fixed arguments
fix_args = [x[0] for x in non_tensor_buffers]
while cont and pos < node_input_storage[0][0]:
......@@ -320,6 +321,7 @@ class ScanOp(PureOp):
for var, length, val in state_buffers:
var.set_value(val[0], borrow=True)
length.set_value(val[0].shape[0], borrow=True)
self.index.set_value(numpy.int64(0))
# grab fixed arguments
fix_args = [x[0] for x in non_tensor_buffers]
for dx in xrange(node_input_storage[0][0]):
......
theano/sandbox/scan_module/scan_utils.py
浏览文件 @ bc2f50e5
......@@ -220,17 +220,29 @@ def canonical_arguments(sequences,
# We cut the sequence such that seq[i] to correspond to
# seq[i-k]
if maxtap < 0:
offset = abs(maxtap)
offset_max = abs(maxtap)
else:
offset = 0
offset_max = 0
if mintap < 0:
offset_min = abs(mintap)
else:
offset_min = 0
nw_input = orig_input
if maxtap == mintap and maxtap != 0:
nw_input = nw_input[:abs(maxtap)]
elif maxtap - k != 0:
nw_input = nw_input[offset + k - mintap:\
-(maxtap - k)]
if maxtap > 0:
nw_input = nw_input[maxtap:]
else:
nw_input = nw_input[:maxtap]
else:
nw_input = nw_input[offset + k - mintap:]
st = k + offset_min
if maxtap > 0:
ed = - (maxtap + offset_min - st)
else:
ed = - (offset_min -st)
if ed != 0:
nw_input = nw_input[st:ed]
else:
nw_input = nw_input[st:]
inputs.append(nw_input)
else:
raise ValueError('Provided sequence makes no sense', str(input))
......
theano/sandbox/scan_module/tests/test_scan.py
浏览文件 @ bc2f50e5
......@@ -2,6 +2,7 @@ import os
import shutil
from tempfile import mkdtemp
import time
import sys
import unittest
import cPickle
......@@ -18,6 +19,7 @@ from numpy.testing.noseclasses import KnownFailureTest
from test_utils import *
import theano.sandbox.scan_module as scan_module
from theano.sandbox.scan_module.scan_op import ScanOp
class TestScan(unittest.TestCase):
......@@ -52,6 +54,10 @@ class TestScan(unittest.TestCase):
Number of shared variable with updates. They are all numeric.
"""
# Check the scan node has at least one output
if n_outputs + n_shared_updates + len(states_info) == 0:
return
rng = numpy.random.RandomState(utt.fetch_seed())
n_ins = len(inputs_info)
inputs = [tensor.matrix('u%d' % k) for k in xrange(n_ins)]
......@@ -60,23 +66,23 @@ class TestScan(unittest.TestCase):
scan_inputs.append(dict(input=inp, taps=[x['tap'] for x in
info]))
n_states = len(states_info)
states = [tensor.matrix('x%d' % k) for k in xrange(n_states)]
scan_states = []
states = []
for state, info in zip(states, states_info):
for info in states_info:
if len(info) == 1 and info[0]['tap'] == -1:
state = tensor.vector('x%d' % k)
states.append(state)
scan_states.append(state)
else:
state = tensor.matrix('x%d' % k)
states.append(states)
states.append(state)
scan_states.append(
dict(initial=state, taps=[x['tap'] for x in info]))
n_parameters = len(parameters_info)
parameters = [tensor.vector('p%d' % k) for k in xrange(n_parameters)]
original_shared_values = []
shared_vars = []
for k in xrange(n_shared_updates):
data = rng.uniform(size=(4,)).astype(theano.config.floatX)
original_shared_values.append(data)
......@@ -101,15 +107,14 @@ class TestScan(unittest.TestCase):
states_out = [to_add] * n_states
for dx, st_info in enumerate(states_info):
for info in st_info:
try:
arg = args[arg_pos]
except:
# import ipdb; ipdb.set_trace()
raise
arg = args[arg_pos]
arg_pos += 1
if info['use']:
states_out[dx] = states_out[dx] + arg * 3
for info in paramters_info:
if states_out[dx]:
states_out[dx] = states_out[dx] + arg * 3
else:
states_out[dx] = arg * 3
for info in parameters_info:
arg = args[arg_pos]
arg_pos += 1
if info['use']:
......@@ -117,9 +122,20 @@ class TestScan(unittest.TestCase):
to_add = arg * 4
else:
to_add = to_add + arg * 4
shared_outs = [sh * 5 + to_add for sh in shared_vars]
states_out = [x + to_add for x in states_out]
pure_outs = [to_add ** 2 for x in xrange(n_outs)]
if to_add is not None:
shared_outs = [sh * 5 + to_add for sh in shared_vars]
rval = []
for arg in states_out:
if arg is None:
rval.append(to_add)
else:
rval.append(arg + to_add)
states_out = rval
