def calculate_fid(input_1, input_2, **kwargs):
feat_layer_name = get_kwarg('feature_layer_fid', kwargs)
feat_extractor = create_feature_extractor(
get_kwarg('feature_extractor', kwargs), [feat_layer_name], **kwargs)
metric = fid_inputs_to_metric(input_1, input_2, feat_extractor,
feat_layer_name, **kwargs)
return metric
def estimate_implementation_exactness(cuda, batch_size=8, rng_seed=2020):
model_tf = TestInception.get_inception_tf()
model_pt = create_feature_extractor(
'inception-v3-compat',
['2048', 'logits_unbiased'],
cuda=cuda,
)
ds = prepare_input_from_id('cifar10-train',
datasets_root=tempfile.gettempdir())
rng = np.random.RandomState(rng_seed)
batch_pt = torch.cat([
ds[i].unsqueeze(0)
for i in rng.choice(len(ds), batch_size, replace=False)
],
dim=0)
f1_pt, f2_pt = TestInception.forward_pt(model_pt, batch_pt, cuda)
f1_tf, f2_tf = TestInception.forward_tf(model_tf, batch_pt)
return {
'2048': {
'pt': [f1_pt],
'tf': [f1_tf]
},
'logits_unbiased': {
'pt': [f2_pt],
'tf': [f2_tf]
}
}
def calculate_isc(input_id, **kwargs):
feature_extractor = get_kwarg('feature_extractor', kwargs)
feat_layer_name = get_kwarg('feature_layer_isc', kwargs)
feat_extractor = create_feature_extractor(feature_extractor,
[feat_layer_name], **kwargs)
metric = isc_input_id_to_metric(input_id, feat_extractor, feat_layer_name,
**kwargs)
return metric
def calculate_kid(**kwargs):
feature_extractor = get_kwarg('feature_extractor', kwargs)
feat_layer_name = get_kwarg('feature_layer_kid', kwargs)
feat_extractor = create_feature_extractor(feature_extractor,
[feat_layer_name], **kwargs)
featuresdict_1 = extract_featuresdict_from_input_id_cached(
1, feat_extractor, **kwargs)
featuresdict_2 = extract_featuresdict_from_input_id_cached(
2, feat_extractor, **kwargs)
metric = kid_featuresdict_to_metric(featuresdict_1, featuresdict_2,
feat_layer_name, **kwargs)
return metric
def calculate_kid(input_1, input_2, **kwargs):
feat_layer_name = get_kwarg('feature_layer_kid', kwargs)
feat_extractor = create_feature_extractor(
get_kwarg('feature_extractor', kwargs), [feat_layer_name], **kwargs)
cacheable_input1_name = get_input_cacheable_name(
input_1, get_kwarg('cache_input1_name', kwargs))
cacheable_input2_name = get_input_cacheable_name(
input_2, get_kwarg('cache_input2_name', kwargs))
featuresdict_1 = extract_featuresdict_from_input_cached(
input_1, cacheable_input1_name, feat_extractor, **kwargs)
featuresdict_2 = extract_featuresdict_from_input_cached(
input_2, cacheable_input2_name, feat_extractor, **kwargs)
metric = kid_featuresdict_to_metric(featuresdict_1, featuresdict_2,
feat_layer_name, **kwargs)
return metric
def calculate_metrics(**kwargs):
"""
Calculates metrics for the given inputs. Keyword arguments:
.. _ISC: https://arxiv.org/pdf/1606.03498.pdf
.. _FID: https://arxiv.org/pdf/1706.08500.pdf
.. _KID: https://arxiv.org/pdf/1801.01401.pdf
.. _PPL: https://arxiv.org/pdf/1812.04948.pdf
Args:
input1 (str or torch.utils.data.Dataset or GenerativeModelBase):
First input, which can be either of the following values:
- Name of a registered input. See :ref:`registry <Registry>` for the complete list of preregistered
inputs, and :meth:`register_dataset` for registering a new input. The following options refine the
behavior wrt dataset location and downloading:
:paramref:`~calculate_metrics.datasets_root`,
:paramref:`~calculate_metrics.datasets_download`.
