hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)
backupfrom https://github.com/ttpro1995/crowd_counting_framework
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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)
s d2c5d384ed61127c4fd3351692381b80a30c74aa Thai Thien 2020-08-04 17:02:33
WIP fce5736b0f6104fa3549884fbaa1f6508086af06 Thai Thien 2020-08-03 17:14:11
debug sha keepful e14c3307499356c7a7ed0d5f33818bd798276d94 Thai Thien 2020-08-03 15:44:02
adamw2_bigtail13i_t3_sha f769948ddd511865f62cddc80957663508e25976 Thai Thien 2020-08-02 04:25:08
g 56048b30fb4a19ae52c641b07754756f43ddcefb Thai Thien 2020-08-02 04:00:46
a d4e0e0d63613536a18f7ae0f39e4798f12abe7e8 Thai Thien 2020-08-02 03:59:14
adamw1_bigtail13i_t4_sha bc07d19cfcc88c4271d1c6f1984c1c96ff5a9413 Thai Thien 2020-08-01 16:19:17
adamw1_bigtail13i_t3_sha 17c169d6ef648e8c4f2c5383fb9226cfce48d626 Thai Thien 2020-08-01 16:11:33
adamw1_bigtail13i_t2_sha 8bc23f1401eacdc2e28f30a825503f0dd96bacc9 Thai Thien 2020-08-01 15:59:29
adamw1_bigtail13i_t2_shb aadc7244daabdad64a548342865a22beda073d94 Thai Thien 2020-08-01 15:47:08
gpu4 95c356f61c49d9f1927da7f86135a9bb60d21a59 Thai Thien 2020-08-01 14:46:30
adamw1_bigtail13i_t1_sha 34434bd013a4b05134c5ce42aa8f9956d424467d Thai Thien 2020-08-01 14:44:56
a 140443bb072f8086b6cef253bcadfb0d925ba606 Thai Thien 2020-08-01 12:09:09
adamw 57f61e52a024e91f57347c85a8c14813df16d15d Thai Thien 2020-08-01 12:05:36
adam1_bigtail13i_t1_shb 713dd1d5a0f13a2c881b00687fabb1174383da5b Thai Thien 2020-08-01 10:22:17
g1_BigTail14i_t8_sha eb361a5e64d38062fa6e5aa61ae21adc5a536aef Thai Thien 2020-07-31 15:45:50
g1_BigTail14i_t7_sha 32ff746fa0c272af36eadcd713cda94ef8f1b8e2 Thai Thien 2020-07-31 14:57:15
g1_BigTail14i_t6_sha 0656f2ac088bd2790b560931839bfa1d4bed98c4 Thai Thien 2020-07-30 19:32:13
sgd -6 g1_BigTail14i_t6_sha.sh 4c2877d478c40382109800e5a4b873d7a11b345c Thai Thien 2020-07-30 19:29:58
t5 070bf50a1a9bba8b8406c5c58b5806e39e99b56e Thai Thien 2020-07-30 19:26:48
Commit d2c5d384ed61127c4fd3351692381b80a30c74aa - s
Author: Thai Thien
Author date (UTC): 2020-08-04 17:02
Committer name: Thai Thien
Committer date (UTC): 2020-08-04 17:02
Parent(s): fce5736b0f6104fa3549884fbaa1f6508086af06
Signing key:
Tree: 6f457805c2738d6234dfbc0720c7653b2f166302
File Lines added Lines deleted
debug/verify_model_shb_best.py 6 3
File debug/verify_model_shb_best.py changed (mode: 100644) (index 120e444..a615b4e)
... ... from hard_code_variable import HardCodeVariable
4 4 from data_util import ShanghaiTechDataPath from data_util import ShanghaiTechDataPath
5 5 from visualize_util import save_img, save_density_map from visualize_util import save_img, save_density_map
6 6 import os import os
7 import numpy as np
7 8 from data_flow import get_train_val_list, get_dataloader, create_training_image_list from data_flow import get_train_val_list, get_dataloader, create_training_image_list
9 import cv2
8 10
9 11 def visualize_evaluation_shanghaitech_keepfull(model): def visualize_evaluation_shanghaitech_keepfull(model):
10 12 model = model.cuda() model = model.cuda()
 
... ... def visualize_evaluation_shanghaitech_keepfull(model):
29 31 save_density_map(label.numpy()[0][0], save_path) save_density_map(label.numpy()[0][0], save_path)
30 32 pred = model(img.cuda()) pred = model(img.cuda())
31 33 predicted_density_map = pred.detach().cpu().clone().numpy() predicted_density_map = pred.detach().cpu().clone().numpy()
32 save_density_map(predicted_density_map[0][0], save_pred_path)
34 predicted_density_map_enlarge = cv2.resize(np.squeeze(predicted_density_map[0][0]), (int(predicted_density_map.shape[3] * 8), int(predicted_density_map.shape[2] * 8)), interpolation=cv2.INTER_CUBIC) / 64
35 save_density_map(predicted_density_map_enlarge, save_pred_path)
33 36 print("pred " + save_pred_path + " value " + str(predicted_density_map.sum())) print("pred " + save_pred_path + " value " + str(predicted_density_map.sum()))
34
35
37 print("cont compare " + str(predicted_density_map.sum()) + " " + str(predicted_density_map_enlarge.sum()))
38 print("shape compare " + str(predicted_density_map.shape) + " " + str(predicted_density_map_enlarge.shape))
36 39 """ """
37 40 Document on save load model Document on save load model
38 41 https://pytorch.org/tutorials/beginner/saving_loading_models.html https://pytorch.org/tutorials/beginner/saving_loading_models.html
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