index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
47,721 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/_deeplab.py | 159,160 | 135,168 | forward | ASPP | 160,168 | 160,168 | class ASPP(nn.Module):
def __init__(self, in_channels, atrous_rates):
super(ASPP, self).__init__()
out_channels = 256
modules = []
modules.append(nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.R... | class ASPP(nn.Module):
def __init__(self, in_channels, atrous_rates):
super(ASPP, self).__init__()
out_channels = 256
modules = []
modules.append(nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.R... | res = []
for conv in self.convs:
res.append(conv(x))
res = torch.cat(res, dim=1)
return self.project(res)
| res = []
for conv in self.convs:
res.append(conv(x))
res = torch.cat(res, dim=1)
return self.project(res)
| 14 | 356 | 60 | 416 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... |
47,722 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/_deeplab.py | 130,131 | 122,135 | forward | ASPPPooling | 131,135 | 131,135 | class ASPPPooling(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(ASPPPooling, self).__init__(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True))
def forw... | class ASPPPooling(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(ASPPPooling, self).__init__(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True))
def forw... | size = x.shape[-2:]
x = super(ASPPPooling, self).forward(x)
return F.interpolate(x, size=size, mode='bilinear', align_corners=False)
| size = x.shape[-2:]
x = super(ASPPPooling, self).forward(x)
return F.interpolate(x, size=size, mode='bilinear', align_corners=False)
| 12 | 137 | 62 | 199 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... |
47,723 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/_deeplab.py | 105,106 | 87,113 | _init_weight | AtrousSeparableConvolution | 106,113 | 106,113 | class AtrousSeparableConvolution(nn.Module):
""" Atrous Separable Convolution
"""
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, bias=True):
super(AtrousSeparableConvolution, self).__init__()
self.body = nn.Sequential(
... | class AtrousSeparableConvolution(nn.Module):
""" Atrous Separable Convolution
"""
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, bias=True):
super(AtrousSeparableConvolution, self).__init__()
self.body = nn.Sequential(
... | for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
| for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
| 16 | 263 | 106 | 369 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... |
47,724 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/_deeplab.py | 79,80 | 63,87 | _init_weight | DeepLabHead | 80,87 | 80,87 | class DeepLabHead(nn.Module):
def __init__(self, in_channels, num_classes, aspp_dilate=[12, 24, 36]):
super(DeepLabHead, self).__init__()
self.classifier = nn.Sequential(
ASPP(in_channels, aspp_dilate),
nn.Conv2d(256, 256, 3, padding=1, bias=False),
nn.BatchNorm2... | class DeepLabHead(nn.Module):
def __init__(self, in_channels, num_classes, aspp_dilate=[12, 24, 36]):
super(DeepLabHead, self).__init__()
self.classifier = nn.Sequential(
ASPP(in_channels, aspp_dilate),
nn.Conv2d(256, 256, 3, padding=1, bias=False),
nn.BatchNorm2... | for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
| for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
| 16 | 228 | 106 | 334 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... |
47,725 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/_deeplab.py | 49,50 | 30,63 | forward | DeepLabHeadV3Plus | 50,55 | 50,55 | class DeepLabHeadV3Plus(nn.Module):
def __init__(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36]):
super(DeepLabHeadV3Plus, self).__init__()
self.project = nn.Sequential(
nn.Conv2d(low_level_channels, 48, 1, bias=False),
nn.BatchNorm2d(48),
... | class DeepLabHeadV3Plus(nn.Module):
def __init__(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36]):
super(DeepLabHeadV3Plus, self).__init__()
self.project = nn.Sequential(
nn.Conv2d(low_level_channels, 48, 1, bias=False),
nn.BatchNorm2d(48),
... | low_level_feature = self.project( feature['low_level'] )
output_feature = self.aspp(feature['out'])
output_feature = F.interpolate(output_feature, size=low_level_feature.shape[2:], mode='bilinear', align_corners=False)
return self.classifier( torch.cat( [ low_level_feature, output_featur... | low_level_feature = self.project( feature['low_level'] )
output_feature = self.aspp(feature['out'])
output_feature = F.interpolate(output_feature, size=low_level_feature.shape[2:], mode='bilinear', align_corners=False)
return self.classifier( torch.cat( [ low_level_feature, output_featur... | 24 | 404 | 114 | 518 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolu... |
47,727 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/s2m_resnet.py | 48,49 | 28,71 | forward | Bottleneck | 49,71 | 49,71 | class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = in... | class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = in... | identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = se... | identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = se... | 43 | 314 | 165 | 479 | import torch
import torch.nn as nn
try:
from torchvision.models.utils import load_state_dict_from_url
except ModuleNotFoundError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet... | import torch
import torch.nn as nn
try:
from torchvision.models.utils import load_state_dict_from_url
except ModuleNotFoundError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet... |
47,728 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/s2m_resnet.py | 122,123 | 71,164 | _make_layer | ResNet | 123,146 | 123,146 | class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = ... | class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = ... | norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(sel... | norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(sel... | 68 | 1,032 | 288 | 1,320 | import torch
import torch.nn as nn
try:
from torchvision.models.utils import load_state_dict_from_url
except ModuleNotFoundError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet... | import torch
import torch.nn as nn
try:
from torchvision.models.utils import load_state_dict_from_url
except ModuleNotFoundError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet... |
47,729 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/utils.py | 71,72 | 23,79 | forward | IntermediateLayerGetter | 72,79 | 72,79 | class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules have been registered
into the model in the same order as they are used.
This means that one should **not** reuse the same nn.Module
twice in... | class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules have been registered
into the model in the same order as they are used.
This means that one should **not** reuse the same nn.Module
twice in... | out = OrderedDict()
for name, module in self.named_children():
x = module(x)
if name in self.return_layers:
out_name = self.return_layers[name]
out[out_name] = x
return out
| out = OrderedDict()
for name, module in self.named_children():
x = module(x)
if name in self.return_layers:
out_name = self.return_layers[name]
out[out_name] = x
return out
| 23 | 640 | 76 | 716 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__in... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__in... |
47,730 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m/utils.py | 15,16 | 9,23 | forward | _SimpleSegmentationModel | 16,23 | 16,23 | class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__init__()
self.backbone = backbone
self.classifier = classifier
def forward(self, x):
FILL_FUNC_BODY | class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__init__()
self.backbone = backbone
self.classifier = classifier
def forward(self, x):
FILL_FUNC_BODY | input_shape = x.shape[-2:]
features = self.backbone(x)
x = self.classifier(features)
x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=False)
return x
| input_shape = x.shape[-2:]
features = self.backbone(x)
x = self.classifier(features)
x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=False)
return x
| 17 | 80 | 80 | 160 | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__in... | # Credit: https://github.com/VainF/DeepLabV3Plus-Pytorch
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
class _SimpleSegmentationModel(nn.Module):
def __init__(self, backbone, classifier):
super(_SimpleSegmentationModel, self).__in... |
47,731 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/s2m_controller.py | 21,22 | 8,41 | interact | S2MController | 22,41 | 22,41 | class S2MController:
"""
A controller for Scribble-to-Mask (for user interaction, not for DAVIS)
Takes the image, previous mask, and scribbles to produce a new mask
ignore_class is usually 255
0 is NOT the ignore class -- it is the label for the background
"""
def __init__(self, s2m_net:S2M... | class S2MController:
"""
A controller for Scribble-to-Mask (for user interaction, not for DAVIS)
Takes the image, previous mask, and scribbles to produce a new mask
ignore_class is usually 255
0 is NOT the ignore class -- it is the label for the background
"""
def __init__(self, s2m_net:S2M... | print(self.device)
image = image.to(self.device, non_blocking=True)
prev_mask = prev_mask.unsqueeze(0)
h, w = image.shape[-2:]
unaggre_mask = torch.zeros((self.num_objects, h, w), dtype=torch.float32, device=image.device)
for ki in range(1, self.num_objects+1):
... | print(self.device)
image = image.to(self.device, non_blocking=True)
prev_mask = prev_mask.unsqueeze(0)
h, w = image.shape[-2:]
unaggre_mask = torch.zeros((self.num_objects, h, w), dtype=torch.float32, device=image.device)
for ki in range(1, self.num_objects+1):
... | 54 | 190 | 337 | 527 | import torch
import numpy as np
from ..interact.s2m.s2m_network import deeplabv3plus_resnet50 as S2M
from util.tensor_util import pad_divide_by, unpad
class S2MController:
"""
A controller for Scribble-to-Mask (for user interaction, not for DAVIS)
Takes the image, previous mask, and scribbles to produce ... | import torch
import numpy as np
from ..interact.s2m.s2m_network import deeplabv3plus_resnet50 as S2M
from util.tensor_util import pad_divide_by, unpad
class S2MController:
"""
A controller for Scribble-to-Mask (for user interaction, not for DAVIS)
Takes the image, previous mask, and scribbles to produce ... |
47,732 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/timer.py | 19,20 | 3,34 | count | Timer | 20,27 | 20,27 | class Timer:
def __init__(self):
self._acc_time = 0
self._paused = True
def start(self):
if self._paused:
self.last_time = time.time()
self._paused = False
return self
def pause(self):
self.count()
self._paused = True
return s... | class Timer:
def __init__(self):
self._acc_time = 0
self._paused = True
def start(self):
if self._paused:
self.last_time = time.time()
self._paused = False
return self
def pause(self):
self.count()
self._paused = True
return s... | if self._paused:
return self._acc_time
t = time.time()
self._acc_time += t - self.last_time
self.last_time = t
return self._acc_time
| if self._paused:
return self._acc_time
t = time.time()
self._acc_time += t - self.last_time
self.last_time = t
return self._acc_time
| 17 | 212 | 64 | 276 | import time
class Timer:
def __init__(self):
self._acc_time = 0
self._paused = True
def start(self):
if self._paused:
self.last_time = time.time()
self._paused = False
return self
def pause(self):
self.count()
self._paused = True
... | import time
class Timer:
def __init__(self):
self._acc_time = 0
self._paused = True
def start(self):
if self._paused:
self.last_time = time.time()
self._paused = False
return self
def pause(self):
self.count()
self._paused = True
... |
47,735 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/aggregate.py | 6,7 | 1,18 | aggregate | 7,18 | 7,18 | import torch
import torch.nn.functional as F
# Soft aggregation from STM
def aggregate(prob, dim, return_logits=False):
FILL_FUNC_BODY | import torch
import torch.nn.functional as F
# Soft aggregation from STM
def aggregate(prob, dim, return_logits=False):
FILL_FUNC_BODY | new_prob = torch.cat([
torch.prod(1-prob, dim=dim, keepdim=True),
prob
], dim).clamp(1e-7, 1-1e-7)
logits = torch.log((new_prob /(1-new_prob)))
prob = F.softmax(logits, dim=dim)
if return_logits:
return logits, prob
else:
return prob | new_prob = torch.cat([
torch.prod(1-prob, dim=dim, keepdim=True),
prob
], dim).clamp(1e-7, 1-1e-7)
logits = torch.log((new_prob /(1-new_prob)))
prob = F.softmax(logits, dim=dim)
if return_logits:
return logits, prob
else:
return prob | 26 | 50 | 126 | 176 | import torch
import torch.nn.functional as F
# Soft aggregation from STM
def aggregate(prob, dim, return_logits=False):
FILL_FUNC_BODY | import torch
import torch.nn.functional as F
# Soft aggregation from STM
def aggregate(prob, dim, return_logits=False):
FILL_FUNC_BODY | |
47,736 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/group_modules.py | 15,16 | 1,83 | interpolate_groups | 16,22 | 16,22 | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | batch_size, num_objects = g.shape[:2]
g = F.interpolate(g.flatten(start_dim=0, end_dim=1),
scale_factor=ratio, mode=mode, align_corners=align_corners)
g = g.view(batch_size, num_objects, *g.shape[1:])
return g
| batch_size, num_objects = g.shape[:2]
g = F.interpolate(g.flatten(start_dim=0, end_dim=1),
scale_factor=ratio, mode=mode, align_corners=align_corners)
g = g.view(batch_size, num_objects, *g.shape[1:])
return g
| 18 | 827 | 101 | 928 | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | |
47,737 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/group_modules.py | 30,31 | 29,36 | forward | GConv2D | 31,36 | 31,36 | class GConv2D(nn.Conv2d):
def forward(self, g):
FILL_FUNC_BODY | class GConv2D(nn.Conv2d):
def forward(self, g):
FILL_FUNC_BODY | batch_size, num_objects = g.shape[:2]
g = super().forward(g.flatten(start_dim=0, end_dim=1))
return g.view(batch_size, num_objects, *g.shape[1:])
| batch_size, num_objects = g.shape[:2]
g = super().forward(g.flatten(start_dim=0, end_dim=1))
return g.view(batch_size, num_objects, *g.shape[1:])
| 12 | 34 | 71 | 105 | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... |
47,738 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/group_modules.py | 48,49 | 36,58 | forward | GroupResBlock | 49,58 | 49,58 | class GroupResBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
if in_dim == out_dim:
self.downsample = None
else:
self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)
self.conv1 = GConv2D(in_dim, out_dim, kernel_size=... | class GroupResBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
if in_dim == out_dim:
self.downsample = None
else:
self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)
self.conv1 = GConv2D(in_dim, out_dim, kernel_size=... | out_g = self.conv1(F.relu(g))
out_g = self.conv2(F.relu(out_g))
if self.downsample is not None:
g = self.downsample(g)
return out_g + g
| out_g = self.conv1(F.relu(g))
out_g = self.conv2(F.relu(out_g))
if self.downsample is not None:
g = self.downsample(g)
return out_g + g
| 18 | 173 | 82 | 255 | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... |
47,739 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/group_modules.py | 66,67 | 58,83 | forward | MainToGroupDistributor | 67,83 | 67,83 | class MainToGroupDistributor(nn.Module):
def __init__(self, x_transform=None, method='cat', reverse_order=False):
super().__init__()
self.x_transform = x_transform
self.method = method
self.reverse_order = reverse_order
def forward(self, x, g):
FILL_FUNC_BODY | class MainToGroupDistributor(nn.Module):
def __init__(self, x_transform=None, method='cat', reverse_order=False):
super().__init__()
self.x_transform = x_transform
self.method = method
self.reverse_order = reverse_order
def forward(self, x, g):
FILL_FUNC_BODY | num_objects = g.shape[1]
if self.x_transform is not None:
x = self.x_transform(x)
if self.method == 'cat':
if self.reverse_order:
g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)
else:
g = torch.cat([x.uns... | num_objects = g.shape[1]
if self.x_transform is not None:
x = self.x_transform(x)
if self.method == 'cat':
if self.reverse_order:
g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)
else:
g = torch.cat([x.uns... | 42 | 99 | 209 | 308 | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... | """
Group-specific modules
They handle features that also depends on the mask.
