本文整理匯總了Python中collections.OrderedDict方法的典型用法代碼示例。如果您正苦於以下問題:Python collections.OrderedDict方法的具體用法?Python collections.OrderedDict怎麽用?Python collections.OrderedDict使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類collections
的用法示例。
在下文中一共展示了collections.OrderedDict方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_assignment_map_from_checkpoint
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
"""Compute the union of the current variables and checkpoint variables."""
assignment_map = {}
initialized_variable_names = {}
name_to_variable = collections.OrderedDict()
for var in tvars:
name = var.name
m = re.match("^(.*):\\d+$", name)
if m is not None:
name = m.group(1)
name_to_variable[name] = var
init_vars = tf.train.list_variables(init_checkpoint)
assignment_map = collections.OrderedDict()
for x in init_vars:
(name, var) = (x[0], x[1])
if name not in name_to_variable:
continue
assignment_map[name] = name
initialized_variable_names[name] = 1
initialized_variable_names[name + ":0"] = 1
return (assignment_map, initialized_variable_names)
示例2: __init__
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def __init__(self):
self.args = None
self.alignDistance = 0
self.samples = collections.OrderedDict()
self.genome = None
self.sources = {}
self.annotationSets = collections.OrderedDict()
# for storing axes, annotations, etc, by allele
self.alleleTracks = collections.defaultdict(collections.OrderedDict)
self.trackCompositor = None
self.dotplots = {}
self.info = {}
self.reset()
示例3: __init__
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def __init__(self, chromPartsCollection, pixelWidth, dividerSize=25):
# length is in genomic coordinates, starts is in pixels
self.dividerSize = dividerSize
self.partsToLengths = collections.OrderedDict()
self.partsToStartPixels = collections.OrderedDict()
self.chromPartsCollection = chromPartsCollection
for part in chromPartsCollection:
self.partsToLengths[part.id] = len(part)
self.pixelWidth = pixelWidth
totalLength = sum(self.partsToLengths.values()) + (len(self.partsToLengths)-1)*dividerSize
self.basesPerPixel = totalLength / float(pixelWidth)
curStart = 0
for regionID in self.partsToLengths:
self.partsToStartPixels[regionID] = curStart
curStart += (self.partsToLengths[regionID]+dividerSize) / self.basesPerPixel
示例4: compute_f1
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def compute_f1(answer_stats, prefix=''):
"""Computes F1, precision, recall for a list of answer scores.
Args:
answer_stats: List of per-example scores.
prefix (''): Prefix to prepend to score dictionary.
Returns:
Dictionary mapping string names to scores.
"""
has_gold, has_pred, is_correct, _ = list(zip(*answer_stats))
precision = safe_divide(sum(is_correct), sum(has_pred))
recall = safe_divide(sum(is_correct), sum(has_gold))
f1 = safe_divide(2 * precision * recall, precision + recall)
return OrderedDict({
prefix + 'n': len(answer_stats),
prefix + 'f1': f1,
prefix + 'precision': precision,
prefix + 'recall': recall
})
示例5: get_metrics_with_answer_stats
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def get_metrics_with_answer_stats(long_answer_stats, short_answer_stats):
"""Generate metrics dict using long and short answer stats."""
def _get_metric_dict(answer_stats, prefix=''):
"""Compute all metrics for a set of answer statistics."""
opt_result, pr_table = compute_pr_curves(
answer_stats, targets=[0.5, 0.75, 0.9])
f1, precision, recall, threshold = opt_result
metrics = OrderedDict({
'best-threshold-f1': f1,
'best-threshold-precision': precision,
'best-threshold-recall': recall,
'best-threshold': threshold,
})
for target, recall, precision, _ in pr_table:
metrics['recall-at-precision>={:.2}'.format(target)] = recall
metrics['precision-at-precision>={:.2}'.format(target)] = precision
# Add prefix before returning.
return dict([(prefix + k, v) for k, v in six.iteritems(metrics)])
metrics = _get_metric_dict(long_answer_stats, 'long-')
metrics.update(_get_metric_dict(short_answer_stats, 'short-'))
return metrics
示例6: _init_fields
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def _init_fields(self):
self.name = None # User-specified name, defaults to build func name if None.
