def __init__(self):
try:
flags.DEFINE_string('classes', './data/coco.names',
'path to classes file')
flags.DEFINE_string('weights', './checkpoints/yolov3.tf',
'path to weights file')
flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_string('image', './data/girl.png',
'path to input image')
flags.DEFINE_string('tfrecord', None, 'tfrecord instead of image')
flags.DEFINE_string('output', './output.jpg',
'path to output image')
flags.DEFINE_integer('num_classes', 80,
'number of classes in the model')
except NameError:
print("再代入禁止")
self.yolo = YoloV3(classes=flags.FLAGS.num_classes)
self.yolo.load_weights(flags.FLAGS.weights).expect_partial()
#logging.info('weights loaded')
self.class_names = [
c.strip() for c in open(flags.FLAGS.classes).readlines()
]
FLAGS = flags.FLAGS
## Dataset/method options
flags.DEFINE_string('datasource', 'sinusoid',
'sinusoid or omniglot or miniimagenet or dclaw')
flags.DEFINE_string('expt_number', '0', '1 or 2 etc')
flags.DEFINE_string(
'expt_name', 'intershuffle',
'non_exclusive or intrashuffle or intershuffle or sin_noise')
flags.DEFINE_string(
'dclaw_pn', '1',
'1 or 2 or 3; dataset permutation number for dclaw. Does differnt train/val/test splits'
)
flags.DEFINE_integer(
'num_classes', 5,
'number of classes used in classification (e.g. 5-way classification).')
# oracle means task id is input (only suitable for sinusoid)
flags.DEFINE_string('baseline', None, 'oracle, or None')
## Training options
flags.DEFINE_integer('pretrain_iterations', 0,
'number of pre-training iterations.')
flags.DEFINE_integer(
'metatrain_iterations', 15000,
'number of metatraining iterations.') # 15k for omniglot, 50k for sinusoid
flags.DEFINE_integer('meta_batch_size', 25,
'number of tasks sampled per meta-update')
flags.DEFINE_float('meta_lr', 0.001, 'the base learning rate of the generator')
flags.DEFINE_integer(
'update_batch_size', 5,
# limitations under the License.
from tensorflow.compiler import vitis_vai
from dataset import get_images_infor_from_file, ImagenetSequence
from tensorflow.compat.v1 import flags
import tensorflow as tf
import numpy as np
import threading
import time
keras = tf.keras
# Get frozen ConcreteFunction
flags.DEFINE_string('input_graph', '', 'TensorFlow \'h5\' file to load.')
flags.DEFINE_string('eval_image_path', '/scratch/data/Imagenet/val_dataset',
'The directory where put the eval images')
flags.DEFINE_integer('nthreads', 8, 'thread number')
flags.DEFINE_integer('batch_iter', 2000, 'eval iterations')
flags.DEFINE_string('mode', 'perf', 'normal or perf mode')
FLAGS = flags.FLAGS
filePath = "./words.txt"
def run_func():
r = model(x[0])[0]
fp = open(filePath, "r")
data1 = fp.readlines()
fp.close()
result = tf.math.top_k(r, 5)
for k in range(5):
cnt = 0
for line in data1:
FLAGS = flags.FLAGS
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
normalizer = preprocessing.MaxAbsScaler()
audio_dir = os.path.join(os.getcwd(), 'music-data')
SONG_FN = 'dubstep.p'
#filenames = glob.glob(audio_dir + '/fma_small/' + '/*[0-9]/*')
with open(SONG_FN, 'rb') as f:
filenames = pickle.load(f)
flags.DEFINE_integer('batch', 32, 'Batch size')
flags.DEFINE_integer('epochs', 10, 'Number of iterations to train on the entire dataset')
flags.DEFINE_integer('latent', 100, 'Dimensionality of the latent space')
flags.DEFINE_string('model_path', '.', 'Path to model checkpoint')
flags.DEFINE_string('output_dir', '.', 'Path to model checkpoints and logs')
flags.DEFINE_string('dtype', 'float32', 'Floating point data type of tensorflow graph')
flags.DEFINE_boolean('train', False, 'Train the music GAN')
flags.DEFINE_integer('seed', -1, 'Random seed for data shuffling and latent vector generator')
flags.DEFINE_boolean('logging', False, 'Whether or not to log and checkpoint the training model')
flags.DEFINE_integer('sampling_rate', 14400, 'Sampling rate of loaded music files')
flags.DEFINE_float('g_lr', 1e-4, 'Learning rate of the generator')
flags.DEFINE_float('d_lr', 1e-6, 'Learning rate of the discriminator')
flags.DEFINE_float('dropout', 0.1, 'Dropout rate of the discriminator')
flags.DEFINE_integer('g_attn', 2, 'Number of multi-head attention layers in the generator')
flags.DEFINE_integer('d_attn', 4, 'Number of multi-head attention layers in the disciminator')
flags.DEFINE_float('noise', 0.05, 'Level of noise added to discriminator input data')
def define():
"""Define common flags."""