Features are typically of shape
batch_size * num_objects * num_channels * H * W
All of them are permutation equivariant w.r.t. to the num_objects dimension
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def i... |
47,740 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/losses.py | 8,9 | 1,69 | dice_loss | 9,23 | 9,23 | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
FILL_FUNC_BODYclass BootstrappedCE(nn.Module):
def __init__(self, start_warm, end_warm, top_p=0.15):
super().__init__()
self.start_warm = start_warm
s... | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
FILL_FUNC_BODYclass BootstrappedCE(nn.Module):
def __init__(self, start_warm, end_warm, top_p=0.15):
super().__init__()
self.start_warm = start_warm
s... | num_objects = input_mask.shape[1]
losses = []
for i in range(num_objects):
mask = input_mask[:,i].flatten(start_dim=1)
# background not in mask, so we add one to cls_gt
gt = (cls_gt==(i+1)).float().flatten(start_dim=1)
numerator = 2 * (mask * gt).sum(-1)
denominator =... | num_objects = input_mask.shape[1]
losses = []
for i in range(num_objects):
mask = input_mask[:,i].flatten(start_dim=1)
# background not in mask, so we add one to cls_gt
gt = (cls_gt==(i+1)).float().flatten(start_dim=1)
numerator = 2 * (mask * gt).sum(-1)
denominator =... | 55 | 656 | 230 | 886 | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
FILL_FUNC_BODYclass BootstrappedCE(nn.Module):
def __init__(self, start_warm, end_warm, top_p=0.15):
super().__init__()
self.start_warm = start_warm
s... | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
FILL_FUNC_BODYclass BootstrappedCE(nn.Module):
def __init__(self, start_warm, end_warm, top_p=0.15):
super().__init__()
self.start_warm = start_warm
s... | |
47,741 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/losses.py | 31,32 | 23,46 | forward | BootstrappedCE | 32,46 | 32,46 | class BootstrappedCE(nn.Module):
def __init__(self, start_warm, end_warm, top_p=0.15):
super().__init__()
self.start_warm = start_warm
self.end_warm = end_warm
self.top_p = top_p
def forward(self, input, target, it):
FILL_FUNC_BODY | class BootstrappedCE(nn.Module):
def __init__(self, start_warm, end_warm, top_p=0.15):
super().__init__()
self.start_warm = start_warm
self.end_warm = end_warm
self.top_p = top_p
def forward(self, input, target, it):
FILL_FUNC_BODY | if it < self.start_warm:
return F.cross_entropy(input, target), 1.0
raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)
num_pixels = raw_loss.numel()
if it > self.end_warm:
this_p = self.top_p
else:
this_p = self.top_p + (1-s... | if it < self.start_warm:
return F.cross_entropy(input, target), 1.0
raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)
num_pixels = raw_loss.numel()
if it > self.end_warm:
this_p = self.top_p
else:
this_p = self.top_p + (1-s... | 40 | 108 | 204 | 312 | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
num_objects = input_mask.shape[1]
losses = []
for i in range(num_objects):
mask = input_mask[:,i].flatten(start_dim=1)
# background not in mask, so we ... | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
num_objects = input_mask.shape[1]
losses = []
for i in range(num_objects):
mask = input_mask[:,i].flatten(start_dim=1)
# background not in mask, so we ... |
47,742 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/losses.py | 52,53 | 46,69 | compute | LossComputer | 53,69 | 53,69 | class LossComputer:
def __init__(self, config):
super().__init__()
self.config = config
self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])
def compute(self, data, num_objects, it):
FILL_FUNC_BODY | class LossComputer:
def __init__(self, config):
super().__init__()
self.config = config
self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])
def compute(self, data, num_objects, it):
FILL_FUNC_BODY | losses = defaultdict(int)
b, t = data['rgb'].shape[:2]
losses['total_loss'] = 0
for ti in range(1, t):
for bi in range(b):
loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)
losses['p'] += ... | losses = defaultdict(int)
b, t = data['rgb'].shape[:2]
losses['total_loss'] = 0
for ti in range(1, t):
for bi in range(b):
loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)
losses['p'] += ... | 50 | 84 | 252 | 336 | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
num_objects = input_mask.shape[1]
losses = []
for i in range(num_objects):
mask = input_mask[:,i].flatten(start_dim=1)
# background not in mask, so we ... | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
def dice_loss(input_mask, cls_gt):
num_objects = input_mask.shape[1]
losses = []
for i in range(num_objects):
mask = input_mask[:,i].flatten(start_dim=1)
# background not in mask, so we ... |
47,743 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/memory_util.py | 7,8 | 1,81 | get_similarity | 8,41 | 8,41 | import math
import numpy as np
import torch
from typing import Optional
def get_similarity(mk, ms, qk, qe):
FILL_FUNC_BODYdef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):
# normalize similarity with top-k softmax
# similarity: B x N x [HW/P]
# use inplace with care... | import math
import numpy as np
import torch
from typing import Optional
def get_similarity(mk, ms, qk, qe):
FILL_FUNC_BODYdef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):
# normalize similarity with top-k softmax
# similarity: B x N x [HW/P]
# use inplace with care... | # used for training/inference and memory reading/memory potentiation
# mk: B x CK x [N] - Memory keys
# ms: B x 1 x [N] - Memory shrinkage
# qk: B x CK x [HW/P] - Query keys
# qe: B x CK x [HW/P] - Query selection
# Dimensions in [] are flattened
CK = mk.shape[1]
mk = mk.flatten(s... | # used for training/inference and memory reading/memory potentiation
# mk: B x CK x [N] - Memory keys
# ms: B x 1 x [N] - Memory shrinkage
# qk: B x CK x [HW/P] - Query keys
# qe: B x CK x [HW/P] - Query selection
# Dimensions in [] are flattened
CK = mk.shape[1]
mk = mk.flatten(s... | 179 | 535 | 505 | 1,040 | import math
import numpy as np
import torch
from typing import Optional
def get_similarity(mk, ms, qk, qe):
FILL_FUNC_BODYdef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):
# normalize similarity with top-k softmax
# similarity: B x N x [HW/P]
# use inplace with care... | import math
import numpy as np
import torch
from typing import Optional
def get_similarity(mk, ms, qk, qe):
FILL_FUNC_BODYdef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):
# normalize similarity with top-k softmax
# similarity: B x N x [HW/P]
# use inplace with care... | |
47,744 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 229,230 | 214,251 | forward | Decoder | 230,251 | 230,251 | class Decoder(nn.Module):
def __init__(self, val_dim, hidden_dim):
super().__init__()
self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)
if hidden_dim > 0:
self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)
else:
self.hidd... | class Decoder(nn.Module):
def __init__(self, val_dim, hidden_dim):
super().__init__()
self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)
if hidden_dim > 0:
self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)
else:
self.hidd... | batch_size, num_objects = memory_readout.shape[:2]
if self.hidden_update is not None:
g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))
else:
g16 = self.fuser(f16, memory_readout)
g8 = self.up_16_8(f8, g16)
g4 = self.up_8_4(f4, g8)
... | batch_size, num_objects = memory_readout.shape[:2]
if self.hidden_update is not None:
g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))
else:
g16 = self.fuser(f16, memory_readout)
g8 = self.up_16_8(f8, g16)
g4 = self.up_8_4(f4, g8)
... | 71 | 292 | 352 | 644 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,745 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 31,32 | 22,44 | forward | FeatureFusionBlock | 32,44 | 32,44 | class FeatureFusionBlock(nn.Module):
def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):
super().__init__()
self.distributor = MainToGroupDistributor()
self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)
self.attention = CBAM(g_mid_dim)
self.block2 = GroupRes... | class FeatureFusionBlock(nn.Module):
def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):
super().__init__()
self.distributor = MainToGroupDistributor()
self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)
self.attention = CBAM(g_mid_dim)
self.block2 = GroupRes... | batch_size, num_objects = g.shape[:2]
g = self.distributor(x, g)
g = self.block1(g)
r = self.attention(g.flatten(start_dim=0, end_dim=1))
r = r.view(batch_size, num_objects, *r.shape[1:])
g = self.block2(g+r)
return g
| batch_size, num_objects = g.shape[:2]
g = self.distributor(x, g)
g = self.block1(g)
r = self.attention(g.flatten(start_dim=0, end_dim=1))
r = r.view(batch_size, num_objects, *r.shape[1:])
g = self.block2(g+r)
return g
| 25 | 157 | 124 | 281 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,746 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 86,87 | 77,102 | forward | HiddenReinforcer | 87,102 | 87,102 | class HiddenReinforcer(nn.Module):
# Used in the value encoder, a single GRU
def __init__(self, g_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)
nn.init.xavier_normal_(self.tran... | class HiddenReinforcer(nn.Module):
# Used in the value encoder, a single GRU
def __init__(self, g_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)
nn.init.xavier_normal_(self.tran... | g = torch.cat([g, h], 2)
# defined slightly differently than standard GRU,
# namely the new value is generated before the forget gate.
# might provide better gradient but frankly it was initially just an
# implementation error that I never bothered fixing
values = self... | g = torch.cat([g, h], 2)
# defined slightly differently than standard GRU,
# namely the new value is generated before the forget gate.
# might provide better gradient but frankly it was initially just an
# implementation error that I never bothered fixing
values = self... | 62 | 138 | 213 | 351 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,747 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 58,59 | 44,77 | forward | HiddenUpdater | 59,77 | 59,77 | class HiddenUpdater(nn.Module):
# Used in the decoder, multi-scale feature + GRU
def __init__(self, g_dims, mid_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)
self.g8_conv = GConv2D(g_dims[1], mid_dim,... | class HiddenUpdater(nn.Module):
# Used in the decoder, multi-scale feature + GRU
def __init__(self, g_dims, mid_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)
self.g8_conv = GConv2D(g_dims[1], mid_dim,... | g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \
self.g4_conv(downsample_groups(g[2], ratio=1/4))
g = torch.cat([g, h], 2)
