def comp_arch(self, action):
arch = copy.deepcopy(self)
arch.action = action
n = arch.n
V = arch.V
p = 0
for i in range(n):
if action[p]:
V[i].replace(layer.Identity())
p += 1
in_shapes = [V[i].in_shape for i in range(n)]
out_shapes = [V[i].out_shape for i in range(n)]
for g in self.groups:
F = max(1, int((1.0 - action[p]) * g.F))
for j in g.inter:
V[j].shrink(F, F)
for j in g.in_only:
Fo = list(V[j].out_shape)[1]
V[j].shrink(F, Fo)
for j in g.out_only:
Fi = list(V[j].in_shape)[1]
V[j].shrink(Fi, F)
p += 1
for i in range(n):
for j in range(i + 1, n):
if out_shapes[i] == in_shapes[j]:
if action[p]:
arch.E[i][j] = True
p += 1
arch.in_links, arch.out_links = gr.get_links(arch.E)
arch.init_rep()
arch.to(opt.device)
return arch
def __init__(self, n, V, E):
super(Architecture, self).__init__()
self.n = n
self.V = V
for i in range(n):
self.add_module('layer_%d' % (i), V[i])
self.groups = gr.get_groups(V)
self.E = E
self.in_links, self.out_links = gr.get_links(E)
self.init_rep()
def generate_plot(self, plots=100):
'''Use generate and test-method to select best possible plot and
character combination.
'''
all_plots = []
for i in range(plots):
action_list, actor1, actor2 = self.get_characters_and_actions()
links = graph.get_links(self.action_graph, action_list)
evaluation = self.evaluate_plot(actor1, actor2,
action_list, links)
all_plots.append((actor1, actor2, action_list, links, evaluation))
all_plots.sort(key=lambda x: x[4], reverse=True)
return all_plots[0]
def comp_arch(self, action):
arch = copy.deepcopy(self)
arch.action = action
n = arch.n
V = arch.V
p = 0
for i in range(n):
if action[p]:
V[i].replace(layer.Identity())
p += 1
in_shapes = [V[i].in_shape for i in range(n)]
out_shapes = [V[i].out_shape for i in range(n)]
flat_output = 0
for g in self.groups:
F = max(1, int((1.0 - action[p]) * g.F))
for j in g.out_only:
if (isinstance(V[j].base, (Flatten)) and
not action[j]):
if j == 0:
F = g.F
else:
F = flat_output
break
for j in g.inter:
V[j].shrink(F, F)
for j in g.in_only:
Fo = list(V[j].out_shape)[1]
V[j].shrink(F, Fo)
for j in g.out_only:
Fi = list(V[j].in_shape)[1]
V[j].shrink(Fi, F)
for j in g.in_only:
if (isinstance(V[j].base, (Flatten)) and
not action[j]):
flat_output = int(F*torch.prod(torch.tensor(V[j].in_shape[2:])).item())
break
p += 1
for i in range(n):
for j in range(i + 1, n):
if out_shapes[i] == in_shapes[j]:
if action[p]:
arch.E[i][j] = True
p += 1
arch.in_links, arch.out_links = gr.get_links(arch.E)
arch.init_rep()
arch.to(opt.device)
return arch
def __init__(self, n, V, E):
super(Architecture, self).__init__()
self.n = n
self.V = V
for i in range(n):
self.add_module('layer_%d' % (i), V[i])
self.groups = gr.get_groups(V)
#for g in self.groups:
# print(g.F)
# print("in layer")
# print(g.in_layers)
# print("out layer")
# print(g.out_layers)
# print("__________________")
self.E = E
self.in_links, self.out_links = gr.get_links(E)
self.init_rep()
def comp_arch_rand_sfn(self):
def shrink_n(F, ratio):
m = opt.ar_channel_mul
return max(1, int(ceil((1.0 - ratio) * F / m))) * m
arch = copy.deepcopy(self)
n = arch.n
V = arch.V
p1 = random.choice(opt.ar_p1)
for i in range(n):
if (random.random() < p1 and V[i].in_shape == V[i].out_shape
and i not in [11, 50, 125]):
V[i].replace(models.Identity())
opt.ar_p2[1] = min(0.9, opt.ar_p2[1])
for g in self.groups:
p2 = random.uniform(*opt.ar_p2)
for j in g.inter:
Fi = shrink_n(list(V[j].in_shape)[1], p2)
Fo = shrink_n(list(V[j].out_shape)[1], p2)
V[j].shrink(Fi, Fo)
for j in g.in_only:
Fi = shrink_n(list(V[j].in_shape)[1], p2)
Fo = list(V[j].out_shape)[1]
V[j].shrink(Fi, Fo)
for j in g.out_only:
Fi = list(V[j].in_shape)[1]
Fo = shrink_n(list(V[j].out_shape)[1], p2)
V[j].shrink(Fi, Fo)
F1 = list(V[2].out_shape)[1]
F4 = list(V[13].out_shape)[1]
F3 = list(V[13].in_shape)[1]
F2 = F4 - F3
V[11].shrink(F1, F2)
V[12].shrink(F2, F2)
F1 = list(V[41].out_shape)[1]
F4 = list(V[52].out_shape)[1]
F3 = list(V[52].in_shape)[1]
F2 = F4 - F3
V[50].shrink(F1, F2)
V[51].shrink(F2, F2)
F1 = list(V[116].out_shape)[1]
F4 = list(V[127].out_shape)[1]
F3 = list(V[127].in_shape)[1]
F2 = F4 - F3
V[125].shrink(F1, F2)
V[126].shrink(F2, F2)
p3 = random.choice(opt.ar_p3)
for i in range(n):
for j in range(i + 1, n):
if (random.random() < p3 and V[i].out_shape == V[j].in_shape
and not isinstance(V[j].base,
(models.Concat, models.Identity))):
arch.E[i][j] = True
arch.in_links, arch.out_links = gr.get_links(arch.E)
arch.init_rep()
arch.to(opt.device)
return arch