From: https://www.kaggle.com/saladjay/densetnet-mixup-focalloss-v11
Author: 邓雍杰Jay
Score: 0.549
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
import os, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import skimage.io
from skimage.transform import resize
from imgaug import augmenters as iaa
from tqdm import tqdm
import PIL
from PIL import Image
import cv2
from sklearn.utils import class_weight, shuffle
from keras.losses import binary_crossentropy
from keras.applications.densenet import preprocess_input
import keras.backend as K
import tensorflow as tf
from sklearn.metrics import f1_score, fbeta_score
from keras.utils import Sequence
WORKERS = 2
CHANNEL = 3
import warnings
warnings.filterwarnings("ignore")
beta_f2=2
nb_classes = 1103
#batch_size = 100
img_size = 224
nb_epochs = 25
imet_path = '../input/imet-2019-fgvc6/'
#imet_path = 'F:/ChromeDownload/IMET/'
model_weights_path = '../input/densenet121weights/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5'
#model_weights_path = 'F:/Edge Downloads/keras_model_weights/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5'
#model_weights_path = 'F:/Edge Downloads/keras_model_weights/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5'
#model_weights_path = 'F:/Edge Downloads/keras_model_weights/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5'
train_df_path = imet_path + 'train.csv'
label_df_path = imet_path + 'labels.csv'
sub_df_path = imet_path + 'sample_submission.csv'
train_img_path = imet_path + 'train/'
test_img_path = imet_path + 'test/'
from keras.legacy import interfaces
from keras.optimizers import Optimizer
from keras import backend as K
class AdamAccumulate_v1(Optimizer):
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,
epsilon=None, decay=0., amsgrad=False, accum_iters=20, **kwargs):
super(AdamAccumulate, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.effective_iterations = K.variable(0, dtype='int64', name='effective_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, dtype='int64')
@interfaces.legacy_get_updates_support
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update(self.iterations, self.iterations + 1)]
flag = K.equal(self.iterations % self.accum_iters, self.accum_iters - 1)
flag = K.cast(flag, K.floatx())
self.updates.append(K.update(self.effective_iterations,
self.effective_iterations + K.cast(flag, 'int64')))
lr = self.lr
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay * K.cast(self.effective_iterations,
K.dtype(self.decay))))
t = K.cast(self.effective_iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
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, gg in zip(params, grads, ms, vs, vhats, gs):
gg_t = (1 - flag) * (gg + g)
m_t = (self.beta_1 * m) + (1. - self.beta_1) * (gg + flag * g) / K.cast(self.accum_iters, K.floatx())
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(
(gg + flag * g) / K.cast(self.accum_iters, K.floatx()))
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - flag * lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - flag * lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append((m, flag * m_t + (1 - flag) * m))
self.updates.append((v, flag * v_t + (1 - flag) * v))
self.updates.append((gg, gg_t))
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, 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()))
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.floor(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)))
# self.iterations incremented after processing a batch
# batch: 1 2 3 4 5 6 7 8 9
# self.iterations: 0 1 2 3 4 5 6 7 8
# update_switch = 1: x x (if accum_iters=4)
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()))
# Load dataset info
path_to_train = imet_path+'train/'
data = pd.read_csv(imet_path+'train.csv')
train_dataset_info = []
for name, labels in zip(data['id'], data['attribute_ids'].str.split(' ')):
train_dataset_info.append({
'path':os.path.join(path_to_train, name),
'labels':np.array([int(label) for label in labels])})
train_dataset_info = np.array(train_dataset_info)
gamma = 2.0
epsilon = K.epsilon()
def old_focal_loss(y_true, y_pred):
pt = y_pred * y_true + (1-y_pred) * (1-y_true)
pt = K.clip(pt, epsilon, 1-epsilon)
CE = -K.log(pt)
FL = K.pow(1-pt, gamma) * CE
loss = K.sum(FL, axis=1)