# yapf: disable
# common_flags.define() may be called multiple times in unit tests.
global _common_flags_defined
if _common_flags_defined:
return
_common_flags_defined = True
flags.DEFINE_integer('batch_size', 32,
'Batch size.')
flags.DEFINE_integer('crop_width', None,
'Width of the central crop for images.')
flags.DEFINE_integer('crop_height', None,
'Height of the central crop for images.')
flags.DEFINE_string('train_log_dir', '/tmp/attention_ocr/train',
'Directory where to write event logs.')
flags.DEFINE_string('dataset_name', 'fsns',
'Name of the dataset. Supported: fsns')
flags.DEFINE_string('split_name', 'train',
'Dataset split name to run evaluation for: test,train.')
flags.DEFINE_string('dataset_dir', None,
'Dataset root folder.')
flags.DEFINE_string('checkpoint', '',
'Path for checkpoint to restore weights from.')
flags.DEFINE_string('master',
'',
'BNS name of the TensorFlow master to use.')
# Model hyper parameters
flags.DEFINE_float('learning_rate', 0.004,
'learning rate')
flags.DEFINE_string('optimizer', 'momentum',
'the optimizer to use')
flags.DEFINE_float('momentum', 0.9,
'momentum value for the momentum optimizer if used')
flags.DEFINE_bool('use_augment_input', True,
'If True will use image augmentation')
# Method hyper parameters
# conv_tower_fn
flags.DEFINE_string('final_endpoint', 'Mixed_5d',
'Endpoint to cut inception tower')
# sequence_logit_fn
flags.DEFINE_bool('use_attention', True,
'If True will use the attention mechanism')
flags.DEFINE_bool('use_autoregression', True,
'If True will use autoregression (a feedback link)')
flags.DEFINE_integer('num_lstm_units', 256,
'number of LSTM units for sequence LSTM')
flags.DEFINE_float('weight_decay', 0.00004,
'weight decay for char prediction FC layers')
flags.DEFINE_float('lstm_state_clip_value', 10.0,
'cell state is clipped by this value prior to the cell'
' output activation')
# 'sequence_loss_fn'
flags.DEFINE_float('label_smoothing', 0.1,
'weight for label smoothing')
flags.DEFINE_bool('ignore_nulls', True,
'ignore null characters for computing the loss')
flags.DEFINE_bool('average_across_timesteps', False,
'divide the returned cost by the total label weight')
import os
import tensorflow as tf
from tensorflow import app
from tensorflow.contrib import slim
from tensorflow.compat.v1 import flags
import common_flags
import model_export_lib
FLAGS = flags.FLAGS
common_flags.define()
flags.DEFINE_string('export_dir', None, 'Directory to export model files to.')
flags.DEFINE_integer(
'image_width', None,
'Image width used during training (or crop width if used)'
' If not set, the dataset default is used instead.')
flags.DEFINE_integer(
'image_height', None,
'Image height used during training(or crop height if used)'
' If not set, the dataset default is used instead.')
flags.DEFINE_string('work_dir', '/tmp',
'A directory to store temporary files.')
