MCPcopy Create free account

hub / github.com/VainF/DeepLabV3Plus-Pytorch / functions

Functions192 in github.com/VainF/DeepLabV3Plus-Pytorch

⨍ Functions 192◇ Types & classes 46
↓ 12 callersFunction_load_model
(arch_type, backbone, num_classes, output_stride, pretrained_backbone)
network/modeling.py:112
↓ 12 callersMethod_make_block
(self, in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True, dilate=False)
network/backbone/xception.py:175
↓ 9 callersFunction_resnet
(arch, block, layers, pretrained, progress, **kwargs)
network/backbone/resnet.py:216
↓ 6 callersMethodupdate
(self, id, val)
metrics/stream_metrics.py:99
↓ 6 callersMethodvis_scalar
(self, name, x, y, opts=None)
utils/visualizer.py:18
↓ 5 callersMethod__init__
(self, in_channels, atrous_rates)
network/_deeplab.py:134
↓ 4 callersMethod_check_input
(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True)
utils/ext_transforms.py:452
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1, dilate=False)
network/backbone/resnet.py:174
↓ 4 callersMethoddecode_target
decode semantic mask to RGB image
datasets/voc.py:156
↓ 4 callersMethodvis_table
(self, name, tbl, opts=None)
utils/visualizer.py:52
↓ 3 callersMethod__init__
(self, c=48, num_blocks=[1, 4, 3], num_classes=1000)
network/backbone/hrnetv2.py:174
↓ 3 callersFunction_make_divisible
This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen
network/backbone/mobilenetv2.py:16
↓ 3 callersFunctionbitget
(byteval, idx)
datasets/voc.py:53
↓ 3 callersFunctionconv1x1
1x1 convolution
network/backbone/resnet.py:33
↓ 3 callersFunctionconv3x3
3x3 convolution with padding
network/backbone/resnet.py:27
↓ 2 callersMethod__init__
(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True, dilation=1)
network/backbone/xception.py:59
↓ 2 callersMethod__init__
(self, in_planes, out_planes, kernel_size=3, stride=1, dilation=1, groups=1)
network/backbone/mobilenetv2.py:37
↓ 2 callersMethod__init__
(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=6
network/backbone/resnet.py:123
↓ 2 callersFunction_hrnet
(arch, channels, num_blocks, pretrained, progress, **kwargs)
network/backbone/hrnetv2.py:293
↓ 2 callersMethod_make_transition_layers
(c, transition_number)
network/backbone/hrnetv2.py:248
↓ 2 callersMethodfeatures
(self, input)
network/backbone/xception.py:181
↓ 2 callersFunctiongen_bar_updater
(pbar)
datasets/utils.py:8
↓ 2 callersFunctionsave_ckpt
save current model
main.py:274
↓ 2 callersMethodto_str
(results)
metrics/stream_metrics.py:38
↓ 2 callersFunctionvalidate
Do validation and return specified samples
main.py:156
↓ 1 callersMethod__init__
(self, model, return_layers, hrnet_flag=False)
network/utils.py:52
↓ 1 callersMethod_fast_hist
(self, label_true, label_pred)
metrics/stream_metrics.py:49
↓ 1 callersMethod_get_target_suffix
(self, mode, target_type)
datasets/cityscapes.py:137
↓ 1 callersMethod_init_weight
(self)
network/_deeplab.py:53
↓ 1 callersMethod_init_weight
(self)
network/_deeplab.py:77
↓ 1 callersMethod_init_weight
(self)
network/_deeplab.py:103
↓ 1 callersFunction_segm_hrnet
(name, backbone_name, num_classes, pretrained_backbone)
network/modeling.py:10
↓ 1 callersFunction_segm_mobilenet
(name, backbone_name, num_classes, output_stride, pretrained_backbone)
network/modeling.py:84
↓ 1 callersFunction_segm_resnet
(name, backbone_name, num_classes, output_stride, pretrained_backbone)
network/modeling.py:32
↓ 1 callersFunction_segm_xception
(name, backbone_name, num_classes, output_stride, pretrained_backbone)
network/modeling.py:60
↓ 1 callersFunctioncheck_integrity
(fpath, md5=None)
datasets/utils.py:18
↓ 1 callersFunctioncheck_pth
(arch)
network/backbone/hrnetv2.py:18
↓ 1 callersFunctiondownload_extract
(url, root, filename, md5)
datasets/voc.py:160
↓ 1 callersFunctiondownload_url
Download a file from a url and place it in root. Args: url (str): URL to download file from root (str): Directory to place downloa
datasets/utils.py:47
↓ 1 callersMethodencode_target
(cls, target)
datasets/cityscapes.py:105
↓ 1 callersFunctionfixed_padding
(kernel_size, dilation)
network/backbone/mobilenetv2.py:45
↓ 1 callersMethodforward
(self, x)
network/_deeplab.py:157
↓ 1 callersFunctionget_argparser
()
predict.py:23
↓ 1 callersFunctionget_argparser
()
main.py:23
↓ 1 callersFunctionget_dataset
Dataset And Augmentation
main.py:99
↓ 1 callersMethodget_params
Get parameters for ``rotate`` for a random rotation. Returns: sequence: params to be passed to ``rotate`` for random rotation.
utils/ext_transforms.py:181
↓ 1 callersMethodget_params
Get parameters for ``crop`` for a random crop. Args: img (PIL Image): Image to be cropped. output_size (tuple): Expect
utils/ext_transforms.py:350
↓ 1 callersMethodget_params