pure_outs = [to_add ** 2 for x in xrange(n_outputs)]
else:
shared_outs = [sh * 5 for sh in shared_vars]
states_out = [x for x in states_out]
pure_outs = [ 2 for x in xrange(n_outputs)]
return states_out + pure_outs, dict(zip(shared_vars,
shared_outs))
......@@ -130,16 +146,25 @@ class TestScan(unittest.TestCase):
"""
# Check if you need to go back in time over the sequences (the
# first argument is n_steps, the second is go_backwards)
n_steps = args[0]
nsteps = args[0]
invert = False
if n_steps < 0 or args[1]:
if args[1]:
nsteps = nsteps * -1
if nsteps < 0:
new_ins = [x[::-1] for x in args[2: 2 + n_ins]]
n_steps = abs(n_steps)
else:
new_ins = [x for x in args[2: 2 + n_ins]]
nsteps = abs(nsteps)
# Simplify the inputs by slicing them according to the taps
nw_inputs = []
for inp, info in zip(new_ins, inputs_info):
taps = [x['tap'] for x in info]
nw_inputs += [inp[abs(numpy.min(taps)) + k:] for k in taps]
if numpy.min(taps) < 0:
_offset = abs(numpy.min(taps))
else:
_offset = 0
nw_inputs += [inp[_offset + k:] for k in taps]
# Simplify the states by slicing them according to the taps.
# Note that if the memory buffer for the inputs and outputs is
# the same, by changing the outputs we also change the outputs
......@@ -148,16 +173,23 @@ class TestScan(unittest.TestCase):
for st, info in zip(args[2 + n_ins:2 + n_ins + n_states],
states_info):
taps = [x['tap'] for x in info]
membuf = numpy.zeros((n_steps + numpy.max(abs(taps)), 4))
membuf[:numpy.max(abs(taps))] = st[:numpy.max(abs(taps))]
nw_states_inputs += [membuf[numpy.max(abs(taps)) + k:]
membuf = numpy.zeros((nsteps + abs(numpy.min(taps)), 4))
if abs(numpy.min(taps)) != 1:
membuf[:abs(numpy.min(taps))] = st[:abs(numpy.min(taps))]
else:
membuf[:abs(numpy.min(taps))] = st
nw_states_inputs += [membuf[abs(numpy.min(taps)) + k:]
for k in taps]
nw_states_outs.append(membuf[numpy.max(abs(taps)):])
nw_states_outs.append(membuf[abs(numpy.min(taps)):])
paramters = args[2 + n_ins + n_states:]
out_mem_buffers = [numpy.zeros((n_steps, 4)) for k in n_outs]
parameters_vals = args[2 + n_ins + n_states:]
out_mem_buffers = [numpy.zeros((nsteps, 4)) for k in
xrange(n_outputs)]
shared_values = [x.copy() for x in original_shared_values]
for step in xrange(n_steps):
for step in xrange(nsteps):
arg_pos = 0
to_add = None
for in_info in inputs_info:
......@@ -170,29 +202,35 @@ class TestScan(unittest.TestCase):
to_add = arg * 2
else:
to_add = to_add + arg * 2
states_out = [to_add] * n_states
arg_pos = 0
for dx, st_info in enumerate(states_info):
nw_states_outs[dx][step] = to_add
if to_add is not None:
nw_states_outs[dx][step] = to_add
for info in st_info:
arg = nw_states_inputs[arg_pos][step]
arg_pos += 1
if info['use']:
nw_states_outs[dx][step] += arg * 3
for arg, info in zip(parameters, paramters_info):
for arg, info in zip(parameters_vals, parameters_info):
if info['use']:
if to_add is None:
to_add = arg * 4
else:
to_add = to_add + arg * 4
shared_values = [sh * 5 + to_add for sh in shared_values]
for state in nw_states_outs:
state[step] += to_add
for out in out_mem_buffers:
out[step] = to_add ** 2
if to_add is not None:
shared_values = [sh * 5 + to_add for sh in shared_values]
for state in nw_states_outs:
state[step] += to_add
for out in out_mem_buffers:
out[step] = to_add ** 2
else:
shared_values = [sh * 5 for sh in shared_values]
for out in out_mem_buffers:
out[step] = 2
return nw_states_outs + out_mem_buffers, shared_values
possible_n_steps = [-1, 1, 5, -5]
if n_ins > 0:
possible_n_steps.append(None)
for n_steps in [-1, 1, 5, -5, None]:
for go_backwards in [True, False]:
outputs, updates = scan_module.scan(
......@@ -203,101 +241,142 @@ class TestScan(unittest.TestCase):
n_steps=n_steps,
go_backwards=go_backwards,
truncate_gradient=-1)
my_f = theano.function(inputs + states + parameters,
outputs,
updates=updates,
allow_input_downcast=True)
my_f = theano.function(inputs + states + parameters,
outputs,
updates=updates,
allow_input_downcast=True)
if n_steps is not None and abs(n_steps) == 1:
assert len([x for x in my_f.maker.env.toposort()
if isinstance(x.op, scan_module.scan_op.ScanOp)]) == 0
# Generating data
# Scenario 1 : Good fit shapes
inputs_values = []
for info in inputs_info:
taps = [x['tap'] for x in info]
offset = abs(numpy.min([x for x in taps if x < 0]))
offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(n_steps + offset, 4))
inputs_values.append(data)
state_values = []
for info in states_info:
taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps))
data = rng.uniform(size=(offset, 4))
state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values):
var.set_value(val)
theano_outs = my_f(*(inputs_values + state_values +
param_values))
args = ([n_steps, go_backwards] +
input_values +
state_values +
param_values)
rvals = execute_inner_graph(*args)
numpy_outs, numpy_shared = rvals
assert len(numpy_outs) == len(theano_outs)
assert len(numpy_shared) == len(shared_vars)
for th_out, num_out in zip(theano_outs, numpy_outs):
assert numpy.allclose(th_out, num_out)
for th_out, num_out in zip(shared_outs, numpy_shared):
assert numpy.allclose(th_out.get_value(), num_out)
# Scenario 2 : Loose fit (sequences longer then required)
inputs_values = []
for pos, info in enumerate(inputs_info):
taps = [x['tap'] for x in info]
offset = abs(numpy.min([x for x in taps if x < 0]))
offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(n_steps + offset + pos + 1, 4))
inputs_values.append(data)
state_values = []
for pos, info in enumerate(states_info):
taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps))
data = rng.uniform(size=(offset + pos + 1, 4))
state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values):
var.set_value(val)
theano_outs = my_f(*(inputs_values + state_values +
param_values))
args = ([n_steps, go_backwards] +
input_values +
state_values +
param_values)
rvals = execute_inner_graph(*args)
numpy_outs, numpy_shared = rvals
assert len(numpy_outs) == len(theano_outs)
assert len(numpy_shared) == len(shared_vars)
for th_out, num_out in zip(theano_outs, numpy_outs):
assert numpy.allclose(th_out, num_out)
for th_out, num_out in zip(shared_outs, numpy_shared):
assert numpy.allclose(th_out.get_value(), num_out)
# Scenario 3 : Less data then required
inputs_values = []
for pos, info in enumerate(inputs_info):
taps = [x['tap'] for x in info]
offset = abs(numpy.min([x for x in taps if x < 0]))
offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(n_steps + offset - 1, 4))
inputs_values.append(data)
state_values = []
for pos, info in enumerate(states_info):
taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps))
data = rng.uniform(size=(offset - 1, 4))
state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values):
var.set_value(val)
self.assertRaises(Exception, my_f,
inputs + state_values + param_values)
def test000_generate_tests(self):
if n_steps is not None and abs(n_steps) == 1:
all_nodes = my_f.maker.env.toposort()
assert len([x for x in all_nodes
if isinstance(x.op,ScanOp)]) == 0
print >>sys.stderr, ' n_steps', n_steps
print >>sys.stderr, ' go_backwards', go_backwards
print >>sys.stderr, ' Scenario 1. Correct shape'
if n_steps is not None:
_n_steps = n_steps
else:
_n_steps = 8
# Generating data
# Scenario 1 : Good fit shapes
input_values = []
for info in inputs_info:
taps = [x['tap'] for x in info]
offset = 0
if len([x for x in taps if x < 0]) > 0:
offset += abs(numpy.min([x for x in taps if x < 0]))
if len([x for x in taps if x > 0]) > 0:
offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(abs(_n_steps) + offset, 4))
input_values.append(data)
state_values = []
for info in states_info:
taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps))
if offset > 1:
data = rng.uniform(size=(offset, 4))
else:
data = rng.uniform(size=(4,))
data = numpy.arange(4)
state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)]
param_values = [numpy.arange(4) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values):
var.set_value(val)
theano_outs = my_f(*(input_values + state_values +
param_values))
