def test_json_write_and_load(self):
dict_to_write = dict(a=1, b=2, c=3)
output_file = os.path.join(self.log_dir, 'output.json')
common.write_to_json(dict_to_write, output_file)
check = common.load_from_json(output_file)
assert dict_to_write == check
def _log_params(self, params):
"""
Takes the argument options to save into a json file.
"""
params_file = os.path.join(self.log_dir, 'params.json')
# Check for discrepancy with previous training config.
if os.path.exists(params_file):
check = common.load_from_json(params_file)
if params != check:
diffs = []
for k in params:
if k in check and params[k] != check[k]:
diffs.append('{}: Expected {} but got {}.'.format(
k, check[k], params[k]))
diff_string = '\n'.join(diffs)
raise ValueError(
"Current hyperparameter configuration is different from previously:\n{}"
.format(diff_string))
common.write_to_json(params, params_file)
def evaluate(metric,
netG,
log_dir,
evaluate_range=None,
evaluate_step=None,
num_runs=3,
start_seed=0,
overwrite=False,
write_to_json=True,
device=None,
**kwargs):
"""
Evaluates a generator over several runs.
Args:
metric (str): The name of the metric for evaluation.
netG (Module): Torch generator model to evaluate.
log_dir (str): The path to the log directory.
evaluate_range (tuple): The 3 valued tuple for defining a for loop.
evaluate_step (int): The specific checkpoint to load. Used in place of evaluate_range.
device (str): Device identifier to use for computation.
num_runs (int): The number of runs to compute FID for each checkpoint.
start_seed (int): Starting random seed to use.
write_to_json (bool): If True, writes to an output json file in log_dir.
overwrite (bool): If True, then overwrites previous metric score.
Returns:
None
"""
# Check evaluation range/steps
if evaluate_range and evaluate_step or not (evaluate_step
or evaluate_range):
raise ValueError(
"Only one of evaluate_step or evaluate_range can be defined.")
if evaluate_range:
if (type(evaluate_range) != tuple
or not all(map(lambda x: type(x) == int, evaluate_range))
or not len(evaluate_range) == 3):
raise ValueError(
"evaluate_range must be a tuple of ints (start, end, step).")
# Check metric arguments
if metric == 'kid':
if 'num_samples' not in kwargs:
raise ValueError(
"num_samples must be provided for KID computation.")
output_file = os.path.join(
log_dir, 'kid_{}k.json'.format(kwargs['num_samples'] // 1000))
elif metric == 'fid':
if 'num_real_samples' not in kwargs or 'num_fake_samples' not in kwargs:
raise ValueError(
"num_real_samples and num_fake_samples must be provided for FID computation."
)
output_file = os.path.join(
log_dir,
'fid_{}k_{}k.json'.format(kwargs['num_real_samples'] // 1000,
kwargs['num_fake_samples'] // 1000))
elif metric == 'inception_score':
if 'num_samples' not in kwargs:
raise ValueError(
"num_samples must be provided for IS computation.")
output_file = os.path.join(
log_dir,
'inception_score_{}k.json'.format(kwargs['num_samples'] // 1000))
else:
choices = ['fid', 'kid', 'inception_score']
raise ValueError("Invalid metric {} selected. Choose from {}.".format(
metric, choices))
# Check checkpoint dir
ckpt_dir = os.path.join(log_dir, 'checkpoints', 'netG')
if not os.path.exists(ckpt_dir):
raise ValueError(
"Checkpoint directory {} cannot be found in log_dir.".format(
ckpt_dir))
# Check device
if device is None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Setup output file
if os.path.exists(output_file):
scores_dict = common.load_from_json(output_file)
scores_dict = dict([(int(k), v) for k, v in scores_dict.items()])
else:
scores_dict = {}
# Decide naming convention
names_dict = {
'fid': 'FID',
'inception_score': 'Inception Score',
'kid': 'KID',
}
# # Set output file and restore if available.
# if metric == 'fid':
# output_file = os.path.join(
# log_dir,
# 'fid_{}k_{}k.json'.format(kwargs['num_real_samples'] // 1000,
# kwargs['num_fake_samples'] // 1000))
# elif metric == 'inception_score':
# output_file = os.path.join(
# log_dir,
# 'inception_score_{}k.json'.format(kwargs['num_samples'] // 1000))
# elif metric == 'kid':
# output_file = os.path.join(
# log_dir, 'kid_{}k.json'.format(
# kwargs['num_samples'] // 1000))
# if os.path.exists(output_file):
# scores_dict = common.load_from_json(output_file)
# scores_dict = dict([(int(k), v) for k, v in scores_dict.items()])
# else:
# scores_dict = {}
# Evaluate across a range
start, end, interval = evaluate_range or (evaluate_step, evaluate_step,
evaluate_step)
for step in range(start, end + 1, interval):
# Skip computed scores
if step in scores_dict and write_to_json and not overwrite:
print("INFO: {} at step {} has been computed. Skipping...".format(
names_dict[metric], step))
continue
# Load and restore the model checkpoint
ckpt_file = os.path.join(ckpt_dir, 'netG_{}_steps.pth'.format(step))
if not os.path.exists(ckpt_file):
print("INFO: Checkpoint at step {} does not exist. Skipping...".
