List of commits:
Subject Hash Author Date (UTC)
fixed: not discard under-size in 256 8a0ef0a9bfb0801933dd4403685fe65ebb5dac6a Thai Thien 2020-04-08 16:10:46
data_flow fixed 256, we ignore too small image in Batch for now 34a56f59674dee5267dba356b3ebe469baa91baf Thai Thien 2020-04-08 15:46:34
fix typo e9e5fe46043233c703e0a872afd1510434d4b5b0 Thai Thien 2020-04-07 18:49:20
tryting to reproduce ccnn again b3681bbf85bb50eb5430581d8779415520d78dc2 Thai Thien 2020-04-07 18:25:30
fix bug, new dataflow 28706ac8dd35ff6f7d17c7d01656b047dd95cffe Thai Thien 2020-04-07 17:54:57
ccnn_v2_t2_sha_scheduler 0bd4b83a3e2adb4d054537fde38c56ec3eca0d68 Thai Thien 2020-04-07 02:16:53
lower ccnn sgd lr 9f10c1c664cf9be2b3093ba72815740b31521287 Thai Thien 2020-04-06 17:41:39
5 07b51e18899506183b197366b8a2e6319dec0215 Thai Thien 2020-04-06 17:30:33
3 more test 03cca5576a44e925db8626c905f324d42c205923 Thai Thien 2020-04-06 17:22:40
modify bigtail2 t2 fcd49caa43303c11049cb6c40b5e5c62f1fcdab3 Thai Thien 2020-04-06 17:06:15
try to reproduce 0946ba05287dcbc00d7743347f6f75d7f7601636 Thai Thien 2020-04-06 16:55:50
custom ccnn v2 t4 sha lower lr b2381872bc284fc16af9c7d3129f1bee418f8915 Thai Thien 2020-04-06 02:11:55
ccnn v2 t5 d2c4a1182c5ca93b9e6b2bc4dadc4ddc8c9c521b Thai Thien 2020-04-05 10:41:18
try to reproduce that 86 4a715c2e62e1f92051d5a4abbba983821e6bb422 Thai Thien 2020-04-05 10:30:47
add c5 e6c82e35a4920700840bde0e891ca5d6cf285a7c Thai Thien 2020-04-05 10:05:44
some big tail 3705300d179b004c1049ec402461de6c9f67bbd1 Thai Thien 2020-04-05 09:49:06
t5 b704da887280b721e0402db32919a1e7b8753269 Thai Thien 2020-04-04 11:18:46
verify if it work 9aa7f6d734d1fb13b9fb02e6d77d813139b6122c Thai Thien 2020-04-04 11:15:25
m3 t123 exp f76cec015c34648ec45f02865562f6d05d507eb2 Thai Thien 2020-04-03 15:27:22
m1 shb 35455b092280af7e7f3140601b909dc58b5c2590 Thai Thien 2020-04-02 10:42:43
Commit 8a0ef0a9bfb0801933dd4403685fe65ebb5dac6a - fixed: not discard under-size in 256
Author: Thai Thien
Author date (UTC): 2020-04-08 16:10
Committer name: Thai Thien
Committer date (UTC): 2020-04-08 16:10
Parent(s): 34a56f59674dee5267dba356b3ebe469baa91baf
Signer:
Signing key:
Signing status: N
Tree: 16865e5f6101cad14d2e38c82d39cac14690a661
File Lines added Lines deleted
data_flow.py 1 1
playground/ccnnv2_playground.py 6 19
File data_flow.py changed (mode: 100644) (index a9da806..7e02d74)
... ... def load_data_shanghaitech_256(img_path, train=True):
273 273 crop_size = (crop_sq_size, crop_sq_size) crop_size = (crop_sq_size, crop_sq_size)
274 274 dx = int(random.random() * (img.size[0] - crop_sq_size)) dx = int(random.random() * (img.size[0] - crop_sq_size))
275 275 dy = int(random.random() * (img.size[1] - crop_sq_size)) dy = int(random.random() * (img.size[1] - crop_sq_size))
276 if dx < 0 or dy < 0: # we crop more than we can chew, so...
276 if img.size[0] - crop_sq_size < 0 or img.size[1] - crop_sq_size < 0: # we crop more than we can chew, so...
277 277 return None, None return None, None
278 278 img = img.crop((dx, dy, crop_size[0] + dx, crop_size[1] + dy)) img = img.crop((dx, dy, crop_size[0] + dx, crop_size[1] + dy))
279 279 target = target[dy:crop_size[1] + dy, dx:crop_size[0] + dx] target = target[dy:crop_size[1] + dy, dx:crop_size[0] + dx]
File playground/ccnnv2_playground.py copied from file sanity_check_dataloader.py (similarity 57%) (mode: 100644) (index 9f28698..c7b96b4)
1 from models import CompactCNNV2
1 2 from args_util import sanity_check_dataloader_parse from args_util import sanity_check_dataloader_parse
2 3 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
3 4 import torch import torch
4 5 import os import os
5 6
6
7 7 if __name__ == "__main__": if __name__ == "__main__":
8 8 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
9 9 print(device) print(device)
 
... ... if __name__ == "__main__":
22 22
23 23 # create data loader # create data loader
24 24 train_loader, val_loader, test_loader = get_dataloader(train_list, val_list, test_list, dataset_name=dataset_name, batch_size=5) train_loader, val_loader, test_loader = get_dataloader(train_list, val_list, test_list, dataset_name=dataset_name, batch_size=5)
25
25 model = CompactCNNV2()
26 # model = model.cuda()
26 27 print("============== TRAIN LOADER ====================================================") print("============== TRAIN LOADER ====================================================")
27 28 min_1 = 500 min_1 = 500
28 29 min_2 = 500 min_2 = 500
29 30 for img, label in train_loader: for img, label in train_loader:
30 print("img shape:" + str(img.shape) + " == " + "label shape " + str(label.shape))
31 size_1 = img.shape[2]
32 size_2 = img.shape[3]
33 if min_1 > size_1:
34 min_1 = size_1
35 if min_2 > size_2:
36 min_2 = size_2
37 if size_1 < 256 or size_2 < 256:
38 count_below_256+=1
39 # example: img shape:torch.Size([1, 3, 716, 1024]) == label shape torch.Size([1, 1, 89, 128])
40
41 print("============== VAL LOADER ====================================================")
42 for img, label in val_loader:
43 print("img shape:" + str(img.shape) + " == " + "label shape " + str(label.shape))
44 print(min_1)
45 print(min_2)
46 print("count < 256 = ", count_below_256)
31 out = model(img)
32 print(out.shape)
33 exit()
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