- Path to a directory with samples. The following options refine the behavior wrt directory
traversal and samples filtering:
:paramref:`~calculate_metrics.samples_find_deep`,
:paramref:`~calculate_metrics.samples_find_ext`, and
:paramref:`~calculate_metrics.samples_ext_lossy`.
- Path to a generative model in the :obj:`ONNX<torch:torch.onnx>` or `PTH` (:obj:`JIT<torch:torch.jit>`)
format. This option also requires the following kwargs:
:paramref:`~calculate_metrics.input1_model_z_type`,
:paramref:`~calculate_metrics.input1_model_z_size`, and
:paramref:`~calculate_metrics.input1_model_num_classes`.
- Instance of :class:`~torch:torch.utils.data.Dataset` encapsulating a fixed set of samples.
- Instance of :class:`GenerativeModelBase`, implementing the generative model.
Default: `None`.
input2 (str or torch.utils.data.Dataset or GenerativeModelBase):
Second input, which can be either of the following values:
- Name of a registered input. See :ref:`registry <Registry>` for the complete list of preregistered
inputs, and :meth:`register_dataset` for registering a new input. The following options refine the
behavior wrt dataset location and downloading:
:paramref:`~calculate_metrics.datasets_root`,
:paramref:`~calculate_metrics.datasets_download`.
- Path to a directory with samples. The following options refine the behavior wrt directory
traversal and samples filtering:
:paramref:`~calculate_metrics.samples_find_deep`,
:paramref:`~calculate_metrics.samples_find_ext`, and
:paramref:`~calculate_metrics.samples_ext_lossy`.
- Path to a generative model in the :obj:`ONNX<torch:torch.onnx>` or `PTH` (:obj:`JIT<torch:torch.jit>`)
format. This option also requires the following kwargs:
:paramref:`~calculate_metrics.input2_model_z_type`,
:paramref:`~calculate_metrics.input2_model_z_size`, and
:paramref:`~calculate_metrics.input2_model_num_classes`.
- Instance of :class:`~torch:torch.utils.data.Dataset` encapsulating a fixed set of samples.
- Instance of :class:`GenerativeModelBase`, implementing the generative model.
Default: `None`.
cuda (bool): Sets executor device to GPU. Default: `True`.
batch_size (int): Batch size used to process images; the larger the more memory is used on the executor device
(see :paramref:`~calculate_metrics.cuda`). Default: `64`.
isc (bool): Calculate ISC_ (Inception Score). Default: `False`.
fid (bool): Calculate FID_ (Frechet Inception Distance). Default: `False`.
kid (bool): Calculate KID_ (Kernel Inception Distance). Default: `False`.
ppl (bool): Calculate PPL_ (Perceptual Path Length). Default: `False`.
feature_extractor (str): Name of the feature extractor (see :ref:`registry <Registry>`). Default:
`inception-v3-compat`.
feature_layer_isc (str): Name of the feature layer to use with ISC metric. Default: `logits_unbiased`.
feature_layer_fid (str): Name of the feature layer to use with FID metric. Default: `"2048"`.
feature_layer_kid (str): Name of the feature layer to use with KID metric. Default: `"2048"`.
feature_extractor_weights_path (str): Path to feature extractor weights (downloaded if `None`). Default: `None`.
isc_splits (int): Number of splits in ISC. Default: `10`.
kid_subsets (int): Number of subsets in KID. Default: `100`.
kid_subset_size (int): Subset size in KID. Default: `1000`.
kid_degree (int): Degree of polynomial kernel in KID. Default: `3`.
kid_gamma (float): Polynomial kernel gamma in KID (automatic if `None`). Default: `None`.
kid_coef0 (float): Polynomial kernel coef0 in KID. Default: `1.0`.
ppl_epsilon (float): Interpolation step size in PPL. Default: `1e-4`.
ppl_reduction (str): Reduction type to apply to the per-sample output values. Default: `mean`.
ppl_sample_similarity (str): Name of the sample similarity to use in PPL metric computation (see :ref:`registry
<Registry>`). Default: `lpips-vgg16`.
ppl_sample_similarity_resize (int): Force samples to this size when computing similarity, unless set to `None`.
