code stringlengths 3 6.57k |
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optimizer_D.step() |
np.mean(np.argmax(label_pred.data.cpu() |
numpy() |
labels_A.data.cpu() |
numpy() |
task_performance.append(acc) |
len(task_performance) |
task_performance.pop(0) |
classifier(imgs_B) |
np.mean(np.argmax(pred_B.data.cpu() |
numpy() |
labels_B.numpy() |
target_performance.append(target_acc) |
len(target_performance) |
target_performance.pop(0) |
print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f] [CLF acc: %3d%% (%3d%%) |
len(dataloader_A) |
d_loss.item() |
g_loss.item() |
np.mean(task_performance) |
np.mean(target_performance) |
len(dataloader_A) |
torch.cat((imgs_A.data[:5], fake_B.data[:5], imgs_B.data[:5]) |
save_image(sample, 'images/%d.png' % batches_done, nrow=int(math.sqrt(batch_size) |
get_requests_session() |
requests.sessions.Session() |
session.mount('http://', HTTPAdapter(pool_connections=25, pool_maxsize=25, pool_block=True) |
session.mount('https://', HTTPAdapter(pool_connections=25, pool_maxsize=25, pool_block=True) |
Copyright (c) |
GeometryOutputNode(sgtk.platform.Application) |
init_app(self) |
self.import_module("tk_houdini_geometrynode") |
module.ToolkitGeometryNodeHandler(self) |
convert_to_geometry_nodes(self) |
self.handler.convert_sg_to_geometry_nodes() |
convert_from_geometry_nodes(self) |
self.handler.convert_geometry_to_sg_nodes() |
get_nodes(self) |
sgtk.platform.current_engine() |
app.get_nodes() |
self.log_debug("Retrieving tk-houdini-geometrynode nodes...") |
self.import_module("tk_houdini_geometrynode") |
get_all_tk_geometry_nodes() |
self.log_debug("Found %s tk-houdini-geometrynode nodes." % (len(nodes) |
Copyright (c) |
test_test_time_augmentation_on_cpu() |
mmcv.Config.fromfile(config_file) |
dict(type='BN', requires_grad=True) |
init_segmentor(config, checkpoint_file, device='cpu') |
osp.join(osp.dirname(__file__) |
inference_segmentor(model, img) |
Foundation (ASF) |
typehints.with_output_types(types.Entity) |
ReadFromDatastore(PTransform) |
__init__(self, query, num_splits=0) |
super(ReadFromDatastore, self) |
__init__() |
ValueError("query.project cannot be empty") |
ValueError("query cannot be empty") |
ValueError("num_splits must be greater than or equal 0") |
expand(self, pcoll) |
Create([self._query]) |
Reshuffle() |
ParDo(ReadFromDatastore._QueryFn() |
display_data(self) |
str(self._query) |
typehints.with_input_types(types.Query) |
typehints.with_output_types(types.Query) |
_SplitQueryFn(DoFn) |
__init__(self, num_splits) |
super(ReadFromDatastore._SplitQueryFn, self) |
__init__() |
process(self, query, *args, **kwargs) |
helper.get_client(query.project, query.namespace) |
query_splitter.validate_split(query) |
self.get_estimated_num_splits(client, query) |
logging.info("Splitting the query into %d splits", estimated_num_splits) |
display_data(self) |
query_latest_statistics_timestamp(client) |
timestamp (in microseconds) |
client.query(kind=kind, order=["-timestamp", ]) |
list(query.fetch(limit=1) |
RuntimeError("Datastore total statistics unavailable.") |
get_estimated_size_bytes(client, query) |
query_latest_statistics_timestamp(client) |
client.query(kind=kind) |
query.add_filter('kind_name', '=', kind_name) |
query.add_filter('timestamp', '=', latest_timestamp) |
list(query.fetch(limit=1) |
get_estimated_num_splits(client, query) |
get_estimated_size_bytes(client, query) |
logging.info('Estimated size bytes for query: %s', estimated_size_bytes) |
float(estimated_size_bytes) |
logging.warning('Failed to fetch estimated size bytes: %s', e) |
max(num_splits, ReadFromDatastore._NUM_QUERY_SPLITS_MIN) |
typehints.with_input_types(types.Query) |
typehints.with_output_types(types.Entity) |
_QueryFn(DoFn) |
process(self, query, *unused_args, **unused_kwargs) |
helper.get_client(query.project, query.namespace) |
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