Spaces:
Running
Running
File size: 8,090 Bytes
9016314 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
import os
import logging
from collections import defaultdict
import numpy as np
import pickle
import tensorflow as tf
from pprint import pformat
from .utils import visualize, plot_functions, plot_img_functions
class Runner(object):
def __init__(self, args, model):
self.args = args
self.sess = model.sess
self.model = model
def set_dataset(self, trainset, validset, testset):
self.trainset = trainset
self.validset = validset
self.testset = testset
def train(self):
train_metrics = []
num_batches = self.trainset.num_batches
self.trainset.initialize()
for i in range(num_batches):
batch = self.trainset.next_batch()
metric, summ, step, _ = self.model.execute(
[self.model.metric, self.model.summ_op,
self.model.global_step, self.model.train_op],
batch)
if (self.args.summ_freq > 0) and (i % self.args.summ_freq == 0):
self.model.writer.add_summary(summ, step)
train_metrics.append(metric)
train_metrics = np.concatenate(train_metrics, axis=0)
return np.mean(train_metrics)
def valid(self):
valid_metrics = []
num_batches = self.validset.num_batches
self.validset.initialize()
for i in range(num_batches):
batch = self.validset.next_batch()
metric = self.model.execute(self.model.metric, batch)
valid_metrics.append(metric)
valid_metrics = np.concatenate(valid_metrics, axis=0)
return np.mean(valid_metrics)
def valid_mse(self):
valid_mse = []
num_batches = self.validset.num_batches
self.validset.initialize()
for i in range(num_batches):
batch = self.validset.next_batch()
sample = self.model.execute(self.model.sample, batch)
mse = np.mean(np.sum(np.square(sample-batch['x']), axis=tuple(range(2,sample.ndim))), axis=1)
valid_mse.append(mse)
valid_mse = np.concatenate(valid_mse, axis=0)
return np.mean(valid_mse)
def valid_chd(self):
pass
def valid_emd(self):
pass
def test(self):
test_metrics = []
num_batches = self.testset.num_batches
self.testset.initialize()
for i in range(num_batches):
batch = self.testset.next_batch()
metric = self.model.execute(self.model.metric, batch)
test_metrics.append(metric)
test_metrics = np.concatenate(test_metrics)
return np.mean(test_metrics)
def test_mse(self):
test_mse = []
num_batches = self.testset.num_batches
self.testset.initialize()
for i in range(num_batches):
batch = self.testset.next_batch()
sample = self.model.execute(self.model.sample, batch)
mse = np.mean(np.sum(np.square(sample-batch['x']), axis=tuple(range(2,sample.ndim))), axis=1)
test_mse.append(mse)
test_mse = np.concatenate(test_mse, axis=0)
return np.mean(test_mse)
def test_chd(self):
pass
def test_emd(self):
pass
def run(self):
logging.info('==== start training ====')
best_train_metric = -np.inf
best_valid_metric = -np.inf
best_test_metric = -np.inf
for epoch in range(self.args.epochs):
train_metric = self.train()
valid_metric = self.valid()
test_metric = self.test()
# save
if train_metric > best_train_metric:
best_train_metric = train_metric
if valid_metric > best_valid_metric:
best_valid_metric = valid_metric
self.model.save()
if test_metric > best_test_metric:
best_test_metric = test_metric
logging.info("Epoch %d, train: %.4f/%.4f, valid: %.4f/%.4f test: %.4f/%.4f" %
(epoch, train_metric, best_train_metric,
valid_metric, best_valid_metric,
test_metric, best_test_metric))
# evaluate
if epoch % 100 == 0:
logging.info('==== start evaluating ====')
self.evaluate(folder=f'{epoch}', load=False)
self.model.save('last')
# finish
logging.info('==== start evaluating ====')
self.evaluate(load=True)
def evaluate(self, folder='test', load=True):
save_dir = f'{self.args.exp_dir}/evaluate/{folder}/'
os.makedirs(save_dir, exist_ok=True)
if load: self.model.load()
# # likelihood
if 'likel' in self.args.eval_metrics:
valid_likel = self.valid()
test_likel = self.test()
logging.info(f"likelihood => valid: {valid_likel} test: {test_likel}")
# # mse
if 'mse' in self.args.eval_metrics:
valid_mse = self.valid_mse()
test_mse = self.test_mse()
logging.info(f"mse => valid: {valid_mse} test: {test_mse}")
if 'chd' in self.args.eval_metrics:
valid_chd = self.valid_chd()
test_chd = self.test_chd()
logging.info(f"chd => valid: {valid_chd} test: {test_chd}")
if 'emd' in self.args.eval_metrics:
valid_emd = self.valid_emd()
test_emd = self.test_emd()
logging.info(f"emd => valid: {valid_emd} test: {test_emd}")
if 'sam' in self.args.eval_metrics:
# train set
self.trainset.initialize()
batch = self.trainset.next_batch()
train_sample = self.model.execute(self.model.sample, batch)
visualize(train_sample, batch, f'{save_dir}/train_sam')
# valid set
self.validset.initialize()
batch = self.validset.next_batch()
valid_sample = self.model.execute(self.model.sample, batch)
visualize(valid_sample, batch, f'{save_dir}/valid_sam')
# test set
self.testset.initialize()
batch = self.testset.next_batch()
test_sample = self.model.execute(self.model.sample, batch)
visualize(test_sample, batch, f'{save_dir}/test_sam')
if 'fns' in self.args.eval_metrics:
# train set
self.trainset.initialize()
batch = self.trainset.next_batch()
train_mean, train_std = self.model.execute([self.model.mean, self.model.std], batch)
plot_functions(train_mean, train_std, batch, f'{save_dir}/train_fn')
# valid set
self.validset.initialize()
batch = self.validset.next_batch()
valid_mean, valid_std = self.model.execute([self.model.mean, self.model.std], batch)
plot_functions(valid_mean, valid_std, batch, f'{save_dir}/valid_fn')
# test set
self.testset.initialize()
batch = self.testset.next_batch()
test_mean, test_std = self.model.execute([self.model.mean, self.model.std], batch)
plot_functions(test_mean, test_std, batch, f'{save_dir}/test_fn')
if 'imfns' in self.args.eval_metrics:
# train set
self.trainset.initialize()
batch = self.trainset.next_batch()
train_mean, train_std = self.model.execute([self.model.mean, self.model.std], batch)
plot_img_functions(train_mean, train_std, batch, f'{save_dir}/train_fn')
# valid set
self.validset.initialize()
batch = self.validset.next_batch()
valid_mean, valid_std = self.model.execute([self.model.mean, self.model.std], batch)
plot_img_functions(valid_mean, valid_std, batch, f'{save_dir}/valid_fn')
# test set
self.testset.initialize()
batch = self.testset.next_batch()
test_mean, test_std = self.model.execute([self.model.mean, self.model.std], batch)
plot_img_functions(test_mean, test_std, batch, f'{save_dir}/test_fn')
|