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Functions238 in github.com/BYchao100/Deep-Lossy-Plus-Residual-Coding

⨍ Functions 238◇ Types & classes 68
↓ 34 callersFunctionconv1x1
1x1 convolution.
DLPR_ll/custom_layers.py:8
↓ 34 callersFunctionconv1x1
1x1 convolution.
DLPR_nll/custom_layers.py:8
↓ 12 callersMethod__init__
(self, in_ch, out_ch, *args, **kwargs)
DLPR_nll/nll_model.py:68
↓ 11 callersMethod__init__
(self, in_ch, out_ch, *args, **kwargs)
DLPR_nll/nll_model_eval.py:69
↓ 10 callersMethod__init__
(self, in_ch, out_ch, *args, **kwargs)
DLPR_ll/ll_model.py:68
↓ 9 callersMethod__init__
(self, in_ch, out_ch, *args, **kwargs)
DLPR_ll/ll_model_eval.py:68
↓ 9 callersMethodload_state_dict
(self, state_dict)
DLPR_nll/nll_model.py:326
↓ 8 callersMethodrun
(self, input, tau)
DLPR_nll/custom_layers.py:133
↓ 8 callersFunctionsubpel_conv1x1
3x3 sub-pixel convolution for up-sampling.
DLPR_ll/custom_layers.py:13
↓ 8 callersFunctionsubpel_conv1x1
3x3 sub-pixel convolution for up-sampling.
DLPR_nll/custom_layers.py:13
↓ 7 callersMethodload_state_dict
(self, state_dict)
DLPR_ll/ll_model.py:258
↓ 5 callersMethod_logits_cumulative
(self, inputs: Tensor, stop_gradient: bool)
DLPR_ll/compression_model.py:211
↓ 5 callersMethod_logits_cumulative
(self, inputs: Tensor, stop_gradient: bool)
DLPR_nll/compression_model.py:211
↓ 5 callersMethoddecompress
(self, strings, shape)
DLPR_ll/ll_model_eval.py:224
↓ 5 callersMethoddecompress
(self, strings, shape)
DLPR_nll/nll_model_eval.py:255
↓ 4 callersFunctiondownsample_conv1x1
3x3 sub-pixel convolution for down-sampling.
DLPR_ll/custom_layers.py:20
↓ 4 callersFunctiondownsample_conv1x1
3x3 sub-pixel convolution for down-sampling.
DLPR_nll/custom_layers.py:20
↓ 3 callersMethod__init__
(self, cond_num, *args, **kwargs)
DLPR_ll/custom_layers.py:116
↓ 3 callersMethod__init__
(self, cond_num, *args, **kwargs)
DLPR_nll/custom_layers.py:116
↓ 3 callersMethod_get_medians
(self)
DLPR_ll/compression_model.py:160
↓ 3 callersMethod_get_medians
(self)
DLPR_nll/compression_model.py:160
↓ 3 callersMethodaux_loss
Return the aggregated loss over the auxiliary entropy bottleneck module(s).
DLPR_ll/compression_model.py:52
↓ 3 callersMethodaux_loss
Return the aggregated loss over the auxiliary entropy bottleneck module(s).