# defined slightly differently than standard GRU,
# namely the new value is generated before the forget gate.
# ... | g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \
self.g4_conv(downsample_groups(g[2], ratio=1/4))
g = torch.cat([g, h], 2)
# defined slightly differently than standard GRU,
# namely the new value is generated before the forget gate.
# ... | 72 | 244 | 282 | 526 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,748 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 166,167 | 153,178 | forward | KeyEncoder | 167,178 | 167,178 | class KeyEncoder(nn.Module):
def __init__(self):
super().__init__()
network = resnet.resnet50(pretrained=True)
self.conv1 = network.conv1
self.bn1 = network.bn1
self.relu = network.relu # 1/2, 64
self.maxpool = network.maxpool
self.res2 = network.layer1 # 1/... | class KeyEncoder(nn.Module):
def __init__(self):
super().__init__()
network = resnet.resnet50(pretrained=True)
self.conv1 = network.conv1
self.bn1 = network.bn1
self.relu = network.relu # 1/2, 64
self.maxpool = network.maxpool
self.res2 = network.layer1 # 1/... | x = self.conv1(f)
x = self.bn1(x)
x = self.relu(x) # 1/2, 64
x = self.maxpool(x) # 1/4, 64
f4 = self.res2(x) # 1/4, 256
f8 = self.layer2(f4) # 1/8, 512
f16 = self.layer3(f8) # 1/16, 1024
return f16, f8, f4
| x = self.conv1(f)
x = self.bn1(x)
x = self.relu(x) # 1/2, 64
x = self.maxpool(x) # 1/4, 64
f4 = self.res2(x) # 1/4, 256
f8 = self.layer2(f4) # 1/8, 512
f16 = self.layer3(f8) # 1/16, 1024
return f16, f8, f4
| 40 | 183 | 163 | 346 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,749 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 207,208 | 194,214 | forward | KeyProjection | 208,214 | 208,214 | class KeyProjection(nn.Module):
def __init__(self, in_dim, keydim):
super().__init__()
self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
# shrinkage
self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)
# selection
self.e_proj = nn.Conv2d(in_... | class KeyProjection(nn.Module):
def __init__(self, in_dim, keydim):
super().__init__()
self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
# shrinkage
self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)
# selection
self.e_proj = nn.Conv2d(in_... | shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None
selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None
return self.key_proj(x), shrinkage, selection
| shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None
selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None
return self.key_proj(x), shrinkage, selection
| 20 | 213 | 76 | 289 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,750 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 186,187 | 178,194 | forward | UpsampleBlock | 187,194 | 187,194 | class UpsampleBlock(nn.Module):
def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):
super().__init__()
self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)
self.distributor = MainToGroupDistributor(method='add')
self.out_conv = GroupResBlock(g_up_di... | class UpsampleBlock(nn.Module):
def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):
super().__init__()
self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)
self.distributor = MainToGroupDistributor(method='add')
self.out_conv = GroupResBlock(g_up_di... | skip_f = self.skip_conv(skip_f)
g = upsample_groups(up_g, ratio=self.scale_factor)
g = self.distributor(skip_f, g)
g = self.out_conv(g)
return g
| skip_f = self.skip_conv(skip_f)
g = upsample_groups(up_g, ratio=self.scale_factor)
g = self.distributor(skip_f, g)
g = self.out_conv(g)
return g
| 16 | 166 | 78 | 244 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,751 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/modules.py | 124,125 | 102,153 | forward | ValueEncoder | 125,153 | 125,153 | class ValueEncoder(nn.Module):
def __init__(self, value_dim, hidden_dim, single_object=False):
super().__init__()
self.single_object = single_object
network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)
self.conv1 = network.conv1
self.bn1 = ... | class ValueEncoder(nn.Module):
def __init__(self, value_dim, hidden_dim, single_object=False):
super().__init__()
self.single_object = single_object
network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)
self.conv1 = network.conv1
self.bn1 = ... | # image_feat_f16 is the feature from the key encoder
if not self.single_object:
g = torch.stack([masks, others], 2)
else:
g = masks.unsqueeze(2)
g = self.distributor(image, g)
batch_size, num_objects = g.shape[:2]
g = g.flatten(start_dim=0, end_di... | # image_feat_f16 is the feature from the key encoder
if not self.single_object:
g = torch.stack([masks, others], 2)
else:
g = masks.unsqueeze(2)
g = self.distributor(image, g)
batch_size, num_objects = g.shape[:2]
g = g.flatten(start_dim=0, end_di... | 89 | 340 | 350 | 690 | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... | """
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The traili... |
47,752 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/resnet.py | 14,15 | 1,166 | load_weights_add_extra_dim | 15,35 | 15,35 | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
FILL_FUNC_BODYmod... | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
FILL_FUNC_BODYmod... | new_dict = OrderedDict()
for k1, v1 in target.state_dict().items():
if not 'num_batches_tracked' in k1:
if k1 in source_state:
tar_v = source_state[k1]
if v1.shape != tar_v.shape:
# Init the new segmentation channel with zeros
... | new_dict = OrderedDict()
for k1, v1 in target.state_dict().items():
if not 'num_batches_tracked' in k1:
if k1 in source_state:
tar_v = source_state[k1]
if v1.shape != tar_v.shape:
# Init the new segmentation channel with zeros
... | 55 | 1,800 | 237 | 2,037 | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
FILL_FUNC_BODYmod... | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
FILL_FUNC_BODYmod... | |
47,753 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/resnet.py | 59,60 | 46,78 | forward | BasicBlock | 60,78 | 60,78 | class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.Re... | class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.Re... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return o... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return o... | 34 | 208 | 127 | 335 | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
new_dict = Or... | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
new_dict = Or... |
47,754 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/resnet.py | 94,95 | 78,117 | forward | Bottleneck | 95,117 | 95,117 | class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d... | class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = se... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = se... | 43 | 290 | 165 | 455 | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
new_dict = Or... | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
new_dict = Or... |
47,755 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/model/resnet.py | 138,139 | 117,154 | _make_layer | ResNet | 139,154 | 139,154 | class ResNet(nn.Module):
def __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(i... | class ResNet(nn.Module):
def __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(i... | downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.exp... | downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.exp... | 48 | 436 | 201 | 637 | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
new_dict = Or... | """
resnet.py - A modified ResNet structure
We append extra channels to the first conv by some network surgery
"""
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo
def load_weights_add_extra_dim(target, source_state, extra_dim=1):
new_dict = Or... |
47,758 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/util/load_subset.py | 13,14 | 1,17 | load_sub_yv | 14,17 | 14,17 | """
load_subset.py - Presents a subset of data
DAVIS - only the training set
YouTubeVOS - I manually filtered some erroneous ones out but I haven't checked all
"""
def load_sub_davis(path='util/davis_subset.txt'):
with open(path, mode='r') as f:
subset = set(f.read().splitlines())
return subset
def l... | """
load_subset.py - Presents a subset of data
DAVIS - only the training set
YouTubeVOS - I manually filtered some erroneous ones out but I haven't checked all
"""
def load_sub_davis(path='util/davis_subset.txt'):
with open(path, mode='r') as f:
subset = set(f.read().splitlines())
return subset
def l... | with open(path, mode='r') as f:
subset = set(f.read().splitlines())
return subset
| with open(path, mode='r') as f:
subset = set(f.read().splitlines())
return subset
| 10 | 139 | 35 | 174 | """
load_subset.py - Presents a subset of data
DAVIS - only the training set
YouTubeVOS - I manually filtered some erroneous ones out but I haven't checked all
"""
def load_sub_davis(path='util/davis_subset.txt'):
with open(path, mode='r') as f:
subset = set(f.read().splitlines())
return subset
def l... | """
load_subset.py - Presents a subset of data
DAVIS - only the training set
YouTubeVOS - I manually filtered some erroneous ones out but I haven't checked all
"""
def load_sub_davis(path='util/davis_subset.txt'):
with open(path, mode='r') as f:
subset = set(f.read().splitlines())
return subset
def l... | |
47,759 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/util/log_integrator.py | 56,57 | 10,81 | finalize | Integrator | 57,81 | 57,81 | class Integrator:
def __init__(self, logger, distributed=True, local_rank=0, world_size=1):
self.values = {}
self.counts = {}
self.hooks = [] # List is used here to maintain insertion order
self.logger = logger
self.distributed = distributed
self.local_rank = local... | class Integrator:
def __init__(self, logger, distributed=True, local_rank=0, world_size=1):
self.values = {}
self.counts = {}
self.hooks = [] # List is used here to maintain insertion order
self.logger = logger
self.distributed = distributed
self.local_rank = local... |
for hook in self.hooks:
k, v = hook(self.values)
self.add_tensor(k, v)
for k, v in self.values.items():
if k[:4] == 'hide':
continue
avg = v / self.counts[k]
if self.distributed:
# Inplace operation
... |
for hook in self.hooks:
k, v = hook(self.values)
self.add_tensor(k, v)
for k, v in self.values.items():
if k[:4] == 'hide':
continue
avg = v / self.counts[k]
if self.distributed:
# Inplace operation
... | 57 | 452 | 239 | 691 | """
Integrate numerical values for some iterations
Typically used for loss computation / logging to tensorboard
Call finalize and create a new Integrator when you want to display/log
"""
import torch
class Integrator:
def __init__(self, logger, distributed=True, local_rank=0, world_size=1):
self.values =... | """
Integrate numerical values for some iterations
Typically used for loss computation / logging to tensorboard
Call finalize and create a new Integrator when you want to display/log
"""
import torch
class Integrator:
def __init__(self, logger, distributed=True, local_rank=0, world_size=1):
self.values =... |
47,760 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/util/logger.py | 16,17 | 1,102 | detach_to_cpu | 17,19 | 17,19 | """
Dumps things to tensorboard and console
"""
import os
import warnings
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
FILL_FUNC_BODYdef fix_wi... | """
Dumps things to tensorboard and console
"""
import os
import warnings
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
FILL_FUNC_BODYdef fix_wi... | return x.detach().cpu()
| return x.detach().cpu()
| 2 | 1,067 | 13 | 1,080 | """
Dumps things to tensorboard and console
"""
import os
import warnings
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
FILL_FUNC_BODYdef fix_wi... | """
Dumps things to tensorboard and console
"""
import os
import warnings
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
FILL_FUNC_BODYdef fix_wi... | |
47,761 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/util/logger.py | 78,79 | 22,102 | log_seg | TensorboardLogger | 79,87 | 79,87 | class TensorboardLogger:
def __init__(self, short_id, id, git_info):
self.short_id = short_id
if self.short_id == 'NULL':
self.short_id = 'DEBUG'
if id is None:
self.no_log = True
warnings.warn('Logging has been disbaled.')
else:
self.... | class TensorboardLogger:
def __init__(self, short_id, id, git_info):
self.short_id = short_id
if self.short_id == 'NULL':
self.short_id = 'DEBUG'
if id is None:
self.no_log = True
warnings.warn('Logging has been disbaled.')
else:
self.... | if self.no_log:
warnings.warn('Logging has been disabled.')
return
x = detach_to_cpu(x)
x = self.inv_seg_trans(x)
x = tensor_to_numpy(x)
self.logger.add_image(tag, x, step)
| if self.no_log:
warnings.warn('Logging has been disabled.')
