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etingof/pysnmp | pysnmp/smi/mibs/SNMPv2-SMI.py | MibTableRow.getInstIdFromIndices | def getInstIdFromIndices(self, *indices):
"""Return column instance identification from indices"""
try:
return self._idxToIdCache[indices]
except TypeError:
cacheable = False
except KeyError:
cacheable = True
idx = 0
instId = ()
... | python | def getInstIdFromIndices(self, *indices):
"""Return column instance identification from indices"""
try:
return self._idxToIdCache[indices]
except TypeError:
cacheable = False
except KeyError:
cacheable = True
idx = 0
instId = ()
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etingof/pysnmp | pysnmp/smi/mibs/SNMPv2-SMI.py | MibTableRow.getInstNameByIndex | def getInstNameByIndex(self, colId, *indices):
"""Build column instance name from components"""
return self.name + (colId,) + self.getInstIdFromIndices(*indices) | python | def getInstNameByIndex(self, colId, *indices):
"""Build column instance name from components"""
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etingof/pysnmp | pysnmp/smi/mibs/SNMPv2-SMI.py | MibTableRow.getInstNamesByIndex | def getInstNamesByIndex(self, *indices):
"""Build column instance names from indices"""
instNames = []
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return tuple(instNames) | python | def getInstNamesByIndex(self, *indices):
"""Build column instance names from indices"""
instNames = []
for columnName in self._vars.keys():
instNames.append(
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etingof/pysnmp | pysnmp/hlapi/v3arch/asyncore/sync/cmdgen.py | nextCmd | def nextCmd(snmpEngine, authData, transportTarget, contextData,
*varBinds, **options):
"""Creates a generator to perform one or more SNMP GETNEXT queries.
On each iteration, new SNMP GETNEXT request is send
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etingof/pysnmp | pysnmp/entity/rfc3413/cmdrsp.py | CommandResponderBase._storeAccessContext | def _storeAccessContext(snmpEngine):
"""Copy received message metadata while it lasts"""
execCtx = snmpEngine.observer.getExecutionContext('rfc3412.receiveMessage:request')
return {
'securityModel': execCtx['securityModel'],
'securityName': execCtx['securityName'],
... | python | def _storeAccessContext(snmpEngine):
"""Copy received message metadata while it lasts"""
execCtx = snmpEngine.observer.getExecutionContext('rfc3412.receiveMessage:request')
return {
'securityModel': execCtx['securityModel'],
'securityName': execCtx['securityName'],
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etingof/pysnmp | pysnmp/entity/rfc3413/cmdrsp.py | NextCommandResponder._getManagedObjectsInstances | def _getManagedObjectsInstances(self, varBinds, **context):
"""Iterate over Managed Objects fulfilling SNMP query.
Returns
-------
:py:class:`list` - List of Managed Objects Instances to respond with or
`None` to indicate that not all objects have been gathered
s... | python | def _getManagedObjectsInstances(self, varBinds, **context):
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:py:class:`list` - List of Managed Objects Instances to respond with or
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etingof/pysnmp | pysnmp/proto/rfc1155.py | NetworkAddress.clone | def clone(self, value=univ.noValue, **kwargs):
"""Clone this instance.
If *value* is specified, use its tag as the component type selector,
and itself as the component value.
:param value: (Optional) the component value.
:type value: :py:obj:`pyasn1.type.base.Asn1ItemBase`
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"""Clone this instance.
If *value* is specified, use its tag as the component type selector,
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:param value: (Optional) the component value.
:type value: :py:obj:`pyasn1.type.base.Asn1ItemBase`
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etingof/pysnmp | pysnmp/smi/instrum.py | MibInstrumController._defaultErrorHandler | def _defaultErrorHandler(varBinds, **context):
"""Raise exception on any error if user callback is missing"""
errors = context.get('errors')
if errors:
err = errors[-1]
raise err['error'] | python | def _defaultErrorHandler(varBinds, **context):
"""Raise exception on any error if user callback is missing"""
errors = context.get('errors')
if errors:
err = errors[-1]
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etingof/pysnmp | pysnmp/smi/instrum.py | MibInstrumController.readMibObjects | def readMibObjects(self, *varBinds, **context):
"""Read Managed Objects Instances.
Given one or more py:class:`~pysnmp.smi.rfc1902.ObjectType` objects, read
all or none of the referenced Managed Objects Instances.
