from flor import Flog
if Flog.flagged():
flog = Flog(False)
Flog.flagged() and flog.write({'file_path':
'/Users/rogarcia/sandbox/iris/iris_annotated.py', 'lsn': 0})
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import train_test_split
import time
start_time = time.time()
Flog.flagged() and flog.write({'locals': [{'start_time': flog.serialize(
start_time)}], 'lineage': 'start_time = time.time()', 'lsn': 1})
iris = datasets.load_iris()
Flog.flagged() and flog.write({'locals': [{'iris': flog.serialize(iris)}],
'lineage': 'iris = datasets.load_iris()', 'lsn': 2})
X_tr, X_te, y_tr, y_te = train_test_split(iris.data, iris.target, test_size
=FLOR_PARAM('test_size', 0.15), random_state=FLOR_PARAM('random_state',
430))
Flog.flagged() and flog.write({'locals': [{'X_tr': flog.serialize(X_tr)}, {
'X_te': flog.serialize(X_te)}, {'y_tr': flog.serialize(y_tr)}, {'y_te':
flog.serialize(y_te)}], 'lineage':
'X_tr, X_te, y_tr, y_te = train_test_split(iris.data, iris.target, test_size =FLOR_PARAM("test_size", 0.15), random_state=FLOR_PARAM("random_state", 430))'
, 'lsn': 3})
clf = svm.SVC(gamma=FLOR_PARAM('gamma', 0.001), C=FLOR_PARAM('C', 100.0))
Flog.flagged() and flog.write({'locals': [{'clf': flog.serialize(clf)}],
'lineage':
'clf = svm.SVC(gamma=FLOR_PARAM("gamma", 0.001), C=FLOR_PARAM("C", 100.0))'
, 'lsn': 4})
clf.fit(X_tr, y_tr)
score = FLOR_METRIC('score', clf.score(X_te, y_te))
Flog.flagged() and flog.write({'locals': [{'score': flog.serialize(score)}],
from flor import Flog
if Flog.flagged():
flog = Flog(False)
Flog.flagged() and flog.write({
'file_path': '/Users/rogarcia/sandbox/iris_loop/iris_raw.py',
'lsn': 0
})
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import train_test_split
import random
import time
start_time = time.time()
Flog.flagged() and flog.write(
{
'locals': [{
'start_time': flog.serialize(start_time)
}],
'lineage': 'start_time = time.time()',
'lsn': 1
})
iris = datasets.load_iris()
Flog.flagged() and flog.write({
'locals': [{
'iris': flog.serialize(iris)
}],
'lineage': 'iris = datasets.load_iris()',
'lsn': 2
})
X_tr, X_te, y_tr, y_te = train_test_split(iris.data,
iris.target,
def train(epoch):
try:
if Flog.flagged(option='start_function'):
flog = Flog()
Flog.flagged() and flog.write({'file_path': 'train.py', 'lsn': 0})
Flog.flagged() and flog.write({'start_function': 'train', 'lsn': 1})
Flog.flagged() and flog.write({
'lsn':
2,
'params': [{
'0.raw.epoch': flog.serialize(epoch, 'epoch')
}]
})
net.train()
for batch_index, (images,
labels) in enumerate(cifar100_training_loader):
Flog.flagged() and flog.write({'start_loop': 33, 'lsn': 4})
if epoch <= args.warm:
Flog.flagged() and flog.write({
'conditional_fork': '(epoch <= args.warm)',
'lsn': 6
})
warmup_scheduler.step()
else:
Flog.flagged() and flog.write({
'conditional_fork': 'not ((epoch <= args.warm))',
'lsn': 7
})
images = Variable(images)
Flog.flagged() and flog.write(
{
'locals': [{
'images': flog.serialize(images, 'images')
}],
'lineage': 'images = Variable(images)',
'lsn': 8
})
labels = Variable(labels)
Flog.flagged() and flog.write(
{
'locals': [{
'labels': flog.serialize(labels, 'labels')
}],
'lineage': 'labels = Variable(labels)',
'lsn': 9
})
labels = labels.cuda()
Flog.flagged() and flog.write(
{
'locals': [{
'labels': flog.serialize(labels, 'labels')
