def test_multithreaded(self):
# legacy test, but left because it's just another test
m = Metrics()
m2 = Metrics(shared=m.share())
m3 = Metrics(shared=m.share())
m2.add('key', SumMetric(1))
m3.add('key', SumMetric(2))
m.add('key', SumMetric(3))
m.report()['key'] == 6
def test_recent(self):
m = Metrics()
m2 = Metrics(shared=m.share())
m.add('test', SumMetric(1))
assert m.report() == {'test': 1}
assert m.report_recent() == {'test': 1}
m.clear_recent()
m.add('test', SumMetric(2))
assert m.report() == {'test': 3}
assert m.report_recent() == {'test': 2}
assert m2.report() == {'test': 3}
assert m2.report_recent() == {}
m2.add('test', SumMetric(3))
assert m2.report() == {'test': 6}
assert m.report() == {'test': 6}
assert m2.report_recent() == {'test': 3}
assert m.report_recent() == {'test': 2}
m2.clear_recent()
assert m2.report() == {'test': 6}
assert m.report() == {'test': 6}
assert m2.report_recent() == {}
assert m.report_recent() == {'test': 2}
m.clear_recent()
assert m2.report() == {'test': 6}
assert m.report() == {'test': 6}
assert m.report_recent() == {}
def test_multithreaded(self):
m = Metrics(threadsafe=True)
m2 = Metrics(threadsafe=True, shared=m.share())
m3 = Metrics(threadsafe=True, shared=m.share())
m2.add('key', SumMetric(1))
m2.flush()
m3.add('key', SumMetric(2))
m3.flush()
m.add('key', SumMetric(3))
m.flush()
m.report()['key'] == 6
def test_simpleadd(self):
m = Metrics()
m.add('key', SumMetric(1))
m.add('key', SumMetric(2))
assert m.report()['key'] == 3
m.clear()
assert 'key' not in m.report()
m.add('key', SumMetric(1.5))
m.add('key', SumMetric(2.5))
assert m.report()['key'] == 4.0
def test_simpleadd(self):
m = Metrics(threadsafe=False)
m.add('key', SumMetric(1))
m.add('key', SumMetric(2))
assert m.report()['key'] == 3
m.clear()
assert 'key' not in m.report()
m.add('key', SumMetric(1.5))
m.add('key', SumMetric(2.5))
assert m.report()['key'] == 4.0
# shouldn't throw exception
m.flush()
def test_largebuffer(self):
m = Metrics(threadsafe=True)
m2 = Metrics(threadsafe=True, shared=m.share())
# intentionally just over the int overflow
for _ in range(32768 + 1):
m2.add('key', SumMetric(1))
m2.flush()
assert m.report()['key'] == 32768 + 1
def test_largebuffer(self):
# legacy test. left as just another test
m = Metrics()
m2 = Metrics(shared=m.share())
# intentionally just over the int overflow
for _ in range(32768 + 1):
m2.add('key', SumMetric(1))
assert m.report()['key'] == 32768 + 1
def test_shared(self):
m = Metrics()
m2 = Metrics(shared=m.share())
m3 = Metrics(shared=m.share())
m2.add('key', SumMetric(1))
m3.add('key', SumMetric(2))
m.add('key', SumMetric(3))
assert m.report()['key'] == 6
def test_verymultithreaded(self):
# legacy test, but useful all the same, for ensuring
# metrics doesn't care about the order things are done
m = Metrics()
nt = 128
ms = [Metrics(shared=m.share()) for _ in range(nt)]
# intentionally just over the int overflow
for _ in range(32768 + 1):
ms[random.randint(0, nt - 1)].add('key', SumMetric(1))
thread_ids = list(range(nt))
random.shuffle(thread_ids)
assert m.report()['key'] == 32768 + 1
def test_verymultithreaded(self):
m = Metrics(threadsafe=True)
nt = 128
ms = [Metrics(threadsafe=True, shared=m.share()) for _ in range(nt)]
# intentionally just over the int overflow
for _ in range(32768 + 1):
ms[random.randint(0, nt - 1)].add('key', SumMetric(1))
thread_ids = list(range(nt))
random.shuffle(thread_ids)
for tid in thread_ids:
ms[tid].flush()
assert m.report()['key'] == 32768 + 1
def test_shared(self):
m = Metrics(threadsafe=False)
m2 = Metrics(threadsafe=False, shared=m.share())
m3 = Metrics(threadsafe=False, shared=m.share())
m2.add('key', SumMetric(1))
m3.add('key', SumMetric(2))
m2.flush() # just make sure this doesn't throw exception, it's a no-op
m.add('key', SumMetric(3))
assert m.report()['key'] == 6
# shouldn't throw exception
m.flush()
m2.flush()
m3.flush()
class SplitTeacher(Teacher):
"""FVQA Teacher, which loads the json VQA data and implements its own
`act` method for interacting with student agent.