Parameters
----------
varBinds: :py:class:`tuple` of :py... | python | def readMibObjects(self, *varBinds, **context):
"""Read Managed Objects Instances.
Given one or more py:class:`~pysnmp.smi.rfc1902.ObjectType` objects, read
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etingof/pysnmp | pysnmp/smi/instrum.py | MibInstrumController.readNextMibObjects | def readNextMibObjects(self, *varBinds, **context):
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Given one or more py:class:`~pysnmp.smi.rfc1902.ObjectType` objects, read
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"""Read Managed Objects Instances next to the given ones.
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etingof/pysnmp | pysnmp/smi/instrum.py | MibInstrumController.writeMibObjects | def writeMibObjects(self, *varBinds, **context):
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etingof/pysnmp | pysnmp/hlapi/v1arch/asyncore/cmdgen.py | bulkCmd | def bulkCmd(snmpDispatcher, authData, transportTarget,
nonRepeaters, maxRepetitions, *varBinds, **options):
"""Initiate SNMP GETBULK query over SNMPv2c.
Based on passed parameters, prepares SNMP GETBULK packet
(:RFC:`1905#section-4.2.3`) and schedules its transmission by
I/O framework at a ... | python | def bulkCmd(snmpDispatcher, authData, transportTarget,
nonRepeaters, maxRepetitions, *varBinds, **options):
"""Initiate SNMP GETBULK query over SNMPv2c.
Based on passed parameters, prepares SNMP GETBULK packet
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markreidvfx/pyaaf2 | aaf2/file.py | AAFFile.save | def save(self):
"""
Writes current changes to disk and flushes modified objects in the
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self... | python | def save(self):
"""
Writes current changes to disk and flushes modified objects in the
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markreidvfx/pyaaf2 | aaf2/file.py | AAFFile.close | def close(self):
"""
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self.f.close() | python | def close(self):
"""
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self.save()
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markreidvfx/pyaaf2 | docs/source/conf.py | run_apidoc | def run_apidoc(_):
"""This method is required by the setup method below."""
import os
dirname = os.path.dirname(__file__)
ignore_paths = [os.path.join(dirname, '../../aaf2/model'),]
# https://github.com/sphinx-doc/sphinx/blob/master/sphinx/ext/apidoc.py
argv = [
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"""This method is required by the setup method below."""
import os
dirname = os.path.dirname(__file__)
ignore_paths = [os.path.join(dirname, '../../aaf2/model'),]
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markreidvfx/pyaaf2 | aaf2/mobid.py | MobID.from_dict | def from_dict(self, d):
"""
Set MobID from a dict
"""
self.length = d.get("length", 0)
self.instanceHigh = d.get("instanceHigh", 0)
self.instanceMid = d.get("instanceMid", 0)
self.instanceLow = d.get("instanceLow", 0)
material = d.get("material", {'Data1'... | python | def from_dict(self, d):
"""
Set MobID from a dict
"""
self.length = d.get("length", 0)
self.instanceHigh = d.get("instanceHigh", 0)
self.instanceMid = d.get("instanceMid", 0)
self.instanceLow = d.get("instanceLow", 0)
material = d.get("material", {'Data1'... | [
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markreidvfx/pyaaf2 | aaf2/mobid.py | MobID.to_dict | def to_dict(self):
"""
MobID representation as dict
"""
material = {'Data1': self.Data1,
'Data2': self.Data2,
'Data3': self.Data3,
'Data4': list(self.Data4)
}
return {'material':material,
... | python | def to_dict(self):
"""
MobID representation as dict
"""
material = {'Data1': self.Data1,
'Data2': self.Data2,
'Data3': self.Data3,
'Data4': list(self.Data4)
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return {'material':material,
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markreidvfx/pyaaf2 | aaf2/ama.py | wave_infochunk | def wave_infochunk(path):
"""
Returns a bytearray of the WAVE RIFF header and fmt
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"""
with open(path,'rb') as file:
if file.read(4) != b"RIFF":
return None
data_size = file.read(4) # container size
if file.read(4) != b"WAVE":... | python | def wave_infochunk(path):
"""
Returns a bytearray of the WAVE RIFF header and fmt
chunk for a `WAVEDescriptor` `Summary`
"""
with open(path,'rb') as file:
if file.read(4) != b"RIFF":
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markreidvfx/pyaaf2 | aaf2/cfb.py | DirEntry.pop | def pop(self):
"""
remove self from binary search tree
"""
entry = self
parent = self.parent
root = parent.child()
dir_per_sector = self.storage.sector_size // 128
max_dirs_entries = self.storage.dir_sector_count * dir_per_sector
count = 0
... | python | def pop(self):
"""
remove self from binary search tree
"""
entry = self
parent = self.parent
root = parent.child()
dir_per_sector = self.storage.sector_size // 128
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.remove | def remove(self, path):
"""
Removes both streams and storage DirEntry types from file.