return loss
train_df = pd.read_csv('../input/imet-2019-fgvc6/train.csv')
train_labels = np.zeros((train_df.shape[0], 1103))
for row_index, row in enumerate(train_df['attribute_ids']):
for label in row.split():
train_labels[row_index, int(label)] = 1
labels_sum = np.sum(train_labels,axis=0)
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
train_index = None
valid_index = None
class data_generator(Sequence):
def mix_up(x, y):
x = np.array(x, np.float32)
lam = np.random.beta(0.4, 0.4)
ori_index = np.arange(int(len(x)))
index_array = np.arange(int(len(x)))
np.random.shuffle(index_array)
mixed_x = lam * x[ori_index] + (1 - lam) * x[index_array]
mixed_y = lam * y[ori_index] + (1 - lam) * y[index_array]
return mixed_x, mixed_y
def create_train(dataset_info, batch_size, shape, augument=True, mix=False):
assert shape[2] == 3
while True:
dataset_info = shuffle(dataset_info)
for start in range(0, len(dataset_info), batch_size):
end = min(start + batch_size, len(dataset_info))
batch_images = []
X_train_batch = dataset_info[start:end]
batch_labels = np.zeros((len(X_train_batch), nb_classes))
for i in range(len(X_train_batch)):
image = data_generator.load_image(
X_train_batch[i]['path'], shape)
if augument:
image = data_generator.augment(image)
#batch_images.append(preprocess_input(image))
batch_images.append((image)/255.0)
batch_labels[i][X_train_batch[i]['labels']] = 1
if(mix):
batch_images, batch_labels = data_generator.mix_up(batch_images, batch_labels)
yield np.array(batch_images, np.float32), batch_labels
def create_valid(valid_dataset_info, batch_size, shape, augument=False):
global valid_index
assert shape[2] == 3
while True:
#valid_dataset_info = dataset_info[valid_index]
for start in range(0, len(valid_dataset_info), batch_size):
end = min(start + batch_size, len(valid_dataset_info))
batch_images = []
X_train_batch = valid_dataset_info[start:end]
batch_labels = np.zeros((len(X_train_batch), nb_classes))
for i in range(len(X_train_batch)):
image = data_generator.load_image(
X_train_batch[i]['path'], shape)
if augument:
image = data_generator.augment(image)
#batch_images.append(preprocess_input(image))
batch_images.append((image)/255.0)
batch_labels[i][X_train_batch[i]['labels']] = 1
yield np.array(batch_images, np.float32), batch_labels
def load_image(path, shape):
#img_pil = Image.open(path+'.png')
#img_cv = np.array(img_pil.filter(PIL.ImageFilter.FIND_EDGES))
img_cv = cv2.imread(path+'.png')
img_cv = cv2.cvtColor(img_cv,cv2.COLOR_BGR2RGB)
if img_cv.shape[1]==300 and img_cv.shape[0]>=450:
over_high = img_cv.shape[0]-300
start = np.random.randint(over_high//3,over_high,1)[0]
img_cv = img_cv[start:start+300,:,:]
elif img_cv.shape[0]==300 and img_cv.shape[1]>450:
over_width = img_cv.shape[1]-300
start = np.random.randint(over_width//3,over_width,1)[0]
img_cv = img_cv[:,start:start+300,:]
img_cv = cv2.resize(img_cv,(img_size,img_size))
return img_cv
def augment(image):
augment_img = iaa.Sequential([
iaa.SomeOf((0,4),[
#iaa.Crop(percent=(0, 0.1)),
iaa.ContrastNormalization((0.5, 1.5)),
iaa.Multiply((0.9, 1.1), per_channel=0.2),
iaa.Fliplr(0.5),
iaa.GaussianBlur(sigma=(0, 0.6)),
#iaa.Affine(
# scale={"x": (0.9, 1.1), "y": (0.9, 1.1)},
# translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)},
# rotate=(-10, 10),
# )
])], random_order=True)
image_aug = augment_img.augment_image(image)
return image_aug
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, load_model
from keras.layers import (Activation, Dropout, Flatten, Dense, GlobalMaxPooling2D,
BatchNormalization, Input, Conv2D, GlobalAveragePooling2D,Concatenate)
from keras import applications
from keras.callbacks import ModelCheckpoint
from keras import metrics
from keras.optimizers import Adam
from keras import backend as K
import keras
from keras.models import Model
# reference link: https://gist.github.com/drscotthawley/d1818aabce8d1bf082a6fb37137473ae
from keras.callbacks import Callback
def get_1cycle_schedule(lr_max=1e-3, n_data_points=8000, epochs=200, batch_size=40, verbose=0):
"""
Creates a look-up table of learning rates for 1cycle schedule with cosine annealing
See @sgugger's & @jeremyhoward's code in fastai library: https://github.com/fastai/fastai/blob/master/fastai/train.py
Wrote this to use with my Keras and (non-fastai-)PyTorch codes.