Use "fvqa:split:X" to choose between splits 0-4 (inclusive), or just
"fvqa" to use the default split (0).
"""
def __init__(self, opt, shared=None):
super().__init__(opt)
dt = opt['datatype'].split(':')[0]
if dt not in ('train', 'test'):
raise RuntimeError('Not valid datatype (only train/test).')
task = opt.get('task', 'fvqa:split:0')
task_num = 0 # default to train/split 0
split = task.split(':')
if len(split) > 2:
task_num = split[2]
if task_num not in [str(i) for i in range(5)]:
raise RuntimeError(
'Invalid train/test split ID (0-4 inclusive)')
if not hasattr(self, 'factmetrics'):
if shared and shared.get('factmetrics'):
self.factmetrics = shared['factmetrics']
else:
self.factmetrics = Metrics(opt)
self.datatype = opt['datatype']
questions_path, trainset_path, self.image_path = _path(opt)
if shared and 'ques' in shared:
self.ques = shared['ques']
else:
self._setup_data(questions_path, trainset_path, dt, task_num)
self.len = len(self.ques)
self.asked_question = False
# for ordered data in batch mode (especially, for validation and
# testing), each teacher in the batch gets a start index and a step
# size so they all process disparate sets of the data
self.step_size = opt.get('batchsize', 1)
self.data_offset = opt.get('batchindex', 0)
self.image_loader = ImageLoader(opt)
self.reset()
def num_examples(self):
return self.len
def num_episodes(self):
return self.len
def report(self):
r = super().report()
r['factmetrics'] = self.factmetrics.report()
return r
def reset(self):
# Reset the dialog so that it is at the start of the epoch,
# and all metrics are reset.
super().reset()
self.lastY = None
self.episode_idx = self.data_offset - self.step_size
self.epochDone = False
def reset_metrics(self):
super().reset_metrics()
self.factmetrics.clear()
def observe(self, observation):
"""Process observation for metrics."""
if self.lastY is not None:
if self.asked_question:
self.metrics.update(observation, self.lastY[0])
else:
self.factmetrics.update(observation, self.lastY[1])
self.lastY = None
return observation
def act(self):
if self.asked_question:
self.asked_question = False
action = {
'text': 'Which fact supports this answer?',
'episode_done': True
}
if self.datatype.startswith('train'):
action['labels'] = self.lastY[1]
if self.datatype != 'train' and self.episode_idx + self.step_size >= self.num_episodes(
):
self.epochDone = True
return action
if self.datatype == 'train':
self.episode_idx = random.randrange(self.len)
else:
self.episode_idx = (self.episode_idx +
self.step_size) % self.num_episodes()
self.asked_question = True
qa = self.ques[self.episode_idx]
question = qa['question']
img_path = self.image_path + qa['img_file']
action = {
'image': self.image_loader.load(img_path),
'text': question,
'episode_done': False
}
human_readable = qa['fact_surface'].replace('[', '').replace(']', '')
self.lastY = [[qa['answer']], [human_readable]]
if self.datatype.startswith('train'):
action['labels'] = self.lastY[0]
return action
def share(self):
shared = super().share()
shared['factmetrics'] = self.factmetrics
shared['ques'] = self.ques
if hasattr(self, 'facts'):
shared['facts'] = self.facts
return shared
def _setup_data(self, questions_path, trainset_path, datatype, task_num):
print('loading: ' + questions_path)
with open(questions_path) as questions_file:
questions = json.load(questions_file)
train_test_images = set()
with open(
os.path.join(trainset_path,
'{}_list_{}.txt'.format(datatype,
task_num))) as imageset:
for line in imageset:
train_test_images.add(line.strip())
self.ques = [
questions[k] for k in sorted(questions.keys())
if questions[k]['img_file'] in train_test_images
]
class SplitTeacher(Teacher):
"""FVQA Teacher, which loads the json VQA data and implements its own
`act` method for interacting with student agent.
Use "fvqa:split:X" to choose between splits 0-4 (inclusive), or just
"fvqa" to use the default split (0).