flags.DEFINE_integer('version_number', 1, 'Version number of the model')
flags.DEFINE_bool(
'export_for_serving', True,
'Whether the exported model accepts serialized tf.Example '
'protos as input')
def get_checkpoint_path():
flags.DEFINE_string('model', './train_dir/resnet50_model_195.h5',
'TensorFlow \'GraphDef\' file to load.')
flags.DEFINE_bool('eval_tfrecords', True, 'If True then use tf_records data .')
flags.DEFINE_string('data_dir', '/data3/datasets/Kaggle/fruits-360/tf_records',
'The directory where put the eval images')
flags.DEFINE_bool('eval_images', False, 'If True then use tf_records data .')
flags.DEFINE_string('eval_image_path',
'/data3/datasets/Kaggle/fruits-360/val_for_tf2',
'The directory where put the eval images')
flags.DEFINE_string('eval_image_list',
'/data3/datasets/Kaggle/fruits-360/val_labels.txt',
'file has validation images list')
flags.DEFINE_string('save_path', "train_dir", 'The directory where save model')
flags.DEFINE_string('filename', "resnet50_model_{epoch}.h5",
'The name of sved model')
flags.DEFINE_integer('label_offset', 1, 'label offset')
flags.DEFINE_string('gpus', '0', 'The gpus used for running evaluation.')
flags.DEFINE_bool('eval_only', False,
'If True then do not train model, only eval model.')
flags.DEFINE_bool(
'save_whole_model', False,
'as applications h5 file just include weights if true save whole model to h5 file.'
)
flags.DEFINE_bool('use_synth_data', False,
'If True then use synth data other than imagenet.')
flags.DEFINE_bool(
'save_best_only', False,
'If True then only save a model if `val_loss` has improved..')
flags.DEFINE_integer('train_step', None, 'Train step number')
flags.DEFINE_integer('batch_size', 32, 'Train batch size')
flags.DEFINE_integer('epochs', 200, 'Train epochs')
import numpy as np
import random
import tensorflow as tf
from load_data import DataGenerator
from tensorflow.compat.v1 import flags
from tensorflow.keras import layers
from matplotlib import pyplot as plt
FLAGS = flags.FLAGS
flags.DEFINE_integer(
'num_classes', 2,
'number of classes used in classification (e.g. 5-way classification).')
flags.DEFINE_integer(
'num_samples', 1,
'number of examples used for inner gradient update (K for K-shot learning).'
)
flags.DEFINE_integer('meta_batch_size', 128,
'Number of N-way classification tasks per batch')
def loss_function(preds, labels):
"""
Computes MANN loss
Args:
preds: [B, K+1, N, N] network output
labels: [B, K+1, N, N] labels
Returns:
scalar cross-entropy loss
import logging
import tensorflow as tf
from tensorflow.contrib import slim
from tensorflow import app
from tensorflow.compat.v1 import flags
from tensorflow.contrib.tfprof import model_analyzer
import data_provider
import common_flags
FLAGS = flags.FLAGS
common_flags.define()
# yapf: disable
flags.DEFINE_integer('task', 0,
'The Task ID. This value is used when training with '
'multiple workers to identify each worker.')
flags.DEFINE_integer('ps_tasks', 0,
'The number of parameter servers. If the value is 0, then'
' the parameters are handled locally by the worker.')
flags.DEFINE_integer('save_summaries_secs', 60,
'The frequency with which summaries are saved, in '
'seconds.')
flags.DEFINE_integer('save_interval_secs', 600,
'Frequency in seconds of saving the model.')
flags.DEFINE_integer('max_number_of_steps', int(1e10),
'The maximum number of gradient steps.')
