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()
]
'T': 101.04768,
'W': 186.07931,
'V': 99.06841,
'Y': 163.06333,
'M(ox)': 147.035405,
'groupCH3': 14.01565,
'groupOH': 17.00274,
'groupH': 1.007825,
'groupH2O': 18.01057,
'groupCH3CO': 42.01057,
'groupO': 15.994915,
'groupNH3': 17.02655
}
FLAGS = flags.FLAGS
flags.DEFINE_string('input_data', '', 'Input data filepath.')
flags.DEFINE_string('output_data_dir', '', 'Input data filepath.')
flags.DEFINE_bool('clean_peptides', False,
'True if peptide modifications are in [x] format.')
flags.DEFINE_string('sequence_col', _MOD_SEQUENCE,
'Modified sequence column name in the input file.')
flags.DEFINE_string('charge_col', _CHARGE,
'Charge column name in the input file.')
flags.DEFINE_string('fragmentation_col', _FRAGMENTATION,
'Fragmentation column name in the input file.')
flags.DEFINE_string('analyzer_col', _MASS_ANALYZER,
'Mass analyzer column name in the input file.')
def generate_json_inputs(data, encoding):
"""Generates inputs to-be stored into a JSON file.
"""Entry point for the training and testing jobs."""
# pylint: disable=invalid-name
import csv
import pickle
import random
from data_generator import DataGenerator
from maml import MAML
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import flags
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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# 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)
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')
flags.DEFINE_integer('heads', 8, 'Number of heads in ALL multi-head attention blocks')
flags.DEFINE_integer('d_model', 768, 'Multi-head attention dimensionality')
flags.DEFINE_boolean('save_data', False, 'Save all training data to a file that will be loaded into memory')
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 io
import pandas as pd
import tensorflow as tf
import tensorflow.compat.v1.flags as flags
import sys
sys.path.append("../../models/research")
from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict
#flags = tf.app.flags
flags.DEFINE_string("csv_input", "", "Path to the CSV input")
flags.DEFINE_string("output_path", "", "Path to output TFRecord")
flags.DEFINE_string(
"label_map",
"",
"Path to the `label_map.pbtxt` contains the <class_name>:<class_index> pairs generated by `xml_to_csv.py` or manually.",
)
# if your image has more labels input them as
# flags.DEFINE_string('label0', '', 'Name of class[0] label')
# flags.DEFINE_string('label1', '', 'Name of class[1] label')
# and so on.
flags.DEFINE_string("img_path", "", "Path to images")
FLAGS = flags.FLAGS
def split(df, group):
--export_dir=/tmp/attention_ocr_export
"""
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')
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.compat.v1 import flags
from tensorflow.keras.optimizers import RMSprop
from dataset import synth_input_fn
from dataset import input_fn, NUM_IMAGES
from dataset import get_images_infor_from_file, ImagenetSequence
keras = tf.keras
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_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.')
flags.DEFINE_string('checkpoint_inception', '',
'Checkpoint to recover inception weights from.')
flags.DEFINE_float('clip_gradient_norm', 2.0,
'If greater than 0 then the gradients would be clipped by '
'it.')
flags.DEFINE_bool('sync_replicas', False,
'If True will synchronize replicas during training.')
flags.DEFINE_integer('replicas_to_aggregate', 1,
'The number of gradients updates before updating params.')
flags.DEFINE_integer('total_num_replicas', 1,
'Total number of worker replicas.')
flags.DEFINE_integer('startup_delay_steps', 15,
import numpy as np
import PIL.Image
import tensorflow as tf
from tensorflow.compat.v1 import flags
from tensorflow.python.training import monitored_session
import common_flags
import datasets
import data_provider
FLAGS = flags.FLAGS
common_flags.define()
# e.g. ./datasets/data/fsns/temp/fsns_train_%02d.png
flags.DEFINE_string('image_path_pattern', '',
'A file pattern with a placeholder for the image index.')
def get_dataset_image_size(dataset_name):
# Ideally this info should be exposed through the dataset interface itself.
# But currently it is not available by other means.
ds_module = getattr(datasets, dataset_name)
height, width, _ = ds_module.DEFAULT_CONFIG['image_shape']
return width, height
def load_images(file_pattern, batch_size, dataset_name):
width, height = get_dataset_image_size(dataset_name)
images_actual_data = np.ndarray(shape=(batch_size, height, width, 3),
dtype='uint8')
for i in range(batch_size):
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)
_ION_COLUMNS = ['b', 'b-H2O', 'b-NH3', 'y', 'y-H2O', 'y-NH3']
_MOD_SEQUENCE = 'ModifiedSequence'
_PRECURSOR_CHARGE = 'PrecursorCharge'
_CHARGE = 'Charge'
_FRAGMENTATION = 'Fragmentation'
_MASS_ANALYZER = 'MassAnalyzer'
_LENGTH = 'Length'
_POSITION_COL = 'FragmentNumber'
_ION_COL = 'FragmentType'
_ABUNDANCE_COL = 'RelativeIntensity'
_LOSS_TYPE = 'FragmentLossType'
_FRAG_CHARGE = 'FragmentCharge'
FLAGS = flags.FLAGS
flags.DEFINE_string('metadata_file', None, 'Path to a TSV file with metadata.')
flags.DEFINE_string('input_data_pattern', None, 'Input data filename pattern.')
flags.DEFINE_enum('label_dim', '2', ['2', '6'],
'Number of features in the output/label time step')
flags.DEFINE_string('output_data_dir', None,
'Directory with prediction outputs.')
flags.DEFINE_bool('neutral_losses', False,
'True if H2O and NH3 losses are modeled.')
flags.DEFINE_bool(
'batch_prediction', True,
'True if batch prediction instead of online was used to generate outputs.')
flags.DEFINE_string(
'add_input_data_pattern', None,
('Input data filename pattern for additional features to-be included to '
'the final outptu. These inputs should be formatted in the same way as'
'the model outputs - ie, JSON format with "key" and "output" values,'
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'])
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)
dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
model = common_flags.create_model(dataset.num_char_classes,