return
x = detach_to_cpu(x)
x = self.inv_seg_trans(x)
x = tensor_to_numpy(x)
self.logger.add_image(tag, x, step)
| 19 | 847 | 91 | 938 | """
Dumps things to tensorboard and console
"""
import os
import warnings
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
return x.detach().cp... | """
Dumps things to tensorboard and console
"""
import os
import warnings
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
return x.detach().cp... |
47,762 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/util/tensor_util.py | 34,35 | 1,48 | unpad | 35,48 | 35,48 | import torch.nn.functional as F
def compute_tensor_iu(seg, gt):
intersection = (seg & gt).float().sum()
union = (seg | gt).float().sum()
return intersection, union
def compute_tensor_iou(seg, gt):
intersection, union = compute_tensor_iu(seg, gt)
iou = (intersection + 1e-6) / (union + 1e-6)
... | import torch.nn.functional as F
def compute_tensor_iu(seg, gt):
intersection = (seg & gt).float().sum()
union = (seg | gt).float().sum()
return intersection, union
def compute_tensor_iou(seg, gt):
intersection, union = compute_tensor_iu(seg, gt)
iou = (intersection + 1e-6) / (union + 1e-6)
... | if len(img.shape) == 4:
if pad[2]+pad[3] > 0:
img = img[:,:,pad[2]:-pad[3],:]
if pad[0]+pad[1] > 0:
img = img[:,:,:,pad[0]:-pad[1]]
elif len(img.shape) == 3:
if pad[2]+pad[3] > 0:
img = img[:,pad[2]:-pad[3],:]
if pad[0]+pad[1] > 0:
... | if len(img.shape) == 4:
if pad[2]+pad[3] > 0:
img = img[:,:,pad[2]:-pad[3],:]
if pad[0]+pad[1] > 0:
img = img[:,:,:,pad[0]:-pad[1]]
elif len(img.shape) == 3:
if pad[2]+pad[3] > 0:
img = img[:,pad[2]:-pad[3],:]
if pad[0]+pad[1] > 0:
... | 41 | 377 | 186 | 563 | import torch.nn.functional as F
def compute_tensor_iu(seg, gt):
intersection = (seg & gt).float().sum()
union = (seg | gt).float().sum()
return intersection, union
def compute_tensor_iou(seg, gt):
intersection, union = compute_tensor_iu(seg, gt)
iou = (intersection + 1e-6) / (union + 1e-6)
... | import torch.nn.functional as F
def compute_tensor_iu(seg, gt):
intersection = (seg & gt).float().sum()
union = (seg | gt).float().sum()
return intersection, union
def compute_tensor_iou(seg, gt):
intersection, union = compute_tensor_iu(seg, gt)
iou = (intersection + 1e-6) / (union + 1e-6)
... | |
47,763 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/cli/benchmark.py | 83,91 | 1,117 | list_benchmarks | 92,115 | 91,115 | # Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | from smarts.benchmark import BENCHMARK_LISTING_FILE
from smarts.benchmark import list_benchmarks as _list_benchmarks
benchmarks = _list_benchmarks(
Path(benchmark_listing)
if benchmark_listing is not None
else BENCHMARK_LISTING_FILE,
)["benchmarks"]
print("BENCHMARK_NAME".l... | """Lists available benchmarks that can be used for `scl benchmark run`."""
from smarts.benchmark import BENCHMARK_LISTING_FILE
from smarts.benchmark import list_benchmarks as _list_benchmarks
benchmarks = _list_benchmarks(
Path(benchmark_listing)
if benchmark_listing is not None
... | 49 | 998 | 259 | 1,257 | # Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,764 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/cli/diagnostic.py | 32,35 | 1,41 | run | 35,40 | 35,40 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | from smarts.diagnostic import run as _run
_run.main(scenarios)
| from smarts.diagnostic import run as _run
_run.main(scenarios)
| 7 | 476 | 33 | 509 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,765 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/cli/envision.py | 31,44 | 1,50 | start_server | 44,49 | 44,49 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | from envision.server import run
run(max_capacity_mb=max_capacity, port=port)
| from envision.server import run
run(max_capacity_mb=max_capacity, port=port)
| 6 | 581 | 37 | 618 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,766 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/cli/studio.py | 91,96 | 1,116 | replay | 96,112 | 96,112 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | from envision.client import Client as Envision
for path in directory:
jsonl_paths = list(Path(path).glob("*.jsonl"))
click.echo(
f"Replaying {len(jsonl_paths)} record(s) at path={path} with "
f"timestep={timestep}s"
)
with ThreadPool(len(jsonl_paths)) as... | from envision.client import Client as Envision
for path in directory:
jsonl_paths = list(Path(path).glob("*.jsonl"))
click.echo(
f"Replaying {len(jsonl_paths)} record(s) at path={path} with "
f"timestep={timestep}s"
)
with ThreadPool(len(jsonl_paths)) as... | 37 | 1,168 | 169 | 1,337 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,767 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/cli/waymo.py | 34,41 | 1,115 | overview | 41,57 | 41,57 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | from smarts.waymo import waymo_utils
scenarios = waymo_utils.get_tfrecord_info(tfrecord_file)
rows = [
[k, v["timestamps"], v["vehicles"], v["pedestrians"]]
for k, v in scenarios.items()
]
headers = [
"Scenario ID",
"Timestamps",
"Vehicles",
"Pedestri... | from smarts.waymo import waymo_utils
scenarios = waymo_utils.get_tfrecord_info(tfrecord_file)
rows = [
[k, v["timestamps"], v["vehicles"], v["pedestrians"]]
for k, v in scenarios.items()
]
headers = [
"Scenario ID",
"Timestamps",
"Vehicles",
"Pedestri... | 31 | 1,134 | 147 | 1,281 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,768 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/client_config.py | 39,40 | 1,57 | _default_override | 40,43 | 40,43 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | return SingleAttributeOverride(True, None)
| return SingleAttributeOverride(True, None)
| 3 | 599 | 16 | 615 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | |
47,769 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/client_config.py | 52,54 | 43,57 | default | EnvisionStateFilter | 55,57 | 54,57 | @dataclass(frozen=True)
class EnvisionStateFilter:
"""A state filtering tool."""
actor_data_filter: Dict[str, SingleAttributeOverride]
"""Actor filtering."""
simulation_data_filter: Dict[str, SingleAttributeOverride]
"""Simulation filtering."""
@classmethod
def default(cls):
FILL_FUNC_BODY | @dataclass(frozen=True)
class EnvisionStateFilter:
"""A state filtering tool."""
actor_data_filter: Dict[str, SingleAttributeOverride]
"""Actor filtering."""
simulation_data_filter: Dict[str, SingleAttributeOverride]
"""Simulation filtering."""
@classmethod
def default(cls):
"""Giv... |
return cls(defaultdict(_default_override), defaultdict(_default_override))
| """Give a new default filter."""
return cls(defaultdict(_default_override), defaultdict(_default_override))
| 3 | 108 | 25 | 133 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,771 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/data_formatter.py | 220,221 | 206,259 | empty_gen | DataFormatterLayer | 221,224 | 221,224 | class DataFormatterLayer(ContextManager, Iterator):
"""A formatting layer that maps into the above layer of the current data formatter."""
def __init__(
self,
data_formatter: "EnvisionDataFormatter",
iterable: Optional[Iterable],
op: Operation,
... | class DataFormatterLayer(ContextManager, Iterator):
"""A formatting layer that maps into the above layer of the current data formatter."""
def __init__(
self,
data_formatter: "EnvisionDataFormatter",
iterable: Optional[Iterable],
op: Operation,
... | return
yield
| return
yield
| 2 | 478 | 10 | 488 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,772 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/data_formatter.py | 185,190 | 139,273 | add | EnvisionDataFormatter | 191,206 | 190,206 | class EnvisionDataFormatter:
"""A formatter to put envision state into a reduced format."""
def __init__(
self,
formatter_params: EnvisionDataFormatterArgs,
):
# self.seen_objects = context.seen_objects if context else set()
self.id: Any = id
self._data: List[Any] = ... | class EnvisionDataFormatter:
"""A formatter to put envision state into a reduced format."""
def __init__(
self,
formatter_params: EnvisionDataFormatterArgs,
):
# self.seen_objects = context.seen_objects if context else set()
self.id: Any = id
self._data: List[Any] = ... | outval = value
f = _sequence_formatter_map.get(type(value))
if op & Operation.REDUCE:
outval = self._reduction_context.resolve_value(outval)
if op & Operation.FLATTEN:
outval = unpack(outval)
if not isinstance(outval, (Sequence, np.ndarray)):
... | """Format the given object to the current layer. Specified operations are performed."""
outval = value
f = _sequence_formatter_map.get(type(value))
if op & Operation.REDUCE:
outval = self._reduction_context.resolve_value(outval)
if op & Operation.FLATTEN:
... | 45 | 1,162 | 177 | 1,339 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,773 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/data_formatter.py | 107,109 | 82,129 | has_ids | ReductionContext | 110,112 | 109,112 | class ReductionContext:
"""Mappings between an object and its reduction to an ID."""
def __init__(
self,
mapping: Optional[Dict[Hashable, int]] = None,
removed: Optional[List[int]] = None,
enabled: bool = True,
) -> None:
self._current_id = 0
self._mapping = ... | class ReductionContext:
"""Mappings between an object and its reduction to an ID."""
def __init__(
self,
mapping: Optional[Dict[Hashable, int]] = None,
removed: Optional[List[int]] = None,
enabled: bool = True,
) -> None:
self._current_id = 0
self._mapping = ... | return len(self._mapping) > 0
| """If this reducing tool has currently reduced objects."""
return len(self._mapping) > 0
| 4 | 386 | 14 | 400 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,774 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/etypes.py | 105,106 | 1,118 | format_actor_id | 115,118 | 106,118 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | if is_multi:
return f"{actor_id}-{{{vehicle_id[:4]}}}"
return actor_id
| """A conversion utility to ensure that an actor id conforms to envision's actor id standard.
Args:
actor_id: The base id of the actor.
vehicle_id: The vehicle id of the vehicle the actor associates with.
is_multi: If an actor associates with multiple vehicles.
Returns:
An en... | 6 | 1,165 | 38 | 1,203 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,775 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/utils/multiprocessing_queue.py | 89,90 | 57,93 | clear | Queue | 91,93 | 90,93 | class Queue(multiprocessing.queues.Queue):
"""A portable implementation of multiprocessing.Queue.
Because of multithreading / multiprocessing semantics, Queue.qsize() may
raise the NotImplementedError exception on Unix platforms like Mac OS X
where sem_getvalue() is not implemented. This subclass addres... | class Queue(multiprocessing.queues.Queue):
"""A portable implementation of multiprocessing.Queue.
Because of multithreading / multiprocessing semantics, Queue.qsize() may
raise the NotImplementedError exception on Unix platforms like Mac OS X
where sem_getvalue() is not implemented. This subclass addres... | while not self.empty():
self.get()
| """Remove all elements from the Queue."""
while not self.empty():
self.get()
| 4 | 387 | 16 | 403 | ## MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... | ## MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... |
47,776 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/envision/utils/multiprocessing_queue.py | 46,47 | 31,57 | increment | SharedCounter | 48,51 | 47,51 | class SharedCounter(object):
"""A synchronized shared counter.
The locking done by multiprocessing.Value ensures that only a single
process or thread may read or write the in-memory ctypes object. However,
in order to do n += 1, Python performs a read followed by a write, so a
second process may rea... | class SharedCounter(object):
"""A synchronized shared counter.