Note that in Keras, the LearningRateScheduler callback (https://keras.io/callbacks/#learningratescheduler) only operates once per epoch, not per batch
So see below for Keras callback
Keyword arguments:
lr_max chosen by user after lr_finder
n_data_points data points per epoch (e.g. size of training set)
epochs number of epochs
batch_size batch size
Output:
lrs look-up table of LR's, with length equal to total # of iterations
Then you can use this in your PyTorch code by counting iteration number and setting
optimizer.param_groups[0]['lr'] = lrs[iter_count]
"""
if verbose > 0:
print("Setting up 1Cycle LR schedule...")
pct_start, div_factor = 0.3, 25. # @sgugger's parameters in fastai code
lr_start = lr_max/div_factor
lr_end = lr_start/1e4
n_iter = (n_data_points * epochs // batch_size) + 1 # number of iterations
a1 = int(n_iter * pct_start)
a2 = n_iter - a1
# make look-up table
lrs_first = np.linspace(lr_start, lr_max, a1) # linear growth
lrs_second = (lr_max-lr_end)*(1+np.cos(np.linspace(0,np.pi,a2)))/2 + lr_end # cosine annealing
lrs = np.concatenate((lrs_first, lrs_second))
return lrs
class OneCycleScheduler(Callback):
"""My modification of Keras' Learning rate scheduler to do 1Cycle learning
which increments per BATCH, not per epoch
Keyword arguments
**kwargs: keyword arguments to pass to get_1cycle_schedule()
Also, verbose: int. 0: quiet, 1: update messages.
Sample usage (from my train.py):
lrsched = OneCycleScheduler(lr_max=1e-4, n_data_points=X_train.shape[0],
epochs=epochs, batch_size=batch_size, verbose=1)
"""
def __init__(self, **kwargs):
super(OneCycleScheduler, self).__init__()
self.verbose = kwargs.get('verbose', 0)
self.lrs = get_1cycle_schedule(**kwargs)
self.iteration = 0
def on_batch_begin(self, batch, logs=None):
lr = self.lrs[self.iteration]
K.set_value(self.model.optimizer.lr, lr) # here's where the assignment takes place
if self.verbose > 0:
print('\nIteration %06d: OneCycleScheduler setting learning '
'rate to %s.' % (self.iteration, lr))
self.iteration += 1
def on_epoch_end(self, epoch, logs=None): # this is unchanged from Keras LearningRateScheduler
logs = logs or {}
logs['lr'] = K.get_value(self.model.optimizer.lr)
self.iteration = 0
from keras.callbacks import Callback
import keras.backend as K
import numpy as np
import matplotlib.pyplot as plt
class CyclicLR(Callback):
"""This callback implements a cyclical learning rate policy (CLR).
The method cycles the learning rate between two boundaries with
some constant frequency, as detailed in this paper (https://arxiv.org/abs/1506.01186).
The amplitude of the cycle can be scaled on a per-iteration or
per-cycle basis.
This class has three built-in policies, as put forth in the paper.
"triangular":
A basic triangular cycle w/ no amplitude scaling.
"triangular2":
A basic triangular cycle that scales initial amplitude by half each cycle.
"exp_range":
A cycle that scales initial amplitude by gamma**(cycle iterations) at each
cycle iteration.
For more detail, please see paper.
