Datasets:
jblitzar
/
github-python-corpus

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from bin.downstream import Solver
else:
raise NotImplementedError
# Execution
solver = Solver(config, paras)
solver.load_data()
solver.set_model()
solver.exec()
# <FILESEP>
# Standard Lib imports
from random import uniform, random, uniform
# Blender imports
import bpy
import colorsys
FALLBACK_WARNING = 'Falling back to pre 2.8 Blender Python API: {}'
ADDON_ID = 'protor_{}'
def random_color(alpha=False):
""" Return a high contrast random color. """
h = random()
s = 1
v = 1
rgb = list(colorsys.hsv_to_rgb(h, s, v))
if alpha:
rgb.append(1)
return rgb
def get_projectors(context, only_selected=False):
""" Get all or only the selected projectors from the scene. """
objs = context.selected_objects if only_selected else context.scene.objects
projectors = []
for obj in objs:
if obj.type == 'CAMERA' and obj.name.startswith('Projector'):
if only_selected:
if obj.select_get():
projectors.append(obj)
else:
projectors.append(obj)
return projectors
def get_projector(context):
""" Return selected Projector or None if no projector is selected. """
projectors = get_projectors(context, only_selected=True)
if len(projectors) == 1:
return projectors[0]
else:
return None
def auto_offset():
offset = 0
def inner(node_width=None, y=None, gap=None):
nonlocal offset
offset += node_width if node_width else 0
y = y if y else 0
gap = gap if gap else 60
offset += gap
return offset, y
return inner
# <FILESEP>
import argparse
def opts():
parser = argparse.ArgumentParser(description='SRDC', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# datasets
parser.add_argument('--data_path_source', type=str, default='./data/datasets/Office31/', help='root of source training set')
parser.add_argument('--data_path_target', type=str, default='./data/datasets/Office31/', help='root of target training set')
parser.add_argument('--data_path_target_t', type=str, default='./data/datasets/Office31/', help='root of target test set')
parser.add_argument('--src', type=str, default='amazon', help='source training set')
parser.add_argument('--tar', type=str, default='webcam_half', help='target training set')
parser.add_argument('--tar_t', type=str, default='webcam_half2', help='target test set')
parser.add_argument('--num_classes', type=int, default=31, help='class number')
# source sample selection
parser.add_argument('--src_soft_select', action='store_true', help='whether to softly select source instances')
parser.add_argument('--src_hard_select', action='store_true', help='whether to hardly select source instances')
parser.add_argument('--src_mix_weight', action='store_true', help='whether to mix 1 and soft weight')
parser.add_argument('--tao_param', type=float, default=0.5, help='threshold parameter of cosine similarity')
# general optimization options
parser.add_argument('--epochs', type=int, default=200, help='number of epochs to train')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--workers', type=int, default=8, metavar='N', help='number of data loading workers (default: 8)')
parser.add_argument('--no_da', action='store_true', help='whether to not use data augmentation')
parser.add_argument('--lr', type=float, default=1e-2, help='learning rate')
parser.add_argument('--lr_plan', type=str, default='dao', help='learning rate decay plan of step or dao')
parser.add_argument('--schedule', type=int, nargs='+', default=[80, 120], help='decrease learning rate at these epochs for step decay')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=1e-4, help='weight decay (L2 penalty)')
parser.add_argument('--nesterov', action='store_true', help='whether to use nesterov SGD')
parser.add_argument('--eps', type=float, default=1e-6, help='a small value to prevent underflow')
# specific optimization options