partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | download_from_url | Download content from a url.
Args:
path: string directory where file will be downloaded
url: string url
Returns:
Full path to downloaded file | translation/tensorflow/process_data.py | def download_from_url(path, url):
"""Download content from a url.
Args:
path: string directory where file will be downloaded
url: string url
Returns:
Full path to downloaded file
"""
filename = url.split("/")[-1]
found_file = find_file(path, filename, max_depth=0)
if found_file is None:
... | def download_from_url(path, url):
"""Download content from a url.
Args:
path: string directory where file will be downloaded
url: string url
Returns:
Full path to downloaded file
"""
filename = url.split("/")[-1]
found_file = find_file(path, filename, max_depth=0)
if found_file is None:
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train | project_masks_on_boxes | Given segmentation masks and the bounding boxes corresponding
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boxes. This prepares the masks for them to be fed to the
loss computation as the targets.
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segmentation... | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py | def project_masks_on_boxes(segmentation_masks, proposals, discretization_size):
"""
Given segmentation masks and the bounding boxes corresponding
to the location of the masks in the image, this function
crops and resizes the masks in the position defined by the
boxes. This prepares the masks for the... | def project_masks_on_boxes(segmentation_masks, proposals, discretization_size):
"""
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train | evaluate_box_proposals | Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results. | object_detection/pytorch/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py | def evaluate_box_proposals(
predictions, dataset, thresholds=None, area="all", limit=None
):
"""Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results.
"""
# Record ma... | def evaluate_box_proposals(
predictions, dataset, thresholds=None, area="all", limit=None
):
"""Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results.
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train | _decode_crop_and_flip | Crops the given image to a random part of the image, and randomly flips.
We use the fused decode_and_crop op, which performs better than the two ops
used separately in series, but note that this requires that the image be
passed in as an un-decoded string Tensor.
Args:
image_buffer: scalar string Tensor r... | image_classification/tensorflow/official/resnet/imagenet_preprocessing.py | def _decode_crop_and_flip(image_buffer, num_channels):
"""Crops the given image to a random part of the image, and randomly flips.
We use the fused decode_and_crop op, which performs better than the two ops
used separately in series, but note that this requires that the image be
passed in as an un-decoded stri... | def _decode_crop_and_flip(image_buffer, num_channels):
"""Crops the given image to a random part of the image, and randomly flips.
We use the fused decode_and_crop op, which performs better than the two ops
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train | _central_crop | Performs central crops of the given image list.
Args:
image: a 3-D image tensor
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
3-D tensor with cropped image. | image_classification/tensorflow/official/resnet/imagenet_preprocessing.py | def _central_crop(image, crop_height, crop_width):
"""Performs central crops of the given image list.
Args:
image: a 3-D image tensor
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
3-D tensor with cropped image.
"""
sh... | def _central_crop(image, crop_height, crop_width):
"""Performs central crops of the given image list.
Args:
image: a 3-D image tensor
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
3-D tensor with cropped image.
"""
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train | _mean_image_subtraction | Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C].
means: a C-vector of values to subtract from each chann... | image_classification/tensorflow/official/resnet/imagenet_preprocessing.py | def _mean_image_subtraction(image, means, num_channels):
"""Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C... | def _mean_image_subtraction(image, means, num_channels):
"""Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
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train | _smallest_size_at_least | Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
width: an int32 scalar tensor indicating the current widt... | image_classification/tensorflow/official/resnet/imagenet_preprocessing.py | def _smallest_size_at_least(height, width, resize_min):
"""Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
... | def _smallest_size_at_least(height, width, resize_min):
"""Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
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train | _aspect_preserving_resize | Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
resize_min: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing the resized image. | image_classification/tensorflow/official/resnet/imagenet_preprocessing.py | def _aspect_preserving_resize(image, resize_min):
"""Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
resize_min: A python integer or scalar `Tensor` indicating the size of
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Returns:
resized_image: A 3-D tensor containing the... | def _aspect_preserving_resize(image, resize_min):
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image: A 3-D image `Tensor`.
resize_min: A python integer or scalar `Tensor` indicating the size of
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train | _resize_image | Simple wrapper around tf.resize_images.
This is primarily to make sure we use the same `ResizeMethod` and other
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Args:
image: A 3-D image `Tensor`.
height: The target height for the resized image.
width: The target width for the resized image.
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"""Simple wrapper around tf.resize_images.
This is primarily to make sure we use the same `ResizeMethod` and other
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Args:
image: A 3-D image `Tensor`.
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This is primarily to make sure we use the same `ResizeMethod` and other
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train | preprocess_image | Preprocesses the given image.
Preprocessing includes decoding, cropping, and resizing for both training
and eval images. Training preprocessing, however, introduces some random
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Args:
image_buffer: scalar string Tensor representing the raw JPEG image buffer.
... | image_classification/tensorflow/official/resnet/imagenet_preprocessing.py | def preprocess_image(image_buffer, output_height, output_width,
num_channels, is_training=False):
"""Preprocesses the given image.
Preprocessing includes decoding, cropping, and resizing for both training
and eval images. Training preprocessing, however, introduces some random
distortion o... | def preprocess_image(image_buffer, output_height, output_width,
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train | sequence_beam_search | Search for sequence of subtoken ids with the largest probability.
Args:
symbols_to_logits_fn: A function that takes in ids, index, and cache as
arguments. The passed in arguments will have shape:
ids -> [batch_size * beam_size, index]
index -> [] (scalar)
cache -> nested dictionary ... | translation/tensorflow/transformer/model/beam_search.py | def sequence_beam_search(
symbols_to_logits_fn, initial_ids, initial_cache, vocab_size, beam_size,
alpha, max_decode_length, eos_id):
"""Search for sequence of subtoken ids with the largest probability.
Args:
symbols_to_logits_fn: A function that takes in ids, index, and cache as
arguments. The p... | def sequence_beam_search(
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alpha, max_decode_length, eos_id):
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train | _shape_list | Return a list of the tensor's shape, and ensure no None values in list. | translation/tensorflow/transformer/model/beam_search.py | def _shape_list(tensor):
"""Return a list of the tensor's shape, and ensure no None values in list."""