# Example
```python
clr = CyclicLR(base_lr=0.001, max_lr=0.006,
step_size=2000., mode='triangular')
model.fit(X_train, Y_train, callbacks=[clr])
```
Class also supports custom scaling functions:
```python
clr_fn = lambda x: 0.5*(1+np.sin(x*np.pi/2.))
clr = CyclicLR(base_lr=0.001, max_lr=0.006,
step_size=2000., scale_fn=clr_fn,
scale_mode='cycle')
model.fit(X_train, Y_train, callbacks=[clr])
```
# Arguments
base_lr: initial learning rate which is the
lower boundary in the cycle.
max_lr: upper boundary in the cycle. Functionally,
it defines the cycle amplitude (max_lr - base_lr).
The lr at any cycle is the sum of base_lr
and some scaling of the amplitude; therefore
max_lr may not actually be reached depending on
scaling function.
step_size: number of training iterations per
half cycle. Authors suggest setting step_size
2-8 x training iterations in epoch.
mode: one of {triangular, triangular2, exp_range}.
Default 'triangular'.
Values correspond to policies detailed above.
If scale_fn is not None, this argument is ignored.
gamma: constant in 'exp_range' scaling function:
gamma**(cycle iterations)
scale_fn: Custom scaling policy defined by a single
argument lambda function, where
0 <= scale_fn(x) <= 1 for all x >= 0.
mode paramater is ignored
scale_mode: {'cycle', 'iterations'}.
Defines whether scale_fn is evaluated on
cycle number or cycle iterations (training
iterations since start of cycle). Default is 'cycle'.
"""
def __init__(self, base_lr=1e-5, max_lr=1e-2, step_size=1000., mode='triangular',
gamma=1., scale_fn=None, scale_mode='cycle'):
super(CyclicLR, self).__init__()
self.base_lr = base_lr
self.max_lr = max_lr
self.step_size = step_size
self.mode = mode
self.gamma = gamma
if scale_fn == None:
if self.mode == 'triangular':
self.scale_fn = lambda x: 1.
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = lambda x: 1/(2.**(x-1))
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = lambda x: gamma**(x)
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.clr_iterations = 0.
self.trn_iterations = 0.
self.history = {}
self._reset()
def _reset(self, new_base_lr=None, new_max_lr=None,
new_step_size=None):
"""Resets cycle iterations.
Optional boundary/step size adjustment.
"""
if new_base_lr != None:
self.base_lr = new_base_lr
if new_max_lr != None:
self.max_lr = new_max_lr
if new_step_size != None:
self.step_size = new_step_size
self.clr_iterations = 0.
def clr(self):
cycle = np.floor(1+self.clr_iterations/(2*self.step_size))
x = np.abs(self.clr_iterations/self.step_size - 2*cycle + 1)
if self.scale_mode == 'cycle':
return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(cycle)
else:
return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(self.clr_iterations)
def on_train_begin(self, logs={}):
logs = logs or {}
if self.clr_iterations == 0:
K.set_value(self.model.optimizer.lr, self.base_lr)
else:
K.set_value(self.model.optimizer.lr, self.clr())
def on_batch_end(self, epoch, logs=None):
logs = logs or {}
self.trn_iterations += 1
self.clr_iterations += 1
self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))
self.history.setdefault('iterations', []).append(self.trn_iterations)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
K.set_value(self.model.optimizer.lr, self.clr())
from keras.regularizers import l2
def create_model(input_shape, n_out):
input_tensor = Input(shape=input_shape)
#base_model = ResNet50V2(include_top=False,weights=None,input_tensor=input_tensor)
#base_model.load_weights('../input/keras-pretrain-model-weights/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5')
