1. data generate
2. Model
3. optimize, loss, etc
4. ensemble
1. data generate
- Using simplified file
def draw_cv2_color(raw_strokes, size=256, lw=6, time_color=True):
img = np.zeros((BASE_SIZE, BASE_SIZE,3), np.uint8)
for t, stroke in enumerate(raw_strokes):
inertia_x = 0
inertia_y = 0
for i in range(len(stroke[0]) - 1):
color = int(255 - 245*(float(t)/len(raw_strokes))) if time_color else 255 ## strokes order
print(color)
sx = stroke[0][i]
sy = stroke[1][i]
ex = stroke[0][i + 1]
ey = stroke[1][i + 1]
color_v = (np.sqrt((sx-ex)*(sx-ex) + (sy-ey)*(sy-ey)) / np.sqrt(size*size)) * 255 ## strokes distance like speed (1~0)
color_a = (np.sqrt((inertia_x-ex)*(inertia_x-ex) + (inertia_y-ey)*(inertia_y-ey)) / np.sqrt(size*size*4)) * 255 ## strokes distance like acceleration (1~0)
_ = cv2.line(img, (sx, sy), (ex, ey), (color,color_v,color_a), lw)
inertia_x = 2*ex -sx
inertia_y = 2*ey-sy
if size != BASE_SIZE:
return cv2.resize(img, (size, size))
else:
return img
- Using raw file
## channel order sequence, speed, acceleration and start point interval time, last stroke last point total drawing time
def draw_cv2_color_new(raw_strokes, size=256, lw=6, time_color=True, last_drop_r = 0.0):
stx_min, sty_min = 99999, 99999
stx_max, sty_max = 0,0
ett=0 # How fast to complete less than 20 seconds
for t, stroke in enumerate(raw_strokes):
if t == len(raw_strokes) -1:
ett = int(stroke[2][-1])
for i in range(len(stroke[0])):
stx_min = min(stx_min, int(stroke[0][i]))
stx_max = max(stx_max, int(stroke[0][i]))
sty_min = min(sty_min, int(stroke[1][i]))
sty_max = max(sty_max, int(stroke[1][i]))
limit_ett = 20*1000
ofs = 15
if int(sty_max-sty_min+2*ofs) > 6000 or int(stx_max-stx_min+2*ofs) > 6000:
img = np.zeros((6000,6000,3), np.uint8)
else:
img = np.zeros((int(sty_max-sty_min+2*ofs), int(stx_max-stx_min+2*ofs),3), np.uint8)
for t, stroke in enumerate(raw_strokes):
inertia_x = 0
inertia_y = 0
pre_st_t = 0
for i in range(len(stroke[0]) - 1):
color = int(255 - 245*float(t)/len(raw_strokes)) if time_color else 255 ## stroke order
sx = int(stroke[0][i]) - stx_min +ofs
sy = int(stroke[1][i]) - sty_min +ofs
st = stroke[2][i]
ex = int(stroke[0][i + 1])- stx_min +ofs
ey = int(stroke[1][i + 1])- sty_min +ofs
et = stroke[2][i+1]
time = et-st
if time ==0:
time = 1
color_v = min(int((np.sqrt((sx-ex)*(sx-ex) + (sy-ey)*(sy-ey)) / time)*255.0), 255) ## speed
color_a = min(int((np.sqrt((inertia_x-ex)*(inertia_x-ex) + (inertia_y-ey)*(inertia_y-ey)) / np.sqrt(time*time))*255.0), 255) ## acceleration (1~0)
_ = cv2.line(img, (sx, sy), (ex, ey), (color,color_v,color_a), lw)
if i==0:
color_inter = int((float(et-pre_st_t)/limit_ett)*245)+10
_ = cv2.circle(img, (sx, sy), lw, (0,0,color_inter), -1) ##interval time
if i==len(stroke[0])-2 and t == len(raw_strokes) -1:
color_end = int((float(ett)/(limit_ett)*245))+10
_ = cv2.circle(img, (sx, sy), lw, (0,color_end,0), -1) ##end time
inertia_x = 2*ex -sx
inertia_y = 2*ey-sy
pre_st_t=et
return cv2.resize(img, (size, size)) #lw reflects how big the picture is drawn, also the aspect ratio is reflected
2. Model Structure
- Best single model
: InceptionResnetV2 (139,139,3) size input local valid score is 0.9516.
: using raw file, using 'imagenet' weights, batch size 180
base_model = InceptionResNetV2(input_shape=input_shape, weights='imagenet',include_top= False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='rule')(x)
x = Dropout(0.3)(x)
predictions = Dense(340, activation='softmax', name='lastfc')(x)
model = Model(inputs=base_model.input, outputs=predictions)
base_model = InceptionResNetV2(input_shape=input_shape, weights='imagenet',include_top= False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='rule')(x)
x = Dropout(0.3)(x)
predictions = Dense(340, activation='softmax', name='lastfc')(x)
model = Model(inputs=base_model.input, outputs=predictions)
3. Optimizer, Loss, etc.
- In first train about 1epoch(50M set) , I used adam and learning rate 0.002,
categorical cross entropy loss
- Second train , I used adam accumulation 500 iters and learning rate 0.002,
categorical cross entropy 10% and top3 loss 90%.