}],
'lineage': 'labels = labels.cuda()',
'lsn': 10
})
images = images.cuda()
Flog.flagged() and flog.write(
{
'locals': [{
'images': flog.serialize(images, 'images')
}],
'lineage': 'images = images.cuda()',
'lsn': 11
})
optimizer.zero_grad()
outputs = net(images)
Flog.flagged() and flog.write(
{
'locals': [{
'outputs': flog.serialize(outputs, 'outputs')
}],
'lineage': 'outputs = net(images)',
'lsn': 12
})
loss = loss_function(outputs, labels)
Flog.flagged() and flog.write(
{
'locals': [{
'loss': flog.serialize(loss, 'loss')
}],
'lineage': 'loss = loss_function(outputs, labels)',
'lsn': 13
})
loss.backward()
optimizer.step()
n_iter = (epoch -
1) * len(cifar100_training_loader) + batch_index + 1
Flog.flagged() and flog.write({
'locals': [{
'n_iter': flog.serialize(n_iter, 'n_iter')
}],
'lineage':
'n_iter = (epoch - 1) * len(cifar100_training_loader) + batch_index + 1',
'lsn':
14
})
last_layer = list(net.children())[-1]
Flog.flagged() and flog.write({
'locals': [{
'last_layer':
flog.serialize(last_layer, 'last_layer')
}],
'lineage':
'last_layer = list(net.children())[-1]',
'lsn':
15
})
for name, para in last_layer.named_parameters():
Flog.flagged() and flog.write({'start_loop': 52, 'lsn': 16})
if 'weight' in name:
Flog.flagged() and flog.write({
'conditional_fork': '("weight" in name)',
'lsn': 18
})
writer.add_scalar('LastLayerGradients/grad_norm2_weights',
para.grad.norm(), n_iter)
else:
Flog.flagged() and flog.write({
'conditional_fork': 'not (("weight" in name))',
'lsn': 19
})
if 'bias' in name:
Flog.flagged() and flog.write({
'conditional_fork': '("bias" in name)',
'lsn': 20
})
writer.add_scalar('LastLayerGradients/grad_norm2_bias',
para.grad.norm(), n_iter)
else:
Flog.flagged() and flog.write({
'conditional_fork': 'not (("bias" in name))',
'lsn': 21
})
Flog.flagged() and flog.write({'end_loop': 52, 'lsn': 17})
print(
'Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLR: {:0.6f}'
.format(loss.item(),
optimizer.param_groups[0]['lr'],
epoch=epoch,
trained_samples=batch_index * args.b + len(images),
total_samples=len(cifar100_training_loader.dataset)))
writer.add_scalar('Train/loss', loss.item(), n_iter)
Flog.flagged() and flog.write({'end_loop': 33, 'lsn': 5})
for name, param in net.named_parameters():
Flog.flagged() and flog.write({'start_loop': 69, 'lsn': 22})
layer, attr = os.path.splitext(name)
Flog.flagged() and flog.write({
'locals': [{
'layer': flog.serialize(layer, 'layer')
}, {
'attr': flog.serialize(attr, 'attr')
}],
'lineage':
'layer, attr = os.path.splitext(name)',
'lsn':
24
})
attr = attr[1:]
Flog.flagged() and flog.write(
{
'locals': [{
'attr': flog.serialize(attr, 'attr')
}],
'lineage': 'attr = attr[1:]',
'lsn': 25
})
writer.add_histogram('{}/{}'.format(layer, attr), param, epoch)
Flog.flagged() and flog.write({'end_loop': 69, 'lsn': 23})
finally:
Flog.flagged() and flog.write({'end_function': 'train', 'lsn': 3})
Flog.flagged(option='end_function') and flog.writer.close()
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import train_test_split
from flor import OpenLog
import time
start_time = time.time()
with OpenLog('iris_raw', depth_limit=2):
from flor import Flog
if Flog.flagged():
flog = Flog()
Flog.flagged() and flog.write(
{'file_path': '/Users/rogarcia/git/flor/examples/iris/iris.py'})