Default: `64`.
ppl_sample_similarity_dtype (str): Check samples are of compatible dtype when computing similarity, unless set
to `None`. Default: `uint8`.
ppl_discard_percentile_lower (int): Removes the lower percentile of samples before reduction. Default: `1`.
ppl_discard_percentile_higher (int): Removes the higher percentile of samples before reduction. Default: `99`.
ppl_z_interp_mode (str): Noise interpolation mode in PPL (see :ref:`registry <Registry>`). Default: `lerp`.
samples_shuffle (bool): Perform random samples shuffling before computing splits. Default: `True`.
samples_find_deep (bool): Find all samples in paths recursively. Default: `False`.
samples_find_ext (str): List of comma-separated extensions (no blanks) to look for when traversing input path.
Default: `png,jpg,jpeg`.
samples_ext_lossy (str): List of comma-separated extensions (no blanks) to warn about lossy compression.
Default: `jpg,jpeg`.
datasets_root (str): Path to built-in torchvision datasets root. Default: `$ENV_TORCH_HOME/fidelity_datasets`.
datasets_download (bool): Download torchvision datasets to :paramref:`~calculate_metrics.dataset_root`.
Default: `True`.
cache_root (str): Path to file cache for features and statistics. Default: `$ENV_TORCH_HOME/fidelity_cache`.
cache (bool): Use file cache for features and statistics. Default: `True`.
input1_cache_name (str): Assigns a cache entry to input1 (when not a registered input) and forces caching of
features on it. Default: `None`.
input1_model_z_type (str): Type of noise, only required when the input is a path to a generator model (see
:ref:`registry <Registry>`). Default: `normal`.
input1_model_z_size (int): Dimensionality of noise (only required when the input is a path to a generator
model). Default: `None`.
input1_model_num_classes (int): Number of classes for conditional (0 for unconditional) generation (only
required when the input is a path to a generator model). Default: `0`.
input1_model_num_samples (int): Number of samples to draw (only required when the input is a generator model).
This option affects the following metrics: ISC, FID, KID. Default: `None`.
input2_cache_name (str): Assigns a cache entry to input2 (when not a registered input) and forces caching of
features on it. Default: `None`.
input2_model_z_type (str): Type of noise, only required when the input is a path to a generator model (see
:ref:`registry <Registry>`). Default: `normal`.
input2_model_z_size (int): Dimensionality of noise (only required when the input is a path to a generator
model). Default: `None`.
input2_model_num_classes (int): Number of classes for conditional (0 for unconditional) generation (only
required when the input is a path to a generator model). Default: `0`.
input2_model_num_samples (int): Number of samples to draw (only required when the input is a generator model).
This option affects the following metrics: FID, KID. Default: `None`.
rng_seed (int): Random numbers generator seed for all operations involving randomness. Default: `2020`.
save_cpu_ram (bool): Use less CPU RAM at the cost of speed. May not lead to improvement with every metric.
Default: `False`.
verbose (bool): Output progress information to STDERR. Default: `True`.
