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Functions66 in github.com/daa233/generative-inpainting-pytorch

⨍ Functions 66◇ Types & classes 10
↓ 42 callersFunctiongen_conv
(input_dim, output_dim, kernel_size=3, stride=1, padding=0, rate=1, activation='elu')
model/networks.py:457
↓ 7 callersMethod__init__
(self, config, use_cuda=True, device_ids=None)
model/networks.py:400
↓ 6 callersFunctiondefault_loader
(path)
utils/tools.py:17
↓ 4 callersFunctiondis_conv
(input_dim, output_dim, kernel_size=5, stride=2, padding=0, rate=1, activation='lrelu')
model/networks.py:464
↓ 4 callersMethoddis_forward
(self, netD, ground_truth, x_inpaint)
trainer.py:81
↓ 4 callersFunctionis_image_file
(filename)
utils/tools.py:462
↓ 4 callersFunctionrandom_bbox
Generate a random tlhw with configuration. Args: config: Config should have configuration including img Returns: tuple: (top
utils/tools.py:89
↓ 4 callersFunctionsame_padding
(images, ksizes, strides, rates)
utils/tools.py:39
↓ 3 callersFunctionextract_image_patches
Extract patches from images and put them in the C output dimension. :param padding: :param images: [batch, channels, in_rows, in_cols]. A
utils/tools.py:58
↓ 3 callersFunctionget_config
(config)
utils/tools.py:474
↓ 3 callersFunctionget_model_list
(dirname, key, iteration=0)
utils/tools.py:480
↓ 3 callersFunctionlocal_patch
(x, bbox_list)
utils/tools.py:148
↓ 3 callersFunctionnormalize
(x)
utils/tools.py:36
↓ 2 callersFunctionbbox2mask
(bboxes, height, width, max_delta_h, max_delta_w)
utils/tools.py:127
↓ 2 callersMethodcalc_gradient_penalty
(self, netD, real_data, fake_data)
trainer.py:91
↓ 2 callersFunctionget_logger
Get the root logger :param checkpoint_path: only specify this when the first time call it :return: the root logger
utils/logger.py:18
↓ 2 callersFunctionmask_image
(x, bboxes, config)
utils/tools.py:157
↓ 1 callersMethod_find_samples_in_subfolders
Finds the class folders in a dataset. Args: dir (string): Root directory path. Returns: tuple: (class
data/dataset.py:43
↓ 1 callersFunctioncompute_color
(u, v)
utils/tools.py:338
↓ 1 callersFunctiondate_uid
Generate a unique id based on date. Returns: str: Return uid string, e.g. '20171122171307111552'.
utils/logger.py:7
↓ 1 callersFunctionflow_to_image
Transfer flow map to image. Part of code forked from flownet.
utils/tools.py:236
↓ 1 callersFunctionload_weights
(path, device)
test_tf_model.py:46
↓ 1 callersFunctionmain
()
train.py:24
↓ 1 callersFunctionmain
()
test_single.py:26
↓ 1 callersFunctionmain
()
test_tf_model.py:24
↓ 1 callersFunctionmake_color_wheel
()
utils/tools.py:400
↓ 1 callersFunctionpil_loader
(path)
utils/tools.py:10
↓ 1 callersFunctionpt_compute_color
(u, v)
utils/tools.py:367
↓ 1 callersFunctionpt_make_color_wheel
()
utils/tools.py:431
↓ 1 callersFunctionreduce_mean
(x, axis=None, keepdim=False)
utils/tools.py:212
↓ 1 callersFunctionreduce_sum
(x, axis=None, keepdim=False)
utils/tools.py:228
↓ 1 callersMethodresume
(self, checkpoint_dir, iteration=0, test=False)
trainer.py:135
↓ 1 callersMethodsave_model
(self, checkpoint_dir, iteration)
trainer.py:124
↓ 1 callersFunctionspatial_discounting_mask
Generate spatial discounting mask constant. Spatial discounting mask is first introduced in publication: Generative Image Inpainting with
utils/tools.py:178
↓ 1 callersFunctiontest_bbox2mask
()
utils/tools.py:138
↓ 1 callersFunctiontest_contextual_attention
(args)
model/networks.py:356
↓ 1 callersFunctiontest_random_bbox
()
utils/tools.py:119
↓ 1 callersFunctionupcast
(x)
test_tf_model.py:54
Method__getitem__
(self, index)
data/dataset.py:22
Method__init__
(self, config)
trainer.py:15
Method__init__
(self, config, use_cuda, device_ids)
model/networks.py:15
Method__init__
(self, input_dim, cnum, use_cuda=True, device_ids=None)
model/networks.py:32
Method__init__
(self, input_dim, cnum, use_cuda=True, device_ids=None)
model/networks.py:95
Method__init__
(self, ksize=3, stride=1, rate=1, fuse_k=3, softmax_scale=10, fuse=False, use_cuda=False, dev
model/networks.py:185
Method__init__
(self, config, use_cuda=True, device_ids=None)
model/networks.py:419
Method__init__
(self, input_dim, cnum, use_cuda=True, device_ids=None)
model/networks.py:438
Method__init__
(self, input_dim, output_dim, kernel_size, stride, padding=0, conv_padding=0, dilation=1, wei
model/networks.py:472
Method__init__
(self, data_path, image_shape, with_subfolder=False, random_crop=True, return_name=False)
data/dataset.py:11
Method__len__
(self)
data/dataset.py:74
Functiondeprocess
(img)
utils/tools.py:468
Functionfloat_to_uint8
(img)
model/networks.py:362
Methodforward
(self, x, bboxes, masks, ground_truth, compute_loss_g=False)
trainer.py:35
Methodforward
(self, x, mask)
model/networks.py:25
Methodforward
(self, x, mask)
model/networks.py:58
Methodforward
(self, xin, x_stage1, mask)
model/networks.py:137
Methodforward
Contextual attention layer implementation. Contextual attention is first introduced in publication: Generative Image Inpainting w
model/networks.py:197
Methodforward
(self, x)
model/networks.py:410
Methodforward
(self, x)
model/networks.py:429
Methodforward
(self, x)
model/networks.py:448
Methodforward
(self, x)
model/networks.py:543
Functionhighlight_flow
Convert flow into middlebury color code image.
utils/tools.py:302
Methodinference
(self, x, masks)
trainer.py:116
Functionpt_flow_to_image
Transfer flow map to image. Part of code forked from flownet.
utils/tools.py:265
Functionpt_highlight_flow
Convert flow into middlebury color code image.
utils/tools.py:320
Functionreduce_std
(x, axis=None, keepdim=False)
utils/tools.py:220
Functiontensor_img_to_npimg
Turn a tensor image with shape CxHxW to a numpy array image with shape HxWxC :param tensor_img: :return: a numpy array image with shape H
utils/tools.py:21