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.compat.v1 import flags
from tensorflow.keras import optimizers
import sys, os
import config as conf
#set up and parse custom flags
flags.DEFINE_integer('model_version', conf.version, "Width of the image")
flags.DEFINE_boolean('rebuild', False, "Drop the checkpoint weights and rebuild model from scratch")
flags.DEFINE_string('lib_folder', conf.lib_folder, "Local library folder")
FLAGS = flags.FLAGS
#mount the library folder
sys.path.append(os.path.abspath(FLAGS.lib_folder))
from data import MNISTProcessor
import visualizer as v
#load data
data_processor = MNISTProcessor(conf.data_path, conf.train_labels,
conf.train_images, '', '')
x_data_train, y_data_train = data_processor.load_train(normalize=True).get_training_data()
#initialize the network
input_layer = Input(shape=(784,), name='input')
network = Dense(152, activation='tanh', name='dense_1')(input_layer)
network = Dense(76, activation='tanh', name='dense_2')(network)
network = Dense(38, activation='tanh', name='dense_3')(network)
network = Dense(4, activation='tanh', name='dense_4')(network)
network = Dense(38, activation='tanh', name='dense_5')(network)
import split_train_test
import fit_lstm
import evaluate
import SAVE
import make_prediction
from tensorflow.compat.v1 import flags
import pandas as pd
import os
flags.DEFINE_string("path", "./data/catgwise/생활+건강/", "path to data file")
flags.DEFINE_string("click_data", "clicks_ma_ratio", "clicks_minmax, clicks_first_ratio, clicks_ma_ratio")
flags.DEFINE_integer("s", 60, "seasonality")
flags.DEFINE_float("dropout", 0, "dropout rate(default=0)")
flags.DEFINE_integer("epoch", 40, "epoch")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("pred_time", 30, "how much time to predict")
flags.DEFINE_string("pred_index", "05-01-2020", "when beginning prediction(month-date-year), default:'01-01-2020'")
flags.DEFINE_boolean("bi", True,"true if bidirectional")
FLAGS = flags.FLAGS
catg_lst = os.listdir(FLAGS.path)
#temppollsell=[[True, True, True], [True, True, False], [True, False, True], [True, False, False],
#[False, True, True], [False, True, False], [False, False, True], [False,False,False]]
temppollsell=[[True, True, False], [True, False, False],
[False, True, False], [False,False,False]]
for category in catg_lst:
data_path = "{}{}".format(FLAGS.path, category)
file = pd.read_csv(data_path, encoding='CP949')
file['date'] = pd.to_datetime(file['date'])
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers
from tensorflow.compat.v1 import flags
import tensorflow.keras as Keras
from keras.callbacks import ModelCheckpoint
import sys, os
import config as conf
#set up and parse custom flags
flags.DEFINE_integer('model_version', conf.version, "Width of the image")
flags.DEFINE_boolean(
'rebuild', False,
"Drop the checkpoint weights and rebuild model from scratch")
flags.DEFINE_string('lib_folder', conf.lib_folder, "Local library folder")
flags.DEFINE_integer('encoder_version', 1, "Autoencoder version to use")
FLAGS = flags.FLAGS
#mount the library folder
sys.path.append(os.path.abspath(FLAGS.lib_folder))
from data import MNISTProcessor
#load data
data_processor = MNISTProcessor(conf.data_path, conf.train_labels,
conf.train_images, '', '')
x_data_train, y_data_train = data_processor.load_train(
normalize=True).get_training_data()
# Load the autoencoder model, including its weights and then process images
autoencoder = Keras.models.load_model(conf.autoencoder_model_path + '/' +
str(FLAGS.encoder_version))
A simple usage example:
python eval.py
"""
import tensorflow as tf
from tensorflow.contrib import slim
from tensorflow import app
from tensorflow.compat.v1 import flags
import data_provider
import common_flags
FLAGS = flags.FLAGS
common_flags.define()
# yapf: disable
flags.DEFINE_integer('num_batches', 100,
'Number of batches to run eval for.')
flags.DEFINE_string('eval_log_dir', '/tmp/attention_ocr/eval',
'Directory where the evaluation results are saved to.')
flags.DEFINE_integer('eval_interval_secs', 60,
'Frequency in seconds to run evaluations.')
flags.DEFINE_integer('number_of_steps', None,
'Number of times to run evaluation.')
# yapf: enable
def main(_):
if not tf.io.gfile.exists(FLAGS.eval_log_dir):
tf.io.gfile.makedirs(FLAGS.eval_log_dir)