# x = Conv2D(32, kernel_size=(1,1), activation='relu')(base_model.output)
# x = Flatten()(x)
model = applications.DenseNet121(weights=None,include_top=False,input_tensor=input_tensor)
model.load_weights(model_weights_path)
x0 = model.output
x1 = GlobalAveragePooling2D()(x0)
x2 = GlobalMaxPooling2D()(x0)
x = Concatenate()([x1,x2])
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation='relu', kernel_regularizer=l2(5e-4))(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
#x = GlobalAveragePooling2D()(base_model.output)
#x = Dropout(0.5)(x)
#x = Dense(1024, activation='relu', kernel_regularizer=l2(5e-4))(x)
#x = Dropout(0.5)(x)
final_output = Dense(n_out, activation='sigmoid', name='final_output')(x)
model = Model(input_tensor, final_output)
return model
# create callbacks list
from keras.callbacks import (ModelCheckpoint, LearningRateScheduler,
EarlyStopping, ReduceLROnPlateau,CSVLogger)
from sklearn.model_selection import train_test_split
epochs = 17; warmup_batch_size = 128;mixup_batch_size = 64
batch_size = 64
checkpoint = ModelCheckpoint('denseNet121_focal.h5', monitor='val_loss', verbose=1,
save_best_only=True, mode='min', save_weights_only = True)
reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3,
verbose=1, mode='auto', epsilon=0.0001)
early = EarlyStopping(monitor="val_loss",
mode="min",
patience=7)
csv_logger = CSVLogger(filename='training_log.csv',
separator=',',
append=True)
# split data into train, valid
indexes = np.arange(train_dataset_info.shape[0])
train_indexes, valid_indexes = train_test_split(indexes, test_size=0.15, random_state=8)
# create train and valid datagens
train_generator = data_generator.create_train(
train_dataset_info[train_indexes], batch_size, (img_size,img_size,3), augument=True, mix=False)
train_mixup = data_generator.create_train(
train_dataset_info[train_indexes], mixup_batch_size, (img_size,img_size,3), augument=False, mix=True)
train_generator_warmup = data_generator.create_train(
train_dataset_info[train_indexes], warmup_batch_size, (img_size,img_size,3), augument=False)
validation_generator = data_generator.create_valid(
train_dataset_info[valid_indexes], batch_size, (img_size,img_size,3), augument=False)
lrsched = OneCycleScheduler(lr_max=1e-4, n_data_points=len(train_indexes),
epochs=1, batch_size=warmup_batch_size, verbose=0)
# callbacks_list = [checkpoint, csv_logger, lrsched]
# callbacks_list = [checkpoint, csv_logger, reduceLROnPlat]
model = create_model(
input_shape=(img_size,img_size,3),
n_out=nb_classes)
%%time
# warm up model
for layer in model.layers:
layer.trainable = False
for i in range(-6,0):
model.layers[i].trainable = True
model.compile(
loss='binary_crossentropy',
#loss='categorical_crossentropy',
#loss = [focal_loss(labels_sum)],
optimizer=Adam(1e-3))
# optimizer=AdamAccumulate(lr=1e-3, accum_iters=2))
model.fit_generator(
train_generator_warmup,
steps_per_epoch=np.ceil(float(len(train_indexes)) / float(warmup_batch_size)),
epochs=2,
max_queue_size=16,
workers=2,
use_multiprocessing=True,
verbose=1,
class_weight = 'auto'
#callbacks = lrsched
)
%%time
# train all layers
for layer in model.layers:
layer.trainable = True
# callbacks_list = [checkpoint, csv_logger, reduceLROnPlat]
model.compile(
loss='binary_crossentropy',
#loss='categorical_crossentropy',
#loss = [focal_loss(labels_sum)],
#loss=focal_loss,
#optimizer=Adam(lr=1e-4))
optimizer=AdamAccumulate(lr=1e-4, accum_iters=4))
model.fit_generator(
train_mixup,
steps_per_epoch=np.ceil(float(len(train_indexes)) / float(mixup_batch_size)),