The locking done by multiprocessing.Value ensures that only a single
process or thread may read or write the in-memory ctypes object. However,
in order to do n += 1, Python performs a read followed by a write, so a
second process may rea... | with self.count.get_lock():
self.count.value += n
| """Increment the counter by n (default = 1)"""
with self.count.get_lock():
self.count.value += n
| 5 | 279 | 24 | 303 | ## MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... | ## MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... |
47,777 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/e12_rllib/configs.py | 6,7 | 1,70 | gen_parser | 7,70 | 7,70 | import argparse
import multiprocessing
from pathlib import Path
def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser:
FILL_FUNC_BODY | import argparse
import multiprocessing
from pathlib import Path
def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser:
FILL_FUNC_BODY | parser = argparse.ArgumentParser(prog)
parser.add_argument(
"scenarios",
help="A list of scenarios. Each element can be either the scenario to"
"run or a directory of scenarios to sample from. See `scenarios/`"
"folder for some samples you can use.",
type=str,
nar... | parser = argparse.ArgumentParser(prog)
parser.add_argument(
"scenarios",
help="A list of scenarios. Each element can be either the scenario to"
"run or a directory of scenarios to sample from. See `scenarios/`"
"folder for some samples you can use.",
type=str,
nar... | 189 | 55 | 681 | 736 | import argparse
import multiprocessing
from pathlib import Path
def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser:
FILL_FUNC_BODY | import argparse
import multiprocessing
from pathlib import Path
def gen_parser(prog: str, default_result_dir: str) -> argparse.ArgumentParser:
FILL_FUNC_BODY | |
47,778 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/e5_agent_zoo.py | 33,34 | 1,77 | main | 34,72 | 34,72 | """This is an example to show how SMARTS multi-agent works. This example uses the same kind of
agent multiple times but different agents with different action and observation shapes can be mixed
in."""
import random
import sys
from pathlib import Path
# This may be necessary to get the repository root into path
SMARTS... | """This is an example to show how SMARTS multi-agent works. This example uses the same kind of
agent multiple times but different agents with different action and observation shapes can be mixed
in."""
import random
import sys
from pathlib import Path
# This may be necessary to get the repository root into path
SMARTS... | name = "random_lane_control-v0"
print(f"=== Before registering `{name}` ===")
print(registry.agent_registry)
registry.register(
name, rla_entrypoint
) # This registers "__main__:random_lane_control-v0"
print(f"=== After registering `{name}` ===")
print(registry.agent_registry)
... | name = "random_lane_control-v0"
print(f"=== Before registering `{name}` ===")
print(registry.agent_registry)
registry.register(
name, rla_entrypoint
) # This registers "__main__:random_lane_control-v0"
print(f"=== After registering `{name}` ===")
print(registry.agent_registry)
... | 144 | 350 | 545 | 895 | """This is an example to show how SMARTS multi-agent works. This example uses the same kind of
agent multiple times but different agents with different action and observation shapes can be mixed
in."""
import random
import sys
from pathlib import Path
# This may be necessary to get the repository root into path
SMARTS... | """This is an example to show how SMARTS multi-agent works. This example uses the same kind of
agent multiple times but different agents with different action and observation shapes can be mixed
in."""
import random
import sys
from pathlib import Path
# This may be necessary to get the repository root into path
SMARTS... | |
47,779 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/e5_agent_zoo.py | 20,21 | 19,24 | act | RandomLaneAgent | 21,24 | 21,24 | class RandomLaneAgent(Agent):
def act(self, obs, **kwargs):
FILL_FUNC_BODY | class RandomLaneAgent(Agent):
def act(self, obs, **kwargs):
FILL_FUNC_BODY | return random.randint(0, 3)
| return random.randint(0, 3)
| 3 | 31 | 18 | 49 | """This is an example to show how SMARTS multi-agent works. This example uses the same kind of
agent multiple times but different agents with different action and observation shapes can be mixed
in."""
import random
import sys
from pathlib import Path
# This may be necessary to get the repository root into path
SMARTS... | """This is an example to show how SMARTS multi-agent works. This example uses the same kind of
agent multiple times but different agents with different action and observation shapes can be mixed
in."""
import random
import sys
from pathlib import Path
# This may be necessary to get the repository root into path
SMARTS... |
47,780 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/e6_agent_action_space.py | 32,33 | 1,52 | display_agent_type_spaces | 33,43 | 33,43 | """This is an example to show how agent interface action formatting configuration works."""
from pathlib import Path
from examples.tools.argument_parser import empty_parser
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.controllers import Controllers
from smarts.core.controllers.act... | """This is an example to show how agent interface action formatting configuration works."""
from pathlib import Path
from examples.tools.argument_parser import empty_parser
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.controllers import Controllers
from smarts.core.controllers.act... | method_name, _, _ = f"{AgentInterface.from_type=}".partition("=")
print(
f"Note that `{method_name}` generates a pre-configured agent type with an existing action space."
)
for name, agent_type in AgentType.__members__.items():
agent_type: AgentType
print(f"{name.ljust(30)}: {Ag... | method_name, _, _ = f"{AgentInterface.from_type=}".partition("=")
print(
f"Note that `{method_name}` generates a pre-configured agent type with an existing action space."
)
for name, agent_type in AgentType.__members__.items():
agent_type: AgentType
print(f"{name.ljust(30)}: {Ag... | 29 | 442 | 128 | 570 | """This is an example to show how agent interface action formatting configuration works."""
from pathlib import Path
from examples.tools.argument_parser import empty_parser
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.controllers import Controllers
from smarts.core.controllers.act... | """This is an example to show how agent interface action formatting configuration works."""
from pathlib import Path
from examples.tools.argument_parser import empty_parser
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.controllers import Controllers
from smarts.core.controllers.act... | |
47,781 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/argument_parser.py | 60,61 | 1,79 | default_argument_parser | 67,79 | 61,79 | import argparse
from typing import Optional
def empty_parser(program: Optional[str] = None):
"""This factory method returns an empty `argparse.ArgumentParser` with primitive
configuration.
You can extend it with more `parser.add_argument(...)` calls or obtain the
arguments via `parser.parse_args()`.
... | import argparse
from typing import Optional
def empty_parser(program: Optional[str] = None):
"""This factory method returns an empty `argparse.ArgumentParser` with primitive
configuration.
You can extend it with more `parser.add_argument(...)` calls or obtain the
arguments via `parser.parse_args()`.
... | parser = minimal_argument_parser(program=program)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument(
"--sim_name",
help="Simulation name.",
type=str,
default=None,
)
parser.add_argument(
"--sumo_port", help="Run SUMO with a specified port... | """This factory method returns a vanilla `argparse.ArgumentParser` with a
slightly broader subset of arguments that should be supported.
You can extend it with more `parser.add_argument(...)` calls or obtain the
arguments via `parser.parse_args()`.
"""
parser = minimal_argument_parser(program=... | 26 | 571 | 128 | 699 | import argparse
from typing import Optional
def empty_parser(program: Optional[str] = None):
"""This factory method returns an empty `argparse.ArgumentParser` with primitive
configuration.
You can extend it with more `parser.add_argument(...)` calls or obtain the
arguments via `parser.parse_args()`.
... | import argparse
from typing import Optional
def empty_parser(program: Optional[str] = None):
"""This factory method returns an empty `argparse.ArgumentParser` with primitive
configuration.
You can extend it with more `parser.add_argument(...)` calls or obtain the
arguments via `parser.parse_args()`.
... | |
47,782 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/primitive_agents.py | 25,26 | 1,109 | open_entrypoint | 26,35 | 26,35 | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | try:
open_agent = importlib.import_module("open_agent")
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
f"Ensure that the open-agent has been installed with `pip install open-agent"
)
return open_agent.entrypoint(debug=debug, aggressiveness=aggressiveness)
| try:
open_agent = importlib.import_module("open_agent")
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
f"Ensure that the open-agent has been installed with `pip install open-agent"
)
return open_agent.entrypoint(debug=debug, aggressiveness=aggressiveness)
| 25 | 989 | 94 | 1,083 | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | |
47,783 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/primitive_agents.py | 36,37 | 35,53 | act | ChaseViaPointsAgent | 37,53 | 37,53 | class ChaseViaPointsAgent(Agent):
def act(self, obs: Observation):
FILL_FUNC_BODY | class ChaseViaPointsAgent(Agent):
def act(self, obs: Observation):
FILL_FUNC_BODY | if (
len(obs.via_data.near_via_points) < 1
or obs.ego_vehicle_state.road_id != obs.via_data.near_via_points[0].road_id
):
return (obs.waypoint_paths[0][0].speed_limit, 0)
nearest = obs.via_data.near_via_points[0]
if nearest.lane_index == obs.ego_vehic... | if (
len(obs.via_data.near_via_points) < 1
or obs.ego_vehicle_state.road_id != obs.via_data.near_via_points[0].road_id
):
return (obs.waypoint_paths[0][0].speed_limit, 0)
nearest = obs.via_data.near_via_points[0]
if nearest.lane_index == obs.ego_vehic... | 34 | 34 | 207 | 241 | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... |
47,784 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/primitive_agents.py | 11,12 | 10,16 | act | KeepLaneAgent | 12,16 | 12,16 | class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | val = ["keep_lane", "slow_down", "change_lane_left", "change_lane_right"]
return random.choice(val)
| val = ["keep_lane", "slow_down", "change_lane_left", "change_lane_right"]
return random.choice(val)
| 8 | 28 | 46 | 74 | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODYdef kla_entrypoint(*, max_episode_ste... | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODYdef kla_entrypoint(*, max_episode_ste... |
47,785 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/primitive_agents.py | 98,99 | 97,102 | act | StandardLaneFollowerAgent | 99,102 | 99,102 | class StandardLaneFollowerAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | class StandardLaneFollowerAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | return (obs["waypoint_paths"]["speed_limit"][0][0], 0)
| return (obs["waypoint_paths"]["speed_limit"][0][0], 0)
| 3 | 30 | 29 | 59 | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... |
47,786 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/primitive_agents.py | 65,66 | 64,88 | act | TrackingAgent | 66,88 | 66,88 | class TrackingAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | class TrackingAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | lane_index = 0
num_trajectory_points = min([10, len(obs.waypoint_paths[lane_index])])
# Desired speed is in m/s
desired_speed = 50 / 3.6
trajectory = [
[
obs.waypoint_paths[lane_index][i].pos[0]
for i in range(num_trajectory_points)
... | lane_index = 0
num_trajectory_points = min([10, len(obs.waypoint_paths[lane_index])])
# Desired speed is in m/s
desired_speed = 50 / 3.6
trajectory = [
[
obs.waypoint_paths[lane_index][i].pos[0]
for i in range(num_trajectory_points)
... | 50 | 27 | 238 | 265 | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... | import importlib
import random
from typing import Optional
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.core.observations import Observation
from smarts.zoo import Agent, AgentSpec
class KeepLaneAgent(Agent):
def act(self, obs):
val = ["keep_lane", "slow_down", "change_la... |
47,787 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/stress_sumo.py | 12,14 | 1,32 | spawn_sumo | 14,24 | 14,24 | try:
import ray
except Exception as e:
from smarts.core.utils.custom_exceptions import RayException
raise RayException.required_to("stress_sumo.py")
from smarts.core.scenario import Scenario
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
@ray.remote
def spawn_sumo(worker_idx, batc... | try:
import ray
except Exception as e:
from smarts.core.utils.custom_exceptions import RayException
raise RayException.required_to("stress_sumo.py")
from smarts.core.scenario import Scenario
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
@ray.remote
def spawn_sumo(worker_idx, batc... | sumo_sim = SumoTrafficSimulation(headless=True)
scenarios_iterator = Scenario.scenario_variations(
["scenarios/sumo/loop"],
["Agent007"],
)
sumo_sim.setup(Scenario.next(scenarios_iterator, f"{batch_id}-{worker_idx}"))
sumo_sim.teardown()
| sumo_sim = SumoTrafficSimulation(headless=True)
scenarios_iterator = Scenario.scenario_variations(
["scenarios/sumo/loop"],
["Agent007"],
)
sumo_sim.setup(Scenario.next(scenarios_iterator, f"{batch_id}-{worker_idx}"))
sumo_sim.teardown()
| 12 | 202 | 117 | 319 | try:
import ray
except Exception as e:
from smarts.core.utils.custom_exceptions import RayException
raise RayException.required_to("stress_sumo.py")
from smarts.core.scenario import Scenario
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
@ray.remote
def spawn_sumo(worker_idx, batc... | try:
import ray
except Exception as e:
from smarts.core.utils.custom_exceptions import RayException
raise RayException.required_to("stress_sumo.py")
from smarts.core.scenario import Scenario
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
@ray.remote
def spawn_sumo(worker_idx, batc... | |
47,788 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/examples/tools/sumo_multi_clients.py | 75,76 | 1,92 | main | 76,90 | 76,90 | import os
import subprocess
import threading
import time
from smarts.core.utils.sumo_utils import SUMO_PATH, sumolib, traci
PORT = 8001
"""
Conclusions:
1. connected clients < num-clients: SUMO will block, only start once all clients have connected.
2. connected clients > num-clients: Extra connection will be closed... | import os
import subprocess
import threading
import time
from smarts.core.utils.sumo_utils import SUMO_PATH, sumolib, traci
PORT = 8001
"""
Conclusions:
1. connected clients < num-clients: SUMO will block, only start once all clients have connected.
2. connected clients > num-clients: Extra connection will be closed... | t1 = threading.Thread(target=init_client, args=())
t1.start()
t2 = threading.Thread(target=run_client_2, args=())
t2.start()
t3 = threading.Thread(target=run_client_3, args=())
t3.start()
t1.join()
t2.join()
t3.join()
| t1 = threading.Thread(target=init_client, args=())
t1.start()
t2 = threading.Thread(target=run_client_2, args=())
t2.start()
t3 = threading.Thread(target=run_client_3, args=())
t3.start()
t1.join()
t2.join()
t3.join()
| 18 | 662 | 122 | 784 | import os
import subprocess
import threading
import time
from smarts.core.utils.sumo_utils import SUMO_PATH, sumolib, traci
PORT = 8001
"""
Conclusions:
1. connected clients < num-clients: SUMO will block, only start once all clients have connected.
