def main():
###build dataset
train_ds = DataIter(cfg.DATA.root_path, cfg.DATA.train_txt_path, True)
test_ds = DataIter(cfg.DATA.root_path, cfg.DATA.val_txt_path, False)
###build trainer
trainer = Train(train_ds=train_ds, val_ds=test_ds)
trainer.load_weight()
if cfg.TRAIN.vis:
for step in range(train_ds.size):
images, labels = train_ds()
for i in range(images.shape[0]):
example_image = np.array(images[i], dtype=np.uint8)
example_image = np.transpose(example_image, [1, 2, 0])
example_label = np.array(labels[i])
_h, _w, _ = example_image.shape
print(example_label.shape)
cv2.imshow('example', example_image)
cv2.waitKey(0)
### train
trainer.custom_loop()
def __init__(self):
self.train_ds = DataIter(cfg.DATA.root_path, cfg.DATA.train_txt_path,
True)
self.val_ds = DataIter(cfg.DATA.root_path, cfg.DATA.val_txt_path,
False)
self.inputs = []
self.outputs = []
self.val_outputs = []
self.ite_num = 1
self._graph = tf.Graph()
self.summaries = []
self.ema_weights = False
def main():
data = pd.read_json('folds.json', lines=True)
losscolector = []
folds = [0, 1, 2, 3, 4]
for fold in folds:
###build dataset
train_data = data[data['fold'] != fold]
val_data = data[data['fold'] == fold]
train_ds = DataIter(train_data, shuffle=True, training_flag=True)
val_ds = DataIter(val_data, shuffle=False, training_flag=False)
###build trainer
trainer = Train(train_ds=train_ds, val_ds=val_ds, fold=fold)
print('it is here')
if cfg.TRAIN.vis:
print('show it, here')
for step in range(train_ds.size):
images, data, labels = train_ds()
# images, mask, labels = cutmix_numpy(images, mask, labels, 0.5)
print(images.shape)
for i in range(images.shape[0]):
example_image = np.array(images[i, 0])
example_label = np.array(labels[i])
cv2.imshow('ss', example_image)
cv2.waitKey(0)
### train
loss, model = trainer.custom_loop()
losscolector.append([loss, model])
avg_loss = 0
for k, loss_and_model in enumerate(losscolector):
print('fold %d : loss %.5f modelname: %s' %
(k, loss_and_model[0], loss_and_model[1]))
avg_loss += loss_and_model[0]
print('average loss is ', avg_loss / 5.)
def main():
###build dataset
train_ds = DataIter(cfg.DATA.root_path, cfg.DATA.train_txt_path, True)
test_ds = DataIter(cfg.DATA.root_path, cfg.DATA.val_txt_path, False)
##build model
model = ShuffleNetV2(model_size='1.0x')
###build trainer
trainer = Train(model, train_ds=train_ds, val_ds=test_ds)
if cfg.TRAIN.vis:
for step in range(train_ds.size):
images, labels = train_ds()
for i in range(images.shape[0]):
example_image = np.array(images[i], dtype=np.uint8)
example_label = np.array(labels[i])
landmark = example_label[0:136].reshape([-1, 2])
_h, _w, _ = example_image.shape
print(landmark.shape)
for _index in range(landmark.shape[0]):
x_y = landmark[_index]
cv2.circle(example_image,
center=(int(x_y[0] * _w), int(x_y[1] * _w)),
color=(255, 255, 255),
radius=1,
thickness=-1)
cv2.imshow('example', example_image)
cv2.waitKey(0)
### train
trainer.custom_loop()
def __init__(self):
self.train_ds = DataIter(cfg.DATA.root_path,
cfg.DATA.train_txt_path,
training_flag=True)
self.val_ds = DataIter(cfg.DATA.root_path,
cfg.DATA.val_txt_path,
training_flag=False)
self.inputs = []
self.outputs = []
self.val_outputs = []
self.ite_num = 1
self._graph = tf.Graph()
with self._graph.as_default():
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
tf_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=tf_config)
self.summaries = []
self.ema_weights = False
from lib.core.model.ShuffleNet_Series.ShuffleNetV2.network import ShuffleNetV2
from lib.core.model.semodel.SeResnet import se_resnet50
import torch
import time
import argparse
import sklearn.metrics
from tqdm import tqdm
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import cv2
from train_config import config as cfg
cfg.TRAIN.batch_size = 1
ds = DataIter(cfg.DATA.root_path, cfg.DATA.val_txt_path, False)
def vis(model_path):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
###build model
model = se_resnet50()
model.load_state_dict(torch.load(model_path, map_location=device),
strict=False)
model.to(device)
model.eval()
cls1_pre_list = []
def main():
train_feature_file = '../lish-moa/train_features.csv'
target_file = '../lish-moa/train_targets_scored.csv'
noscore_target = '../lish-moa/train_targets_nonscored.csv'
train_features = pd.read_csv(train_feature_file)
labels = pd.read_csv(target_file)
extra_labels = pd.read_csv(noscore_target)
test_features = pd.read_csv('../lish-moa/test_features.csv')
pub_test_features=test_features.copy()