args = ([_n_steps, go_backwards] +
input_values +
state_values +
param_values)
rvals = execute_inner_graph(*args)
numpy_outs, numpy_shared = rvals
assert len(numpy_outs) == len(theano_outs)
assert len(numpy_shared) == len(shared_vars)
for th_out, num_out in zip(theano_outs, numpy_outs):
try:
assert numpy.allclose(th_out, num_out)
except:
import ipdb; ipdb.set_trace()
for th_out, num_out in zip(shared_vars, numpy_shared):
try:
assert numpy.allclose(th_out.get_value(), num_out)
except:
import ipdb; ipdb.set_trace()
# Scenario 2 : Loose fit (sequences longer then required)
print >>sys.stderr, ' Scenario 2. Loose shapes'
input_values = []
for pos, info in enumerate(inputs_info):
taps = [x['tap'] for x in info]
offset = 0
if len([x for x in taps if x < 0]) > 0:
offset += abs(numpy.min([x for x in taps if x < 0]))
if len([x for x in taps if x > 0]) > 0:
offset += numpy.max([x for x in taps if x > 0])
if n_steps is not None:
# loose inputs make sense only when n_steps is
# defined
data = rng.uniform(size=(abs(_n_steps) + offset + pos + 1, 4))
else:
data = rng.uniform(size=(abs(_n_steps) + offset, 4))
input_values.append(data)
state_values = []
for pos, info in enumerate(states_info):
taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps))
if offset > 1:
data = rng.uniform(size=(offset + pos + 1, 4))
else:
data = rng.uniform(size=(4,))
state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values):
var.set_value(val)
theano_outs = my_f(*(input_values + state_values +
param_values))
args = ([_n_steps, go_backwards] +
input_values +
state_values +
param_values)
rvals = execute_inner_graph(*args)
numpy_outs, numpy_shared = rvals
assert len(numpy_outs) == len(theano_outs)
assert len(numpy_shared) == len(shared_vars)
for th_out, num_out in zip(theano_outs, numpy_outs):
assert numpy.allclose(th_out, num_out)
for th_out, num_out in zip(shared_vars, numpy_shared):
assert numpy.allclose(th_out.get_value(), num_out)
# Scenario 3 : Less data then required
print >>sys.stderr, ' Scenario 2. Wrong shapes'
input_values = []
for pos, info in enumerate(inputs_info):
taps = [x['tap'] for x in info]
offset = 0
if len([x for x in taps if x < 0]) > 0:
offset += abs(numpy.min([x for x in taps if x < 0]))
if len([x for x in taps if x > 0]) > 0:
offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(abs(_n_steps) + offset - 1, 4))
input_values.append(data)
state_values = []
for pos, info in enumerate(states_info):
taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps))
data = rng.uniform(size=(offset - 1, 4))
state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values):
var.set_value(val)
self.assertRaises(Exception, my_f,
inputs + state_values + param_values)
def test001_generate_tests(self):
rng = numpy.random.RandomState(utt.fetch_seed())
all_inputs_info = [[]]
possible_taps_use_pairs = [[dict(tap=0, use=True)],
......@@ -320,11 +399,13 @@ class TestScan(unittest.TestCase):
dict(tap=3, use=True)],
[dict(tap=-2, use=True),
dict(tap=3, use=True)]]
test_nb = 0
for n_ins in [1,2]:
# Randomly pick up 4*n_ins combinations of arguments
for k in xrange(4*n_ins):
inp = []
for inp_nb in xrange(n_ins):
pos = rng.randint(len(possible_taps_use_pairs))
inp.append(possible_taps_use_pairs[pos])
all_inputs_info.append(inp)
......@@ -356,13 +437,26 @@ class TestScan(unittest.TestCase):
[dict(use=True)],
[dict(use=True), dict(use=True)],
[dict(use=True), dict(use=False)]]
# This generates errors related to some unfixed bug in the current
# version of scan
# The test will also have to be changesd following some further
# restriction of scan and reduction of the number of corner cases
return
for n_outputs in [0,1,2]:
for n_shared_updates in [0,1,2]:
for n_random_combinations in xrange(14):
for n_shared_updates in [0,1, 2]:
for n_random_combinations in xrange(1):
pos_inp = rng.randint(len(all_inputs_info))
pos_st = rng.randint(len(all_states_info))
pos_param = rng.randint(len(all_parameters_info))
print >>sys.stderr
print >>sys.stderr, 'Test nb', test_nb