2. connected clients > num-clients: Extra connection will be closed... | import os
import subprocess
import threading
import time
from smarts.core.utils.sumo_utils import SUMO_PATH, sumolib, traci
PORT = 8001
"""
Conclusions:
1. connected clients < num-clients: SUMO will block, only start once all clients have connected.
2. connected clients > num-clients: Extra connection will be closed... | |
47,789 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/scenarios/sumo/intersections/2lane_circle/agent_prefabs.py | 9,10 | 8,13 | act | KeepLaneAgent | 10,13 | 10,13 | class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | return "keep_lane"
| return "keep_lane"
| 2 | 28 | 14 | 42 | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, DoneCriteria
from smarts.core.controllers import ActionSpaceType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODYclass Stop... | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, DoneCriteria
from smarts.core.controllers import ActionSpaceType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class KeepLaneAgent(Agent):
def act(self, obs):
FILL_FUNC_BODYclass Stop... |
47,790 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/scenarios/sumo/intersections/2lane_circle/agent_prefabs.py | 14,15 | 13,18 | act | StoppedAgent | 15,18 | 15,18 | class StoppedAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | class StoppedAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | return (0, 0)
| return (0, 0)
| 3 | 27 | 14 | 41 | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, DoneCriteria
from smarts.core.controllers import ActionSpaceType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class KeepLaneAgent(Agent):
def act(self, obs):
return "keep_lan... | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, DoneCriteria
from smarts.core.controllers import ActionSpaceType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class KeepLaneAgent(Agent):
def act(self, obs):
return "keep_lan... |
47,791 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/scenarios/sumo/intersections/6lane/scenario.py | 11,12 | 1,59 | actor_gen | 12,22 | 12,22 | import os
from pathlib import Path
from smarts.sstudio import gen_scenario
from smarts.sstudio.sstypes import Mission, Route, Scenario, SocialAgentActor, Traffic
from smarts.sstudio.sstypes.entry_tactic import IdEntryTactic
from smarts.sstudio.sstypes.route import RandomRoute
from smarts.sstudio.sstypes.traffic import... | import os
from pathlib import Path
from smarts.sstudio import gen_scenario
from smarts.sstudio.sstypes import Mission, Route, Scenario, SocialAgentActor, Traffic
from smarts.sstudio.sstypes.entry_tactic import IdEntryTactic
from smarts.sstudio.sstypes.route import RandomRoute
from smarts.sstudio.sstypes.traffic import... | return [
SocialAgentActor(
name=f"{id_}-non-interactive-agent-{speed}-v0",
agent_locator="zoo.policies:non-interactive-agent-v0",
policy_kwargs={"speed": speed},
)
for speed in [10, 30, 80]
]
| return [
SocialAgentActor(
name=f"{id_}-non-interactive-agent-{speed}-v0",
agent_locator="zoo.policies:non-interactive-agent-v0",
policy_kwargs={"speed": speed},
)
for speed in [10, 30, 80]
]
| 15 | 541 | 105 | 646 | import os
from pathlib import Path
from smarts.sstudio import gen_scenario
from smarts.sstudio.sstypes import Mission, Route, Scenario, SocialAgentActor, Traffic
from smarts.sstudio.sstypes.entry_tactic import IdEntryTactic
from smarts.sstudio.sstypes.route import RandomRoute
from smarts.sstudio.sstypes.traffic import... | import os
from pathlib import Path
from smarts.sstudio import gen_scenario
from smarts.sstudio.sstypes import Mission, Route, Scenario, SocialAgentActor, Traffic
from smarts.sstudio.sstypes.entry_tactic import IdEntryTactic
from smarts.sstudio.sstypes.route import RandomRoute
from smarts.sstudio.sstypes.traffic import... | |
47,792 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/scenarios/sumo/zoo_intersection/agent_prefabs.py | 13,14 | 1,33 | demo_agent_callable | 14,19 | 14,19 | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class SimpleAgent(Agent):
def act(self, obs):
return "keep_lane"
# You can register a callable that will build your A... | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class SimpleAgent(Agent):
def act(self, obs):
return "keep_lane"
# You can register a callable that will build your A... | if interface is None:
interface = AgentInterface.from_type(AgentType.Laner)
return AgentSpec(interface=interface, agent_builder=SimpleAgent)
| if interface is None:
interface = AgentInterface.from_type(AgentType.Laner)
return AgentSpec(interface=interface, agent_builder=SimpleAgent)
| 10 | 290 | 49 | 339 | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class SimpleAgent(Agent):
def act(self, obs):
return "keep_lane"
# You can register a callable that will build your A... | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class SimpleAgent(Agent):
def act(self, obs):
return "keep_lane"
# You can register a callable that will build your A... | |
47,793 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/scenarios/sumo/zoo_intersection/agent_prefabs.py | 8,9 | 7,13 | act | SimpleAgent | 9,13 | 9,13 | class SimpleAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | class SimpleAgent(Agent):
def act(self, obs):
FILL_FUNC_BODY | return "keep_lane"
# You can register a callable that will build your AgentSpec
| return "keep_lane"
# You can register a callable that will build your AgentSpec
| 13 | 26 | 29 | 55 | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class SimpleAgent(Agent):
def act(self, obs):
FILL_FUNC_BODYdef demo_agent_callable(target_prefix=None, interface=None):
if... | from smarts.core.agent import Agent
from smarts.core.agent_interface import AgentInterface, AgentType
from smarts.zoo.agent_spec import AgentSpec
from smarts.zoo.registry import register
class SimpleAgent(Agent):
def act(self, obs):
FILL_FUNC_BODYdef demo_agent_callable(target_prefix=None, interface=None):
if... |
47,794 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/benchmark/driving_smarts/v2023/metric_formula_drive.py | 99,100 | 1,148 | costs_to_score | 122,144 | 100,144 | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | dist_to_destination = costs.dist_to_destination
humanness_error = _score_humanness_error(costs=costs)
rule_violation = score_rule_violation(costs=costs)
time = costs.steps
overall = (
0.25 * (1 - dist_to_destination)
+ 0.25 * (1 - time)
+ 0.25 * (1 - humanness_error)
... | """Compute score from costs.
+-------------------+--------+-----------------------------------------------------------+
| | Range | Remarks |
+===================+========+===========================================================+
... | 54 | 1,363 | 233 | 1,596 | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | |
47,795 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/benchmark/driving_smarts/v2023/metric_formula_drive.py | 61,62 | 53,99 | params | Formula | 67,82 | 62,82 | class Formula(FormulaBase):
"""Sets the (i) cost function parameters, and (ii) score computation formula,
for an environment.
"""
def __init__(self):
pass
def params(self) -> Params:
FILL_FUNC_BODY def score(self, records: Dict[str, Dict[str, Record]]) -> Score:
"""
Comp... | class Formula(FormulaBase):
"""Sets the (i) cost function parameters, and (ii) score computation formula,
for an environment.
"""
def __init__(self):
pass
def params(self) -> Params:
"""Return parameters to configure and initialize cost functions.
Returns:
Para... | params = Params(
collisions=Collisions(active=False),
comfort=Comfort(active=True),
dist_to_destination=DistToDestination(active=True),
dist_to_obstacles=DistToObstacles(active=False),
jerk_linear=JerkLinear(active=False),
lane_center_offse... | """Return parameters to configure and initialize cost functions.
Returns:
Params: Cost function parameters.
"""
params = Params(
collisions=Collisions(active=False),
comfort=Comfort(active=True),
dist_to_destination=DistToDestination(activ... | 17 | 275 | 191 | 466 | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... |
47,796 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/benchmark/driving_smarts/v2023/metric_formula_platoon.py | 99,100 | 1,148 | costs_to_score | 122,144 | 100,144 | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | dist_to_destination = costs.dist_to_destination
humanness_error = _score_humanness_error(costs=costs)
rule_violation = score_rule_violation(costs=costs)
vehicle_gap = costs.vehicle_gap
overall = (
0.25 * (1 - dist_to_destination)
+ 0.25 * (1 - vehicle_gap)
+ 0.25 * (1 - human... | """Compute score from costs.
+-------------------+--------+-----------------------------------------------------------+
| | Range | Remarks |
+===================+========+===========================================================+
... | 54 | 1,369 | 247 | 1,616 | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | |
47,797 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/benchmark/driving_smarts/v2023/metric_formula_platoon.py | 82,83 | 53,99 | score | Formula | 93,99 | 83,99 | class Formula(FormulaBase):
"""Sets the (i) cost function parameters, and (ii) score computation formula,
for an environment.
"""
def __init__(self):
pass
def params(self) -> Params:
"""Return parameters to configure and initialize cost functions.
Returns:
Para... | class Formula(FormulaBase):
"""Sets the (i) cost function parameters, and (ii) score computation formula,
for an environment.
"""
def __init__(self):
pass
def params(self) -> Params:
"""Return parameters to configure and initialize cost functions.
Returns:
Para... |
agent_weight = agent_weights(records=records)
agent_score = agent_scores(records=records, func=costs_to_score)
return weighted_score(scores=agent_score, weights=agent_weight)
| """
Computes several sub-component scores and one total combined score named
"Overall" on the wrapped environment.
Args:
records (Dict[str, Dict[str, Record]]): Records.
Returns:
Score: "Overall" score and other sub-component scores.
"""
... | 10 | 385 | 72 | 457 | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... | # MIT License
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ... |
47,798 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/actor.py | 62,63 | 48,81 | get_dimensions | ActorState | 64,66 | 63,66 | @dataclass
class ActorState:
"""Actor state information."""
actor_id: str # must be unique within the simulation
actor_type: Optional[str] = None
source: Optional[str] = None # the source of truth for this Actor's state
role: ActorRole = ActorRole.Unknown
updated: bool = False
def get_po... | @dataclass
class ActorState:
"""Actor state information."""
actor_id: str # must be unique within the simulation
actor_type: Optional[str] = None
source: Optional[str] = None # the source of truth for this Actor's state
role: ActorRole = ActorRole.Unknown
updated: bool = False
def get_po... | return None
| """Get the dimensions of this actor. Some actors do not have physical dimensions."""
return None
| 2 | 348 | 7 | 355 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,799 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/actor_capture_manager.py | 65,69 | 40,220 | _make_new_vehicle | ActorCaptureManager | 69,88 | 69,88 | class ActorCaptureManager:
"""The base for managers that handle transition of control of actors."""
def step(self, sim: SMARTS):
"""Step the manager. Assume modification of existence and control of the simulation actors.
Args:
sim (smarts.core.smarts.SMARTS): The smarts simulation ... | class ActorCaptureManager:
"""The base for managers that handle transition of control of actors."""
def step(self, sim: SMARTS):
"""Step the manager. Assume modification of existence and control of the simulation actors.
Args:
sim (smarts.core.smarts.SMARTS): The smarts simulation ... | if social:
return cls.__make_new_social_vehicle(sim, agent_id, initial_speed)
agent_interface = sim.agent_manager.agent_interface_for_agent_id(agent_id)
plan = Plan(sim.road_map, mission)
# Apply agent vehicle association.
vehicle = sim.vehicle_index.build_agent_vehic... | if social:
return cls.__make_new_social_vehicle(sim, agent_id, initial_speed)
agent_interface = sim.agent_manager.agent_interface_for_agent_id(agent_id)
plan = Plan(sim.road_map, mission)
# Apply agent vehicle association.
vehicle = sim.vehicle_index.build_agent_vehic... | 40 | 1,695 | 220 | 1,915 | # MIT License
#
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,800 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent.py | 62,63 | 1,74 | deprecated_agent_spec | 64,73 | 63,73 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | from smarts.zoo.agent_spec import AgentSpec as AgentSpecAlias
warnings.warn(
"The AgentSpec class has moved to the following module: smarts.zoo.agent_spec. Calling it from this module will be deprecated.",
DeprecationWarning,
)
return AgentSpecAlias(*args, **kwargs)
| """Deprecated version of AgentSpec, see smarts.zoo.agent_spec"""
from smarts.zoo.agent_spec import AgentSpec as AgentSpecAlias
warnings.warn(
"The AgentSpec class has moved to the following module: smarts.zoo.agent_spec. Calling it from this module will be deprecated.",
DeprecationWarning,
... | 30 | 704 | 96 | 800 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | |
47,801 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent.py | 41,43 | 28,51 | act | Agent | 47,51 | 43,51 | class Agent(metaclass=ABCMeta):
"""The base class for agents"""
@classmethod
def from_function(cls, agent_function: Callable[[Any], Any]) -> "Agent":
"""A utility function to create an agent from a lambda or other callable object.
