def test_homo(self, gamma=3):
n_trunc = 1000
kq, kd = 15, 30
lam = 0.2
diffusion = Diffusion(self.cross_gallery_fc)
inverse = diffusion.get_laplacian_inverse(n_trunc, kd)
knn = KNN(self.cross_gallery_fc, method='cosine')
sims, ids = knn.search(self.cross_query_fc, kq)
sims[sims < 0] = 0
sims /= np.sum(sims, axis=-1).reshape(-1, 1)
sims = sims**gamma
scores_qg = np.empty(
(len(self.test_query_set), len(self.test_gallery_set)),
dtype=np.float32)
for i in range(len(self.test_query_set)):
scores_qg[i] = (sims[i] @ inverse[ids[i]])
diffusion = Diffusion(self.cross_query_fc)
inverse = diffusion.get_laplacian_inverse(n_trunc, kd)
knn = KNN(self.cross_query_fc, method='cosine')
sims, ids = knn.search(self.cross_gallery_fc, kq)
sims[sims < 0] = 0
sims /= np.sum(sims, axis=-1).reshape(-1, 1)
sims = sims**gamma
scores_gq = np.empty(
(len(self.test_gallery_set), len(self.test_query_set)),
dtype=np.float32)
for i in range(len(self.test_gallery_set)):
scores_gq[i] = (sims[i] @ inverse[ids[i]])
scores = lam * scores_qg + (1 - lam) * scores_gq.T
self.evaluate(-scores)
def test_middle(self):
"""
Changing middle compartment, affects outer compartments by the same amount
"""
# TODO: why does 'Compartment' fail the test (presumable an anion issue causing different steady-state cli)
self.compBase = SimpleCompartment("c1", pkcc2=1e-8, z=-0.85)
self.comp = self.compBase.copy("left")
self.comp2 = self.comp.copy("right")
# set diffusion value
cli_D = 2.03
cli_D *= 1e-7 # um2 to dm2 (D in dm2/ss)
ki_D = 1.96
ki_D *= 1e-7 # um2 to dm2 (D in dm2/s)
nai_D = 1.33
nai_D *= 1e-7
# create diffusion connection
diffusion_object = Diffusion(self.comp, self.comp2, ions={'cli': cli_D, 'ki': ki_D, 'nai': nai_D})
diffusion_object = Diffusion(self.comp2, self.compBase, ions={'cli': cli_D, 'ki': ki_D, 'nai': nai_D})
self.sim.run(stop=100, dt=0.001, block_after=False)
self.compBase.gx = 1e-8
self.sim.run(continuefor=1, dt=1e-6, block_after=False)
self.assertEqual(self.comp.cli, self.comp2.cli)
self.compBase.gx = 0e-8
self.sim.run(continuefor=1, dt=1e-6, block_after=False)
self.assertEqual(self.comp.cli, self.comp2.cli)
def search():
st = time.time()
n_query = len(queries)
print("min..", np.min(queries))
print("max..", np.max(queries))
diffusion = Diffusion(np.vstack([queries, gallery]), args.cache_dir)
print("diffusion shape",diffusion.features.shape)
# offline type : scipy.sparse.csr.csr_matrix (희소행렬)
# offline shape : (5118 ,5118) >> query와 gallery의 합
offline = diffusion.get_offline_results(args.truncation_size, args.kd)
# offline.data 안에 nan 값이 존재함
print("offline..", len(offline.data))
print("offline features shape..",offline.shape)
offline.data = np.nan_to_num(offline.data, copy=False)
# 컬럼별로 확률화(SUM=1), feature=(1-a)(1-aS)의 역행렬
features = preprocessing.normalize(offline, norm="l2", axis=1)
print("features..",features.shape)
scores = features[:n_query] @ features[n_query:].T
np.save("pirsData/scores/"+ args.cate +"_scores.npy", -scores.todense())
print("1> features[:n_query].shape :", features[:n_query].shape)
print("2> features[n_query:].shape :", features[n_query:].shape)
print("3> scores.shape :", scores.shape)
# scores.shape : (55, 5063) = (쿼리, 갤러리) = (row, col)
ranks = np.argsort(-scores.todense())
#np.save("pirsData/ranks/"+ args.cate +"_ranks.npy", ranks)
print("ranks[0]...\n", ranks[:10,:10])
print("time check...>>>", args.cate,">>>", time.time()-st, ">>>", len(queries))
def search():
n_query = len(queries)
diffusion = Diffusion(np.vstack([queries, gallery]), args.cache_dir)