DLPR_nll/compression_model.py:52
↓ 3 callersMethodrun
(self, input, tau)
DLPR_nll/nll_model_eval.py:198
↓ 3 callersMethodupdate
(self, scale_table=None, force=False)
DLPR_ll/ll_model.py:202
↓ 3 callersMethodupdate
(self, scale_table=None, force=False)
DLPR_nll/nll_model.py:224
↓ 2 callersMethod_build_indexes
(size)
DLPR_ll/compression_model.py:292
↓ 2 callersMethod_build_indexes
(size)
DLPR_nll/compression_model.py:292
↓ 2 callersMethod_extend_ndims
(tensor, n)
DLPR_ll/compression_model.py:305
↓ 2 callersMethod_extend_ndims
(tensor, n)
DLPR_nll/compression_model.py:305
↓ 2 callersFunctionbuild_transforms
(transform_type)
DLPR_ll/utils/data/transform.py:21
↓ 2 callersFunctionbuild_transforms
(transform_type)
DLPR_nll/utils/data/transform.py:21
↓ 2 callersMethodcompress
(self, input)
DLPR_ll/ll_model_eval.py:209
↓ 2 callersMethodcompress
(self, input)
DLPR_nll/nll_model_eval.py:240
↓ 2 callersMethodforward
(self, input, cond)
DLPR_ll/custom_layers.py:124
↓ 2 callersMethodforward
(self, input, cond)
DLPR_nll/custom_layers.py:124
↓ 2 callersFunctionmaskedconv7x7_parallel
(in_ch: int, out_ch: int, mask_type="5P")
DLPR_ll/custom_layers.py:27
↓ 2 callersFunctionmaskedconv7x7_parallel
(in_ch: int, out_ch: int, mask_type="5P")
DLPR_nll/custom_layers.py:27
↓ 2 callersFunctionpatch2img
(patch, img_sz)
DLPR_ll/decode.py:57
↓ 2 callersFunctionpatch2img
(patch, img_sz)
DLPR_ll/test.py:71
↓ 2 callersFunctionpatch2img
(patch, img_sz)
DLPR_nll/decode.py:47
↓ 2 callersFunctionpatch2img
(patch, img_sz)
DLPR_nll/test.py:67
↓ 2 callersFunctionwindow_partition
Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)
DLPR_ll/win_attention.py:6
↓ 2 callersFunctionwindow_partition
Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)
DLPR_nll/win_attention.py:6
↓ 1 callersMethod__init__
(self, dim=192, window_size=(8, 8), num_heads=8, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)
DLPR_ll/win_attention.py:50
↓ 1 callersMethod__init__
(self, entropy_bottleneck_channels, init_weights=True)
DLPR_ll/compression_model.py:45
↓ 1 callersMethod__init__
(self, dim=192, window_size=(8, 8), num_heads=8, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)
DLPR_nll/win_attention.py:50
↓ 1 callersMethod__init__
(self, entropy_bottleneck_channels, init_weights=True)
DLPR_nll/compression_model.py:45
↓ 1 callersMethod_initialize_weights
(self)
DLPR_ll/compression_model.py:61
↓ 1 callersMethod_initialize_weights
(self)
DLPR_nll/compression_model.py:61
↓ 1 callersMethod_likelihood
(self, inputs: Tensor)
DLPR_ll/compression_model.py:233
↓ 1 callersMethod_likelihood
(self, inputs: Tensor)
DLPR_nll/compression_model.py:233
↓ 1 callersFunctioncoding_order_table7x7
(patch_sz=64, mask_type="3P")
DLPR_ll/decode.py:18
↓ 1 callersFunctioncoding_order_table7x7
(patch_sz=64, mask_type="3P")
DLPR_ll/test.py:18
↓ 1 callersFunctioncoding_order_table7x7
(patch_sz=64, mask_type="3P")
DLPR_ll/encode.py:16
↓ 1 callersFunctioncoding_order_table7x7
(patch_sz=64, mask_type="3P")
DLPR_nll/decode.py:17
↓ 1 callersFunctioncoding_order_table7x7
(patch_sz=64, mask_type="3P")
DLPR_nll/test.py:18
↓ 1 callersFunctioncoding_order_table7x7
(patch_sz=64, mask_type="3P")
DLPR_nll/encode.py:17
↓ 1 callersFunctioncompress
(model, input, COT, tau=0, patch_sz=64, mix_num=5)
DLPR_ll/test.py:87
↓ 1 callersFunctioncompress
(model, input, COT, tau=0, patch_sz=64, mix_num=5)
DLPR_ll/encode.py:76
↓ 1 callersFunctioncompress
(model, input, COT, tau=0, patch_sz=64, mix_num=5)
DLPR_nll/test.py:83
↓ 1 callersFunctioncompress
(model, input, COT, tau=0, patch_sz=64, mix_num=5)
DLPR_nll/encode.py:64
↓ 1 callersFunctionconfigure_optimizers
(model, lr, aux_lr)
DLPR_ll/train.py:17
↓ 1 callersFunctionconfigure_optimizers