self.scope = None # Unique TF graph scope, derived from the user-specified name.
self.static_kwargs = dict() # Arguments passed to the user-supplied build func.
self.num_inputs = 0 # Number of input tensors.
self.num_outputs = 0 # Number of output tensors.
self.input_shapes = [[]] # Input tensor shapes (NC or NCHW), including minibatch dimension.
self.output_shapes = [[]] # Output tensor shapes (NC or NCHW), including minibatch dimension.
self.input_shape = [] # Short-hand for input_shapes[0].
self.output_shape = [] # Short-hand for output_shapes[0].
self.input_templates = [] # Input placeholders in the template graph.
self.output_templates = [] # Output tensors in the template graph.
self.input_names = [] # Name string for each input.
self.output_names = [] # Name string for each output.
self.vars = OrderedDict() # All variables (localname => var).
self.trainables = OrderedDict() # Trainable variables (localname => var).
self._build_func = None # User-supplied build function that constructs the network.
self._build_func_name = None # Name of the build function.
self._build_module_src = None # Full source code of the module containing the build function.
self._run_cache = dict() # Cached graph data for Network.run().
示例7: _allreduce_coalesced
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
bucket_size_bytes = bucket_size_mb * 1024 * 1024
buckets = _take_tensors(tensors, bucket_size_bytes)
else:
buckets = OrderedDict()
for tensor in tensors:
tp = tensor.type()
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(tensor)
buckets = buckets.values()
for bucket in buckets:
flat_tensors = _flatten_dense_tensors(bucket)
dist.all_reduce(flat_tensors)
flat_tensors.div_(world_size)
for tensor, synced in zip(
bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
tensor.copy_(synced)
示例8: test_max_pool_2d
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def test_max_pool_2d():
test_cases = OrderedDict([('in_w', [10, 20]), ('in_h', [10, 20]),
('in_channel', [1, 3]), ('out_channel', [1, 3]),
('kernel_size', [3, 5]), ('stride', [1, 2]),
('padding', [0, 1]), ('dilation', [1, 2])])
for in_h, in_w, in_cha, out_cha, k, s, p, d in product(
*list(test_cases.values())):
# wrapper op with 0-dim input
x_empty = torch.randn(0, in_cha, in_h, in_w, requires_grad=True)
wrapper = MaxPool2d(k, stride=s, padding=p, dilation=d)
wrapper_out = wrapper(x_empty)
# torch op with 3-dim input as shape reference
x_normal = torch.randn(3, in_cha, in_h, in_w)
ref = nn.MaxPool2d(k, stride=s, padding=p, dilation=d)
ref_out = ref(x_normal)
assert wrapper_out.shape[0] == 0
assert wrapper_out.shape[1:] == ref_out.shape[1:]
assert torch.equal(wrapper(x_normal), ref_out)
示例9: convert
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def convert(src, dst):
"""Convert keys in pycls pretrained RegNet models to mmdet style."""
# load caffe model
regnet_model = torch.load(src)
blobs = regnet_model['model_state']
# convert to pytorch style
state_dict = OrderedDict()
converted_names = set()
for key, weight in blobs.items():
if 'stem' in key:
convert_stem(key, weight, state_dict, converted_names)
elif 'head' in key:
convert_head(key, weight, state_dict, converted_names)
elif key.startswith('s'):
convert_reslayer(key, weight, state_dict, converted_names)
# check if all layers are converted
for key in blobs:
if key not in converted_names:
print(f'not converted: {key}')
# save checkpoint
checkpoint = dict()
checkpoint['state_dict'] = state_dict
torch.save(checkpoint, dst)
示例10: __init__
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def __init__(self, cmd_root, cli_args, folders=None, files=None):
"""Main tool runner."""