storage type entries need to be empty dirs.
"""
entry = self.find(path)
if not entry:
raise ValueError("%s does not exists" % path)
if entry.type == 'root storage... | python | def remove(self, path):
"""
Removes both streams and storage DirEntry types from file.
storage type entries need to be empty dirs.
"""
entry = self.find(path)
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.rmtree | def rmtree(self, path):
"""
Removes directory structure, similar to shutil.rmtree.
"""
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for item in streams:
self.free_fat_chain(item.sector_id, item.byte_size < self.min_stream_max_size)
... | python | def rmtree(self, path):
"""
Removes directory structure, similar to shutil.rmtree.
"""
for root, storage, streams in self.walk(path, topdown=False):
for item in streams:
self.free_fat_chain(item.sector_id, item.byte_size < self.min_stream_max_size)
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.listdir_dict | def listdir_dict(self, path = None):
"""
Return a dict containing the ``DirEntry`` objects in the directory
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"""
if path is None:
path = self.root
root = self.find(path)
if root is None:
raise Val... | python | def listdir_dict(self, path = None):
"""
Return a dict containing the ``DirEntry`` objects in the directory
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"""
if path is None:
path = self.root
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.makedir | def makedir(self, path, class_id=None):
"""
Create a storage DirEntry name path
"""
return self.create_dir_entry(path, dir_type='storage', class_id=class_id) | python | def makedir(self, path, class_id=None):
"""
Create a storage DirEntry name path
"""
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.makedirs | def makedirs(self, path):
"""
Recursive storage DirEntry creation function.
"""
root = ""
assert path.startswith('/')
p = path.strip('/')
for item in p.split('/'):
root += "/" + item
if not self.exists(root):
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"""
Recursive storage DirEntry creation function.
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.move | def move(self, src, dst):
"""
Moves ``DirEntry`` from src to dst
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if src_entry is None:
raise ValueError("src path does not exist: %s" % src)
if dst.endswith('/'):
dst += src_entry.name
if self.exists(dst):
... | python | def move(self, src, dst):
"""
Moves ``DirEntry`` from src to dst
"""
src_entry = self.find(src)
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raise ValueError("src path does not exist: %s" % src)
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markreidvfx/pyaaf2 | aaf2/cfb.py | CompoundFileBinary.open | def open(self, path, mode='r'):
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markreidvfx/pyaaf2 | aaf2/properties.py | add2set | def add2set(self, pid, key, value):
"""low level add to StrongRefSetProperty"""
prop = self.property_entries[pid]
current = prop.objects.get(key, None)
current_local_key = prop.references.get(key, None)
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current.detach()
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"""low level add to StrongRefSetProperty"""
prop = self.property_entries[pid]
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current_local_key = prop.references.get(key, None)
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MillionIntegrals/vel | vel/rl/modules/q_distributional_head.py | QDistributionalHead.histogram_info | def histogram_info(self) -> dict:
""" Return extra information about histogram """
return {
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'vmin': self.vmin,
'vmax': self.vmax,
'num_atoms': self.atoms
} | python | def histogram_info(self) -> dict:
""" Return extra information about histogram """
return {
'support_atoms': self.support_atoms,
'atom_delta': self.atom_delta,
'vmin': self.vmin,
'vmax': self.vmax,
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MillionIntegrals/vel | vel/rl/modules/q_distributional_head.py | QDistributionalHead.sample | def sample(self, histogram_logits):
""" Sample from a greedy strategy with given q-value histogram """
histogram_probs = histogram_logits.exp() # Batch size * actions * atoms
atoms = self.support_atoms.view(1, 1, self.atoms) # Need to introduce two new dimensions
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""" Sample from a greedy strategy with given q-value histogram """
histogram_probs = histogram_logits.exp() # Batch size * actions * atoms
atoms = self.support_atoms.view(1, 1, self.atoms) # Need to introduce two new dimensions