Returns:
: Dictionary of metrics with a subset of the following keys:
- :const:`torch_fidelity.KEY_METRIC_ISC_MEAN`
- :const:`torch_fidelity.KEY_METRIC_ISC_STD`
- :const:`torch_fidelity.KEY_METRIC_FID`
- :const:`torch_fidelity.KEY_METRIC_KID_MEAN`
- :const:`torch_fidelity.KEY_METRIC_KID_STD`
- :const:`torch_fidelity.KEY_METRIC_PPL_MEAN`
- :const:`torch_fidelity.KEY_METRIC_PPL_STD`
- :const:`torch_fidelity.KEY_METRIC_PPL_RAW`
"""
verbose = get_kwarg('verbose', kwargs)
input1, input2 = get_kwarg('input1', kwargs), get_kwarg('input2', kwargs)
have_isc = get_kwarg('isc', kwargs)
have_fid = get_kwarg('fid', kwargs)
have_kid = get_kwarg('kid', kwargs)
have_ppl = get_kwarg('ppl', kwargs)
need_input1 = have_isc or have_fid or have_kid or have_ppl
need_input2 = have_fid or have_kid
vassert(
have_isc or have_fid or have_kid or have_ppl,
'At least one of "isc", "fid", "kid", "ppl" metrics must be specified')
vassert(
input1 is not None or not need_input1,
'First input is required for "isc", "fid", "kid", and "ppl" metrics')
vassert(input2 is not None or not need_input2,
'Second input is required for "fid" and "kid" metrics')
metrics = {}
if have_isc or have_fid or have_kid:
feature_extractor = get_kwarg('feature_extractor', kwargs)
feature_layer_isc, feature_layer_fid, feature_layer_kid = (None, ) * 3
feature_layers = set()
if have_isc:
feature_layer_isc = get_kwarg('feature_layer_isc', kwargs)
feature_layers.add(feature_layer_isc)
if have_fid:
feature_layer_fid = get_kwarg('feature_layer_fid', kwargs)
feature_layers.add(feature_layer_fid)
if have_kid:
feature_layer_kid = get_kwarg('feature_layer_kid', kwargs)
feature_layers.add(feature_layer_kid)
feat_extractor = create_feature_extractor(feature_extractor,
list(feature_layers),
**kwargs)
# isc: input - featuresdict(cached) - metric
# fid: input - featuresdict(cached) - statistics(cached) - metric
# kid: input - featuresdict(cached) - metric
if (not have_isc) and have_fid and (not have_kid):
# shortcut for a case when statistics are cached and features are not required on at least one input
metric_fid = fid_inputs_to_metric(feat_extractor, **kwargs)
metrics.update(metric_fid)
else:
vprint(verbose, f'Extracting features from input1')
featuresdict_1 = extract_featuresdict_from_input_id_cached(
1, feat_extractor, **kwargs)
featuresdict_2 = None
if input2 is not None:
vprint(verbose, f'Extracting features from input2')
featuresdict_2 = extract_featuresdict_from_input_id_cached(
2, feat_extractor, **kwargs)
if have_isc:
metric_isc = isc_featuresdict_to_metric(
featuresdict_1, feature_layer_isc, **kwargs)
metrics.update(metric_isc)
if have_fid:
cacheable_input1_name = get_cacheable_input_name(1, **kwargs)
cacheable_input2_name = get_cacheable_input_name(2, **kwargs)
fid_stats_1 = fid_featuresdict_to_statistics_cached(
featuresdict_1, cacheable_input1_name, feat_extractor,
feature_layer_fid, **kwargs)
fid_stats_2 = fid_featuresdict_to_statistics_cached(
featuresdict_2, cacheable_input2_name, feat_extractor,
feature_layer_fid, **kwargs)
metric_fid = fid_statistics_to_metric(
fid_stats_1, fid_stats_2, get_kwarg('verbose', kwargs))
metrics.update(metric_fid)
if have_kid:
metric_kid = kid_featuresdict_to_metric(
featuresdict_1, featuresdict_2, feature_layer_kid,
**kwargs)
metrics.update(metric_kid)
if have_ppl:
metric_ppl = calculate_ppl(1, **kwargs)
metrics.update(metric_ppl)
return metrics
def estimate_implementation_exactness(self, cuda):
model_pt = create_feature_extractor('inception-v3-compat', ['2048'],
cuda=cuda)
conv_pt = model_pt.Conv2d_1a_3x3.conv