offline = diffusion.get_offline_results(args.truncation_size, args.kd)
features = preprocessing.normalize(offline, norm="l2", axis=1)
scores = features[:n_query] @ features[n_query:].T
ranks = np.argsort(-scores.todense())
evaluate(ranks)
def test_sis_model():
params = {
'model': 'SIS',
'b': 0.00208,
'd': 0.01,
'c': 1,
'runs': 10,
'steps': 5000,
'diffusion': 'max',
'method': 'add_edge_random',
'k': 15,
'seed': 1,
'plot_transition': False,
'gif_animation': False
}
graph = karate()
ds = Diffusion(graph, params)
increased_diffusion = ds.run_simulation()
params['diffusion'] = None
params['method'] = None
params['k'] = 0
ds = Diffusion(graph, params)
baseline_diffusion = ds.run_simulation()
params['diffusion'] = 'min'
params['method'] = 'ns_node'
params['k'] = 4
ds = Diffusion(graph, params)
decreased_diffusion = ds.run_simulation()
assert sum(decreased_diffusion) < sum(baseline_diffusion) < sum(increased_diffusion)
def test_mols(self):
self.compBase = SimpleCompartment("c1", pkcc2=1e-8, z=-0.85,
cli=0.015292947537423218,
ki=0.023836660428807395,
nai=0.1135388427892471)
self.comp = self.compBase.copy("left")
self.comp2 = self.comp.copy("right")
self.compSingle = SimpleCompartment("cs", pkcc2=1e-8, z=-0.85,
cli=0.015292947537423218,
ki=0.023836660428807395,
nai=0.1135388427892471,
length=3 * default_length)
# set diffusion value
cli_D = 2.03
cli_D *= 1e-7 # um2 to dm2 (D in dm2/s)
ki_D = 1.96
ki_D *= 1e-7 # um2 to dm2 (D in dm2/s)
nai_D = 1.33
nai_D *= 1e-7
# create diffusion connection
diffusion_object = Diffusion(self.comp, self.compBase, ions={'cli': cli_D, 'ki': ki_D, 'nai': nai_D})
diffusion_object = Diffusion(self.comp2, self.compBase, ions={'cli': cli_D, 'ki': ki_D, 'nai': nai_D})
self.sim.run(stop=100, dt=0.001, block_after=False)
self.compBase.gx = 1e-8
self.compSingle.gx = 1e-8
self.sim.run(continuefor=1, dt=1e-6, block_after=False)
xmol_3 = self.compBase.mols(self.compBase.xi) + self.comp.mols(self.comp.xi) + self.comp2.mols(self.comp2.xi)
xmol_single = self.compSingle.mols(self.compSingle.xi)
self.assertAlmostEqual(xmol_3, xmol_single)
cmol_3 = self.compBase.mols(self.compBase.cli) + self.comp.mols(self.comp.cli) + self.comp2.mols(self.comp2.cli)
cmol_single = self.compSingle.mols(self.compSingle.cli)
self.assertAlmostEqual(cmol_3, cmol_single)
def __init__(self, options = None):
super().__init__(options)
self.layout.split(
Layout(name='upper'),
Layout(name='lower')
)
self.layout["lower"].update(self.menu)
self.maps = dict()
self.maps["diffusion"] = Diffusion(80,30, 300)
self.maps["life"] = Life(140,30,5,80)
self.layout["upper"].size = 35
self.populate()
def __init__(self, options=None):
super().__init__(options)
#Upper is interactive map preview
#Lower is menu options
self.layout.split(Layout(name='upper'), Layout(name='lower'))
self.sleepTime = .5
self.maps = dict()
self.maps["diffusion"] = Diffusion(80, 30, 300)
self.maps["life"] = Life(140, 30, 5, 80)
self.layout["upper"].size = 35
self.layout["lower"].update(self.menu)
self.populate()
def test_one_is_two(self):
"""
Changing to the same values of all (2) compartments is the same as changing as if it were compartment
"""
self.compBase = Compartment("c1", length=10e-5, pkcc2=0, z=-0.85,
cli=0.015292947537423218,
ki=0.023836660428807395,
nai=0.1135388427892471)
self.comp = Compartment("c1", length=5e-5, pkcc2=0, z=-0.85,
cli=0.015292947537423218,
ki=0.023836660428807395,
nai=0.1135388427892471)
self.comp2 = self.comp.copy("c2")
# set diffusion value
cli_D = 2.03
cli_D *= 1e-7 # um2 to dm2 (D in dm2/s)