# Get statically known shape (may contain None's for unknown dimensions)
shape = tensor.get_shape().as_list()
# Ensure that the shape values are not None
dynamic_shape = tf.shape(tensor)
for i in range(le... | def _shape_list(tensor):
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train | _flatten_beam_dim | Reshapes first two dimensions in to single dimension.
Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
Reshaped tensor of shape [A*B, ...] | translation/tensorflow/transformer/model/beam_search.py | def _flatten_beam_dim(tensor):
"""Reshapes first two dimensions in to single dimension.
Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
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Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
Reshaped tensor of shape [A*B, ...]
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train | _unflatten_beam_dim | Reshapes first dimension back to [batch_size, beam_size].
Args:
tensor: Tensor to reshape of shape [batch_size*beam_size, ...]
batch_size: Tensor, original batch size.
beam_size: int, original beam size.
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"""Reshapes first dimension back to [batch_size, beam_size].
Args:
tensor: Tensor to reshape of shape [batch_size*beam_size, ...]
batch_size: Tensor, original batch size.
beam_size: int, original beam size.
Returns:
Reshaped tensor of shape... | def _unflatten_beam_dim(tensor, batch_size, beam_size):
"""Reshapes first dimension back to [batch_size, beam_size].
Args:
tensor: Tensor to reshape of shape [batch_size*beam_size, ...]
batch_size: Tensor, original batch size.
beam_size: int, original beam size.
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train | _gather_beams | Gather beams from nested structure of tensors.
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train | _gather_topk_beams | Gather top beams from nested structure. | translation/tensorflow/transformer/model/beam_search.py | def _gather_topk_beams(nested, score_or_log_prob, batch_size, beam_size):
"""Gather top beams from nested structure."""
_, topk_indexes = tf.nn.top_k(score_or_log_prob, k=beam_size)
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train | SequenceBeamSearch.search | Beam search for sequences with highest scores. | translation/tensorflow/transformer/model/beam_search.py | def search(self, initial_ids, initial_cache):
"""Beam search for sequences with highest scores."""
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train | SequenceBeamSearch._create_initial_state | Return initial state dictionary and its shape invariants.
Args:
initial_ids: initial ids to pass into the symbols_to_logits_fn.
int tensor with shape [batch_size, 1]
initial_cache: dictionary storing values to be passed into the
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Returns:
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initial_ids: initial ids to pass into the symbols_to_logits_fn.
int tensor with shape [batch_size, 1]
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train | SequenceBeamSearch._continue_search | Return whether to continue the search loop.
The loops should terminate when
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2) when the worst score in the finished sequences is better than the best
score in the alive sequences (i.e. the finished sequences are provably
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train | SequenceBeamSearch._search_step | Beam search loop body.
Grow alive sequences by a single ID. Sequences that have reached the EOS
token are marked as finished. The alive and finished sequences with the
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A sequence's finished score is calculating by dividing the log probability
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Grow alive sequences by a single ID. Sequences that have reached the EOS
token are marked as finished. The alive and finished sequences with the
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A sequence's finished score is calculating by... | def _search_step(self, state):
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train | SequenceBeamSearch._grow_alive_seq | Grow alive sequences by one token, and collect top 2*beam_size sequences.
2*beam_size sequences are collected because some sequences may have reached
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Args:
state: A dictionary with the current loop state.
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train | SequenceBeamSearch._get_new_alive_state | Gather the top k sequences that are still alive.
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new_seq: New sequences generated by growing the current alive sequences
int32 tensor with shape [batch_size, 2 * beam_size, cur_index + 1]
new_log_probs: Log probabilities of new sequences
float32 tensor with shape [batch_size, beam... | translation/tensorflow/transformer/model/beam_search.py | def _get_new_alive_state(self, new_seq, new_log_probs, new_cache):
"""Gather the top k sequences that are still alive.
Args:
new_seq: New sequences generated by growing the current alive sequences
int32 tensor with shape [batch_size, 2 * beam_size, cur_index + 1]
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new_seq: New sequences generated by growing the current alive sequences
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train | SequenceBeamSearch._get_new_finished_state | Combine new and old finished sequences, and gather the top k sequences.
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new_seq: New sequences generated by growing the current alive sequences
int32 tensor with shape [batch_size, beam_size, i + 1]
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Args:
state: A dictionary with the current loop state.
new_seq: New sequences generated by growing the current alive sequences
int32 tensor with shape... | def _get_new_finished_state(self, state, new_seq, new_log_probs):
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state: A dictionary with the current loop state.
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train | BahdanauAttention.reset_parameters | Sets initial random values for trainable parameters. | rnn_translator/pytorch/seq2seq/models/attention.py | def reset_parameters(self, init_weight):
"""
Sets initial random values for trainable parameters.
"""
stdv = 1. / math.sqrt(self.num_units)
self.linear_att.data.uniform_(-init_weight, init_weight)
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self.normalize_scalar.data.fill_(stdv)
... | def reset_parameters(self, init_weight):
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Sets initial random values for trainable parameters.
"""
stdv = 1. / math.sqrt(self.num_units)
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... | def set_mask(self, context_len, context):
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sets self.mask which is applied before softmax
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train | BahdanauAttention.calc_score | Calculate Bahdanau score
:param att_query: b x t_q x n
:param att_keys: b x t_k x n
returns: b x t_q x t_k scores | rnn_translator/pytorch/seq2seq/models/attention.py | def calc_score(self, att_query, att_keys):
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Calculate Bahdanau score
:param att_query: b x t_q x n
:param att_keys: b x t_k x n
returns: b x t_q x t_k scores
"""
b, t_k, n = att_keys.size()
t_q = att_query.size(1)
att_query = att_query.unsqu... | def calc_score(self, att_query, att_keys):
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Calculate Bahdanau score
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:param att_keys: b x t_k x n
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train | get_position_encoding | Return positional encoding.
Calculates the position encoding as a mix of sine and cosine functions with
geometrically increasing wavelengths.
Defined and formulized in Attention is All You Need, section 3.5.
Args:
length: Sequence length.
hidden_size: Size of the
min_timescale: Minimum scale that ... | translation/tensorflow/transformer/model/model_utils.py | def get_position_encoding(
length, hidden_size, min_timescale=1.0, max_timescale=1.0e4):
"""Return positional encoding.
Calculates the position encoding as a mix of sine and cosine functions with
geometrically increasing wavelengths.
Defined and formulized in Attention is All You Need, section 3.5.