format(step))
continue
netG.restore_checkpoint(ckpt_file=ckpt_file, optimizer=None)
# Compute score for each seed
scores = []
for seed in range(start_seed, start_seed + num_runs):
print("INFO: Computing {} in memory...".format(names_dict[metric]))
# Obtain only the raw score without var
if metric == "fid":
score = compute_fid.fid_score(netG=netG,
seed=seed,
device=device,
split='train',
log_dir=log_dir,
**kwargs
)
elif metric == "inception_score":
score, _ = compute_is.inception_score(netG=netG,
seed=seed,
device=device,
log_dir=log_dir,
**kwargs)
elif metric == "kid":
score, _ = compute_kid.kid_score(netG=netG,
device=device,
seed=seed,
split='train',
log_dir=log_dir,
**kwargs)
scores.append(score)
print("INFO: {} (step {}) [seed {}]: {}".format(
names_dict[metric], step, seed, score))
scores_dict[step] = scores
# Save scores every step
if write_to_json:
common.write_to_json(scores_dict, output_file)
# Print the scores in order
for step in range(start, end + 1, interval):
if step in scores_dict:
scores = scores_dict[step]
mean = np.mean(scores)
std = np.std(scores)
print("INFO: {} (step {}): {} (± {}) ".format(
names_dict[metric], step, mean, std))
# Save to output file
if write_to_json:
common.write_to_json(scores_dict, output_file)
print("INFO: {} Evaluation completed!".format(names_dict[metric]))
return scores_dict
def evaluate_pr(netG,
log_dir,
evaluate_range=None,
evaluate_step=None,
num_runs=1,
start_seed=0,
overwrite=False,
write_to_json=True,
device=None,
**kwargs):
"""
Computes precision and recall.
Args:
netG (Module): Torch generator model to evaluate.
log_dir (str): The path to the log directory.
evaluate_range (tuple): The 3 valued tuple for defining a for loop.
evaluate_step (int): The specific checkpoint to load. Used in place of evaluate_range.
device (str): Device identifier to use for computation.
num_runs (int): The number of runs to compute FID for each checkpoint.
start_seed (int): Starting random seed to use.
write_to_json (bool): If True, writes to an output json file in log_dir.
overwrite (bool): If True, then overwrites previous metric score.
Returns:
dictionary: precision, recall score dictionary.
"""
# Check evaluation range/steps
if evaluate_range and evaluate_step or not (evaluate_step
or evaluate_range):
raise ValueError(
"Only one of evaluate_step or evaluate_range can be defined.")
if evaluate_range:
if (type(evaluate_range) != tuple
or not all(map(lambda x: type(x) == int, evaluate_range))
or not len(evaluate_range) == 3):
raise ValueError(
"evaluate_range must be a tuple of ints (start, end, step).")
output_log_dir = log_dir / 'evaluate' / f'step-{evaluate_step}'
output_log_dir.mkdir(parents=True, exist_ok=True)
# Check metric arguments
if 'num_real_samples' not in kwargs or 'num_fake_samples' not in kwargs:
raise ValueError(
"num_real_samples and num_fake_samples must be provided for PR computation."
)
output_file = os.path.join(
output_log_dir,
'pr_{}k_{}k.json'.format(kwargs['num_real_samples'] // 1000,
kwargs['num_fake_samples'] // 1000))
# Check checkpoint dir
ckpt_dir = os.path.join(log_dir, 'checkpoints', 'netG')
if not os.path.exists(ckpt_dir):
raise ValueError(
"Checkpoint directory {} cannot be found in log_dir.".format(
ckpt_dir))
# Check device
if device is None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Setup output file
if os.path.exists(output_file):
scores_dict = common.load_from_json(output_file)
scores_dict = dict([(int(k), v) for k, v in scores_dict.items()])
else:
scores_dict = {}
# Evaluate across a range
start, end, interval = evaluate_range or (evaluate_step, evaluate_step,
evaluate_step)
for step in range(start, end + 1, interval):
# Skip computed scores
# if step in scores_dict and write_to_json and not overwrite:
# print("INFO: PR at step {} has been computed. Skipping...".format(step))
# continue
# Load and restore the model checkpoint
ckpt_file = os.path.join(ckpt_dir, 'netG_{}_steps.pth'.format(step))
if not os.path.exists(ckpt_file):
print("INFO: Checkpoint at step {} does not exist. Skipping...".
format(step))
continue
netG.restore_checkpoint(ckpt_file=ckpt_file, optimizer=None)
# Compute score for each seed
scores = defaultdict(list)
for seed in range(start_seed, start_seed + num_runs):
print("INFO: Computing PR in memory...")