ki_D = 1.96
ki_D *= 1e-7 # um2 to dm2 (D in dm2/s)
nai_D = 1.33
nai_D *= 1e-7
# create diffusion connection
diffusion_object = Diffusion(self.comp, self.comp2, ions={'cli': cli_D, 'ki': ki_D, 'nai': nai_D})
self.assertEqual(self.compBase.cli, self.comp.cli)
self.sim.run(stop=100, dt=0.001, block_after=False)
self.assertEqual(self.compBase.cli, self.comp.cli)
self.assertEqual(self.comp.cli, self.comp2.cli)
self.compBase.gx = self.comp.gx = self.comp2.gx = 1e-8
self.sim.run(continuefor=1, dt=1e-6, block_after=False)
self.assertEqual(self.compBase.cli, self.comp.cli)
self.assertEqual(self.comp.cli, self.comp2.cli)
def search_old(gamma=3):
diffusion = Diffusion(gallery, args.cache_dir)
offline = diffusion.get_offline_results(args.truncation_size, args.kd)
time0 = time.time()
print('[search] 1) k-NN search')
sims, ids = diffusion.knn.search(queries, args.kq)
sims = sims**gamma
qr_num = ids.shape[0]
print('[search] 2) linear combination')
all_scores = np.empty((qr_num, args.truncation_size), dtype=np.float32)
all_ranks = np.empty((qr_num, args.truncation_size), dtype=np.int)
for i in tqdm(range(qr_num), desc='[search] query'):
scores = sims[i] @ offline[ids[i]]
parts = np.argpartition(-scores,
args.truncation_size)[:args.truncation_size]
ranks = np.argsort(-scores[parts])
all_scores[i] = scores[parts][ranks]
all_ranks[i] = parts[ranks]
print('[search] search costs {:.2f}s'.format(time.time() - time0))
# 3) evaluation
evaluate(all_ranks)
def run_epidemic_experiment(params):
graph = as_733().copy()
results = defaultdict(list)
b_list = np.arange(0, 0.005, 0.001) # transmission probability
for idx, b in enumerate(b_list):
params['b'] = b
if idx == 1:
params['plot_transition'] = True
params['gif_animation'] = True
params['gif_snaps'] = True
else:
params['plot_transition'] = False
params['gif_animation'] = False
params['gif_snaps'] = False
ds = Diffusion(graph, **params)
result = ds.run_simulation()
results[ds.get_effective_strength()] = result
plot_results(graph, params, results)
required=True,
help="""
Path to gallery features
""")
parser.add_argument('--ground_truth_path',
type=str,
help="""
Path to ground-truth
""")
parser.add_argument('-n',
'--truncation_size',
type=int,
default=1000,
help="""
Number of images in the truncated gallery
""")
args = parser.parse_args()
args.kq, args.kd = 10, 50
return args
if __name__ == "__main__":
args = parse_args()
if not os.path.isdir(args.cache_dir):
os.makedirs(args.cache_dir)
if args.ground_truth_path:
evaluator = OxfordParisEvaluator(args.ground_truth_path)
dataset = Dataset(args.query_path, args.gallery_path)
diffusion = Diffusion(dataset.gallery, args.cache_dir)
search(args)
def main(cli_D=2.03,
new_gx=0e-8,
anion_flux=False,
default_xz=-0.85,
jkccup=1e-12,
nrcomps=2,
dz=1e-7,
textra=100,
say='',
stretch=False):
"""
cli_D # um2/s
:return: sim, gui: it is useful to return these objects for access after simulation
"""
print("main")
sim = Simulator().get_instance()
gui = sim.gui()
dt = 0.001 # s
length = 10e-5
comp = Compartment("reference",
z=-0.85,
cli=0.0052,
ki=0.0123,
nai=0.014,
length=length,
radius=default_radius_short,
stretch_w=stretch)
# copies left
compl = comp.copy("dendrite left")
# copies right
compr = []
compr.append(comp.copy("dendrite right " + str(1)))
for i in range(nrcomps):
compr.append(comp.copy("dendrite right " + str(i + 2)))
# find steady-state values of ions
sim.run(stop=100,
dt=0.001,
plot_update_interval=50,
data_collect_interval=5,
block_after=False)
