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Functions273 in github.com/baowenbo/DAIN

⨍ Functions 273◇ Types & classes 65
↓ 138 callersMethodsize
(self)
Stack.py:17
↓ 92 callersFunctionconv
(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1)
PWCNet/PWCNet.py:27
↓ 92 callersFunctionconv
(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1)
PWCNet/models/PWCNet.py:24
↓ 18 callersMethodbackward
(self, gradoutput)
my_package/SeparableConv/SeparableConvLayer.py:57
↓ 18 callersFunctiondeconv
(in_planes, out_planes, kernel_size=4, stride=2, padding=1)
PWCNet/PWCNet.py:36
↓ 18 callersFunctiondeconv
(in_planes, out_planes, kernel_size=4, stride=2, padding=1)
PWCNet/models/PWCNet.py:33
↓ 17 callersMethodupdate
(self, val, n=1)
AverageMeter.py:14
↓ 12 callersFunctionpredict_flow
(in_planes)
PWCNet/PWCNet.py:33
↓ 12 callersFunctionpredict_flow
(in_planes)
PWCNet/models/PWCNet.py:30
↓ 11 callersFunctioncount_network_parameters
(model)
train.py:94
↓ 5 callersMethodconv_relu_maxpool
(input_filter, output_filter, kernel_size, padding,kernel_size_pooling)
networks/DAIN_slowmotion.py:368
↓ 5 callersMethodconv_relu_maxpool
(input_filter, output_filter, kernel_size, padding,kernel_size_pooling)
networks/DAIN.py:353
↓ 5 callersMethodconv_relu_unpool
(input_filter, output_filter, kernel_size, padding,unpooling_factor)
networks/DAIN_slowmotion.py:384
↓ 5 callersMethodconv_relu_unpool
(input_filter, output_filter, kernel_size, padding,unpooling_factor)
networks/DAIN.py:369
↓ 5 callersMethodsave
(self)
MegaDepth/util/html.py:45
↓ 4 callersMethod__init__
(self, lambda_e = 10.0/255.0, lambda_v = 1.0, Nw = 3)
my_package/FilterInterpolation/FilterInterpolationLayer.py:83
↓ 4 callersMethodload_data
()
MegaDepth/data/base_data_loader.py:10
↓ 4 callersMethodpop
(self)
Stack.py:5
↓ 4 callersMethodwarp
warp an image/tensor (im2) back to im1, according to the optical flow x: [B, C, H, W] (im2) flo: [B, 2, H, W] flow
PWCNet/PWCNet.py:159
↓ 4 callersMethodwarp
warp an image/tensor (im2) back to im1, according to the optical flow x: [B, C, H, W] (im2) flo: [B, 2, H, W] flow
PWCNet/PWCNet.py:417
↓ 4 callersMethodwarp
warp an image/tensor (im2) back to im1, according to the optical flow x: [B, C, H, W] (im2) flo: [B, 2, H, W] flow
PWCNet/models/PWCNet.py:140
↓ 4 callersMethodwarp
warp an image/tensor (im2) back to im1, according to the optical flow x: [B, C, H, W] (im2) flo: [B, 2, H, W] flow
PWCNet/models/PWCNet.py:371
↓ 3 callersMethod__init__
(self, optimizer, step_size, gamma=0.1, last_epoch=-1)
lr_scheduler.py:98
↓ 3 callersMethod__init__
(self, block, num_blocks,dense = True,dilation=True)
S2D_models/S2DF.py:99
↓ 3 callersMethodadd_header
(self, str)
MegaDepth/util/html.py:25
↓ 3 callersMethodadd_images
(self, ims, txts, links, width=400)
MegaDepth/util/html.py:33
↓ 3 callersFunctionchunk
(tag, data)
MegaDepth/util/png.py:16
↓ 3 callersFunctioncreate_model
(opt,pretrained=None)
MegaDepth/models/models.py:2
↓ 3 callersMethodforward_prepare
(self, input)
MegaDepth/pytorch_DIW_scratch.py:12
↓ 3 callersMethodforward_singlePath
(self, modulelist, input, name)
networks/DAIN_slowmotion.py:219
↓ 3 callersMethodforward_singlePath
(self, modulelist, input, name)
networks/DAIN.py:212
↓ 3 callersMethodparse
(self)
MegaDepth/options/base_options.py:41
↓ 2 callersFunctionCreateDataLoader
(_root, _list_dir, _input_height, _input_width, is_flip = True, shuffle = True)
MegaDepth/data/data_loader.py:2
↓ 2 callersFunctionCreateDataLoader_TEST
(_root, _list_dir, _input_height, _input_width)