.. code-block:: python
keep_lane_agent = Agent... | class Agent(metaclass=ABCMeta):
"""The base class for agents"""
@classmethod
def from_function(cls, agent_function: Callable[[Any], Any]) -> "Agent":
"""A utility function to create an agent from a lambda or other callable object.
.. code-block:: python
keep_lane_agent = Agent... |
raise NotImplementedError
| """The agent action. See documentation on observations, `AgentSpec`, and `AgentInterface`.
Expects an adapted observation and returns a raw action.
"""
raise NotImplementedError
| 2 | 190 | 15 | 205 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,802 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent.py | 58,59 | 51,62 | act | FunctionAgent | 59,62 | 59,62 | class FunctionAgent(Agent):
"""An agent generated from a function."""
def __init__(self, agent_function) -> None:
assert callable(agent_function)
self._agent_function = agent_function
def act(self, obs, **configs):
FILL_FUNC_BODY | class FunctionAgent(Agent):
"""An agent generated from a function."""
def __init__(self, agent_function) -> None:
assert callable(agent_function)
self._agent_function = agent_function
def act(self, obs, **configs):
FILL_FUNC_BODY | return self._agent_function(obs)
| return self._agent_function(obs)
| 2 | 79 | 16 | 95 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,803 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_buffer.py | 34,38 | 26,40 | acquire_agent | AgentBuffer | 39,40 | 38,40 | class AgentBuffer(metaclass=abc.ABCMeta):
"""Defines a buffer of agents for external use."""
@abc.abstractmethod
def destroy(self):
"""Clean up the buffer resources."""
raise NotImplementedError
@abc.abstractmethod
def acquire_agent(
self, retries: int = 3, timeout: Optiona... | class AgentBuffer(metaclass=abc.ABCMeta):
"""Defines a buffer of agents for external use."""
@abc.abstractmethod
def destroy(self):
"""Clean up the buffer resources."""
raise NotImplementedError
@abc.abstractmethod
def acquire_agent(
self, retries: int = 3, timeout: Optiona... | raise NotImplementedError
| """Get an agent from the buffer."""
raise NotImplementedError
| 2 | 125 | 9 | 134 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,805 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_interface.py | 142,144 | 131,147 | name | CustomRenderBufferDependency | 144,147 | 144,147 | @dataclass
class CustomRenderBufferDependency(RenderDependencyBase):
"""Base for referencing an observation buffer (other than a camera)."""
buffer_dependency_name: Union[str, BufferID]
"""The identification of buffer to reference."""
variable_name: str
"""The variable name inside the shader."""
... | @dataclass
class CustomRenderBufferDependency(RenderDependencyBase):
"""Base for referencing an observation buffer (other than a camera)."""
buffer_dependency_name: Union[str, BufferID]
"""The identification of buffer to reference."""
variable_name: str
"""The variable name inside the shader."""
... | return self.variable_name
| return self.variable_name
| 2 | 124 | 13 | 137 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,806 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_interface.py | 159,161 | 147,173 | name | CustomRenderCameraDependency | 161,163 | 161,163 | @dataclass
class CustomRenderCameraDependency(RenderDependencyBase):
"""Provides a uniform texture access to an existing camera."""
camera_dependency_name: Union[str, CameraSensorID]
"""The name of the camera (type) to target."""
variable_name: str
"""The name of the camera texture variable."""
... | @dataclass
class CustomRenderCameraDependency(RenderDependencyBase):
"""Provides a uniform texture access to an existing camera."""
camera_dependency_name: Union[str, CameraSensorID]
"""The name of the camera (type) to target."""
variable_name: str
"""The name of the camera texture variable."""
... | return self.variable_name
| return self.variable_name
| 2 | 258 | 11 | 269 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,807 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_interface.py | 117,119 | 108,131 | name | CustomRenderVariableDependency | 119,121 | 119,121 | @dataclass
class CustomRenderVariableDependency(RenderDependencyBase):
"""Base for renderer value to pass directly to the shader."""
value: Union[int, float, bool, np.ndarray, list, tuple]
"""The value to pass to the shader."""
variable_name: str
"""The uniform name inside the shader."""
@prop... | @dataclass
class CustomRenderVariableDependency(RenderDependencyBase):
"""Base for renderer value to pass directly to the shader."""
value: Union[int, float, bool, np.ndarray, list, tuple]
"""The value to pass to the shader."""
variable_name: str
"""The uniform name inside the shader."""
@prop... | return self.variable_name
| return self.variable_name
| 2 | 198 | 11 | 209 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,808 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_interface.py | 384,386 | 359,391 | actors_alive | DoneCriteria | 387,391 | 386,391 | @dataclass(frozen=True)
class DoneCriteria:
"""Toggle conditions on which cause removal of an agent from the current episode."""
collision: bool = True
"""End the episode when the agent collides with another vehicle."""
off_road: bool = True
"""End the episode when the agent drives off the road."""... | @dataclass(frozen=True)
class DoneCriteria:
"""Toggle conditions on which cause removal of an agent from the current episode."""
collision: bool = True
"""End the episode when the agent collides with another vehicle."""
off_road: bool = True
"""End the episode when the agent drives off the road."""... | warnings.warn("Use interest.", category=DeprecationWarning)
return self.interest
| """Deprecated. Use interest."""
warnings.warn("Use interest.", category=DeprecationWarning)
return self.interest
| 5 | 335 | 28 | 363 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,809 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_interface.py | 343,345 | 328,349 | actors_pattern | InterestDoneCriteria | 346,349 | 345,349 | @dataclass(frozen=True)
class InterestDoneCriteria:
"""Require scenario marked interest actors to exist."""
actors_filter: Tuple[str, ...] = ()
"""Interface defined interest actors that should exist to continue this agent."""
include_scenario_marked: bool = True
"""If scenario marked interest acto... | @dataclass(frozen=True)
class InterestDoneCriteria:
"""Require scenario marked interest actors to exist."""
actors_filter: Tuple[str, ...] = ()
"""Interface defined interest actors that should exist to continue this agent."""
include_scenario_marked: bool = True
"""If scenario marked interest acto... | return re.compile("|".join(rf"(?:{aoi})" for aoi in self.actors_filter))
| """The expression match pattern for actors covered by this interface specifically."""
return re.compile("|".join(rf"(?:{aoi})" for aoi in self.actors_filter))
| 6 | 173 | 35 | 208 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,810 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agent_interface.py | 101,104 | 97,108 | name | RenderDependencyBase | 105,108 | 104,108 | @dataclass
class RenderDependencyBase(metaclass=abc.ABCMeta):
"""Base class for render dependencies"""
@property
@abc.abstractmethod
def name(self) -> str:
FILL_FUNC_BODY | @dataclass
class RenderDependencyBase(metaclass=abc.ABCMeta):
"""Base class for render dependencies"""
@property
@abc.abstractmethod
def name(self) -> str:
"""The name of this dependency."""
FILL_FUNC_BODY | raise NotImplementedError()
| """The name of this dependency."""
raise NotImplementedError()
| 2 | 74 | 14 | 88 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,812 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agents_provider.py | 239,241 | 232,252 | actions | AgentPhysicsProvider | 241,252 | 241,252 | class AgentPhysicsProvider(AgentsProvider):
"""A provider that manages agent vehicles with Ackermann chassis,
applying their actions via appropriate controllers to their chassis."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Set[ActionSpaceType]:
FILL_FUN... | class AgentPhysicsProvider(AgentsProvider):
"""A provider that manages agent vehicles with Ackermann chassis,
applying their actions via appropriate controllers to their chassis."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Set[ActionSpaceType]:
FILL_FUN... | return {
ActionSpaceType.Continuous,
ActionSpaceType.Lane,
ActionSpaceType.ActuatorDynamic,
ActionSpaceType.LaneWithContinuousSpeed,
ActionSpaceType.Trajectory,
ActionSpaceType.MPC,
ActionSpaceType.Empty,
}
| return {
ActionSpaceType.Continuous,
ActionSpaceType.Lane,
ActionSpaceType.ActuatorDynamic,
ActionSpaceType.LaneWithContinuousSpeed,
ActionSpaceType.Trajectory,
ActionSpaceType.MPC,
ActionSpaceType.Empty,
}
| 10 | 95 | 91 | 186 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,814 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agents_provider.py | 259,261 | 252,264 | actions | DirectControlProvider | 261,264 | 261,264 | class DirectControlProvider(AgentsProvider):
"""A provider that manages agent vehicles that are under direct control.
These vehicles must have a Box chassis to allow for bypassing the application of forces."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Se... | class DirectControlProvider(AgentsProvider):
"""A provider that manages agent vehicles that are under direct control.
These vehicles must have a Box chassis to allow for bypassing the application of forces."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Se... | return {ActionSpaceType.Direct}
| return {ActionSpaceType.Direct}
| 2 | 97 | 15 | 112 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,815 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agents_provider.py | 270,272 | 264,279 | actions | MotionPlannerProvider | 272,279 | 272,279 | class MotionPlannerProvider(AgentsProvider):
"""A provider that reshapes agent vehicle motion to follow a motion plan."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Set[ActionSpaceType]:
FILL_FUNC_BODY | class MotionPlannerProvider(AgentsProvider):
"""A provider that reshapes agent vehicle motion to follow a motion plan."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Set[ActionSpaceType]:
FILL_FUNC_BODY | return {
ActionSpaceType.TargetPose,
ActionSpaceType.RelativeTargetPose,
ActionSpaceType.MultiTargetPose,
}
| return {
ActionSpaceType.TargetPose,
ActionSpaceType.RelativeTargetPose,
ActionSpaceType.MultiTargetPose,
}
| 6 | 80 | 49 | 129 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,816 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/agents_provider.py | 285,287 | 279,288 | actions | TrajectoryInterpolationProvider | 287,288 | 287,288 | class TrajectoryInterpolationProvider(AgentsProvider):
"""A provider used to perform trajectory interpolation on agent actors that request trajectory following."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Set[ActionSpaceType]:
FILL_FUNC_BODY | class TrajectoryInterpolationProvider(AgentsProvider):
"""A provider used to perform trajectory interpolation on agent actors that request trajectory following."""
def __init__(self, sim):
super().__init__(sim)
@property
def actions(self) -> Set[ActionSpaceType]:
FILL_FUNC_BODY | return {ActionSpaceType.TrajectoryWithTime}
| return {ActionSpaceType.TrajectoryWithTime}
| 2 | 83 | 15 | 98 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,817 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/buffer_agent.py | 41,43 | 28,45 | terminate | BufferAgent | 44,45 | 43,45 | class BufferAgent(metaclass=abc.ABCMeta):
"""An agent which is part of a buffer."""
@abstractmethod
def act(self, obs):
"""Gives a future action based on observations."""
raise NotImplementedError
@abstractmethod
def start(self, agent_spec: AgentSpec):
"""Begin operation of... | class BufferAgent(metaclass=abc.ABCMeta):
"""An agent which is part of a buffer."""
@abstractmethod
def act(self, obs):
"""Gives a future action based on observations."""
raise NotImplementedError
@abstractmethod
def start(self, agent_spec: AgentSpec):
"""Begin operation of... | raise NotImplementedError
| """Clean up agent resources."""
raise NotImplementedError
| 2 | 135 | 9 | 144 | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,818 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/controllers/actuator_dynamic_controller.py | 35,37 | 32,66 | perform_action | ActuatorDynamicController | 40,66 | 37,66 | class ActuatorDynamicController:
"""A controller that maintains the last steering angle."""
@classmethod
def perform_action(cls, vehicle, action, state, dt_sec):
FILL_FUNC_BODY | class ActuatorDynamicController:
"""A controller that maintains the last steering angle."""