# validation_data=validation_generator,
# validation_steps=np.ceil(float(len(valid_indexes)) / float(batch_size)),
epochs=7,
verbose=1,
class_weight = 'auto',
max_queue_size=16,
workers=WORKERS,
use_multiprocessing=True)
# callbacks=callbacks_list)
%%time
clr = CyclicLR(base_lr=1e-5,max_lr=6e-5,mode='triangular2', step_size=np.ceil(len(train_indexes)/batch_size))
model.compile(
loss='binary_crossentropy',
#loss='categorical_crossentropy',
#loss = [focal_loss(labels_sum)],
#loss=focal_loss,
optimizer=Adam(lr=3e-5))
# optimizer=AdamAccumulate(lr=1e-4, accum_iters=4))
#callbacks_list = [checkpoint, csv_logger, clr]
callbacks_list = [checkpoint, csv_logger, reduceLROnPlat]
model.fit_generator(
train_generator,
steps_per_epoch=np.ceil(float(len(train_indexes)) / float(batch_size)),
validation_data=validation_generator,
validation_steps=np.ceil(float(len(valid_indexes)) / float(batch_size)),
epochs=epochs,
verbose=1,
class_weight = 'auto',
max_queue_size=16, workers=WORKERS, use_multiprocessing=True,
callbacks=callbacks_list)
print(os.listdir('../working/'))
model.save('../input/denseNet121.h5')
#model.summary()
submit = pd.read_csv('../input/imet-2019-fgvc6/sample_submission.csv')
#model = keras.models.load_model('../input/denseNet121.h5',custom_objects={'focal_loss':focal_loss})
predicted = []
'''Search for the best threshold regarding the validation set'''
BATCH = 512
fullValGen = data_generator.create_valid(
train_dataset_info[valid_indexes], BATCH, (img_size,img_size,3))
n_val = round(train_dataset_info.shape[0]*0.15)//BATCH
print(n_val)
lastFullValPred = np.empty((0, nb_classes))
lastFullValLabels = np.empty((0, nb_classes))
for i in tqdm(range(n_val+1)):
im, lbl = next(fullValGen)
scores = model.predict(im)
lastFullValPred = np.append(lastFullValPred, scores, axis=0)
lastFullValLabels = np.append(lastFullValLabels, lbl, axis=0)
print(lastFullValPred.shape, lastFullValLabels.shape)
def my_f2(y_true, y_pred):
assert y_true.shape[0] == y_pred.shape[0]
tp = np.sum((y_true == 1) & (y_pred == 1), axis=1)
tn = np.sum((y_true == 0) & (y_pred == 0), axis=1)
fp = np.sum((y_true == 0) & (y_pred == 1), axis=1)
fn = np.sum((y_true == 1) & (y_pred == 0), axis=1)
p = tp / (tp + fp + K.epsilon())
r = tp / (tp + fn + K.epsilon())
f2 = (1+beta_f2**2)*p*r / (p*beta_f2**2 + r + 1e-15)
return np.mean(f2)
def find_best_fixed_threshold(preds, targs, do_plot=True):
score = []
thrs = np.arange(0, 0.5, 0.01)
for thr in tqdm(thrs):
score.append(my_f2(targs, (preds > thr).astype(int) ))
score = np.array(score)
pm = score.argmax()
best_thr, best_score = thrs[pm], score[pm].item()
print(f'thr={best_thr:.3f}', f'F2={best_score:.3f}')
if do_plot:
plt.plot(thrs, score)
plt.vlines(x=best_thr, ymin=score.min(), ymax=score.max())
plt.text(best_thr+0.03, best_score-0.01, f'$F_{2}=${best_score:.3f}', fontsize=14);
plt.show()
return best_thr, best_score
best_thr, best_score = find_best_fixed_threshold(lastFullValPred, lastFullValLabels, do_plot=True)
for i, name in tqdm(enumerate(submit['id'])):
path = os.path.join('../input/imet-2019-fgvc6/test/', name)
image = data_generator.load_image(path, (img_size,img_size,3))
#score_predict = model.predict(preprocess_input(image[np.newaxis]))
score_predict = model.predict((image[np.newaxis])/255.0)
# print(score_predict)
label_predict = np.arange(nb_classes)[score_predict[0]>=best_thr]
# print(label_predict)
str_predict_label = ' '.join(str(l) for l in label_predict)
predicted.append(str_predict_label)
submit['attribute_ids'] = predicted
submit.to_csv('submission.csv', index=False)