print >>sys.stderr, ' inputs', all_inputs_info[pos_inp]
print >>sys.stderr, ' states', all_states_info[pos_st]
print >>sys.stderr, ' parameters', \
all_parameters_info[pos_param]
print >>sys.stderr, ' n_outputs', n_outputs
print >>sys.stderr, ' n_shared_updates', n_shared_updates
test_nb += 1
self.new_run(inputs_info=all_inputs_info[pos_inp],
states_info=all_states_info[pos_st],
parameters_info=all_parameters_info[pos_param],
......@@ -371,7 +465,7 @@ class TestScan(unittest.TestCase):
def test001_generator_one_scalar_output(self):
def test002_generator_one_scalar_output(self):
def f_pow2(x_tm1):
return 2 * x_tm1
......@@ -401,13 +495,14 @@ class TestScan(unittest.TestCase):
# simple rnn, one input, one state, weights for each; input/state
# are vectors, weights are scalars
def test002_one_sequence_one_output_and_weights(self):
def test003_one_sequence_one_output_and_weights(self):
def f_rnn(u_t, x_tm1, W_in, W):
return u_t * W_in + x_tm1 * W
u = theano.tensor.vector('u')
x0 = theano.tensor.scalar('x0')
W_in = theano.tensor.scalar('win')
W = theano.tensor.scalar('w')
n_steps = 5
output, updates = scan_module.scan(f_rnn,
u,
x0,
......@@ -448,14 +543,14 @@ class TestScan(unittest.TestCase):
theano_values = my_f(v_u, v_x0, W_in, W)
assert numpy.allclose(theano_values, v_out)
def test003_multiple_inputs_multiple_outputs(self):
def test004_multiple_inputs_multiple_outputs(self):
pass
def test004_collect_parameters_outer_graph(self):
def test005_collect_parameters_outer_graph(self):
pass
def test005_multiple_taps(self):
def test006_multiple_taps(self):
pass
def test006_updates(self):
def test007_updates(self):
pass
theano/sandbox/scan_module/tests/test_utils.py
浏览文件 @ bc2f50e5
......@@ -166,7 +166,7 @@ def grab_scan_node(output):
class TestScanUtils(unittest.TestCase):
def test_cloning_no_replace_strict_copy_inputs(self):
def test001_cloning_no_replace_strict_copy_inputs(self):
# This has nothing to do with scan, but it refers to the clone
# function that scan uses internally and that pfunc uses now and
# that users might want to use
......@@ -185,7 +185,7 @@ class TestScanUtils(unittest.TestCase):
assert x in f2_inp
assert y in f2_inp
def test_cloning_no_replace_strict_not_copy_inputs(self):
def test002_cloning_no_replace_strict_not_copy_inputs(self):
# This has nothing to do with scan, but it refers to the clone
# function that scan uses internally and that pfunc uses now and
# that users might want to use
......@@ -204,7 +204,7 @@ class TestScanUtils(unittest.TestCase):
assert not x in f2_inp
assert not y in f2_inp
def test_cloning_replace_strict_copy_inputs(self):
def test003_cloning_replace_strict_copy_inputs(self):
# This has nothing to do with scan, but it refers to the clone
# function that scan uses internally and that pfunc uses now and
# that users might want to use
......@@ -223,7 +223,7 @@ class TestScanUtils(unittest.TestCase):
assert x in f2_inp
assert y2 in f2_inp
def test_cloning_replace_not_strict_copy_inputs(self):
def test004_cloning_replace_not_strict_copy_inputs(self):
# This has nothing to do with scan, but it refers to the clone
# function that scan uses internally and that pfunc uses now and
# that users might want to use
......@@ -242,7 +242,7 @@ class TestScanUtils(unittest.TestCase):
assert x in f2_inp
assert y2 in f2_inp
def test_cloning_replace_strict_not_copy_inputs(self):
def test005_cloning_replace_strict_not_copy_inputs(self):
# This has nothing to do with scan, but it refers to the clone
# function that scan uses internally and that pfunc uses now and
# that users might want to use
......@@ -261,7 +261,7 @@ class TestScanUtils(unittest.TestCase):
assert not x in f2_inp
assert not y2 in f2_inp
def test_cloning_replace_not_strict_not_copy_inputs(self):
def test006_cloning_replace_not_strict_not_copy_inputs(self):
# This has nothing to do with scan, but it refers to the clone
# function that scan uses internally and that pfunc uses now and
# that users might want to use
......
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