Args... | def get_position_encoding(
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Calculates the position encoding as a mix of sine and cosine functions with
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train | get_decoder_self_attention_bias | Calculate bias for decoder that maintains model's autoregressive property.
Creates a tensor that masks out locations that correspond to illegal
connections, so prediction at position i cannot draw information from future
positions.
Args:
length: int length of sequences in batch.
Returns:
float tens... | translation/tensorflow/transformer/model/model_utils.py | def get_decoder_self_attention_bias(length):
"""Calculate bias for decoder that maintains model's autoregressive property.
Creates a tensor that masks out locations that correspond to illegal
connections, so prediction at position i cannot draw information from future
positions.
Args:
length: int length... | def get_decoder_self_attention_bias(length):
"""Calculate bias for decoder that maintains model's autoregressive property.
Creates a tensor that masks out locations that correspond to illegal
connections, so prediction at position i cannot draw information from future
positions.
Args:
length: int length... | [
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train | get_padding | Return float tensor representing the padding values in x.
Args:
x: int tensor with any shape
padding_value: int value that
Returns:
flaot tensor with same shape as x containing values 0 or 1.
0 -> non-padding, 1 -> padding | translation/tensorflow/transformer/model/model_utils.py | def get_padding(x, padding_value=0):
"""Return float tensor representing the padding values in x.
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x: int tensor with any shape
padding_value: int value that
Returns:
flaot tensor with same shape as x containing values 0 or 1.
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x: int tensor with any shape
padding_value: int value that
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flaot tensor with same shape as x containing values 0 or 1.
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train | get_padding_bias | Calculate bias tensor from padding values in tensor.
Bias tensor that is added to the pre-softmax multi-headed attention logits,
which has shape [batch_size, num_heads, length, length]. The tensor is zero at
non-padding locations, and -1e9 (negative infinity) at padding locations.
Args:
x: int tensor with... | translation/tensorflow/transformer/model/model_utils.py | def get_padding_bias(x):
"""Calculate bias tensor from padding values in tensor.
Bias tensor that is added to the pre-softmax multi-headed attention logits,
which has shape [batch_size, num_heads, length, length]. The tensor is zero at
non-padding locations, and -1e9 (negative infinity) at padding locations.
... | def get_padding_bias(x):
"""Calculate bias tensor from padding values in tensor.
Bias tensor that is added to the pre-softmax multi-headed attention logits,
which has shape [batch_size, num_heads, length, length]. The tensor is zero at
non-padding locations, and -1e9 (negative infinity) at padding locations.
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train | SubmissionChecks._add_result | Adds a result to the dictionary.
Args:
dict_entry: main dict to add entry
entry: slot for this entry (likely an integer)
dt: the timing for the entry
start_time: when the entry started unix time float | compliance/verify_submission/mlperf_submission_helper/checks.py | def _add_result(self, dict_entry, entry, dt, start_time):
"""Adds a result to the dictionary.
Args:
dict_entry: main dict to add entry
entry: slot for this entry (likely an integer)
dt: the timing for the entry
start_time: when the entry started unix time float
"""
time_entry = ... | def _add_result(self, dict_entry, entry, dt, start_time):
"""Adds a result to the dictionary.
Args:
dict_entry: main dict to add entry
entry: slot for this entry (likely an integer)
dt: the timing for the entry
start_time: when the entry started unix time float
"""
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train | SubmissionChecks._sorted_results | Sorts dict of results based on log start_time.
Sorts the results and returns an array with only the values but sorted
by oldest value first.value
Args:
results_dicts: List of result dicts
Returns:
List of only the time but sorted oldest first. | compliance/verify_submission/mlperf_submission_helper/checks.py | def _sorted_results(self, results_dicts):
"""Sorts dict of results based on log start_time.
Sorts the results and returns an array with only the values but sorted
by oldest value first.value
Args:
results_dicts: List of result dicts
Returns:
List of only the time but sorted oldest fir... | def _sorted_results(self, results_dicts):
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results_dicts: List of result dicts
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train | SubmissionChecks.get_compliance | Get the compliance level of the output file. | compliance/verify_submission/mlperf_submission_helper/checks.py | def get_compliance(self, filename):
"""Get the compliance level of the output file."""
print('Running Compliance Check on {}'.format(filename))
print('#' * 80)
start_time, status, dt, qual, target = mlp_compliance.l2_check_file_w_starttime(
filename)
print('#' * 80)
if status:
lev... | def get_compliance(self, filename):
"""Get the compliance level of the output file."""
print('Running Compliance Check on {}'.format(filename))
print('#' * 80)
start_time, status, dt, qual, target = mlp_compliance.l2_check_file_w_starttime(
filename)
print('#' * 80)
if status:
lev... | [
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train | SubmissionChecks.verify_and_extract_time | Verifies and result and returns timing.
Uses submodule mlp_compliance (https://github.com/bitfort/mlp_compliance)
Args:
log_file: Absolute path to result file.
division: open, closed
result_name: name of the benchmark, ncf, ssd, etc
Returns:
Time for the result or `INFINITE_TIME` ... | compliance/verify_submission/mlperf_submission_helper/checks.py | def verify_and_extract_time(self, log_file, division, result_name):
"""Verifies and result and returns timing.
Uses submodule mlp_compliance (https://github.com/bitfort/mlp_compliance)
Args:
log_file: Absolute path to result file.
division: open, closed
result_name: name of the benchmark... | def verify_and_extract_time(self, log_file, division, result_name):
"""Verifies and result and returns timing.
Uses submodule mlp_compliance (https://github.com/bitfort/mlp_compliance)
Args:
log_file: Absolute path to result file.
division: open, closed
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train | _compute_and_write_row_block | Compute row block (shard) of expansion for row i of the left_matrix.
Compute a shard of the randomized Kronecker product and dump it on the fly.
A standard Kronecker product between matrices A and B produces
[[a_11 B, ..., a_1n B],
...
... | data_generation/fractal_graph_expansions/graph_expansion.py | def _compute_and_write_row_block(
i, left_matrix, right_matrix, train_indices_out_path, test_indices_out_path,
remove_empty_rows):
"""Compute row block (shard) of expansion for row i of the left_matrix.
Compute a shard of the randomized Kronecker product and dump it on the fly.