Get a randomized transform to be applied on image. Arguments are same as that of __init__. Returns: Transform which random
utils/ext_transforms.py:472
↓ 1 callersMethodget_results
(self, id)
metrics/stream_metrics.py:107
↓ 1 callersFunctionhrnetv2_32
(pretrained=False, progress=True, number_blocks=[1, 4, 3], **kwargs)
network/backbone/hrnetv2.py:308
↓ 1 callersMethodlogits
(self, features)
network/backbone/xception.py:211
↓ 1 callersFunctionmain
()
predict.py:61
↓ 1 callersFunctionmain
()
main.py:211
↓ 1 callersFunctionmakedir_exist_ok
Python2 support for os.makedirs(.., exist_ok=True)
datasets/utils.py:34
↓ 1 callersMethodreset
(self, id)
metrics/stream_metrics.py:93
↓ 1 callersMethodvis_image
vis image in visdom
utils/visualizer.py:36
↓ 1 callersFunctionvoc_cmap
(N=256, normalized=False)
datasets/voc.py:52
Method__call__
(self, tensor)
utils/utils.py:21
Method__call__
Args: img (PIL Image): Image to be flipped. Returns: PIL Image: Randomly flipped image.
utils/ext_transforms.py:23
Method__call__
(self, img, lbl)
utils/ext_transforms.py:53
Method__call__
Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.
utils/ext_transforms.py:81
Method__call__
Args: img (PIL Image): Image to be scaled. lbl (PIL Image): Label to be scaled. Returns: PIL Imag
utils/ext_transforms.py:99
Method__call__
Args: img (PIL Image): Image to be scaled. lbl (PIL Image): Label to be scaled. Returns: PIL Imag
utils/ext_transforms.py:129
Method__call__
img (PIL Image): Image to be rotated. lbl (PIL Image): Label to be rotated. Returns: PIL Image: Rotated i
utils/ext_transforms.py:190
Method__call__
Args: img (PIL Image): Image to be flipped. lbl (PIL Image): Label to be flipped. Returns: PIL Im
utils/ext_transforms.py:245
Method__call__
(self, img, lbl)
utils/ext_transforms.py:265
Method__call__
Note that labels will not be normalized to [0, 1]. Args: pic (PIL Image or numpy.ndarray): Image to be converted to tenso
utils/ext_transforms.py:281
Method__call__
Args: tensor (Tensor): Tensor image of size (C, H, W) to be normalized. tensor (Tensor): Tensor of label. A dummy inp
utils/ext_transforms.py:312
Method__call__
Args: img (PIL Image): Image to be cropped. lbl (PIL Image): Label to be cropped. Returns: PIL Im
utils/ext_transforms.py:367
Method__call__
Args: img (PIL Image): Image to be scaled. Returns: PIL Image: Rescaled image.
utils/ext_transforms.py:416
Method__call__
Args: img (PIL Image): Input image. Returns: PIL Image: Color jittered image.
utils/ext_transforms.py:502
Method__call__
(self, img)
utils/ext_transforms.py:531
Method__call__
(self, img)
utils/ext_transforms.py:552
Method__getitem__
Args: index (int): Index Returns: tuple: (image, target) where target is the image segmentation.
datasets/voc.py:137
Method__getitem__
Args: index (int): Index Returns: tuple: (image, target) where target is a tuple of all target types if targe
datasets/cityscapes.py:114
Method__init__
(self, alpha=1, gamma=0, size_average=True, ignore_index=255)
utils/loss.py:6
Method__init__
(self, optimizer, max_iters, power=0.9, last_epoch=-1, min_lr=1e-6)
utils/scheduler.py:4
Method__init__
(self, mean, std)
utils/utils.py:15
Method__init__
(self, port='13579', env='main', id=None)
utils/visualizer.py:7
Method__init__
(self, p=0.5)
utils/ext_transforms.py:20
Method__init__
(self, transforms)
utils/ext_transforms.py:50
Method__init__
(self, size)
utils/ext_transforms.py:75
Method__init__
(self, scale_range, interpolation=Image.BILINEAR)
utils/ext_transforms.py:95
Method__init__
(self, scale, interpolation=Image.BILINEAR)
utils/ext_transforms.py:125
Method__init__
(self, degrees, resample=False, expand=False, center=None)
utils/ext_transforms.py:166
Method__init__
(self, p=0.5)
utils/ext_transforms.py:242
Method__init__
(self, diviser=32)
utils/ext_transforms.py:262
Method__init__
(self, normalize=True, target_type='uint8')
utils/ext_transforms.py:278
Method__init__
(self, mean, std)
utils/ext_transforms.py:308
Method__init__
(self, size, padding=0, pad_if_needed=False)
utils/ext_transforms.py:341
Method__init__
(self, size, interpolation=Image.BILINEAR)
utils/ext_transforms.py:411
Method__init__
(self, brightness=0, contrast=0, saturation=0, hue=0)
utils/ext_transforms.py:445
Method__init__
(self, lambd)
utils/ext_transforms.py:527
Method__init__
(self, transforms)
utils/ext_transforms.py:549
Method__init__
Overridden by subclasses
metrics/stream_metrics.py:5
Method__init__
(self, n_classes)
metrics/stream_metrics.py:29
Method__init__
(self)
metrics/stream_metrics.py:87
Method__init__
(self, backbone, classifier)
network/utils.py:8
Method__init__
(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36])
network/_deeplab.py:29
Method__init__
(self, in_channels, num_classes, aspp_dilate=[12, 24, 36])
network/_deeplab.py:62
next →1–100 of 192, ranked by callers