batch_size = 1
# keep_filters = 16 # anything less makes the backends diverge and causes much different results
# conv_pt.weight.data = conv_pt.weight[0:keep_filters]
# conv_pt.out_channels = keep_filters
ds = prepare_input_from_id('cifar10-train',
datasets_root=tempfile.gettempdir())
rng = np.random.RandomState(2020)
x_pt = torch.cat([
ds[i].unsqueeze(0)
for i in rng.choice(len(ds), batch_size, replace=False)
],
dim=0)
if cuda:
x_pt = x_pt.cuda()
x_pt = x_pt.float()
x_pt = interpolate_bilinear_2d_like_tensorflow1x(x_pt,
size=(299, 299),
align_corners=False)
x_pt = (x_pt - 128) / 128
out_tf = self.forward_tf(conv_pt, x_pt)
out_pt_builtin = self.forward_pt(conv_pt, x_pt)
out_pt_manualchw = self.forward_pt_manualchw(conv_pt, x_pt)
out_pt_manualhwc = self.forward_pt_manualhwc(conv_pt, x_pt)
err_abs_tf_pt_builtin = (out_tf - out_pt_builtin).abs()
err_abs_tf_pt_manualchw = (out_tf - out_pt_manualchw).abs()
err_abs_tf_pt_manualhwc = (out_tf - out_pt_manualhwc).abs()
err_abs_pt_builtin_manualchw = (out_pt_builtin -
out_pt_manualchw).abs()
err_abs_pt_builtin_manualhwc = (out_pt_builtin -
out_pt_manualhwc).abs()
suffix = f'convolution_{"gpu" if cuda else "cpu"}'
self.save(out_tf, f'{suffix}_conv_tf.png')
self.save(out_pt_builtin, f'{suffix}_conv_pt_builtin.png')
self.save(err_abs_tf_pt_builtin, f'{suffix}_err_abs_tf_pt_builtin.png')
self.save(err_abs_tf_pt_manualchw,
f'{suffix}_err_abs_tf_pt_manualchw.png')
self.save(err_abs_tf_pt_manualhwc,
f'{suffix}_err_abs_tf_pt_manualhwc.png')
self.save(err_abs_pt_builtin_manualchw,
f'{suffix}_err_abs_pt_builtin_manualchw.png')
self.save(err_abs_pt_builtin_manualhwc,
f'{suffix}_err_abs_pt_builtin_manualhwc.png')
flipping_pixel_err_abs = err_abs_tf_pt_builtin[0, 0, -1, -1].item()
print(
f'{suffix}_bottom_right_flipping_pixel_err_abs={flipping_pixel_err_abs}',
file=sys.stderr)
err_abs = err_abs_tf_pt_builtin.max().item()
print(f'{suffix}_max_pixelwise_err_abs={err_abs}', file=sys.stderr)
err_rel = err_abs / out_tf.abs().max().clamp_min(1e-9).item()
print(f'{suffix}_max_pixelwise_err_rel={err_rel}', file=sys.stderr)
return err_rel
def calculate_metrics(input_1, input_2=None, **kwargs):
have_isc, have_fid, have_kid = get_kwarg('isc', kwargs), get_kwarg('fid', kwargs), get_kwarg('kid', kwargs)
assert have_isc or have_fid or have_kid, 'At least one of "isc", "fid", "kid" metrics must be specified'
assert (not have_fid) and (not have_kid) or input_2 is not None, \
'Both inputs are required for "fid" and "kid" metrics'
verbose = get_kwarg('verbose', kwargs)
feature_layer_isc, feature_layer_fid, feature_layer_kid = (None,) * 3
feature_layers = set()
if have_isc:
feature_layer_isc = get_kwarg('feature_layer_isc', kwargs)
feature_layers.add(feature_layer_isc)
if have_fid:
feature_layer_fid = get_kwarg('feature_layer_fid', kwargs)
feature_layers.add(feature_layer_fid)
if have_kid:
feature_layer_kid = get_kwarg('feature_layer_kid', kwargs)
feature_layers.add(feature_layer_kid)
feat_extractor = create_feature_extractor(
get_kwarg('feature_extractor', kwargs), list(feature_layers), **kwargs
)
# isc: input - featuresdict(cached) - metric
# fid: input - featuresdict(cached) - statistics(cached) - metric
# kid: input - featuresdict(cached) - metric
metrics = {}
if (not have_isc) and have_fid and (not have_kid):
# shortcut for a case when statistics are cached and features are not required on at least one input
metric_fid = fid_inputs_to_metric(input_1, input_2, feat_extractor, feature_layer_fid, **kwargs)
metrics.update(metric_fid)
return metrics