A standard Kronecker produ... | def _compute_and_write_row_block(
i, left_matrix, right_matrix, train_indices_out_path, test_indices_out_path,
remove_empty_rows):
"""Compute row block (shard) of expansion for row i of the left_matrix.
Compute a shard of the randomized Kronecker product and dump it on the fly.
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train | output_randomized_kronecker_to_pickle | Compute randomized Kronecker product and dump it on the fly.
A standard Kronecker product between matrices A and B produces
[[a_11 B, ..., a_1n B],
...
[a_m1 B, ..., a_mn B]]
(if A's size is (m, n) and B's size is (p, q) then A Kr... | data_generation/fractal_graph_expansions/graph_expansion.py | def output_randomized_kronecker_to_pickle(
left_matrix, right_matrix,
train_indices_out_path, test_indices_out_path,
train_metadata_out_path=None, test_metadata_out_path=None,
remove_empty_rows=True):
"""Compute randomized Kronecker product and dump it on the fly.
A standard Kronecker product betwe... | def output_randomized_kronecker_to_pickle(
left_matrix, right_matrix,
train_indices_out_path, test_indices_out_path,
train_metadata_out_path=None, test_metadata_out_path=None,
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train | make_gtp_instance | Takes a path to model files and set up a GTP engine instance. | reinforcement/tensorflow/minigo/gtp.py | def make_gtp_instance(load_file, cgos_mode=False, kgs_mode=False,
minigui_mode=False):
"""Takes a path to model files and set up a GTP engine instance."""
n = DualNetwork(load_file)
if cgos_mode:
player = CGOSPlayer(network=n, seconds_per_move=5, timed_match=True,
... | def make_gtp_instance(load_file, cgos_mode=False, kgs_mode=False,
minigui_mode=False):
"""Takes a path to model files and set up a GTP engine instance."""
n = DualNetwork(load_file)
if cgos_mode:
player = CGOSPlayer(network=n, seconds_per_move=5, timed_match=True,
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train | main | Run Minigo in GTP mode. | reinforcement/tensorflow/minigo/gtp.py | def main(argv):
"""Run Minigo in GTP mode."""
del argv
engine = make_gtp_instance(FLAGS.load_file,
cgos_mode=FLAGS.cgos_mode,
kgs_mode=FLAGS.kgs_mode,
minigui_mode=FLAGS.minigui_mode)
dbg("GTP engine ready\n")
... | def main(argv):
"""Run Minigo in GTP mode."""
del argv
engine = make_gtp_instance(FLAGS.load_file,
cgos_mode=FLAGS.cgos_mode,
kgs_mode=FLAGS.kgs_mode,
minigui_mode=FLAGS.minigui_mode)
dbg("GTP engine ready\n")
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train | validate | Validate a model's performance on a set of holdout data. | reinforcement/tensorflow/minigo/validate.py | def validate(*tf_records):
"""Validate a model's performance on a set of holdout data."""
if FLAGS.use_tpu:
def _input_fn(params):
return preprocessing.get_tpu_input_tensors(
params['batch_size'], tf_records, filter_amount=1.0)
else:
def _input_fn():
r... | def validate(*tf_records):
"""Validate a model's performance on a set of holdout data."""
if FLAGS.use_tpu:
def _input_fn(params):
return preprocessing.get_tpu_input_tensors(
params['batch_size'], tf_records, filter_amount=1.0)
else:
def _input_fn():
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train | main | Validate a model's performance on a set of holdout data. | reinforcement/tensorflow/minigo/validate.py | def main(argv):
"""Validate a model's performance on a set of holdout data."""
_, *validation_paths = argv
if FLAGS.expand_validation_dirs:
tf_records = []
with utils.logged_timer("Building lists of holdout files"):
for record_dir in validation_paths:
tf_records.e... | def main(argv):
"""Validate a model's performance on a set of holdout data."""
_, *validation_paths = argv
if FLAGS.expand_validation_dirs:
tf_records = []
with utils.logged_timer("Building lists of holdout files"):
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train | setup_custom_environment | Load custom environment setup from a Python source file and run the setup
function. | object_detection/pytorch/maskrcnn_benchmark/utils/env.py | def setup_custom_environment(custom_module_path):
"""Load custom environment setup from a Python source file and run the setup
function.
"""
module = import_file("maskrcnn_benchmark.utils.env.custom_module", custom_module_path)
assert hasattr(module, "setup_environment") and callable(
module... | def setup_custom_environment(custom_module_path):
"""Load custom environment setup from a Python source file and run the setup
function.
"""
module = import_file("maskrcnn_benchmark.utils.env.custom_module", custom_module_path)
assert hasattr(module, "setup_environment") and callable(
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train | get_models | Finds all models, returning a list of model number and names
sorted increasing.
Returns: [(13, 000013-modelname), (17, 000017-modelname), ...etc] | reinforcement/tensorflow/minigo/rl_loop/fsdb.py | def get_models():
"""Finds all models, returning a list of model number and names
sorted increasing.
Returns: [(13, 000013-modelname), (17, 000017-modelname), ...etc]
"""
all_models = gfile.Glob(os.path.join(models_dir(), '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
... | def get_models():
"""Finds all models, returning a list of model number and names
sorted increasing.
Returns: [(13, 000013-modelname), (17, 000017-modelname), ...etc]
"""
all_models = gfile.Glob(os.path.join(models_dir(), '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
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train | get_hour_dirs | Gets the directories under selfplay_dir that match YYYY-MM-DD-HH. | reinforcement/tensorflow/minigo/rl_loop/fsdb.py | def get_hour_dirs(root=None):
"""Gets the directories under selfplay_dir that match YYYY-MM-DD-HH."""
root = root or selfplay_dir()
return list(filter(lambda s: re.match(r"\d{4}-\d{2}-\d{2}-\d{2}", s),
gfile.ListDirectory(root))) | def get_hour_dirs(root=None):
"""Gets the directories under selfplay_dir that match YYYY-MM-DD-HH."""
