hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)
backupfrom https://github.com/ttpro1995/crowd_counting_framework
Clone URLs: https://rocketgit.com/user/hahattpro/crowd_counting_framework ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework git://git.rocketgit.com/user/hahattpro/crowd_counting_framework
debug/a-1 develop feature/add_can_adcrowdnet feature/add_context_aware_network feature/attn_can_adcrowd_simple feature/cache_dataloader feature/compact_cnn feature/compare_with_original_count feature/dataloader_not_divide_8 feature/debug_viz feature/documentation feature/improve_density_gen feature/improve_loss_fn feature/loader_full_and_augment feature/refactor-dataflow feature/refactor_ccnn_basecode feature/save_best feature/simple_cometml feature/train_context_aware_network_on_shtA feature/train_dev_test_split hotfix/fix_visualize_data_loader master meow_exp server-training with_apex
List of commits:
Subject Hash Author Date (UTC)
fix target1.unsqueeze(0) bb5c46f121207fd9838ee085314df40513e2ed32 Thai Thien 2020-02-27 15:56:07
because GPU too small, we crop shanghaitech A 0f97517dd8de5d3d75cf49af9aa9d20e4595ea3e Thai Thien 2020-02-27 15:52:21
meow 3b9576511e597b4894c7c95ca06dc5219ef9c524 tthien 2020-02-27 15:44:39
fix wrong py name in script, dataset use only 1 worker 3bd765c9aa79e47049a94547e57deb68c947001c Thai Thien 2020-02-27 15:41:44
minor fix 5505b9a8b28df105e11a99d3462323ae9d507102 Thai Thien 2020-02-27 15:28:37
get ready for short training run with 30 epochs 66dda0858561897cd5f81e10077459adb39d86dd Thai Thien 2020-02-27 15:22:01
implement attn_can_adcrowdnet ffd38664a43d861c20cdc225746b9ce2a00260c7 Thai Thien 2020-02-27 15:10:27
WIP: add can-adcrowdnet 5620b83449b31d00a367c8de77e431e19a5ccfb3 Thai Thien 2020-02-25 11:31:38
add readable timestamp viz ae1fdb49ddb9ea77659529dceb7fb87c2790c8dc Thai Thien 2020-02-24 03:49:57
change save name prefix c53a86f30fb8fd4e8f3a409eb67827d56a43ae5c Thai Thien 2020-02-02 10:48:15
training flow that work fb242273e8f696916f9d1ff4bb76b4e5869799ef Thai Thien 2020-02-02 10:42:01
fix the dataloader for shanghaitech 5f2aee9f316e6555e6a70c6ad037a4e6b491867b Thai Thien 2020-02-02 09:19:50
context aware visualize seem ok 1bdb6ffe77ca4e40ef8f299b2506df2266243db4 Thai Thien 2020-02-02 05:07:10
visualize eval context aware network seem ok f3fe45c23dfeab3730624737efabb0b14d23c25b Thai Thien 2020-02-02 04:50:34
visualize_shanghaitech_pacnn_with_perspective run without error 12366a2de2bd60ff4bd36e6132d44e37dedf7462 Thai Thien 2020-02-02 04:21:16
eval context aware network on ShanghaiTechB can run e8c454d2b6d287c830c1286c9a37884b3cfc615f Thai Thien 2020-02-02 04:09:14
import ShanghaiTechDataPath in data_util e81eb56315d44375ff5c0e747d61456601492f8f Thai Thien 2020-02-02 04:04:36
add model_context_aware_network.py 2a36025c001d85afc064c090f4d22987b328977b Thai Thien 2020-02-02 03:46:38
PACNN (TODO: test this) 44d5ae7ec57c760fb4f105dd3e3492148a0cc075 Thai Thien 2020-02-02 03:40:26
add data path 80134de767d0137a663f343e4606bafc57a1bc1f Thai Thien 2020-02-02 03:38:21
Commit bb5c46f121207fd9838ee085314df40513e2ed32 - fix target1.unsqueeze(0)
Author: Thai Thien
Author date (UTC): 2020-02-27 15:56
Committer name: Thai Thien
Committer date (UTC): 2020-02-27 15:56
Parent(s): 0f97517dd8de5d3d75cf49af9aa9d20e4595ea3e
Signing key:
Tree: c5e84e5ca36089663abd3771a95a576557143efa
File Lines added Lines deleted
data_flow.py 2 1
File data_flow.py changed (mode: 100644) (index 9bbb7c1..77d19e0)
... ... def load_data_shanghaitech(img_path, train=True):
89 89
90 90 target1 = cv2.resize(target, (int(target.shape[1] / 8), int(target.shape[0] / 8)), target1 = cv2.resize(target, (int(target.shape[1] / 8), int(target.shape[0] / 8)),
91 91 interpolation=cv2.INTER_CUBIC) * 64 interpolation=cv2.INTER_CUBIC) * 64
92 target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output
92 # target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output
93 target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output
93 94 return img, target1 return img, target1
94 95
95 96
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