# set diffusion value
cli_D *= 1e-7 # cm2 to dm2 (D in dm2/s)
ki_D = 1.96
ki_D *= 1e-7 # cm2 to dm2 (D in dm2/s)
nai_D = 1.33
nai_D *= 1e-7
diffusion_object = []
# connect with Diffusion
diffusion_object.append(
Diffusion(compl, comp, ions={
'cli': cli_D,
'ki': ki_D,
'nai': nai_D
}))
diffusion_object.append(
Diffusion(comp,
compr[0],
ions={
'cli': cli_D,
'ki': ki_D,
'nai': nai_D
}))
for i in range(nrcomps):
diffusion_object.append(
Diffusion(compr[i],
compr[i + 1],
ions={
'cli': cli_D,
'ki': ki_D,
'nai': nai_D
}))
# heatmap incorporating compartment heights
sc = 1e7
htplot = Colormap("cmap", 0, compr)
totalht, initvals = htplot.heatmap(compl,
comp,
compr,
sc,
0,
all=1,
init_vals=None)
htplot.heatmap(compl, comp, compr, sc, totalht, all=1, init_vals=initvals)
voltage_reversal_graph_comp = gui.add_graph() \
.add_ion_conc(comp, "ecl", line_style='g', y_units_scale=1000, y_plot_units='mV') \
.add_ion_conc(comp, "ek", line_style='b', y_units_scale=1000, y_plot_units='mV') \
.add_voltage(comp, line_style='k', y_units_scale=1000, y_plot_units='mV')
voltage_reversal_graph_compr = gui.add_graph() \
.add_ion_conc(compr[-1], "ecl", line_style='g', y_units_scale=1000, y_plot_units='mV') \
.add_ion_conc(compr[-1], "ek", line_style='b', y_units_scale=1000, y_plot_units='mV') \
.add_voltage(compr[-1], line_style='k', y_units_scale=1000, y_plot_units='mV')
voltage_reversal_graph_compl = gui.add_graph() \
.add_ion_conc(compl, "ecl", line_style='g', y_units_scale=1000, y_plot_units='mV') \
.add_ion_conc(compl, "ek", line_style='b', y_units_scale=1000, y_plot_units='mV') \
.add_voltage(compl, line_style='k', y_units_scale=1000, y_plot_units='mV')
voltage_reversal_graph_compr1 = gui.add_graph() \
.add_ion_conc(compr[0], "ecl", line_style='g', y_units_scale=1000, y_plot_units='mV') \
.add_ion_conc(compr[0], "ek", line_style='b', y_units_scale=1000, y_plot_units='mV') \
.add_voltage(compr[0], line_style='k', y_units_scale=1000, y_plot_units='mV')
# run simulation with diffusion
sim.run(continuefor=10,
dt=dt,
plot_update_interval=5,
data_collect_interval=1)
print(datetime.datetime.now())
print_concentrations(
[comp, compl, compr[-1]],
title="Ion concentrations given diffusion between compartments")
htplot.heatmap(compl, comp, compr, sc, totalht, all=1, init_vals=initvals)
# (optionally) change anion conductance
prev_comp_gx = comp.gx
comp.gx = new_gx
comp.dz = dz
voltage_reversal_graph_comp.save(say + 'reference.eps')
if dz != 0:
z_graph = gui.add_graph() \
.add_ion_conc(comp, "z", line_style='m')
if comp.gx > 0:
x_graph = gui.add_graph() \
.add_ion_conc(comp, "absox", line_style='m') \
.add_ion_conc(compl, "absox", line_style=':m') \
.add_ion_conc(compr[0], "absox", line_style='m--')
# (optionally) change anion flux
if anion_flux:
comp.xz = default_xz
comp.xmz = (comp.z * comp.xi - comp.xz * comp.xi_temp) / comp.xm
print('Anion flux with fixed anions having net charge', comp.xmz,
'while a proportion of', (1 - comp.ratio),
'of all impermeants are temporarily mobile anions of charge',
comp.xz)
z_graph = gui.add_graph() \
.add_ion_conc(comp, "z", line_style='m')
# (optionally) change kcc2
prev_comp_pkcc2 = comp.pkcc2
if jkccup is not None:
comp.jkccup = jkccup
g_graph = gui.add_graph() \
.add_ion_conc(comp, "pkcc2", line_style='k')
vol_graph = gui.add_graph() \
.add_ion_conc(comp, "w", line_style='b') \
.add_ion_conc(compl, "w", line_style=':b') \
.add_ion_conc(compr[0], "w", line_style='b--')
sim.run(continuefor=textra,
dt=dt * 0.001,
plot_update_interval=textra / 32,
data_collect_interval=textra / 32)
