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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

List of commits:

Subject	Hash	Author	Date (UTC)
add L1Mean, add experiment tag on cometml	125f03f6f136bde9fa11b00a749b8a0c3de51534	Thai Thien	2020-06-13 04:15:30
msenone	f68f48cc1a2e28a65da368a9a66ebec9f8694467	Thai Thien	2020-06-13 03:54:44
train script	2b142c16cce5403d0d673077d2b1e442af072e42	Thai Thien	2020-06-12 16:42:25
train script	67923fc98cd4f82880e973f7dbf143c09ffcf8e8	Thai Thien	2020-06-12 16:01:59
ccnnv8 adam t1 sha, H3	0cab419a2c9747611347b6ddb75ca8a5cb20eb79	Thai Thien	2020-06-11 16:21:47
ccnv8 adam t1 shb	edd704f19c3d58d959c13b96100498c8809b309c	Thai Thien	2020-06-11 15:51:21
ccnnv8	4e8e6e864c3f2fea80a3b61addbc26eb8f3ae059	Thai Thien	2020-06-11 15:47:04
bigtail7 cuda4	fdde3eb95706e95cdc0e8d81f93f2e08036d3192	Thai Thien	2020-06-11 15:45:38
train script, bigtail6, bigtail7 shb	ea5758dcd7d8428e694cc84d50c81267eabcef23	Thai Thien	2020-06-11 15:36:54
WIP	3d7e1f10700c05bf7672cbf46ae6cafae8f2330f	Thai Thien	2020-06-11 13:07:18
return x	c750d65fe266979e9cdebf9a53b2af343462fbda	Thai Thien	2020-06-11 12:39:13
bigtail5	b750f0037993fdfdda6433b1982f5612d13aa1a6	Thai Thien	2020-06-11 12:32:28
adam t3 t4	933528ca8870affc741e989520172819382db0c0	Thai Thien	2020-06-10 07:59:26
adam t1 and adam t2	4202f1b609dc90197990e014addce68a04d0b8a8	Thai Thien	2020-06-09 15:23:49
t8	2dda6cd71a7aa50e760dfdcdfbdcdedfce36b836	Thai Thien	2020-06-09 15:12:20
t7	2ff8d0931ae51a93915721202aaa17c38c2918cc	Thai Thien	2020-06-09 15:08:32
t5 t6	7eeeb38e47fc4b59750438d66c911a7a93573b91	Thai Thien	2020-06-09 14:53:05
t4	e58849acc2d608f95130b58cafbe642f5f67a269	Thai Thien	2020-06-08 15:54:08
sgd t3	d6a8816d042d837578e095e748167abdb97acdd2	Thai Thien	2020-06-08 15:48:55
meow	776073961973fb7c5fa04a86b3c642627fa8b1ee	Thai Thien	2020-06-08 15:18:04

Commit 125f03f6f136bde9fa11b00a749b8a0c3de51534 - add L1Mean, add experiment tag on cometml
Author: Thai Thien
Author date (UTC): 2020-06-13 04:15
Committer name: Thai Thien
Committer date (UTC): 2020-06-13 04:15
Parent(s): f68f48cc1a2e28a65da368a9a66ebec9f8694467
Signer:
Signing key:
Signing status: N
Tree: a1b29391813aadf388ec7ce34a95d4016da80ce1

File	Lines added	Lines deleted
experiment_main.py	11	0

File experiment_main.py changed (mode: 100644) (index abdc5db..5915299)
...	...	if __name__ == "__main__":
68	68	# model	# model
69	69	model_name = args.model	model_name = args.model
70	70	experiment.log_other("model", model_name)	experiment.log_other("model", model_name)
	71		experiment.add_tag(model_name)
71	72	if model_name == "M1":	if model_name == "M1":
72	73	model = M1()	model = M1()
73	74	elif model_name == "M2":	elif model_name == "M2":

...	...	if __name__ == "__main__":
121	122	elif args.loss_fn == "L1":	elif args.loss_fn == "L1":
122	123	loss_fn = nn.L1Loss(reduction='sum').to(device)	loss_fn = nn.L1Loss(reduction='sum').to(device)
123	124	print("use L1Loss")	print("use L1Loss")
	125		elif args.loss_fn == "L1Mean":
	126		loss_fn = nn.L1Loss(reduction='mean').to(device)
	127		print("use L1Mean")
124	128	elif args.loss_fn == "MSEMean":	elif args.loss_fn == "MSEMean":
125	129	loss_fn = nn.MSELoss(reduction='mean').to(device)	loss_fn = nn.MSELoss(reduction='mean').to(device)
126	130	print("use MSEMean")	print("use MSEMean")
127	131	elif args.loss_fn == "MSENone":	elif args.loss_fn == "MSENone":
	132		"""
	133		Doesnt work
	134		because
	135		RuntimeError: grad can be implicitly created only for scalar outputs
	136		"""
128	137	loss_fn = nn.MSELoss(reduction='none').to(device)	loss_fn = nn.MSELoss(reduction='none').to(device)
129	138	print("use MSE without any reduction")	print("use MSE without any reduction")
	139		experiment.add_tag(args.loss_fn)
130	140
131	141	if args.optim == "adam":	if args.optim == "adam":
132	142	optimizer = torch.optim.Adam(model.parameters(), args.lr,	optimizer = torch.optim.Adam(model.parameters(), args.lr,

...	...	if __name__ == "__main__":
137	147	weight_decay=args.decay,	weight_decay=args.decay,
138	148	momentum=args.momentum)	momentum=args.momentum)
139	149	print("use sgd")	print("use sgd")
	150		experiment.add_tag(args.optim)
140	151
141	152	trainer = create_supervised_trainer(model, optimizer, loss_fn, device=device)	trainer = create_supervised_trainer(model, optimizer, loss_fn, device=device)
142	153	evaluator_train = create_supervised_evaluator(model,	evaluator_train = create_supervised_evaluator(model,

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