MegaDepth/data/data_loader.py:8
↓ 2 callersMethodFlowProject
(inputs, depth = None)
networks/DAIN_slowmotion.py:301
↓ 2 callersMethodFlowProject
(inputs, depth = None)
networks/DAIN.py:294
↓ 2 callersFunctionconv3x3
3x3 convolution with padding
Resblock/BasicBlock.py:7
↓ 2 callersFunctionconv3x3
3x3 convolution with padding
S2D_models/S2DF.py:20
↓ 2 callersMethodconv_relu
(input_filter, output_filter, kernel_size, padding)
networks/DAIN_slowmotion.py:357
↓ 2 callersMethodconv_relu
(input_filter, output_filter, kernel_size, padding)
networks/DAIN.py:342
↓ 2 callersMethodconv_relu_conv
(input_filter, output_filter, kernel_size, padding)
networks/DAIN_slowmotion.py:339
↓ 2 callersMethodconv_relu_conv
(input_filter, output_filter, kernel_size, padding)
networks/DAIN.py:324
↓ 2 callersMethodevaluate_SDR
(self, input_, targets)
MegaDepth/models/HG_model.py:123
↓ 2 callersMethodevaluate_sc_inv
(self, input_, targets)
MegaDepth/models/HG_model.py:159
↓ 2 callersMethodforward
(self)
MegaDepth/models/base_model.py:18
↓ 2 callersMethodforward_flownets
(self, model, input, time_offsets = None)
networks/DAIN_slowmotion.py:204
↓ 2 callersMethodforward_flownets
(self, model, input, time_offsets = None)
networks/DAIN.py:197
↓ 2 callersFunctiongra_adap_tv_loss
(flow, image, epsilon)
loss_function.py:32
↓ 2 callersMethodis_empty
(self)
Stack.py:19
↓ 2 callersFunctionmake_dataset
(list_dir)
MegaDepth/data/image_folder.py:20
↓ 2 callersFunctionmake_dataset
(root, list_file)
datasets/Vimeo_90K_interp.py:8
↓ 2 callersFunctionmkdir
(path)
MegaDepth/util/util.py:69
↓ 2 callersFunctionpart_loss
(diffs, offsets, occlusions, images, epsilon, use_negPSNR=False)
loss_function.py:61
↓ 2 callersMethodpush
(self,val)
Stack.py:10
↓ 2 callersMethodreset
(self)
AverageMeter.py:8
↓ 2 callersMethodstep
(self, epoch=None)
lr_scheduler.py:26
↓ 2 callersMethodswitch_to_eval
(self)
MegaDepth/models/HG_model.py:170
↓ 1 callersMethodFilterInterpolate
(ref0, ref2, offset, filter,filter_size2, time_offset)
networks/DAIN_slowmotion.py:324
↓ 1 callersMethodFilterInterpolate
(ref0, ref2, offset, filter,filter_size2)
networks/DAIN.py:317
↓ 1 callersMethodFilterInterpolate_ctx
(ctx0,ctx2,offset,filter, timeoffset)
networks/DAIN_slowmotion.py:311
↓ 1 callersMethodFilterInterpolate_ctx
(ctx0,ctx2,offset,filter)
networks/DAIN.py:304
↓ 1 callersMethod__init__
(self)
my_package/FlowProjection/FlowProjectionLayer.py:88
↓ 1 callersMethod__init__
(self)
my_package/FilterInterpolation/FilterInterpolationModule.py:9
↓ 1 callersMethod__init__
(self, inplanes, planes, dilation = 1, stride=1, downsample=None)
Resblock/BasicBlock.py:14
↓ 1 callersMethod__init__
input: md --- maximum displacement (for correlation. default: 4), after warpping
PWCNet/PWCNet.py:46
↓ 1 callersMethod__init__
(self, pad_size=0, kernel_size=0, max_displacement=0, stride1=1, stride2=2, corr_multiply=1)
PWCNet/correlation_package_pytorch1_0/correlation.py:48
↓ 1 callersMethod__init__
input: md --- maximum displacement (for correlation. default: 4), after warpping
PWCNet/models/PWCNet.py:43
↓ 1 callersMethod__next__
(self)
balancedsampler.py:17
↓ 1 callersMethod__next__
(self)
balancedsampler.py:46
↓ 1 callersMethod_init_is_better
(self, mode, threshold, threshold_mode)
lr_scheduler.py:291
↓ 1 callersMethod_initialize_weights
(self)
networks/DAIN_slowmotion.py:56
↓ 1 callersMethod_initialize_weights
(self)
networks/DAIN.py:57
↓ 1 callersMethod_reduce_lr
(self, epoch)
lr_scheduler.py:277
↓ 1 callersMethod_reset
Resets num_bad_epochs counter and cooldown counter.