print(datetime.datetime.now())
print_concentrations(
[comp, compl, compr[-1]],
title="Ion concentrations during event from the dendritic compartment")
# heatmap incorporating compartment heights
htplot.heatmap(compl,
comp,
compr,
sc,
totalht,
all=1,
init_vals=initvals,
title=[
say + 'all_halfway_df.eps', say + 'all_halfway_ecl.eps',
say + 'all_halfway_vm.eps'
])
voltage_reversal_graph_comp.save(say + 'reference.eps')
sim.run(continuefor=textra,
dt=dt * 0.001,
plot_update_interval=textra / 2,
data_collect_interval=textra / 16)
print(datetime.datetime.now())
print_concentrations(
[comp, compl, compr[-1]],
title=
"Ion concentrations immediately after event from the dendritic compartment"
)
# heatmap incorporating compartment heights
htplot.heatmap(compl, comp, compr, sc, totalht, all=1, init_vals=initvals)
comp.gx = prev_comp_gx
comp.jkccup = 0
comp.dz = 0
voltage_reversal_graph_comp.save(say + 'reference.eps')
sim.run(continuefor=textra * 1,
dt=dt * 0.001,
plot_update_interval=textra / 2,
data_collect_interval=textra / 4)
print(datetime.datetime.now())
print_concentrations([comp, compl, compr[-1]],
title="Ion concentrations at almost steady state")
htplot.heatmap(compl,
comp,
compr,
sc,
totalht,
all=1,
init_vals=initvals,
title=[
say + 'all_end_df.eps', say + 'all_end_ecl.eps',
say + 'all_end_vm.eps'
])
voltage_reversal_graph_comp.save(say + 'reference.eps')
sim.run(continuefor=textra * 1,
dt=dt * 0.001,
plot_update_interval=textra / 2,
data_collect_interval=textra / 4)
print(datetime.datetime.now())
print_concentrations([comp, compl, compr[-1]],
title="Ion concentrations at steady state")
# heatmap incorporating compartment heights
htplot.heatmap(compl,
comp,
compr,
sc,
totalht,
all=1,
init_vals=initvals,
title=[
say + 'all_end_df.eps', say + 'all_end_ecl.eps',
say + 'all_end_vm.eps'
])
voltage_reversal_graph_comp.save(say + 'reference.eps')
sim.run(continuefor=textra * 2,
dt=dt * 0.001,
plot_update_interval=textra / 2,
data_collect_interval=textra / 4)
print(datetime.datetime.now())
print_concentrations([comp, compl, compr[-1]],
title="Ion concentrations at steady state")
# heatmap incorporating compartment heights
htplot.heatmap(compl,
comp,
compr,
sc,
totalht,
all=1,
init_vals=initvals,
title=[
say + 'all_end_df.eps', say + 'all_end_ecl.eps',
say + 'all_end_vm.eps'
])
voltage_reversal_graph_comp.save(say + 'reference.eps')
sim.run(continuefor=textra * 2,
dt=dt * 0.001,
plot_update_interval=textra / 2,
data_collect_interval=textra / 4)
print(datetime.datetime.now())
print_concentrations([comp, compl, compr[-1]],
title="Ion concentrations at steady state")
# heatmap incorporating compartment heights
htplot.heatmap(compl,
comp,
compr,
sc,
totalht,
all=1,
init_vals=initvals,
title=[
say + 'all_end_df.eps', say + 'all_end_ecl.eps',
say + 'all_end_vm.eps'
])
voltage_reversal_graph_comp.save(say + 'reference.eps')
sim.run(continuefor=textra * 2,
dt=dt * 0.001,
plot_update_interval=textra / 2,
data_collect_interval=textra / 4)
print(datetime.datetime.now())
print_concentrations([comp, compl, compr[-1]],
title="Ion concentrations at steady state")
# heatmap incorporating compartment heights
htplot.heatmap(compl,
comp,
compr,
sc,
totalht,
all=1,
init_vals=initvals,
title=[
say + 'all_end_df.eps', say + 'all_end_ecl.eps',
say + 'all_end_vm.eps'
])
voltage_reversal_graph_comp.save(say + 'reference.eps')
return sim, gui
def _infer(model, root_path, test_loader=None, local_val=False):
"""
모델과 데이터가 주어졌을 때, 다음과 같은 데이터 구조를 반환하는 함수를 만들어야 합니다.