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MillionIntegrals/vel | vel/sources/nlp/text_url.py | TextUrlSource.download | def download(self):
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pathlib.Path(self.data_path).mkdir(parents=True, exist_ok=True)
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""" Make sure data file is downloaded and stored properly """
if not os.path.exists(self.data_path):
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MillionIntegrals/vel | vel/sources/vision/mnist.py | create | def create(model_config, batch_size, normalize=True, num_workers=0, augmentations=None):
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MillionIntegrals/vel | vel/storage/classic.py | ClassicStorage.reset | def reset(self, configuration: dict) -> None:
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MillionIntegrals/vel | vel/rl/api/algo_base.py | clip_gradients | def clip_gradients(batch_result, model, max_grad_norm):
""" Clip gradients to a given maximum length """
if max_grad_norm is not None:
grad_norm = torch.nn.utils.clip_grad_norm_(
filter(lambda p: p.requires_grad, model.parameters()),
max_norm=max_grad_norm
)
else:
... | python | def clip_gradients(batch_result, model, max_grad_norm):
""" Clip gradients to a given maximum length """
if max_grad_norm is not None:
grad_norm = torch.nn.utils.clip_grad_norm_(
filter(lambda p: p.requires_grad, model.parameters()),
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MillionIntegrals/vel | vel/rl/buffers/circular_replay_buffer.py | CircularReplayBuffer.sample_trajectories | def sample_trajectories(self, rollout_length, batch_info) -> Trajectories:
""" Sample batch of trajectories and return them """
indexes = self.backend.sample_batch_trajectories(rollout_length)
transition_tensors = self.backend.get_trajectories(indexes, rollout_length)
return Trajectorie... | python | def sample_trajectories(self, rollout_length, batch_info) -> Trajectories:
""" Sample batch of trajectories and return them """
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MillionIntegrals/vel | vel/rl/algo/policy_gradient/trpo.py | conjugate_gradient_method | def conjugate_gradient_method(matrix_vector_operator, loss_gradient, nsteps, rdotr_tol=1e-10):
""" Conjugate gradient algorithm """
x = torch.zeros_like(loss_gradient)
r = loss_gradient.clone()
p = loss_gradient.clone()
rdotr = torch.dot(r, r)
for i in range(nsteps):
Avp = matrix_vect... | python | def conjugate_gradient_method(matrix_vector_operator, loss_gradient, nsteps, rdotr_tol=1e-10):
""" Conjugate gradient algorithm """
x = torch.zeros_like(loss_gradient)
r = loss_gradient.clone()
p = loss_gradient.clone()
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MillionIntegrals/vel | vel/rl/algo/policy_gradient/trpo.py | TrpoPolicyGradient.line_search | def line_search(self, model, rollout, original_policy_loss, original_policy_params, original_parameter_vec,
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""" Find the right stepsize to make sure policy improves """
current_parameter_vec = original_parameter_vec.clone()
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MillionIntegrals/vel | vel/rl/algo/policy_gradient/trpo.py | TrpoPolicyGradient.fisher_vector_product | def fisher_vector_product(self, vector, kl_divergence_gradient, model):
""" Calculate product Hessian @ vector """
assert not vector.requires_grad, "Vector must not propagate gradient"
dot_product = vector @ kl_divergence_gradient
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MillionIntegrals/vel | vel/rl/algo/policy_gradient/trpo.py | TrpoPolicyGradient.value_loss | def value_loss(self, model, observations, discounted_rewards):
""" Loss of value estimator """
value_outputs = model.value(observations)
value_loss = 0.5 * F.mse_loss(value_outputs, discounted_rewards)
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""" Loss of value estimator """
value_outputs = model.value(observations)
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MillionIntegrals/vel | vel/rl/algo/policy_gradient/trpo.py | TrpoPolicyGradient.calc_policy_loss | def calc_policy_loss(self, model, policy_params, policy_entropy, rollout):
"""
Policy gradient loss - calculate from probability distribution
Calculate surrogate loss - advantage * policy_probability / fixed_initial_policy_probability
Because we operate with logarithm of -probability (... | python | def calc_policy_loss(self, model, policy_params, policy_entropy, rollout):
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Policy gradient loss - calculate from probability distribution
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MillionIntegrals/vel | vel/rl/api/rollout.py | Transitions.shuffled_batches | def shuffled_batches(self, batch_size):
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if batch_size >= self.size:
yield self
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batch_splits = math_util.divide_ceiling(self.size, batch_size)
indices = list(range(self.size))
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""" Generate randomized batches of data """
if batch_size >= self.size:
yield self
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MillionIntegrals/vel | vel/rl/api/rollout.py | Trajectories.to_transitions | def to_transitions(self) -> 'Transitions':
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# No need to propagate 'rollout_tensors' as they won't mean anything
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""" Convert given rollout to Transitions """
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MillionIntegrals/vel | vel/rl/api/rollout.py | Trajectories.shuffled_batches | def shuffled_batches(self, batch_size):
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MillionIntegrals/vel | vel/rl/api/rollout.py | Trajectories.episode_information | def episode_information(self):
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""" List of information about finished episodes """
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MillionIntegrals/vel | vel/models/rnn/multilayer_rnn_sequence_model.py | MultilayerRnnSequenceModel.forward_state | def forward_state(self, sequence, state=None):
""" Forward propagate a sequence through the network accounting for the state """
if state is None:
state = self.zero_state(sequence.size(0))
data = self.input_block(sequence)
state_outputs = []
# for layer_length, lay... | python | def forward_state(self, sequence, state=None):
""" Forward propagate a sequence through the network accounting for the state """
if state is None:
state = self.zero_state(sequence.size(0))
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MillionIntegrals/vel | vel/models/rnn/multilayer_rnn_sequence_model.py | MultilayerRnnSequenceModel.loss_value | def loss_value(self, x_data, y_true, y_pred):
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""" Calculate a value of loss function """
y_pred = y_pred.view(-1, y_pred.size(2))
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MillionIntegrals/vel | vel/api/learner.py | Learner.initialize_training | def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
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self.model.reset_weights()
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MillionIntegrals/vel | vel/api/learner.py | Learner.run_epoch | def run_epoch(self, epoch_info: EpochInfo, source: 'vel.api.Source'):
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epoch_info.on_epoch_begin()
lr = epoch_info.optimizer.param_groups[-1]['lr']
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""" Run full epoch of learning """
epoch_info.on_epoch_begin()
lr = epoch_info.optimizer.param_groups[-1]['lr']
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""" Run a single training epoch """
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MillionIntegrals/vel | vel/api/learner.py | Learner.validation_epoch | def validation_epoch(self, epoch_info, source: 'vel.api.Source'):
""" Run a single evaluation epoch """
self.eval()
iterator = tqdm.tqdm(source.val_loader(), desc="Validation", unit="iter", file=sys.stdout)
with torch.no_grad():
for batch_idx, (data, target) in enumerate(it... | python | def validation_epoch(self, epoch_info, source: 'vel.api.Source'):
""" Run a single evaluation epoch """
self.eval()
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output, loss = self.model.loss(data, target)
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MillionIntegrals/vel | vel/api/learner.py | Learner.train_batch | def train_batch(self, batch_info, data, target):
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MillionIntegrals/vel | vel/util/situational.py | process_environment_settings | def process_environment_settings(default_dictionary: dict, settings: typing.Optional[dict]=None,
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MillionIntegrals/vel | vel/rl/reinforcers/buffered_off_policy_iteration_reinforcer.py | BufferedOffPolicyIterationReinforcer.roll_out_and_store | def roll_out_and_store(self, batch_info):
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MillionIntegrals/vel | vel/rl/reinforcers/buffered_off_policy_iteration_reinforcer.py | BufferedOffPolicyIterationReinforcer.train_on_replay_memory | def train_on_replay_memory(self, batch_info):
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MillionIntegrals/vel | vel/modules/resnet_v1.py | conv3x3 | def conv3x3(in_channels, out_channels, stride=1):
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"""
3x3 convolution with padding.