iris = datasets.load_iris()
Flog.flagged() and flog.write({
'locals': [{
'iris': flog.serialize(iris)
}],
'lineage': 'iris = datasets.load_iris()'
})
X_tr, X_te, y_tr, y_te = train_test_split(iris.data,
iris.target,
test_size=0.15,
random_state=430)
Flog.flagged() and flog.write({
'locals': [{
'X_tr': flog.serialize(X_tr)
}, {
'X_te': flog.serialize(X_te)
def eval_training(epoch):
try:
if Flog.flagged(option='start_function'):
flog = Flog()
Flog.flagged() and flog.write({'file_path': 'train.py', 'lsn': 0})
Flog.flagged() and flog.write({
'start_function': 'eval_training',
'lsn': 1
})
Flog.flagged() and flog.write({
'lsn':
2,
'params': [{
'0.raw.epoch': flog.serialize(epoch, 'epoch')
}]
})
net.eval()
test_loss = 0.0
Flog.flagged() and flog.write(
{
'locals': [{
'test_loss': flog.serialize(test_loss, 'test_loss')
}],
'lineage': 'test_loss = 0.0',
'lsn': 4
})
correct = 0.0
Flog.flagged() and flog.write(
{
'locals': [{
'correct': flog.serialize(correct, 'correct')
}],
'lineage': 'correct = 0.0',
'lsn': 5
})
for images, labels in cifar100_test_loader:
Flog.flagged() and flog.write({'start_loop': 80, 'lsn': 6})
images = Variable(images)
Flog.flagged() and flog.write(
{
'locals': [{
'images': flog.serialize(images, 'images')
}],
'lineage': 'images = Variable(images)',
'lsn': 8
})
labels = Variable(labels)
Flog.flagged() and flog.write(
{
'locals': [{
'labels': flog.serialize(labels, 'labels')
}],
'lineage': 'labels = Variable(labels)',
'lsn': 9
})
images = images.cuda()
Flog.flagged() and flog.write(
{
'locals': [{
'images': flog.serialize(images, 'images')
}],
'lineage': 'images = images.cuda()',
'lsn': 10
})
labels = labels.cuda()
Flog.flagged() and flog.write(
{
'locals': [{
'labels': flog.serialize(labels, 'labels')
}],
'lineage': 'labels = labels.cuda()',
'lsn': 11
})
outputs = net(images)
Flog.flagged() and flog.write(
{
'locals': [{
'outputs': flog.serialize(outputs, 'outputs')
}],
'lineage': 'outputs = net(images)',
'lsn': 12
})
loss = loss_function(outputs, labels)
Flog.flagged() and flog.write(
{
'locals': [{
'loss': flog.serialize(loss, 'loss')
}],
'lineage': 'loss = loss_function(outputs, labels)',
'lsn': 13
})
test_loss += loss.item()
Flog.flagged() and flog.write({
'locals': [{
'test_loss': flog.serialize(test_loss, 'test_loss')
}],
'lineage':
'test_loss += loss.item()',
'lsn':
14
})
_, preds = outputs.max(1)
Flog.flagged() and flog.write({
'locals': [{
'_': flog.serialize(_, '_')
}, {
'preds': flog.serialize(preds, 'preds')
}],
'lineage':
'_, preds = outputs.max(1)',
'lsn':
15
})
correct += preds.eq(labels).sum()
Flog.flagged() and flog.write(
{
'locals': [{
'correct': flog.serialize(correct, 'correct')
}],
'lineage': 'correct += preds.eq(labels).sum()',
'lsn': 16
})
Flog.flagged() and flog.write({'end_loop': 80, 'lsn': 7})
print('Test set: Average loss: {:.4f}, Accuracy: {:.4f}'.format(
test_loss / len(cifar100_test_loader.dataset),
correct.float() / len(cifar100_test_loader.dataset)))
print()
writer.add_scalar('Test/Average loss',
test_loss / len(cifar100_test_loader.dataset), epoch)
writer.add_scalar('Test/Accuracy',
correct.float() / len(cifar100_test_loader.dataset),
epoch)
__return__ = correct.float() / len(cifar100_test_loader.dataset)
Flog.flagged() and flog.write({
'locals': [{
'__return__': flog.serialize(__return__, '__return__')