root = root or selfplay_dir()
return list(filter(lambda s: re.match(r"\d{4}-\d{2}-\d{2}-\d{2}", s),
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train | game_counts | Prints statistics for the most recent n_back models | reinforcement/tensorflow/minigo/rl_loop/fsdb.py | def game_counts(n_back=20):
"""Prints statistics for the most recent n_back models"""
for _, model_name in get_models[-n_back:]:
games = get_games(model_name)
print("Model: {}, Games: {}".format(model_name, len(games))) | def game_counts(n_back=20):
"""Prints statistics for the most recent n_back models"""
for _, model_name in get_models[-n_back:]:
games = get_games(model_name)
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train | boxlist_nms | Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the top max_proposals are kept
after non-maximum suppression
score_field (st... | object_detection/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py | def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the to... | def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the to... | [
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train | remove_small_boxes | Only keep boxes with both sides >= min_size
Arguments:
boxlist (Boxlist)
min_size (int) | object_detection/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py | def remove_small_boxes(boxlist, min_size):
"""
Only keep boxes with both sides >= min_size
Arguments:
boxlist (Boxlist)
min_size (int)
"""
# TODO maybe add an API for querying the ws / hs
xywh_boxes = boxlist.convert("xywh").bbox
_, _, ws, hs = xywh_boxes.unbind(dim=1)
k... | def remove_small_boxes(boxlist, min_size):
"""
Only keep boxes with both sides >= min_size
Arguments:
boxlist (Boxlist)
min_size (int)
"""
# TODO maybe add an API for querying the ws / hs
xywh_boxes = boxlist.convert("xywh").bbox
_, _, ws, hs = xywh_boxes.unbind(dim=1)
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train | boxlist_iou | Compute the intersection over union of two set of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Arguments:
box1: (BoxList) bounding boxes, sized [N,4].
box2: (BoxList) bounding boxes, sized [M,4].
Returns:
(tensor) iou, sized [N,M].
Reference:
https://github.com/chain... | object_detection/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py | def boxlist_iou(boxlist1, boxlist2):
"""Compute the intersection over union of two set of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Arguments:
box1: (BoxList) bounding boxes, sized [N,4].
box2: (BoxList) bounding boxes, sized [M,4].
Returns:
(tensor) iou, sized [N,M].
... | def boxlist_iou(boxlist1, boxlist2):
"""Compute the intersection over union of two set of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Arguments:
box1: (BoxList) bounding boxes, sized [N,4].
box2: (BoxList) bounding boxes, sized [M,4].
Returns:
(tensor) iou, sized [N,M].
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train | cat_boxlist | Concatenates a list of BoxList (having the same image size) into a
single BoxList
Arguments:
bboxes (list[BoxList]) | object_detection/pytorch/maskrcnn_benchmark/structures/boxlist_ops.py | def cat_boxlist(bboxes):
"""
Concatenates a list of BoxList (having the same image size) into a
single BoxList
Arguments:
bboxes (list[BoxList])
"""
assert isinstance(bboxes, (list, tuple))
assert all(isinstance(bbox, BoxList) for bbox in bboxes)
size = bboxes[0].size
asser... | def cat_boxlist(bboxes):
"""
Concatenates a list of BoxList (having the same image size) into a
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Arguments:
bboxes (list[BoxList])
"""
assert isinstance(bboxes, (list, tuple))
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train | Loss._loc_vec | Generate Location Vectors | single_stage_detector/ssd/base_model.py | def _loc_vec(self, loc):
"""
Generate Location Vectors
"""
gxy = self.scale_xy*(loc[:, :2, :] - self.dboxes[:, :2, :])/self.dboxes[:, 2:, ]
gwh = self.scale_wh*(loc[:, 2:, :]/self.dboxes[:, 2:, :]).log()
return torch.cat((gxy, gwh), dim=1).contiguous() | def _loc_vec(self, loc):
"""
Generate Location Vectors
"""
gxy = self.scale_xy*(loc[:, :2, :] - self.dboxes[:, :2, :])/self.dboxes[:, 2:, ]
gwh = self.scale_wh*(loc[:, 2:, :]/self.dboxes[:, 2:, :]).log()
return torch.cat((gxy, gwh), dim=1).contiguous() | [
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train | Loss.forward | ploc, plabel: Nx4x8732, Nxlabel_numx8732
predicted location and labels
gloc, glabel: Nx4x8732, Nx8732
ground truth location and labels | single_stage_detector/ssd/base_model.py | def forward(self, ploc, plabel, gloc, glabel):
"""
ploc, plabel: Nx4x8732, Nxlabel_numx8732
predicted location and labels
gloc, glabel: Nx4x8732, Nx8732
ground truth location and labels
"""
mask = glabel > 0
pos_num = mask.sum(dim... | def forward(self, ploc, plabel, gloc, glabel):
"""
ploc, plabel: Nx4x8732, Nxlabel_numx8732
predicted location and labels
gloc, glabel: Nx4x8732, Nx8732
ground truth location and labels
"""
mask = glabel > 0
pos_num = mask.sum(dim... | [
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train | Fp16Optimizer.set_grads | Copies gradients from param_with_grad to params
:param params: dst parameters
:param params_with_grad: src parameters | rnn_translator/pytorch/seq2seq/train/fp_optimizers.py | def set_grads(params, params_with_grad):
"""
Copies gradients from param_with_grad to params
:param params: dst parameters
:param params_with_grad: src parameters
"""
for param, param_w_grad in zip(params, params_with_grad):
if param.grad is None:
... | def set_grads(params, params_with_grad):
"""
Copies gradients from param_with_grad to params
:param params: dst parameters
:param params_with_grad: src parameters
"""
for param, param_w_grad in zip(params, params_with_grad):
if param.grad is None:
... | [
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train | Fp16Optimizer.set_weights | Copies parameters from new_params to params
:param params: dst parameters
:param new_params: src parameters | rnn_translator/pytorch/seq2seq/train/fp_optimizers.py | def set_weights(params, new_params):
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Copies parameters from new_params to params
:param params: dst parameters
:param new_params: src parameters
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for param, new_param in zip(params, new_params):
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Copies parameters from new_params to params
:param params: dst parameters
:param new_params: src parameters
"""
for param, new_param in zip(params, new_params):
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train | Fp16Optimizer.initialize_model | Initializes internal state and build fp32 master copy of weights.
:param model: fp16 model | rnn_translator/pytorch/seq2seq/train/fp_optimizers.py | def initialize_model(self, model):
"""
Initializes internal state and build fp32 master copy of weights.