@classmethod
def perform_action(cls, vehicle, action, state, dt_sec):
"""Perform throttle, break, and steering, keeping the previous steering angle as the start
for the next steering angle.
"... | throttle, brake, steering_change = action
# The following is the normalized steering change
# under the assumption that the steering angles at
# wheels can reach to their maximum values in one
# second.
clipped_steering_change = np.clip(
steering_change,
... | """Perform throttle, break, and steering, keeping the previous steering angle as the start
for the next steering angle.
"""
throttle, brake, steering_change = action
# The following is the normalized steering change
# under the assumption that the steering angles at
... | 83 | 88 | 265 | 353 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,819 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/controllers/direct_controller.py | 34,41 | 30,102 | perform_action | DirectController | 50,102 | 41,102 | class DirectController:
"""A controller that directly sets a vehicle's acceleration and angular velocity
(rather than applying forces like torque) based on kinematics."""
@classmethod
def perform_action(
cls,
dt: float,
vehicle,
action: Union[float, Tuple[float, float]],... | class DirectController:
"""A controller that directly sets a vehicle's acceleration and angular velocity
(rather than applying forces like torque) based on kinematics."""
@classmethod
def perform_action(
cls,
dt: float,
vehicle,
action: Union[float, Tuple[float, float]],... | chassis = vehicle.chassis
if isinstance(action, (int, float)):
# special case: setting the initial speed
if isinstance(chassis, BoxChassis):
vehicle.control(vehicle.pose, action, dt)
elif isinstance(chassis, AckermannChassis):
chassis.... | """Performs an action adapting to the underlying chassis.
Args:
dt (float):
A delta time value.
vehicle (Vehicle):
The vehicle to control.
action (Union[float, Tuple[float, float]]):
(speed) XOR (acceleration, angular_ve... | 283 | 182 | 819 | 1,001 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,821 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/controllers/trajectory_interpolation_controller.py | 58,60 | 40,127 | _interpolate | TrajectoryInterpolationController | 65,83 | 60,83 | class TrajectoryInterpolationController:
"""A controller used to perform trajectory interpolation."""
@classmethod
def assert_is_legal_trajectory(cls, trajectory: np.ndarray):
"""Test if the trajectory is correctly formed."""
assert (
len(trajectory[TrajectoryField.TIME_INDEX]) ... | class TrajectoryInterpolationController:
"""A controller used to perform trajectory interpolation."""
@classmethod
def assert_is_legal_trajectory(cls, trajectory: np.ndarray):
"""Test if the trajectory is correctly formed."""
assert (
len(trajectory[TrajectoryField.TIME_INDEX]) ... | start_time = ms0[TrajectoryField.TIME_INDEX]
end_time = ms1[TrajectoryField.TIME_INDEX]
assert (
end_time >= start_time and time >= start_time
), f"{start_time} <= {time} <= {end_time} ?"
ratio = math.fabs((time - start_time) / (end_time - start_time))
left_ov... | """Linearly interpolate between two vehicle motion states.
Returns:
np.ndarray: New vehicle state between vehicle motion state ms0 and ms1
"""
start_time = ms0[TrajectoryField.TIME_INDEX]
end_time = ms1[TrajectoryField.TIME_INDEX]
assert (
end_tim... | 73 | 831 | 292 | 1,123 | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,823 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/data_model.py | 43,44 | 31,46 | to_agent_spec | SocialAgent | 45,46 | 44,46 | @dataclass
class SocialAgent:
"""A serializable representation of a social agent."""
id: str
actor_name: str
is_boid: bool
is_boid_keep_alive: bool
agent_locator: str
policy_kwargs: Dict[str, Any] = field(default_factory=dict)
initial_speed: Optional[float] = None
def to_agent_spec... | @dataclass
class SocialAgent:
"""A serializable representation of a social agent."""
id: str
actor_name: str
is_boid: bool
is_boid_keep_alive: bool
agent_locator: str
policy_kwargs: Dict[str, Any] = field(default_factory=dict)
initial_speed: Optional[float] = None
def to_agent_spec... | return make(locator=self.agent_locator, **self.policy_kwargs)
| """Generate an agent spec."""
return make(locator=self.agent_locator, **self.policy_kwargs)
| 3 | 134 | 23 | 157 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,825 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/events.py | 54,56 | 29,59 | actors_alive_done | Events | 57,59 | 56,59 | class Events(NamedTuple):
"""Classified observations that can trigger agent done status."""
collisions: Tuple[Collision]
"""Collisions with other vehicles (if any)."""
off_road: bool
"""True if vehicle is off the road, else False."""
off_route: bool
"""True if vehicle is off the mission rou... | class Events(NamedTuple):
"""Classified observations that can trigger agent done status."""
collisions: Tuple[Collision]
"""Collisions with other vehicles (if any)."""
off_road: bool
"""True if vehicle is off the road, else False."""
off_route: bool
"""True if vehicle is off the mission rou... | warnings.warn("Use interest_done.", category=DeprecationWarning)
return self.interest_done
| """Deprecated. Use interest_done."""
warnings.warn("Use interest_done.", category=DeprecationWarning)
return self.interest_done
| 5 | 320 | 28 | 348 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,826 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/external_provider.py | 108,109 | 35,145 | step | ExternalProvider | 109,111 | 109,111 | class ExternalProvider(Provider):
"""A provider that is intended to be used for external intervention in the simulation.
Vehicles managed by this provider cannot be hijacked by social agents
and may have privileged VehicleStates."""
def __init__(self, sim):
# start with the default recovery fla... | class ExternalProvider(Provider):
"""A provider that is intended to be used for external intervention in the simulation.
Vehicles managed by this provider cannot be hijacked by social agents
and may have privileged VehicleStates."""
def __init__(self, sim):
# start with the default recovery fla... | return self._provider_state
| return self._provider_state
| 2 | 1,063 | 11 | 1,074 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,827 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/id_actor_capture_manager.py | 98,99 | 37,131 | reset | IdActorCaptureManager | 99,129 | 99,129 | class IdActorCaptureManager(ActorCaptureManager):
"""The base for managers that handle transition of control of actors."""
def __init__(self) -> None:
self._log = logging.getLogger(self.__class__.__name__)
self._actor_for_agent: Dict[str, Tuple[str, NavigationMission]] = {}
def step(self, ... | class IdActorCaptureManager(ActorCaptureManager):
"""The base for managers that handle transition of control of actors."""
def __init__(self) -> None:
self._log = logging.getLogger(self.__class__.__name__)
self._actor_for_agent: Dict[str, Tuple[str, NavigationMission]] = {}
def step(self, ... | self._actor_for_agent.clear()
missions: Dict[str, NavigationMission] = scenario.missions
cancelled_agents = set()
for agent_id, mission in missions.items():
if mission is None:
continue
entry_tactic = mission.entry_tactic
if not isinsta... | self._actor_for_agent.clear()
missions: Dict[str, NavigationMission] = scenario.missions
cancelled_agents = set()
for agent_id, mission in missions.items():
if mission is None:
continue
entry_tactic = mission.entry_tactic
if not isinsta... | 89 | 730 | 433 | 1,163 | # MIT License
#
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
47,828 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/local_agent.py | 46,47 | 32,53 | start | LocalAgent | 48,51 | 47,51 | class LocalAgent(BufferAgent):
"""A local implementation of a buffer agent."""
def __init__(self):
self._agent: Agent = None
self._agent_spec: AgentSpec = None
def act(self, obs):
"""Call the agent's act function asynchronously and return a Future."""
act_future = futures.... | class LocalAgent(BufferAgent):
"""A local implementation of a buffer agent."""
def __init__(self):
self._agent: Agent = None
self._agent_spec: AgentSpec = None
def act(self, obs):
"""Call the agent's act function asynchronously and return a Future."""
act_future = futures.... | self._agent_spec = agent_spec
self._agent = self._agent_spec.build_agent()
| """Send the AgentSpec to the agent runner."""
self._agent_spec = agent_spec
self._agent = self._agent_spec.build_agent()
| 6 | 161 | 31 | 192 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,829 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/local_agent_buffer.py | 36,39 | 27,41 | acquire_agent | LocalAgentBuffer | 39,41 | 39,41 | class LocalAgentBuffer(AgentBuffer):
"""A buffer that manages social agents."""
def __init__(self):
pass
def destroy(self):
pass
def acquire_agent(
self, retries: int = 3, timeout: Optional[float] = None
) -> BufferAgent:
FILL_FUNC_BODY | class LocalAgentBuffer(AgentBuffer):
"""A buffer that manages social agents."""
def __init__(self):
pass
def destroy(self):
pass
def acquire_agent(
self, retries: int = 3, timeout: Optional[float] = None
) -> BufferAgent:
FILL_FUNC_BODY | localAgent = LocalAgent()
return localAgent
| localAgent = LocalAgent()
return localAgent
| 5 | 89 | 15 | 104 | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,831 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/local_traffic_provider.py | 831,833 | 789,890 | _angle_scale | _LaneWindow | 833,872 | 833,872 | class _LaneWindow:
def __init__(
self,
lane: RoadMap.Lane,
time_left: float,
ttc: float,
ttre: float,
gap: float,
lane_coord: RefLinePoint,
agent_gap: Optional[float],
ahead_id: Optional[str],
... | class _LaneWindow:
def __init__(
self,
lane: RoadMap.Lane,
time_left: float,
ttc: float,
ttre: float,
gap: float,
lane_coord: RefLinePoint,
agent_gap: Optional[float],
ahead_id: Optional[str],
... | # we need to correct for not going straight across.
# other things being equal, we target ~30 degrees (sin(30)=.5) on average.
if abs(self.radius) > 1e5 or self.radius == 0:
return safe_division(1.0, math.sin(theta), 1e6)
# here we correct for the local ro... | # we need to correct for not going straight across.
# other things being equal, we target ~30 degrees (sin(30)=.5) on average.
if abs(self.radius) > 1e5 or self.radius == 0:
return safe_division(1.0, math.sin(theta), 1e6)
# here we correct for the local ro... | 151 | 803 | 437 | 1,240 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,832 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/local_traffic_provider.py | 1307,1308 | 1274,1378 | predict_crash_in | _RelWindow | 1311,1369 | 1308,1369 | class _RelWindow:
@dataclass
class _RelativeVehInfo:
dist: float
bearing: float
heading: float
dt: float
def __init__(self, width: int = 5):
self._width = width
self._junction_foes: Dict[
Tuple[str, int]... | class _RelWindow:
@dataclass
class _RelativeVehInfo:
dist: float
bearing: float
heading: float
dt: float
def __init__(self, width: int = 5):
self._width = width
self._junction_foes: Dict[
Tuple[str, int]... | # CBDR is a good approximation at distance over time with
# both vehicles behaving "steadily" (ideally, driving
# straight at a constant speed). It breaks down, thankfully
# in preictable ways, if these assumptions are violated.
#
# Range Dependen... | """Estimate if and when I might collide with veh_id using
the constant bearing, decreasing range (CBDR) techique, but
attempting to correct for non-linearities."""
# CBDR is a good approximation at distance over time with
# both vehicles behaving "steadily" (i... | 372 | 539 | 830 | 1,369 | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to us... |
47,834 | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS | /home/hieuvd/dung_repo/raw/stars_03/huawei-noah_SMARTS/SMARTS/smarts/core/observations.py | 213,215 | 207,219 | hit_via_points | Vias | 216,219 | 215,219 | class Vias(NamedTuple):
"""Listing of nearby collectible ViaPoints and ViaPoints collected in the last step."""
near_via_points: Tuple[ViaPoint]
"""Ordered list of nearby points that have not been hit."""
@property
def hit_via_points(self) -> Tuple[ViaPoint]:
FILL_FUNC_BODY | class Vias(NamedTuple):
"""Listing of nearby collectible ViaPoints and ViaPoints collected in the last step."""
near_via_points: Tuple[ViaPoint]
"""Ordered list of nearby points that have not been hit."""
@property
def hit_via_points(self) -> Tuple[ViaPoint]:
"""List of points that were hi... | return tuple(vp for vp in self.near_via_points if vp.hit)
| """List of points that were hit in the previous step."""
return tuple(vp for vp in self.near_via_points if vp.hit)
| 8 | 111 | 30 | 141 | # MIT License
#
# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... | # MIT License
#
# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation th... |
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