cacheable_input1_name = get_input_cacheable_name(input_1, get_kwarg('cache_input1_name', kwargs))
cacheable_input2_name = get_input_cacheable_name(input_2, get_kwarg('cache_input2_name', kwargs))
if verbose:
print(f'Extracting features from input_1', file=sys.stderr)
featuresdict_1 = extract_featuresdict_from_input_cached(input_1, cacheable_input1_name, feat_extractor, **kwargs)
featuresdict_2 = None
if input_2 is not None:
if verbose:
print(f'Extracting features from input_2', file=sys.stderr)
featuresdict_2 = extract_featuresdict_from_input_cached(
input_2, cacheable_input2_name, feat_extractor, **kwargs
)
if have_isc:
metric_isc = isc_featuresdict_to_metric(featuresdict_1, feature_layer_isc, **kwargs)
metrics.update(metric_isc)
if have_fid:
fid_stats_1 = fid_featuresdict_to_statistics_cached(
featuresdict_1, cacheable_input1_name, feat_extractor, feature_layer_fid, **kwargs
)
fid_stats_2 = fid_featuresdict_to_statistics_cached(
featuresdict_2, cacheable_input2_name, feat_extractor, feature_layer_fid, **kwargs
)
metric_fid = fid_statistics_to_metric(fid_stats_1, fid_stats_2, get_kwarg('verbose', kwargs))
metrics.update(metric_fid)
if have_kid:
metric_kid = kid_featuresdict_to_metric(featuresdict_1, featuresdict_2, feature_layer_kid, **kwargs)
metrics.update(metric_kid)
return metrics
def calculate_metrics(input_1, input_2=None, **kwargs):
r"""
Calculate metrics for the given inputs.
Args:
input_1: str or torch.util.data.Dataset
First positional input, can be either a Dataset instance, or a string containing a path to a directory
of images, or one of the registered input sources (see registry.py).
input_2: str or torch.util.data.Dataset
Second positional input (not used in unary metrics, such as "isc"), can be either a Dataset instance, or a
string containing a path to a directory of images, or one of the registered input sources (see registry.py).
cuda: bool (default: True)
Sets executor device to GPU.
batch_size: int (default: 64)
Batch size used to process images; the larger the more memory is used on the executor device (see "cuda"
argument).
isc: bool (default: False)
Calculate ISC (Inception Score).
fid: bool (default: False)
Calculate FID (Frechet Inception Distance).
kid: bool (default: False)
Calculate KID (Kernel Inception Distance).
feature_extractor: str (default: inception-v3-compat)
Name of the feature extractor (see registry.py).
feature_layer_isc: str (default: logits_unbiased)
Name of the feature layer to use with ISC metric.
feature_layer_fid: str (default: 2048)
Name of the feature layer to use with FID metric.
feature_layer_kid: str (default: 2048)
Name of the feature layer to use with KID metric.
feature_extractor_weights_path: str (default: None)
Path to feature extractor weights (downloaded if None).
isc_splits: int (default: 10)
Number of splits in ISC.
kid_subsets: int (default: 100)
Number of subsets in KID.
kid_subset_size: int (default: 1000)
Subset size in KID.
kid_degree: int (default: 3)
Degree of polynomial kernel in KID.
kid_gamma: float (default: None)
Polynomial kernel gamma in KID (automatic if None).
kid_coef0: float (default: 1)
Polynomial kernel coef0 in KID.
samples_shuffle: bool (default: True)
Perform random samples shuffling before computing splits.
samples_find_deep: bool (default: False)
Find all samples in paths recursively.
samples_find_ext: str (default: png,jpg,jpeg)
List of extensions to look for when traversing input path.
samples_ext_lossy: str (default: jpg,jpeg)
List of extensions to warn about lossy compression.