[ [ query_image_id_1, predicted_database_image_id_1 ],
[ query_image_id_2, predicted_database_image_id_2 ],
...
[ query_image_id_N, predicted_database_image_id_N ] ]
README 설명에서처럼 predicted_database_image_id_n 은 query_image_id_n 에 대해
평가셋 이미지 1,...,n-1,n+1,...,N 를 데이터베이스로 간주했을 때에 가장 쿼리와 같은 카테고리를
가질 것으로 예측하는 이미지입니다. 이미지 아이디는 test_loader 에서 extract 되는 첫번째
인자인 data_id 를 사용합니다.
Args:
model: 이미지를 인풋으로 받아서 feature vector를 반환하는 모델
root_path: 데이터가 저장된 위치
test_loader: 사용되지 않음
local_val: 사용되지 않음
Returns:
top1_reference_ids: 위에서 설명한 list 데이터 구조
"""
if test_loader is None:
test_loader = test_data_loader(
root=os.path.join(root_path, 'test_data'))
# TODO 모델의 아웃풋을 적당히 가공하고 연산하여 각 query에 대해 매치가 되는 데이터베이스
# TODO 이미지의 ID를 찾는 모듈을 구현 (현재 구현은 베이스라인 - L2 정규화 및 내적으로 가장
# TODO 비슷한 이미지 조회).
feats = None
data_ids = None
s_t = time.time()
for idx, data_package in enumerate(test_loader):
if local_val:
data_id, image, _ = data_package
else:
data_id, image = data_package
image = image.cuda()
feat = model(image, extract=True)
feat = feat.detach().cpu().numpy()
feat = feat / np.linalg.norm(feat, axis=1)[:, np.newaxis]
if feats is None:
feats = feat
else:
feats = np.append(feats, feat, axis=0)
if data_ids is None:
data_ids = data_id
else:
data_ids = np.append(data_ids, data_id, axis=0)
if time.time() - s_t > 10:
print('Infer batch {}/{}.'.format(idx + 1, len(test_loader)))
diffusion = Diffusion(feats, cache_dir='./cache')
offline = diffusion.get_offline_results(n_trunc=1000, kd=50)
features = preprocessing.normalize(offline, norm='l2', axis=1)
score_matrix = features @ features.T
np.fill_diagonal(score_matrix, -np.inf)
top1_reference_indices = np.argmax(score_matrix, axis=1)
top1_reference_ids = [[
data_ids[idx], data_ids[top1_reference_indices[idx]]
] for idx in range(len(data_ids))]
return top1_reference_ids
def diffusion(
self,
slice_time,
synergy,
states=None,
time_stamps=None,
nb_steps=1,
infected_node=1,
cooperator_ratio=0.0,
strategies=None,
payoffs=None
):
from diffusion import Diffusion
from pgg import PublicGoodGames
G = self._slices[int(slice_time)]['graph'].copy()
if states == None or time_stamps == None:
# Initialize the Public Good Game
PGG = PublicGoodGames(G=G,
cooperator_ratio=cooperator_ratio, nb_simulation_step=nb_steps, synergy=synergy)
# Get the startegies list
strategies = PGG.get_strategies()
# Initialize the disffusion process with the startegies list
D = Diffusion(G=G, nb_simulation_step=nb_steps,
time_step=self._time_step,
initial_time_stamp=slice_time,
strategies=strategies)
# Run the diffusion Process
D.run_steps(infected_node)
# Run the Game
PGG.run_game()
# Return the strategies list, the payoffs list, the timestamps list and the states list
return (D.get_states(), D.get_time_stamps(), PGG.get_strategies(), PGG.get_payoffs())
elif strategies != None and payoffs != None:
D = Diffusion(G=G, nb_simulation_step=nb_steps,
time_step=self._time_step,
initial_time_stamp=slice_time,
strategies=strategies)
PGG = PublicGoodGames(G=G, nb_simulation_step=nb_steps, synergy=synergy)
PGG.set_strategies(strategies)
PGG.set_payoffs(payoffs)
D.set_nodes_states(states)
D.set_node_time_stamps(time_stamps)
# Run the diffusion Process
D.run_steps(infected_node)
# Run the Game
PGG.run_game()
# Return the strategies list, the payoffs list, the timestamps list and the states list
return (D.get_states(), D.get_time_stamps(), PGG.get_strategies(), PGG.get_payoffs())
def grow(nr=3, textra=10):
print("growing via anions")
sim = Simulator().get_instance()