(model, lr, aux_lr)
DLPR_nll/train.py:18
↓ 1 callersFunctiondecompress
(model, code_lossy, code_res, img_shape, res_range, COT, tau=0, mix_num=5)
DLPR_ll/decode.py:72
↓ 1 callersFunctiondecompress
(model, code_lossy, code_res, img_shape, res_range, COT, tau=0, mix_num=5)
DLPR_ll/test.py:202
↓ 1 callersFunctiondecompress
(model, code_lossy, code_res, img_shape, res_range, COT, tau=0, mix_num=5)
DLPR_nll/decode.py:62
↓ 1 callersFunctiondecompress
(model, code_lossy, code_res, img_shape, res_range, COT, tau=0, mix_num=5)
DLPR_nll/test.py:206
↓ 1 callersFunctioneval_epoch
(model, criterion, eval_dataloader, epoch, tb_writer=None)
DLPR_ll/train.py:88
↓ 1 callersFunctioneval_epoch
(model, criterion, eval_dataloader, epoch, tb_writer=None)
DLPR_nll/train.py:109
↓ 1 callersFunctionget_scale_table
(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS)
DLPR_ll/ll_model_eval.py:19
↓ 1 callersFunctionget_scale_table
(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS)
DLPR_ll/ll_model.py:19
↓ 1 callersFunctionget_scale_table
(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS)
DLPR_nll/nll_model_eval.py:20
↓ 1 callersFunctionget_scale_table
(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS)
DLPR_nll/nll_model.py:20
↓ 1 callersFunctionimg2patch
(img, patch_sz)
DLPR_ll/test.py:53
↓ 1 callersFunctionimg2patch
(img, patch_sz)
DLPR_ll/encode.py:50
↓ 1 callersFunctionimg2patch
(img, patch_sz)
DLPR_nll/test.py:49
↓ 1 callersFunctionimg2patch
(img, patch_sz)
DLPR_nll/encode.py:47
↓ 1 callersFunctionload_image
(image_path)
DLPR_ll/encode.py:68
↓ 1 callersMethodlog_prob_from_logits
(self, x)
DLPR_ll/logisticmixturemodel.py:59
↓ 1 callersMethodlog_prob_from_logits
(self, x)
DLPR_nll/logisticmixturemodel.py:59
↓ 1 callersMethodlog_sum_exp
(self, x)
DLPR_ll/logisticmixturemodel.py:65
↓ 1 callersMethodlog_sum_exp
(self, x)
DLPR_nll/logisticmixturemodel.py:65
↓ 1 callersMethodloss
(self)
DLPR_ll/compression_model.py:206
↓ 1 callersMethodloss
(self)
DLPR_nll/compression_model.py:206
↓ 1 callersMethodparameters_w_cond
(self)
DLPR_nll/nll_model.py:344
↓ 1 callersMethodparameters_wo_cond
(self)
DLPR_nll/nll_model.py:335
↓ 1 callersFunctiontrain
(train_dataloader, eval_dataloader, epochs, ckp_dir, log_dir, resume=False)
DLPR_ll/train.py:154
↓ 1 callersFunctiontrain
(train_dataloader, eval_dataloader, epochs, ckp_dir, log_dir, resume=False)
DLPR_nll/train.py:179
↓ 1 callersFunctiontrain_one_epoch
(model, criterion, train_dataloader, optimizer, aux_optimizer, train_step, tb_writer=None, clip_max_norm=None)
DLPR_ll/train.py:49
↓ 1 callersFunctiontrain_one_epoch
(model, criterion, train_dataloader, optimizer, aux_optimizer, optimizer_nll, train_step, tb_writer=None, clip
DLPR_nll/train.py:63
↓ 1 callersFunctionwindow_reverse
Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image
DLPR_ll/win_attention.py:21
↓ 1 callersFunctionwindow_reverse
Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image
DLPR_nll/win_attention.py:21
Method__call__
(self, pic)
DLPR_ll/utils/data/transform.py:9
Method__call__
(self, pic)
DLPR_nll/utils/data/transform.py:9
Method__getitem__
(self, index)
DLPR_ll/utils/data/datasets.py:21
Method__getitem__
(self, index)
DLPR_nll/utils/data/datasets.py:21
Method__init__
(self, dim=192, num_heads=8, window_size=8, shift_size=0, qkv_bias=True, qk_scale=None, drop=
DLPR_ll/win_attention.py:136
Method__init__
(self, mean, log_sigma, mixture_weights, autoregression_coefficients=None, *args, **kwargs)
DLPR_ll/logisticmixturemodel.py:7
Method__init__
( self, channels: int, *args: Any, tail_mass: float = 1e-9, init_scale
DLPR_ll/compression_model.py:117
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