datasets_root: str (default: None)
Path to built-in torchvision datasets root. Defaults to $ENV_TORCH_HOME/fidelity_datasets.
datasets_download: bool (default: True)
Download torchvision datasets to dataset_root.
cache_root: str (default: None)
Path to file cache for features and statistics. Defaults to $ENV_TORCH_HOME/fidelity_cache.
cache: bool (default: True)
Use file cache for features and statistics.
cache_input1_name: str (default: None)
Assigns a cache entry to input1 (if a path) and forces caching of features on it if not None.
cache_input2_name: str (default: None)
Assigns a cache entry to input2 (if a path) and forces caching of features on it if not None.
rng_seed: int (default: 2020)
Random numbers generator seed for all operations involving randomness.
save_cpu_ram: bool (default: False)
Use less CPU RAM at the cost of speed.
verbose: bool (default: True)
Output progress information to STDERR.
Return: a dictionary of metrics.
"""
have_isc, have_fid, have_kid = get_kwarg('isc', kwargs), get_kwarg('fid', kwargs), get_kwarg('kid', kwargs)
vassert(have_isc or have_fid or have_kid, 'At least one of "isc", "fid", "kid" metrics must be specified')
vassert(
(not have_fid) and (not have_kid) or input_2 is not None, 'Both inputs are required for "fid" and "kid" metrics'
)
verbose = get_kwarg('verbose', kwargs)
feature_layer_isc, feature_layer_fid, feature_layer_kid = (None,) * 3
feature_layers = set()
if have_isc:
feature_layer_isc = get_kwarg('feature_layer_isc', kwargs)
feature_layers.add(feature_layer_isc)
if have_fid:
feature_layer_fid = get_kwarg('feature_layer_fid', kwargs)
feature_layers.add(feature_layer_fid)
if have_kid:
feature_layer_kid = get_kwarg('feature_layer_kid', kwargs)
feature_layers.add(feature_layer_kid)
feat_extractor = create_feature_extractor(
get_kwarg('feature_extractor', kwargs), list(feature_layers), **kwargs
)
# isc: input - featuresdict(cached) - metric
# fid: input - featuresdict(cached) - statistics(cached) - metric
# kid: input - featuresdict(cached) - metric
metrics = {}
if (not have_isc) and have_fid and (not have_kid):
# shortcut for a case when statistics are cached and features are not required on at least one input
metric_fid = fid_inputs_to_metric(input_1, input_2, feat_extractor, feature_layer_fid, **kwargs)
metrics.update(metric_fid)
return metrics
cacheable_input1_name = get_input_cacheable_name(input_1, get_kwarg('cache_input1_name', kwargs))
vprint(verbose, f'Extracting features from input_1')
featuresdict_1 = extract_featuresdict_from_input_cached(input_1, cacheable_input1_name, feat_extractor, **kwargs)
featuresdict_2 = None
if input_2 is not None:
cacheable_input2_name = get_input_cacheable_name(input_2, get_kwarg('cache_input2_name', kwargs))
vprint(verbose, f'Extracting features from input_2')
featuresdict_2 = extract_featuresdict_from_input_cached(
input_2, cacheable_input2_name, feat_extractor, **kwargs
)
if have_isc:
metric_isc = isc_featuresdict_to_metric(featuresdict_1, feature_layer_isc, **kwargs)
metrics.update(metric_isc)
if have_fid:
fid_stats_1 = fid_featuresdict_to_statistics_cached(
featuresdict_1, cacheable_input1_name, feat_extractor, feature_layer_fid, **kwargs
)
fid_stats_2 = fid_featuresdict_to_statistics_cached(
featuresdict_2, cacheable_input2_name, feat_extractor, feature_layer_fid, **kwargs
)
metric_fid = fid_statistics_to_metric(fid_stats_1, fid_stats_2, get_kwarg('verbose', kwargs))
metrics.update(metric_fid)
if have_kid:
metric_kid = kid_featuresdict_to_metric(featuresdict_1, featuresdict_2, feature_layer_kid, **kwargs)
metrics.update(metric_kid)
return metrics