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MillionIntegrals/vel | vel/notebook/loader.py | load | def load(config_path, run_number=0, device='cuda:0'):
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MillionIntegrals/vel | vel/api/info.py | TrainingInfo.restore | def restore(self, hidden_state):
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MillionIntegrals/vel | vel/api/info.py | EpochResultAccumulator.result | def result(self):
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""" Return the epoch result """
final_result = {'epoch_idx': self.global_epoch_idx}
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MillionIntegrals/vel | vel/api/info.py | EpochInfo.on_epoch_end | def on_epoch_end(self):
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MillionIntegrals/vel | vel/api/info.py | BatchInfo.aggregate_key | def aggregate_key(self, aggregate_key):
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MillionIntegrals/vel | vel/rl/commands/rl_train_command.py | RlTrainCommand.run | def run(self):
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reinforcer = self.reinforcer.instantiate(device)
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device = self.model_config.torch_device()
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MillionIntegrals/vel | vel/rl/commands/rl_train_command.py | RlTrainCommand.resume_training | def resume_training(self, reinforcer, callbacks, metrics) -> TrainingInfo:
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MillionIntegrals/vel | vel/rl/commands/rl_train_command.py | RlTrainCommand._openai_logging | def _openai_logging(self, epoch_result):
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MillionIntegrals/vel | vel/util/module_util.py | module_broadcast | def module_broadcast(m, broadcast_fn, *args, **kwargs):
""" Call given function in all submodules with given parameters """
apply_leaf(m, lambda x: module_apply_broadcast(x, broadcast_fn, args, kwargs)) | python | def module_broadcast(m, broadcast_fn, *args, **kwargs):
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Return number of current phase.
Return index of first phase not done after all up to epoch_number were done.
"""
idx = bisect.bisect_left(self.ladder, epoch_number)
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"""
Return number of current phase.
Return index of first phase not done after all up to epoch_number were done.
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idx = bisect.bisect_left(self.ladder, epoch_number)
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MillionIntegrals/vel | vel/rl/env/classic_atari.py | wrapped_env_maker | def wrapped_env_maker(environment_id, seed, serial_id, disable_reward_clipping=False, disable_episodic_life=False,
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MillionIntegrals/vel | vel/util/visdom.py | visdom_send_metrics | def visdom_send_metrics(vis, metrics, update='replace'):
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""" Send set of metrics to visdom """
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MillionIntegrals/vel | vel/api/train_phase.py | TrainPhase.restore | def restore(self, training_info: TrainingInfo, local_batch_idx: int, model: Model, hidden_state: dict):
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Restore learning from intermediate state.
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MillionIntegrals/vel | vel/rl/buffers/backend/prioritized_vec_buffer_backend.py | PrioritizedCircularVecEnvBufferBackend.update_priority | def update_priority(self, tree_idx_list, priority_list):
""" Update priorities of the elements in the tree """
for tree_idx, priority, segment_tree in zip(tree_idx_list, priority_list, self.segment_trees):
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MillionIntegrals/vel | vel/rl/buffers/backend/prioritized_vec_buffer_backend.py | PrioritizedCircularVecEnvBufferBackend._sample_batch_prioritized | def _sample_batch_prioritized(self, segment_tree, batch_size, history, forward_steps=1):
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p_total = segment_tree.total()
segment = p_total / batch_size
# Get batch of valid samples
batch = [
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p_total = segment_tree.total()
segment = p_total / batch_size
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MillionIntegrals/vel | vel/rl/buffers/backend/circular_vec_buffer_backend.py | take_along_axis | def take_along_axis(large_array, indexes):
""" Take along axis """
# Reshape indexes into the right shape
if len(large_array.shape) > len(indexes.shape):
indexes = indexes.reshape(indexes.shape + tuple([1] * (len(large_array.shape) - len(indexes.shape))))
return np.take_along_axis(large_array, ... | python | def take_along_axis(large_array, indexes):
""" Take along axis """
# Reshape indexes into the right shape
if len(large_array.shape) > len(indexes.shape):
indexes = indexes.reshape(indexes.shape + tuple([1] * (len(large_array.shape) - len(indexes.shape))))
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MillionIntegrals/vel | vel/rl/buffers/backend/circular_vec_buffer_backend.py | CircularVecEnvBufferBackend.get_transition | def get_transition(self, frame_idx, env_idx):
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past_frame, future_frame = self.get_frame_with_future(frame_idx, env_idx)