lr_scheduler.py:250
↓ 1 callersMethodadd_table
(self, border=1)
MegaDepth/util/html.py:29
↓ 1 callersMethodbatch_classify
(self, z_A_arr, z_B_arr, ground_truth )
MegaDepth/models/HG_model.py:48
↓ 1 callersFunctioncharbonier_loss
(x,epsilon)
loss_function.py:16
↓ 1 callersMethodcomputeSDR
(self, prediction_d, targets)
MegaDepth/models/HG_model.py:86
↓ 1 callersMethodevaluate_RMSE
(self, input_images, prediction_d, targets)
MegaDepth/models/HG_model.py:144
↓ 1 callersMethodforward
(self, input1, input2)
PWCNet/correlation_package_pytorch1_0/correlation.py:57
↓ 1 callersMethodget_MonoNet5
Generally, the MonoNet is aimed to provide a basic module for generating either offset, or filter, or occlusion. :param channel_in:
networks/DAIN_slowmotion.py:250
↓ 1 callersMethodget_MonoNet5
Generally, the MonoNet is aimed to provide a basic module for generating either offset, or filter, or occlusion. :param channel_in:
networks/DAIN.py:243
↓ 1 callersMethodget_image_dir
(self)
MegaDepth/util/html.py:22
↓ 1 callersMethodget_lr
(self)
lr_scheduler.py:23
↓ 1 callersMethodinitialize
(self)
MegaDepth/options/base_options.py:10
↓ 1 callersMethodinitialize
(self, opt)
MegaDepth/models/base_model.py:8
↓ 1 callersMethodload_MD
(self, img_path, depth_path)
MegaDepth/data/image_folder.py:55
↓ 1 callersMethodload_SfM_ORD
(self, img_path, targets_path)
MegaDepth/data/image_folder.py:139
↓ 1 callersFunctionmotion_sym_loss
(offset, epsilon, occlusion = None)
loss_function.py:49
↓ 1 callersFunctionnegPSNR_loss
(x,epsilon)
loss_function.py:19
↓ 1 callersFunctionraw_data
()
MegaDepth/util/png.py:9
↓ 1 callersMethodrmse_Loss
(self, log_prediction_d, mask, log_gt)
MegaDepth/models/HG_model.py:131
↓ 1 callersMethodsave
(self, label)
MegaDepth/models/base_model.py:37
↓ 1 callersMethodstep
(self, metrics, epoch=None)
lr_scheduler.py:256
↓ 1 callersFunctiontest
(model)
MegaDepth/rmse_error_main.py:33
↓ 1 callersFunctiontest_DepthFlowProjectionModule
(input1,input2)
my_package/test_module.py:576
↓ 1 callersFunctiontest_SDR
(model)
MegaDepth/SDR_compute.py:41
↓ 1 callersFunctiontest_WeightedFlowProjectionModule
(input1 , input2, input3)
my_package/test_module.py:671
FunctionHourGlass
Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
MegaDepth/MegaDepth_model.py:11
FunctionMultipleBasicBlock_4
(input_feature,intermediate_feature = 64)
Resblock/BasicBlock.py:88
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