:param model: fp16 model
"""
logging.info('Initializing fp32 clone weights')
self.fp16_model = model
self.fp16_model.zero_grad()
self.fp32_params = [p... | def initialize_model(self, model):
"""
Initializes internal state and build fp32 master copy of weights.
:param model: fp16 model
"""
logging.info('Initializing fp32 clone weights')
self.fp16_model = model
self.fp16_model.zero_grad()
self.fp32_params = [p... | [
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train | Fp16Optimizer.step | Performs one step of the optimizer.
Applies loss scaling, computes gradients in fp16, converts gradients to
fp32, inverts scaling and applies optional gradient norm clipping.
If gradients are finite, it applies update to fp32 master weights and
copies updated parameters to fp16 model for... | rnn_translator/pytorch/seq2seq/train/fp_optimizers.py | def step(self, loss, optimizer, scheduler, update=True):
"""
Performs one step of the optimizer.
Applies loss scaling, computes gradients in fp16, converts gradients to
fp32, inverts scaling and applies optional gradient norm clipping.
If gradients are finite, it applies update t... | def step(self, loss, optimizer, scheduler, update=True):
"""
Performs one step of the optimizer.
Applies loss scaling, computes gradients in fp16, converts gradients to
fp32, inverts scaling and applies optional gradient norm clipping.
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train | Fp32Optimizer.step | Performs one step of the optimizer.
:param loss: value of loss function
:param optimizer: optimizer
:param update: if True executes weight update | rnn_translator/pytorch/seq2seq/train/fp_optimizers.py | def step(self, loss, optimizer, scheduler, update=True):
"""
Performs one step of the optimizer.
:param loss: value of loss function
:param optimizer: optimizer
:param update: if True executes weight update
"""
loss.backward()
if update:
if se... | def step(self, loss, optimizer, scheduler, update=True):
"""
Performs one step of the optimizer.
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:param update: if True executes weight update
"""
loss.backward()
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train | _parse_example | Return inputs and targets Tensors from a serialized tf.Example. | translation/tensorflow/transformer/utils/dataset.py | def _parse_example(serialized_example):
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data_fields = {
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train | _filter_max_length | Indicates whether the example's length is lower than the maximum length. | translation/tensorflow/transformer/utils/dataset.py | def _filter_max_length(example, max_length=256):
"""Indicates whether the example's length is lower than the maximum length."""
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tf.size(example[1]) <= max_length) | def _filter_max_length(example, max_length=256):
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train | _get_example_length | Returns the maximum length between the example inputs and targets. | translation/tensorflow/transformer/utils/dataset.py | def _get_example_length(example):
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train | _create_min_max_boundaries | Create min and max boundary lists up to max_length.
For example, when max_length=24, min_boundary=4 and boundary_scale=2, the
returned values will be:
buckets_min = [0, 4, 8, 16, 24]
buckets_max = [4, 8, 16, 24, 25]
Args:
max_length: The maximum length of example in dataset.
min_boundary: Minimu... | translation/tensorflow/transformer/utils/dataset.py | def _create_min_max_boundaries(
max_length, min_boundary=_MIN_BOUNDARY, boundary_scale=_BOUNDARY_SCALE):
"""Create min and max boundary lists up to max_length.
For example, when max_length=24, min_boundary=4 and boundary_scale=2, the
returned values will be:
buckets_min = [0, 4, 8, 16, 24]
buckets_ma... | def _create_min_max_boundaries(
max_length, min_boundary=_MIN_BOUNDARY, boundary_scale=_BOUNDARY_SCALE):
"""Create min and max boundary lists up to max_length.
For example, when max_length=24, min_boundary=4 and boundary_scale=2, the
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buckets_min = [0, 4, 8, 16, 24]
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train | _batch_examples | Group examples by similar lengths, and return batched dataset.
Each batch of similar-length examples are padded to the same length, and may
have different number of elements in each batch, such that:
group_batch_size * padded_length <= batch_size.
This decreases the number of padding tokens per batch, which... | translation/tensorflow/transformer/utils/dataset.py | def _batch_examples(dataset, batch_size, max_length):
"""Group examples by similar lengths, and return batched dataset.
Each batch of similar-length examples are padded to the same length, and may
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"""Group examples by similar lengths, and return batched dataset.
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train | _read_and_batch_from_files | Create dataset where each item is a dict of "inputs" and "targets".
Args:
file_pattern: String used to match the input TFRecord files.
batch_size: Maximum number of tokens per batch of examples
max_length: Maximum number of tokens per example
num_cpu_cores: Number of cpu cores for parallel input proc... | translation/tensorflow/transformer/utils/dataset.py | def _read_and_batch_from_files(
file_pattern, batch_size, max_length, num_cpu_cores, shuffle, repeat):
"""Create dataset where each item is a dict of "inputs" and "targets".
Args:
file_pattern: String used to match the input TFRecord files.
batch_size: Maximum number of tokens per batch of examples
... | def _read_and_batch_from_files(
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"""Create dataset where each item is a dict of "inputs" and "targets".
Args:
file_pattern: String used to match the input TFRecord files.
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train | train_input_fn | Load and return dataset of batched examples for use during training. | translation/tensorflow/transformer/utils/dataset.py | def train_input_fn(params):
"""Load and return dataset of batched examples for use during training."""
file_pattern = os.path.join(getattr(params, "data_dir", ""), "*encoded-train*")
return _read_and_batch_from_files(
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"""Load and return dataset of batched examples for use during training."""
file_pattern = os.path.join(getattr(params, "data_dir", ""), "*encoded-train*")
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train | eval_input_fn | Load and return dataset of batched examples for use during evaluation. | translation/tensorflow/transformer/utils/dataset.py | def eval_input_fn(params):
"""Load and return dataset of batched examples for use during evaluation."""
file_pattern = os.path.join(getattr(params, "data_dir", ""), "*encoded-dev*")
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"""Load and return dataset of batched examples for use during evaluation."""
file_pattern = os.path.join(getattr(params, "data_dir", ""), "*encoded-dev*")
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train | reduce_loss_dict | Reduce the loss dictionary from all processes so that process with rank
0 has the averaged results. Returns a dict with the same fields as
loss_dict, after reduction. | object_detection/pytorch/maskrcnn_benchmark/engine/trainer.py | def reduce_loss_dict(loss_dict):
"""
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"""
world_size = get_world_size()
if world_size < 2:
return loss_dict
with t... | def reduce_loss_dict(loss_dict):
"""
Reduce the loss dictionary from all processes so that process with rank
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loss_dict, after reduction.