"""
def __init__(self, opt, shared=None):
super().__init__(opt)
dt = opt['datatype'].split(':')[0]
if dt not in ('train', 'test'):
raise RuntimeError('Not valid datatype (only train/test).')
task = opt.get('task', 'fvqa:split:0')
task_num = 0 # default to train/split 0
split = task.split(':')
if len(split) > 2:
task_num = split[2]
if task_num not in [str(i) for i in range(5)]:
raise RuntimeError('Invalid train/test split ID (0-4 inclusive)')
if not hasattr(self, 'factmetrics'):
if shared and shared.get('factmetrics'):
self.factmetrics = shared['factmetrics']
else:
self.factmetrics = Metrics(opt)
self.datatype = opt['datatype']
questions_path, trainset_path, self.image_path = _path(opt)
if shared and 'ques' in shared:
self.ques = shared['ques']
else:
self._setup_data(questions_path, trainset_path, dt, task_num)
self.len = len(self.ques)
self.asked_question = False
# for ordered data in batch mode (especially, for validation and
# testing), each teacher in the batch gets a start index and a step
# size so they all process disparate sets of the data
self.step_size = opt.get('batchsize', 1)
self.data_offset = opt.get('batchindex', 0)
self.image_loader = ImageLoader(opt)
self.reset()
def __len__(self):
return self.len
def report(self):
r = super().report()
r['factmetrics'] = self.factmetrics.report()
return r
def reset(self):
# Reset the dialog so that it is at the start of the epoch,
# and all metrics are reset.
super().reset()
self.lastY = None
self.episode_idx = self.data_offset - self.step_size
self.epochDone = False
def reset_metrics(self):
super().reset_metrics()
self.factmetrics.clear()
def observe(self, observation):
"""Process observation for metrics."""
if self.lastY is not None:
if self.asked_question:
self.metrics.update(observation, self.lastY[0])
else:
self.factmetrics.update(observation, self.lastY[1])
self.lastY = None
return observation
def act(self):
if self.asked_question:
self.asked_question = False
action = {'text': 'Which fact supports this answer?', 'episode_done': True}
if self.datatype.startswith('train'):
action['labels'] = self.lastY[1]
if self.datatype != 'train' and self.episode_idx + self.step_size >= len(self):
self.epochDone = True
return action
if self.datatype == 'train':
self.episode_idx = random.randrange(self.len)
else:
self.episode_idx = (self.episode_idx + self.step_size) % len(self)
self.asked_question = True
qa = self.ques[self.episode_idx]
question = qa['question']
img_path = self.image_path + qa['img_file']
action = {
'image': self.image_loader.load(img_path),
'text': question,
'episode_done': False
}
human_readable = qa['fact_surface'].replace('[', '').replace(']', '')
self.lastY = [[qa['answer']], [human_readable]]
if self.datatype.startswith('train'):
action['labels'] = self.lastY[0]
return action
def share(self):
shared = super().share()
shared['factmetrics'] = self.factmetrics
shared['ques'] = self.ques
if hasattr(self, 'facts'):
shared['facts'] = self.facts
return shared
def _setup_data(self, questions_path, trainset_path, datatype, task_num):
print('loading: ' + questions_path)
with open(questions_path) as questions_file:
questions = json.load(questions_file)
train_test_images = set()
with open(os.path.join(trainset_path, '{}_list_{}.txt'.format(datatype, task_num))) as imageset:
for line in imageset:
train_test_images.add(line.strip())
self.ques = [questions[k] for k in sorted(questions.keys()) if questions[k]['img_file'] in train_test_images]
class Teacher(Agent):
"""
Basic Teacher agent that keeps track of how many times it's received messages.
Teachers provide the ``report()`` method to get back metrics.
"""
def __init__(self, opt, shared=None):
if not hasattr(self, 'opt'):
self.opt = copy.deepcopy(opt)
if not hasattr(self, 'id'):
self.id = opt.get('task', 'teacher')
if not hasattr(self, 'metrics'):
if shared and shared.get('metrics'):
self.metrics = shared['metrics']
else:
self.metrics = Metrics(opt)
self.epochDone = False
# return state/action dict based upon passed state
def act(self):
"""Act upon the previous observation."""
if self.observation is not None and 'text' in self.observation:
t = {'text': 'Hello agent!'}
return t
def epoch_done(self):
"""Return whether the epoch is done."""
return self.epochDone
# Default unknown length
def num_examples(self):
"""
Return the number of examples (e.g. individual utterances) in the dataset.
Default implementation returns `None`, indicating an unknown number.
"""
return None
def num_episodes(self):
"""
Return the number of episodes (e.g. conversations) in the dataset.
Default implementation returns `None`, indicating an unknown number.
"""
return None
def report(self):
"""Return metrics showing total examples and accuracy if available."""
return self.metrics.report()
def reset(self):
"""Reset the teacher."""
super().reset()
self.reset_metrics()
self.epochDone = False
def reset_metrics(self):
"""Reset metrics."""
self.metrics.clear()
def share(self):
"""In addition to default Agent shared parameters, share metrics."""
shared = super().share()
shared['metrics'] = self.metrics
return shared