gui = sim.gui()
dt = 0.001 # s
comp = []
comp.append(
Compartment("initial growth cone",
z=-0.85,
cli=0.00433925284075134,
ki=0.1109567493822927,
nai=0.0255226350779378,
length=5e-5,
radius=default_radius_short))
# steady state
sim.run(stop=100,
dt=0.001,
plot_update_interval=500,
data_collect_interval=5,
block_after=False)
# set diffusion value
cli_D = 2.03
cli_D *= 1e-7 # cm2 to dm2 (D in dm2/s)
ki_D = 1.96
ki_D *= 1e-7 # cm2 to dm2 (D in dm2/s)
nai_D = 1.33
nai_D *= 1e-7
#another compartment
comp.append(comp[0].copy("compartment 1"))
comp[1].L = 10e-5
comp[1].w = np.pi * comp[1].r**2 * comp[1].L
diffusion_object = [
Diffusion(comp[0],
comp[1],
ions={
'cli': cli_D,
'ki': ki_D,
'nai': nai_D
})
]
# heatmap incorporating compartment heights
sc = 1e5
htplot = Colormap("dendrite", comp[0].w + comp[1].w, comp)
totalht, init_vals = htplot.smallheatmap(comp,
sc,
int(htplot.totalh * sc),
all=0,
init_val=None)
# plot
voltage_reversal_graph_comp = gui.add_graph() \
.add_ion_conc(comp[0], "ecl", line_style='g', y_units_scale=1000, y_plot_units='mV') \
.add_ion_conc(comp[0], "ek", line_style='b', y_units_scale=1000, y_plot_units='mV') \
.add_voltage(comp[0], line_style='k', y_units_scale=1000, y_plot_units='mV')
volume_graph = gui.add_graph()
volume_graph.add_var(volume_graph.time,
"time",
htplot,
"comp0w",
line_style='k')
volume_graph.add_var(volume_graph.time,
"time",
htplot,
"totalh",
line_style='b')
sim.run(continuefor=1,
dt=dt * 0.001,
plot_update_interval=0.5,
data_collect_interval=textra / 16)
# growth
for i in range(nr):
htplot.smallheatmap(comp,
sc,
totalht,
all=0,
init_val=init_vals,
name='graphs/grow_done' + str(i) + '.eps')
comp[0].gx = 1
# stop at certain length
while comp[0].L < 15e-5:
print("Fluxing compartment's length: " + str(comp[0].L))
sim.run(continuefor=0.5,
dt=dt * 0.001,
plot_update_interval=0.25,
data_collect_interval=textra / 16)
if 9.9e-5 < comp[0].L < 10.3e-5:
htplot.smallheatmap(comp,
sc,
totalht,
all=0,
init_val=init_vals,
name='graphs/grow_interim' + str(i) +
'.eps')
comp[0].gx = 0
print_concentrations(comp, str(i))
# split compartments
comp.insert(0, comp[0].copy("compartment " + str(i)))
comp[1].L -= 5e-5
comp[0].L = 5e-5
comp[0].w = np.pi * comp[0].r**2 * comp[0].L
comp[1].w = np.pi * comp[1].r**2 * comp[1].L
print_concentrations(comp, str(i))
# update total height
htplot.comp = comp
# update diffusion
diffusion_object.append(
Diffusion(comp[0],
comp[1],
ions={
'cli': cli_D,
'ki': ki_D,
'nai': nai_D
}))
for a in comp:
print(a.name)
for j in diffusion_object:
print(j.name)
sim.run(continuefor=10,
dt=dt * 0.001,
plot_update_interval=5,
data_collect_interval=textra / 16)
htplot.smallheatmap(comp,
sc,
totalht,
all=1,
init_val=init_vals,
name='graphs/grow_end.eps')
sim.run(continuefor=4,
dt=dt * 0.001,
plot_update_interval=2,
data_collect_interval=0.5)
htplot.smallheatmap(comp,
sc,
totalht,
all=1,
init_val=init_vals,
name='graphs/grow_end.eps')
return sim, gui
def main():
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument('train_images_path')
arg_parser.add_argument('test_images_path')
arg_parser.add_argument('predictions_path')
args = arg_parser.parse_args()
imsize=480
train_path=args.train_images_path
test_path=args.test_images_path
outfile=args.predictions_path
##read data##
data_list=os.listdir(ipath)
train_images = get_imlist(train_path)
test_images = get_imlist(test_path)
##RAMAC##
RAMAC = extract_feature(train_images, 'resnet101', imsize)
RAMAC_test = extract_feature(test_images, 'resnet101', imsize)