"""
world_size = get_world_size()
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train | parse_dtype_info | Convert dtype string to tf dtype, and set loss_scale default as needed.
Args:
flags: namespace object returned by arg parser.
Raises:
ValueError: If an invalid dtype is provided. | image_classification/tensorflow/official/utils/arg_parsers/parsers.py | def parse_dtype_info(flags):
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Args:
flags: namespace object returned by arg parser.
Raises:
ValueError: If an invalid dtype is provided.
"""
if flags.dtype in (i[0] for i in DTYPE_MAP.values()):
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"""Convert dtype string to tf dtype, and set loss_scale default as needed.
Args:
flags: namespace object returned by arg parser.
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ValueError: If an invalid dtype is provided.
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train | calc_iou_tensor | Calculation of IoU based on two boxes tensor,
Reference to https://github.com/kuangliu/pytorch-ssd
input:
box1 (N, 4)
box2 (M, 4)
output:
IoU (N, M) | single_stage_detector/ssd/utils.py | def calc_iou_tensor(box1, box2):
""" Calculation of IoU based on two boxes tensor,
Reference to https://github.com/kuangliu/pytorch-ssd
input:
box1 (N, 4)
box2 (M, 4)
output:
IoU (N, M)
"""
N = box1.size(0)
M = box2.size(0)
be1 = box1.unsq... | def calc_iou_tensor(box1, box2):
""" Calculation of IoU based on two boxes tensor,
Reference to https://github.com/kuangliu/pytorch-ssd
input:
box1 (N, 4)
box2 (M, 4)
output:
IoU (N, M)
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N = box1.size(0)
M = box2.size(0)
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train | Encoder.scale_back_batch | Do scale and transform from xywh to ltrb
suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox | single_stage_detector/ssd/utils.py | def scale_back_batch(self, bboxes_in, scores_in):
"""
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suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
"""
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"""
Do scale and transform from xywh to ltrb
suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
"""
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train | generate_anchors | Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors
are centered on stride / 2, have (approximate) sqrt areas of the specified
sizes, and aspect ratios as given. | object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/anchor_generator.py | def generate_anchors(
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"""
return _generat... | def generate_anchors(
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):
"""Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors
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train | _generate_anchors | Generate anchor (reference) windows by enumerating aspect ratios X
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"""
anchor = np.array([1, 1, base_size, base_size], dtype=np.float) - 1
anchors = _ratio_enum(anchor, asp... | def _generate_anchors(base_size, scales, aspect_ratios):
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train | to_flat | Converts from a Minigo coordinate to a flattened coordinate. | reinforcement/tensorflow/minigo/coords.py | def to_flat(coord):
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train | from_sgf | Converts from an SGF coordinate to a Minigo coordinate. | reinforcement/tensorflow/minigo/coords.py | def from_sgf(sgfc):
"""Converts from an SGF coordinate to a Minigo coordinate."""
if sgfc is None or sgfc == '' or (go.N <= 19 and sgfc == 'tt'):
return None
return _SGF_COLUMNS.index(sgfc[1]), _SGF_COLUMNS.index(sgfc[0]) | def from_sgf(sgfc):
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train | from_gtp | Converts from a GTP coordinate to a Minigo coordinate. | reinforcement/tensorflow/minigo/coords.py | def from_gtp(gtpc):
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gtpc = gtpc.upper()
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gtpc = gtpc.upper()
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train | to_gtp | Converts from a Minigo coordinate to a GTP coordinate. | reinforcement/tensorflow/minigo/coords.py | def to_gtp(coord):
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if coord is None:
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y, x = coord
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train | findContours | Wraps cv2.findContours to maintain compatiblity between versions
3 and 4
Returns:
contours, hierarchy | object_detection/pytorch/maskrcnn_benchmark/utils/cv2_util.py | def findContours(*args, **kwargs):
"""
Wraps cv2.findContours to maintain compatiblity between versions
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Returns:
contours, hierarchy
"""
if cv2.__version__.startswith('4'):
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elif cv2.__version__.startswith('3'):
... | def findContours(*args, **kwargs):
"""
Wraps cv2.findContours to maintain compatiblity between versions
3 and 4
Returns:
contours, hierarchy
"""
if cv2.__version__.startswith('4'):
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train | MCTSNode.maybe_add_child | Adds child node for fcoord if it doesn't already exist, and returns it. | reinforcement/tensorflow/minigo/mcts.py | def maybe_add_child(self, fcoord):
""" Adds child node for fcoord if it doesn't already exist, and returns it. """
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new_position = self.position.play_move(
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""" Adds child node for fcoord if it doesn't already exist, and returns it. """
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train | MCTSNode.add_virtual_loss | Propagate a virtual loss up to the root node.
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train | MCTSNode.backup_value | Propagates a value estimation up to the root node.
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value: the value to be propagated (1 = black wins, -1 = white wins)
up_to: the node to propagate until. | reinforcement/tensorflow/minigo/mcts.py | def backup_value(self, value, up_to):
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train | MCTSNode.is_done | True if the last two moves were Pass or if the position is at a move
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train | MCTSNode.children_as_pi | Returns the child visit counts as a probability distribution, pi
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"""Returns the child visit counts as a probability distribution, pi
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train | MCTSNode.mvp_gg | Returns most visited path in go-gui VAR format e.g. 'b r3 w c17... | reinforcement/tensorflow/minigo/mcts.py | def mvp_gg(self):
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train | _convert_args | Convert a list of command arguments to types specified by the handler.
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args: the list of string arguments to pass to handler.
Returns:
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Args:
handler: a command handler function.
args: the list of string arguments to pass to handler.
Returns:
A new list containing `args` that have been converted to the expected typ... | def _convert_args(handler, args):
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train | Engine.add_cmd_handler | Registers a new command handler object.
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Args:
handler_obj: the handler object to registe... | reinforcement/tensorflow/minigo/gtp_engine.py | def add_cmd_handler(self, handler_obj):
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train | padded_cross_entropy_loss | Calculate cross entropy loss while ignoring padding.