# Obtain only the raw score without var
score = pr_score(netG=netG,
seed=seed,
device=device,
log_dir=log_dir,
**kwargs)
for key in score:
scores[key].append(score[key])
print("INFO: {} (step {}) [seed {}]: {}".format(
key, step, seed, score[key]))
scores_dict[step] = scores
# Save scores every step
if write_to_json:
common.write_to_json(scores_dict, output_file)
# Print the scores in order
for step in range(start, end + 1, interval):
if step in scores_dict:
for key in scores_dict[step]:
scores = scores_dict[step][key]
mean = np.mean(scores)
std = np.std(scores)
print("INFO: {} (step {}): {} (± {}) ".format(
key, step, mean, std))
# Save to output file
if write_to_json:
common.write_to_json(scores_dict, output_file)
print("INFO: PR Evaluation completed!")
return scores_dict
def evaluate_custom(metric,
netG,
log_dir,
num_runs=1,
start_seed=0,
overwrite=False,
write_to_json=True,
device=None,
**kwargs):
output_log_dir = log_dir / 'evaluate' / f'custom'
output_log_dir.mkdir(parents=True, exist_ok=True)
# Check metric arguments
if metric == 'kid':
if 'num_samples' not in kwargs:
raise ValueError(
"num_samples must be provided for KID computation.")
output_file = os.path.join(
output_log_dir,
'kid_{}k.json'.format(kwargs['num_samples'] // 1000))
elif metric == 'fid':
if 'num_real_samples' not in kwargs or 'num_fake_samples' not in kwargs:
raise ValueError(
"num_real_samples and num_fake_samples must be provided for FID computation."
)
output_file = os.path.join(
output_log_dir,
'fid_{}k_{}k.json'.format(kwargs['num_real_samples'] // 1000,
kwargs['num_fake_samples'] // 1000))
elif metric == 'inception_score':
if 'num_samples' not in kwargs:
raise ValueError(
"num_samples must be provided for IS computation.")
output_file = os.path.join(
output_log_dir,
'inception_score_{}k.json'.format(kwargs['num_samples'] // 1000))
else:
choices = ['fid', 'kid', 'inception_score']
raise ValueError("Invalid metric {} selected. Choose from {}.".format(
metric, choices))
# Check device
if device is None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Setup output file
if os.path.exists(output_file):
scores_dict = common.load_from_json(output_file)
scores_dict = dict([(int(k), v) for k, v in scores_dict.items()])
else:
scores_dict = {}
# Decide naming convention
names_dict = {
'fid': 'FID',
'inception_score': 'Inception Score',
'kid': 'KID',
}
step = 0
# Compute score for each seed
scores = []
for seed in range(start_seed, start_seed + num_runs):
print("INFO: Computing {} in memory...".format(names_dict[metric]))
# Obtain only the raw score without var
if metric == "fid":
score = compute_fid.fid_score(netG=netG,
seed=seed,
device=device,
log_dir=log_dir,
**kwargs)
elif metric == "inception_score":
score, _ = compute_is.inception_score(netG=netG,
seed=seed,
device=device,
log_dir=log_dir,
**kwargs)
elif metric == "kid":
score, _ = compute_kid.kid_score(netG=netG,
device=device,
seed=seed,
log_dir=log_dir,
**kwargs)
scores.append(score)
print("INFO: {} (step {}) [seed {}]: {}".format(
names_dict[metric], step, seed, score))
scores_dict[step] = scores
# Save scores every step
if write_to_json:
common.write_to_json(scores_dict, output_file)
# Print the scores in order
if step in scores_dict:
scores = scores_dict[step]
mean = np.mean(scores)
std = np.std(scores)
print("INFO: {} (step {}): {} (± {}) ".format(names_dict[metric], step,
mean, std))
# Save to output file
if write_to_json:
common.write_to_json(scores_dict, output_file)
print("INFO: {} Evaluation completed!".format(names_dict[metric]))
return scores_dict