# Run options
self.cmd_root = cmd_root # Folder on which the command was executed
self.config = load_config(cmd_root, cli_args)
self.file_manager = FileManager(folders=folders, files=files)
self.folders = folders
self.files = files
self.all_results = OrderedDict()
self.all_tools = {}
self.test_results = None
self.failed_checks = set()
self.check = self.config.get_value('check')
self.enforce = self.config.get_value('enforce')
self.diff_mode = self.config.get_value('diff_mode')
self.file_mode = self.config.get_value('file_mode')
self.branch = self.config.get_value('branch')
self.disable_formatters = cli_args.disable_formatters
self.disable_linters = cli_args.disable_linters
self.disable_tests = cli_args.disable_tests
示例11: parse_coverage
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def parse_coverage(self):
"""Parse .coverage json report generated by coverage."""
coverage_string = ("!coverage.py: This is a private format, don't "
"read it directly!")
coverage_path = os.path.join(self.cmd_root, '.coverage')
covered_lines = {}
if os.path.isfile(coverage_path):
with open(coverage_path, 'r') as file_obj:
data = file_obj.read()
data = data.replace(coverage_string, '')
cov = json.loads(data)
covered_lines = OrderedDict()
lines = cov['lines']
for path in sorted(lines):
covered_lines[path] = lines[path]
return covered_lines
示例12: fprop
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def fprop(self, x):
output = OrderedDict()
# first convolutional layer
h_conv1 = tf.nn.relu(self._conv2d(x, self.W_conv1) + self.b_conv1)
h_pool1 = self._max_pool_2x2(h_conv1)
# second convolutional layer
h_conv2 = tf.nn.relu(
self._conv2d(h_pool1, self.W_conv2) + self.b_conv2)
h_pool2 = self._max_pool_2x2(h_conv2)
# first fully connected layer
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, self.W_fc1) + self.b_fc1)
# output layer
logits = tf.matmul(h_fc1, self.W_fc2) + self.b_fc2
output = deterministic_dict(locals())
del output["self"]
output[self.O_PROBS] = tf.nn.softmax(logits=logits)
return output
示例13: read_cpg_profiles
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def read_cpg_profiles(filenames, log=None, *args, **kwargs):
"""Read methylation profiles.
Input files can be gzip compressed.
Returns
-------
dict
`dict (key, value)`, where `key` is the output name and `value` the CpG
table.
"""
cpg_profiles = OrderedDict()
for filename in filenames:
if log:
log(filename)
#cpg_file = dat.GzipFile(filename, 'r')
cpg_file = get_fh(filename, 'r')
output_name = split_ext(filename)
cpg_profile = dat.read_cpg_profile(cpg_file, sort=True, *args, **kwargs)
cpg_profiles[output_name] = cpg_profile
cpg_file.close()
return cpg_profiles
示例14: map_cpg_tables
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def map_cpg_tables(cpg_tables, chromo, chromo_pos):
"""Maps values from cpg_tables to `chromo_pos`.
Positions in `cpg_tables` for `chromo` must be a subset of `chromo_pos`.
Inserts `dat.CPG_NAN` for uncovered positions.
"""
chromo_pos.sort()
mapped_tables = OrderedDict()
for name, cpg_table in six.iteritems(cpg_tables):
cpg_table = cpg_table.loc[cpg_table.chromo == chromo]
cpg_table = cpg_table.sort_values('pos')
mapped_table = map_values(cpg_table.value.values,
cpg_table.pos.values,
chromo_pos)
assert len(mapped_table) == len(chromo_pos)
mapped_tables[name] = mapped_table
return mapped_tables
示例15: __init__
# 需要導入模塊: import collections [as 別名]
# 或者: from collections import OrderedDict [as 別名]
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 初始化權重
self.params = {}
# 用高斯分布初始化
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
# 生成層
self.layers = OrderedDict()
self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
self.layers['Relu1'] = Relu()
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
self.lastLayer = SoftmaxWithLoss()