# ####
# ####
# GENES = [col for col in train_features.columns if col.startswith('g-')]
# CELLS = [col for col in train_features.columns if col.startswith('c-')]
# ####
# # RankGauss - transform to Gauss
# for col in (GENES + CELLS):
# transformer = QuantileTransformer(n_quantiles=100, random_state=0, output_distribution="normal")
# vec_len = len(train_features[col].values)
# vec_len_pub_test = len(pub_test_features[col].values)
# vec_len_test=len(test_features[col].values)
#
# data = np.concatenate([train_features[col].values.reshape(vec_len, 1),
# pub_test_features[col].values.reshape(vec_len_pub_test, 1)], axis=0)
#
# transformer.fit(data)
#
# train_features[col] = \
# transformer.transform(train_features[col].values.reshape(vec_len, 1)).reshape(1, vec_len)[0]
print(train_features.shape)
losscolector=[]
folds=[0,1,2,3,4,5,6]
seeds=[40,41,42,43,44]
n_fold=len(folds)
model_dicts=[{'name':'resnetlike','func':Complexer},
{'name':'densenetlike','func':Denseplexer},
{'name':'tablenet','func':Tablenet},
]
for model_dict in model_dicts:
# for cur_seed in seeds:
for cur_seed in seeds:
seed_everything(cur_seed)
#### 5 fols split
features = train_features.copy()
target_cols = [c for c in labels.columns if c not in ['sig_id']]
features['fold'] = -1
Fold = MultilabelStratifiedKFold(n_splits=n_fold, shuffle=True, random_state=cur_seed)
for fold, (train_index, test_index) in enumerate(Fold.split(features, labels[target_cols])):
features['fold'][test_index] = fold
for fold in folds:
logger.info('train with seed %d' % (cur_seed))
###build dataset
train_ind = features[features['fold'] != fold].index.to_list()
train_features_=features.iloc[train_ind].copy()
train_target_ = labels.iloc[train_ind].copy()
train_extra_Target_ = extra_labels.iloc[train_ind].copy()
val_ind=features.loc[features['fold'] == fold].index.to_list()
val_features_ = features.iloc[val_ind].copy()
val_target_ = labels.iloc[val_ind].copy()
val_extra_Target_ = extra_labels.iloc[val_ind].copy()
train_ds=DataIter(train_features_,train_target_,train_extra_Target_,shuffle=True,training_flag=True)
val_ds=DataIter(val_features_,val_target_,val_extra_Target_,shuffle=False,training_flag=False)
### build model
model=model_dict['func']()
model_name=str(model_dict['name']+str(cur_seed))
if cfg.TRAIN.pretrain_on_no_score:
###build trainer
trainer = Train(model_name='PRETRAIN_'+model_name, model=model, train_ds=train_ds, val_ds=val_ds, fold=fold)
trainer.pretrain=True
### pretrian first with no score
loss,best_model=trainer.custom_loop()
### train
trainer_fine = Train(model_name=model_name, model=model, train_ds=train_ds, val_ds=val_ds, fold=fold)
trainer_fine.load_from(best_model)
trainer_fine.pretrain=False
loss, best_model = trainer_fine.custom_loop()
else:
###build trainer
trainer = Train(model_name=model_name, model=model, train_ds=train_ds, val_ds=val_ds, fold=fold)
trainer.pretrain = False
loss, best_model = trainer.custom_loop()
if cfg.TRAIN.finetune_alldata:
### finetune with all data
train_features_ = features.copy()
train_target_ = labels.copy()
train_extra_Target_ = extra_labels.copy()
val_ind = features.loc[features['fold'] == fold].index.to_list()
val_features_ = features.iloc[val_ind].copy()
val_target_ = labels.iloc[val_ind].copy()
val_extra_Target_ = extra_labels.iloc[val_ind].copy()
train_ds = DataIter(train_features_, train_target_, train_extra_Target_, shuffle=True,
training_flag=True)
val_ds = DataIter(val_features_, val_target_, val_extra_Target_, shuffle=False, training_flag=False)
### build model
model = model_dict['func']()
model_name = str(model_dict['name'] + str(cur_seed))
###build trainer
trainer = Train(model_name=model_name, model=model, train_ds=train_ds, val_ds=val_ds, fold=fold)
trainer.reset(best_model)
loss, best_model = trainer.custom_loop()
losscolector.append([loss,best_model])
avg_loss=0
for k,loss_and_model in enumerate(losscolector):
print('fold %d : loss %.5f modelname: %s'%(k,loss_and_model[0],loss_and_model[1]))
avg_loss+=loss_and_model[0]
print('simple,average loss is ',avg_loss/(len(losscolector)))
def main():
epochs = cfg.TRAIN.epoch
batch_size = cfg.TRAIN.batch_size
enable_function = False
### set gpu memory
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
devices = ['/device:GPU:{}'.format(i) for i in range(cfg.TRAIN.num_gpu)]
strategy = tf.distribute.MirroredStrategy(devices)
##build model
with strategy.scope():
if 'ShuffleNet' in cfg.MODEL.net_structure:
model = SimpleFace_shufflenet()
else:
model = SimpleFace_mobilenet()
##run a time to build
image = np.zeros(shape=(1, 160, 160, 3), dtype=np.float32)
model(image)
###recover weights
if cfg.MODEL.pretrained_model is not None:
model.load_weights(cfg.MODEL.pretrained_model)
###build trainer
trainer = Train(epochs, enable_function, model, batch_size, strategy)
###build dataset
train_ds = DataIter(cfg.DATA.root_path, cfg.DATA.train_txt_path, True)
test_ds = DataIter(cfg.DATA.root_path, cfg.DATA.val_txt_path, False)
train_dataset = tf.data.Dataset.from_generator(
train_ds,
output_types=(tf.float32, tf.float32),
output_shapes=([None, None, None, None], [None,
cfg.MODEL.out_channel]))
test_dataset = tf.data.Dataset.from_generator(
test_ds,
output_types=(tf.float32, tf.float32),
output_shapes=([None, None, None, None], [None,
cfg.MODEL.out_channel]))
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
test_dataset = test_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
train_dist_dataset = strategy.experimental_distribute_dataset(
train_dataset)
test_dist_dataset = strategy.experimental_distribute_dataset(test_dataset)
if cfg.TRAIN.vis:
for images, labels in train_dist_dataset:
for i in range(images.shape[0]):
example_image = np.array(images[i], dtype=np.uint8)
example_label = np.array(labels[i])
landmark = example_label[0:136].reshape([-1, 2])
_h, _w, _ = example_image.shape
print(landmark.shape)
for _index in range(landmark.shape[0]):
x_y = landmark[_index]
cv2.circle(example_image,
center=(int(x_y[0] * _w), int(x_y[1] * _w)),
color=(255, 255, 255),
radius=1,
thickness=-1)
cv2.imshow('example', example_image)
cv2.waitKey(0)
### train
trainer.custom_loop(train_dist_dataset, test_dist_dataset, strategy)
def main():
epochs = cfg.TRAIN.epoch
batch_size = cfg.TRAIN.batch_size
enable_function = False
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
# strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = FaceBoxes()
###run a time to build the model
image = np.zeros(shape=(1, 512, 512, 3), dtype=np.float32)
model.inference(image)
## load pretrained weights
if cfg.MODEL.pretrained_model is not None:
logger.info('load pretrained params from %s' %
cfg.MODEL.pretrained_model)
model.load_weights(cfg.MODEL.pretrained_model)
### build trainer
trainer = Train(epochs, enable_function, model, batch_size, strategy)
### build dataiter
train_ds = DataIter(cfg.DATA.root_path, cfg.DATA.train_txt_path, True)
test_ds = DataIter(cfg.DATA.root_path, cfg.DATA.val_txt_path, False)
### it's a tensorpack data iter, produce a batch every iter
train_dataset = tf.data.Dataset.from_generator(
train_ds,
output_types=(tf.float32, tf.float32, tf.float32),
output_shapes=([None, None, None, None], [None, None,
None], [None, None]))
test_dataset = tf.data.Dataset.from_generator(
test_ds,
output_types=(tf.float32, tf.float32, tf.float32),
output_shapes=([None, None, None, None], [None, None,
None], [None, None]))
####
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
test_dataset = test_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
train_dist_dataset = strategy.experimental_distribute_dataset(
train_dataset)
test_dist_dataset = strategy.experimental_distribute_dataset(test_dataset)
## check the data
if cfg.TRAIN.vis:
for images, labels, matches in train_dist_dataset:
for i in range(images.shape[0]):
example_image = np.array(images[i], dtype=np.uint8)
example_image = cv2.cvtColor(example_image, cv2.COLOR_BGR2RGB)
example_label = np.array(labels[i])
cv2.imshow('example', example_image)
cv2.waitKey(0)
break
##train
trainer.custom_loop(train_dist_dataset, test_dist_dataset, strategy)
import matplotlib.pyplot as plt
from train_config import config as cfg
from lib.core.base_trainer.model import Complexer
from lib.dataset.dataietr import DataIter
data = pd.read_json('folds.json', lines=True)
cfg.TRAIN.batch_size = 1
pre_length = 91
fold = 0
###build dataset
train_data = data[data['fold'] != fold]
val_data = data[data['fold'] == fold]
train_ds = DataIter(train_data, shuffle=True, training_flag=True)
val_ds = DataIter(val_data, shuffle=True, training_flag=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
short_model = Complexer(pre_length=pre_length)
weight = './fold0_epoch_138_val_loss0.250854.pth'
short_model.load_state_dict(torch.load(weight, map_location=device))
short_model.to(device)
for i in range(val_data.size):
images_np, data, target = val_ds()
images = torch.from_numpy(images_np).to(device).float()
data = torch.from_numpy(data).to(device).float()