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""" Single transition with given index """
past_frame, future_frame = self.get_frame_with_future(frame_idx, env_idx)
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MillionIntegrals/vel | vel/rl/buffers/backend/circular_vec_buffer_backend.py | CircularVecEnvBufferBackend.get_transitions_forward_steps | def get_transitions_forward_steps(self, indexes, forward_steps, discount_factor):
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MillionIntegrals/vel | vel/rl/buffers/backend/circular_vec_buffer_backend.py | CircularVecEnvBufferBackend.sample_batch_trajectories | def sample_batch_trajectories(self, rollout_length):
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results.append(self.sample_rollout_single_env(rollout_length))
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""" Return indexes of next random rollout """
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MillionIntegrals/vel | vel/rl/buffers/backend/circular_vec_buffer_backend.py | CircularVecEnvBufferBackend.sample_frame_single_env | def sample_frame_single_env(self, batch_size, forward_steps=1):
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MillionIntegrals/vel | vel/rl/commands/record_movie_command.py | RecordMovieCommand.record_take | def record_take(self, model, env_instance, device, take_number):
""" Record a single movie and store it on hard drive """
frames = []
observation = env_instance.reset()
if model.is_recurrent:
hidden_state = model.zero_state(1).to(device)
frames.append(env_instance.... | python | def record_take(self, model, env_instance, device, take_number):
""" Record a single movie and store it on hard drive """
frames = []
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MillionIntegrals/vel | vel/rl/modules/noise/ou_noise.py | OuNoise.reset_training_state | def reset_training_state(self, dones, batch_info):
""" A hook for a model to react when during training episode is finished """
for idx, done in enumerate(dones):
if done > 0.5:
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MillionIntegrals/vel | vel/rl/modules/noise/ou_noise.py | OuNoise.forward | def forward(self, actions, batch_info):
""" Return model step after applying noise """
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len_action_space = self.action_space.shape[-1]
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... | Return model step after applying noise | [
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MillionIntegrals/vel | vel/util/intepolate.py | interpolate_logscale | def interpolate_logscale(start, end, steps):
""" Interpolate series between start and end in given number of steps - logscale interpolation """
if start <= 0.0:
warnings.warn("Start of logscale interpolation must be positive!")
start = 1e-5
return np.logspace(np.log10(float(start)), np.log1... | python | def interpolate_logscale(start, end, steps):
""" Interpolate series between start and end in given number of steps - logscale interpolation """
if start <= 0.0:
warnings.warn("Start of logscale interpolation must be positive!")
start = 1e-5
return np.logspace(np.log10(float(start)), np.log1... | [
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MillionIntegrals/vel | vel/util/intepolate.py | interpolate_series | def interpolate_series(start, end, steps, how='linear'):
""" Interpolate series between start and end in given number of steps """
return INTERP_DICT[how](start, end, steps) | python | def interpolate_series(start, end, steps, how='linear'):
""" Interpolate series between start and end in given number of steps """
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MillionIntegrals/vel | vel/util/intepolate.py | interpolate_single | def interpolate_single(start, end, coefficient, how='linear'):
""" Interpolate single value between start and end in given number of steps """
return INTERP_SINGLE_DICT[how](start, end, coefficient) | python | def interpolate_single(start, end, coefficient, how='linear'):
""" Interpolate single value between start and end in given number of steps """
return INTERP_SINGLE_DICT[how](start, end, coefficient) | [
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MillionIntegrals/vel | vel/commands/summary_command.py | ModelSummary.run | def run(self, *args):
""" Print model summary """
if self.source is None:
self.model.summary()
else:
x_data, y_data = next(iter(self.source.train_loader()))
self.model.summary(input_size=x_data.shape[1:]) | python | def run(self, *args):
""" Print model summary """
if self.source is None:
self.model.summary()
else:
x_data, y_data = next(iter(self.source.train_loader()))
self.model.summary(input_size=x_data.shape[1:]) | [
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MillionIntegrals/vel | vel/rl/reinforcers/on_policy_iteration_reinforcer.py | OnPolicyIterationReinforcer.initialize_training | def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
""" Prepare models for training """
if model_state is not None:
self.model.load_state_dict(model_state)
else:
self.model.reset_weights()
self.algo.initialize(
... | python | def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
""" Prepare models for training """
if model_state is not None:
self.model.load_state_dict(model_state)
else:
self.model.reset_weights()
self.algo.initialize(
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