}],
'lineage':
'__return__ = correct.float() / len(cifar100_test_loader.dataset)',
'lsn':
17
})
return __return__
finally:
Flog.flagged() and flog.write({
'end_function': 'eval_training',
'lsn': 3
})
Flog.flagged(option='end_function') and flog.writer.close()
from flor import Flog
if Flog.flagged():
flog = Flog()
Flog.flagged() and flog.write({'file_path': 'train.py', 'lsn': 31})
import os
import sys
import argparse
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from conf import settings
from utils import get_network, get_training_dataloader, get_test_dataloader, WarmUpLR
def train(epoch):
try:
if Flog.flagged(option='start_function'):
flog = Flog()
Flog.flagged() and flog.write({'file_path': 'train.py', 'lsn': 0})
Flog.flagged() and flog.write({'start_function': 'train', 'lsn': 1})
Flog.flagged() and flog.write({
'lsn':
2,
'params': [{
def test_model(clf, X_te, y_te):
from flor import Flog
if Flog.flagged():
flog = Flog()
Flog.flagged() and flog.write({
'file_path': '/Users/rogarcia/sandbox/examples/basic.py',
'lsn': 0
})
Flog.flagged() and flog.write({'start_function': 'test_model', 'lsn': 1})
Flog.flagged() and flog.write({
'lsn':
2,
'params': [{
'0.raw.clf': flog.serialize(clf)
}, {
'1.raw.X_te': flog.serialize(X_te)
}, {
'2.raw.y_te': flog.serialize(y_te)
}]
})
score = log.metric(clf.score(X_te, y_te))
Flog.flagged() and flog.write({
'locals': [{
'score': flog.serialize(score)
}],
'lineage': 'score = log.metric(clf.score(X_te, y_te))',
'lsn': 4
})
Flog.flagged() and flog.write({'end_function': 'test_model', 'lsn': 3})
def train_model(n_estimators, X_tr, y_tr):
from flor import Flog
if Flog.flagged():
flog = Flog()
Flog.flagged() and flog.write({
'file_path': '/Users/rogarcia/sandbox/examples/basic.py',
'lsn': 0
})
Flog.flagged() and flog.write({'start_function': 'train_model', 'lsn': 1})
Flog.flagged() and flog.write({
'lsn':
2,
'params': [{
'0.raw.n_estimators': flog.serialize(n_estimators)
}, {
'1.raw.X_tr': flog.serialize(X_tr)
}, {
'2.raw.y_tr': flog.serialize(y_tr)
}]
})
clf = RandomForestClassifier(n_estimators=n_estimators).fit(X_tr, y_tr)
Flog.flagged() and flog.write({
'locals': [{
'clf': flog.serialize(clf)
}],
'lineage':
'clf = RandomForestClassifier(n_estimators=n_estimators).fit(X_tr, y_tr)',
'lsn': 4
})
__return__ = clf
Flog.flagged() and flog.write(
{
'locals': [{
'__return__': flog.serialize(__return__)
}],
'lineage': '__return__ = clf',
'lsn': 6
})
Flog.flagged() and flog.write({'end_function': 'train_model', 'lsn': 5})
return __return__
Flog.flagged() and flog.write({'end_function': 'train_model', 'lsn': 3})
from flor import Flog
if Flog.flagged():
flog = Flog(False)
Flog.flagged() and flog.write({
'file_path': '/Users/rogarcia/sandbox/examples/basic.py',
'lsn': 0
})
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
def train_model(n_estimators, X_tr, y_tr):
from flor import Flog
if Flog.flagged():
flog = Flog()
Flog.flagged() and flog.write({
'file_path': '/Users/rogarcia/sandbox/examples/basic.py',
'lsn': 0
})
Flog.flagged() and flog.write({'start_function': 'train_model', 'lsn': 1})
Flog.flagged() and flog.write({
'lsn':
2,
'params': [{
'0.raw.n_estimators': flog.serialize(n_estimators)
}, {
'1.raw.X_tr': flog.serialize(X_tr)
}, {
'2.raw.y_tr': flog.serialize(y_tr)