##UEL##
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomCrop(size=224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
transform_test = transforms.Compose([
transforms.ToPILImage(),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
net = resnet101(pretrained=True,low_dim=128)
model_path = './model/UEL.t'#After training UEL
net.load_state_dict(torch.load())
imset = DataLoader(path = train_path, transform=transform_test)
train_loader = torch.utils.data.DataLoader(imset, batch_size=32, shuffle=False, num_workers=0)
UEL = obtainf(net, train_loader)
imset = DataLoader(path = test_path, transform=transform_test)
test_loader = torch.utils.data.DataLoader(imset, batch_size=32, shuffle=False, num_workers=0)
UEL_test = obtainf(net, test_loader)
##GEM##
image_size=1024
multiscale='[1, 2**(1/2), 1/2**(1/2)]'
state = torch.load('./model/retrievalSfM120k-vgg16-gem-b4dcdc6.pth')
net_params = {}
net_params['architecture'] = state['meta']['architecture']
net_params['pooling'] = state['meta']['pooling']
net_params['local_whitening'] = state['meta'].get('local_whitening', False)
net_params['regional'] = state['meta'].get('regional', False)
net_params['whitening'] = state['meta'].get('whitening', False)
net_params['mean'] = state['meta']['mean']
net_params['std'] = state['meta']['std']
net_params['pretrained'] = False
# load network
net = init_network(net_params)
net.load_state_dict(state['state_dict'])
# if whitening is precomputed
if 'Lw' in state['meta']:
net.meta['Lw'] = state['meta']['Lw']
ms = list(eval(multiscale))
msp = net.pool.p.item()
net.cuda()
net.eval()
# set up the transform
normalize = transforms.Normalize(
mean=net.meta['mean'],
std=net.meta['std']
)
transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
GEM = extract_vectors(net,train_images , 480, transform, ms=ms, msp=msp).numpy().T
GEM_test = extract_vectors(net,test_images , 480, transform, ms=ms, msp=msp).numpy().T
##Retrieval##
feats=np.concatenate((RAMAC,UEL,GEM),axis=1).astype('float32')
query_feat=np.concatenate((RAMAC_test, UEL_test,GEM_test),axis=1).astype('float32')
##diffusion##
kq, kd = 7, 50
gamma=80
diffusion = Diffusion(feats, '/')
offline = diffusion.get_offline_results(1024, kd)
print('[search] 1) k-NN search')
sims, ids = diffusion.knn.search(query_feat, kq)
sims = sims ** gamma
qr_num = ids.shape[0]
print('[search] 2) linear combination')
all_scores = np.empty((qr_num, 7), dtype=np.float32)
all_ranks = np.empty((qr_num, 7), dtype=np.int)
for i in range(qr_num):
scores = sims[i] @ offline[ids[i]]
parts = np.argpartition(-scores, 7)[:7]
ranks = np.argsort(-scores[parts])
all_scores[i] = scores[parts][ranks]
all_ranks[i] = parts[ranks]
I = all_ranks
##output##
out=pd.DataFrame(list(map(lambda x: x.split('/')[-1].split('.jpg')[0],timages)))
out['1']=pd.DataFrame(I)[0].map(lambda x:data_list[x].split('.')[0] )
out['2']=pd.DataFrame(I)[1].map(lambda x:data_list[x].split('.')[0] )
out['3']=pd.DataFrame(I)[2].map(lambda x:data_list[x].split('.')[0] )
out['4']=pd.DataFrame(I)[3].map(lambda x:data_list[x].split('.')[0] )
out['5']=pd.DataFrame(I)[4].map(lambda x:data_list[x].split('.')[0] )
out['6']=pd.DataFrame(I)[5].map(lambda x:data_list[x].split('.')[0] )
out['7']=pd.DataFrame(I)[6].map(lambda x:data_list[x].split('.')[0] )
out.to_csv(outfile,index=None,header=None)
def test_diffusion_compartments(self, **kwargs):
self.d = Diffusion(self.comp, self.comp2, self.ions)
self.run_diffusion(300, False, **kwargs)