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labels: Tensor of size [batch_size, length_labels]
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logits: Tensor of size [batch_size, length_logits, vocab_size]
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train | get_eval_metrics | Return dictionary of model evaluation metrics. | translation/tensorflow/transformer/utils/metrics.py | def get_eval_metrics(logits, labels, params):
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train | padded_accuracy | Percentage of times that predictions matches labels on non-0s. | translation/tensorflow/transformer/utils/metrics.py | def padded_accuracy(logits, labels):
"""Percentage of times that predictions matches labels on non-0s."""
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train | padded_accuracy_topk | Percentage of times that top-k predictions matches labels on non-0s. | translation/tensorflow/transformer/utils/metrics.py | def padded_accuracy_topk(logits, labels, k):
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train | padded_sequence_accuracy | Percentage of times that predictions matches labels everywhere (non-0). | translation/tensorflow/transformer/utils/metrics.py | def padded_sequence_accuracy(logits, labels):
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train | padded_neg_log_perplexity | Average log-perplexity excluding padding 0s. No smoothing. | translation/tensorflow/transformer/utils/metrics.py | def padded_neg_log_perplexity(logits, labels, vocab_size):
"""Average log-perplexity excluding padding 0s. No smoothing."""
num, den = padded_cross_entropy_loss(logits, labels, 0, vocab_size)
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train | bleu_score | Approximate BLEU score computation between labels and predictions.
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"""Approximate BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have beam search.... | def bleu_score(logits, labels):
"""Approximate BLEU score computation between labels and predictions.
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train | _get_ngrams_with_counter | Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
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Returns:
The Counter containing all n-grams upto max_order in segment
with ... | translation/tensorflow/transformer/utils/metrics.py | def _get_ngrams_with_counter(segment, max_order):
"""Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
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Returns:
The Counter con... | def _get_ngrams_with_counter(segment, max_order):
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train | compute_bleu | Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of references for each translation. Each
reference should be tokenized into a list of tokens.
translation_corpus: list of translations to score. Each translation
should be tokenized into a ... | translation/tensorflow/transformer/utils/metrics.py | def compute_bleu(reference_corpus, translation_corpus, max_order=4,
use_bp=True):
"""Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of references for each translation. Each
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... | def compute_bleu(reference_corpus, translation_corpus, max_order=4,
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Args:
reference_corpus: list of references for each translation. Each
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train | rouge_2_fscore | ROUGE-2 F1 score computation between labels and predictions.
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Args:
logits: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge2_fscore: approx rouge-2 f1 score... | translation/tensorflow/transformer/utils/metrics.py | def rouge_2_fscore(logits, labels):
"""ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
logits: tensor, model predictions
labels: tensor, gold output.
Returns:
... | def rouge_2_fscore(logits, labels):
"""ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
logits: tensor, model predictions
labels: tensor, gold output.
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train | rouge_n | Computes ROUGE-N f1 score of two text collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Args:
eval_sentences: Predicted sentences.
ref_sentences: Sentences from the reference set
n: Size of ngram. Defaults ... | translation/tensorflow/transformer/utils/metrics.py | def rouge_n(eval_sentences, ref_sentences, n=2):
"""Computes ROUGE-N f1 score of two text collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Args:
eval_sentences: Predicted sentences.
ref_sentences: Sentences f... | def rouge_n(eval_sentences, ref_sentences, n=2):
"""Computes ROUGE-N f1 score of two text collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
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"_... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | rouge_l_fscore | ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 sco... | translation/tensorflow/transformer/utils/metrics.py | def rouge_l_fscore(predictions, labels):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Retu... | def rouge_l_fscore(predictions, labels):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Retu... | [
"ROUGE",
"scores",
"computation",
"between",
"labels",
"and",
"predictions",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/translation/tensorflow/transformer/utils/metrics.py#L365-L381 | [
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train | parse_args | Helper function parsing the command line options
@retval ArgumentParser | single_stage_detector/ssd/bind_launch.py | def parse_args():
"""
Helper function parsing the command line options
@retval ArgumentParser
"""
parser = ArgumentParser(description="PyTorch distributed training launch "
"helper utilty that will spawn up "
"multiple d... | def parse_args():
"""
Helper function parsing the command line options
@retval ArgumentParser
"""
parser = ArgumentParser(description="PyTorch distributed training launch "
"helper utilty that will spawn up "
"multiple d... | [
"Helper",
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] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/single_stage_detector/ssd/bind_launch.py#L10-L60 | [
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train | download_and_extract | Extract files from downloaded compressed archive file.
Args:
path: string directory where the files will be downloaded
url: url containing the compressed input and target files
input_filename: name of file containing data in source language
target_filename: name of file containing data in target lang... | translation/data_download.py | def download_and_extract(path, url, input_filename, target_filename):
"""Extract files from downloaded compressed archive file.
Args:
path: string directory where the files will be downloaded
url: url containing the compressed input and target files
input_filename: name of file containing data in sourc... | def download_and_extract(path, url, input_filename, target_filename):
"""Extract files from downloaded compressed archive file.
Args:
path: string directory where the files will be downloaded
url: url containing the compressed input and target files
input_filename: name of file containing data in sourc... | [
"Extract",
"files",
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"compressed",
"archive",
"file",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/translation/data_download.py#L146-L185 | [
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"=... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | main | Obtain training and evaluation data for the Transformer model. | translation/data_download.py | def main(unused_argv):
"""Obtain training and evaluation data for the Transformer model."""
make_dir(FLAGS.raw_dir)
make_dir(FLAGS.data_dir)
# Get paths of download/extracted training and evaluation files.
print("Step 1/4: Downloading data from source")
train_files = get_raw_files(FLAGS.raw_dir, _TRAIN_DAT... | def main(unused_argv):
"""Obtain training and evaluation data for the Transformer model."""
make_dir(FLAGS.raw_dir)
make_dir(FLAGS.data_dir)
# Get paths of download/extracted training and evaluation files.
print("Step 1/4: Downloading data from source")
train_files = get_raw_files(FLAGS.raw_dir, _TRAIN_DAT... | [
"Obtain",
"training",
"and",
"evaluation",
"data",
"for",
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"Transformer",
"model",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/translation/data_download.py#L194-L202 | [
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"print",
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"\"Step 1/4: Downloading data from source\"",
")",... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
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