- I did not have enough time to train to the saturation.
categorical cross entropy loss
- Second train , I used adam accumulation 500 iters and learning rate 0.002,
categorical cross entropy 10% and top3 loss 90%.
- I did not have enough time to train to the saturation.
#https://github.com/keras-team/keras/issues/3556
import keras.backend as K
from keras.legacy import interfaces
from keras.optimizers import Optimizer
class AdamAccumulate(Optimizer):
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,
epsilon=None, decay=0., amsgrad=False, accum_iters=1, **kwargs):
if accum_iters < 1:
raise ValueError('accum_iters must be >= 1')
super(AdamAccumulate, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.lr = K.variable(lr, name='lr')
self.beta_1 = K.variable(beta_1, name='beta_1')
self.beta_2 = K.variable(beta_2, name='beta_2')
self.decay = K.variable(decay, name='decay')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
self.amsgrad = amsgrad
self.accum_iters = K.variable(accum_iters, K.dtype(self.iterations))
self.accum_iters_float = K.cast(self.accum_iters, K.floatx())
@interfaces.legacy_get_updates_support
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
completed_updates = K.cast(K.tf.floordiv(self.iterations, self.accum_iters), K.floatx())
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay * completed_updates))
t = completed_updates + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t)))
update_switch = K.equal((self.iterations + 1) % self.accum_iters, 0)
update_switch = K.cast(update_switch, K.floatx())
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
gs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat, tg in zip(params, grads, ms, vs, vhats, gs):
sum_grad = tg + g
avg_grad = sum_grad / self.accum_iters_float
m_t = (self.beta_1 * m) + (1. - self.beta_1) * avg_grad
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(avg_grad)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, (1 - update_switch) * vhat + update_switch * vhat_t))
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, (1 - update_switch) * m + update_switch * m_t))
self.updates.append(K.update(v, (1 - update_switch) * v + update_switch * v_t))
self.updates.append(K.update(tg, (1 - update_switch) * sum_grad))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, (1 - update_switch) * p + update_switch * new_p))
return self.updates
def get_config(self):
config = {'lr': float(K.get_value(self.lr)),
'beta_1': float(K.get_value(self.beta_1)),
'beta_2': float(K.get_value(self.beta_2)),
'decay': float(K.get_value(self.decay)),
'epsilon': self.epsilon,
'amsgrad': self.amsgrad}
base_config = super(AdamAccumulate, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
4. Ensemble
- I used weighed average ensemble by local valid score and argmax corr.
- I used InceptionResNetV2 (raw, simple), Xception (raw), Resnet50 (simple).
- I did not have time to train both (raw, simple)
- I used InceptionResNetV2 (raw, simple), Xception (raw), Resnet50 (simple).
- I did not have time to train both (raw, simple)
- Average weight calculation is below code
def get_score_w(local_score):
ls = np.array(local_score)
sub = ls - ls.min()
div = sub/sub.max()
add = div + 0.1
nor = add/add.max()
sqr = nor*nor
print(sqr)
return sqr
def get_corr_w(clsnp):
corxlist=[]
for idx1, cls1 in enumerate((clsnp)):
corylist=[]
for idx2, cls2 in enumerate((clsnp)):
cor_max = np.corrcoef(np.argmax(clsnp[idx1],axis=1),np.argmax(clsnp[idx2],axis=1))[0][1]
corylist.append(cor_max)
corxlist.append(corylist)
df = pd.DataFrame(corxlist,columns=names, index=names)
#print(df)
corr_w = []
for i in range(df.shape[0]):
count = 0
thr = 0.95
for v in df.values[i]:
if v > thr :
count+=1
corr_w.append(1.0/count)
return np.array(corr_w), df
def getensemble_w(clsnp, local_score):
score_w = get_score_w(local_score)
for i in range(score_w.shape[0]):
if score_w[i]==1.0:
score_w[i] = 1.2 #max score add 20%
corr_w = get_corr_w(clsnp)[0]
ensemble_w = score_w*corr_w
return ensemble_w
def get_score_w(local_score):
ls = np.array(local_score)
sub = ls - ls.min()
div = sub/sub.max()
add = div + 0.1
nor = add/add.max()
sqr = nor*nor
print(sqr)
return sqr
def get_corr_w(clsnp):
corxlist=[]
for idx1, cls1 in enumerate((clsnp)):
corylist=[]
for idx2, cls2 in enumerate((clsnp)):
cor_max = np.corrcoef(np.argmax(clsnp[idx1],axis=1),np.argmax(clsnp[idx2],axis=1))[0][1]
corylist.append(cor_max)
corxlist.append(corylist)
df = pd.DataFrame(corxlist,columns=names, index=names)
#print(df)
corr_w = []
for i in range(df.shape[0]):
count = 0
thr = 0.95
for v in df.values[i]:
if v > thr :
count+=1
corr_w.append(1.0/count)
return np.array(corr_w), df
def getensemble_w(clsnp, local_score):
score_w = get_score_w(local_score)
for i in range(score_w.shape[0]):
if score_w[i]==1.0:
score_w[i] = 1.2 #max score add 20%
corr_w = get_corr_w(clsnp)[0]
ensemble_w = score_w*corr_w
return ensemble_w
Everybody enjoy the competition~!!
