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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

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List of commits:

Subject	Hash	Author	Date (UTC)
best score checkpoint , timer	bb26cec915aa04d68a0dd00911c273542f9b34b5	Thai Thien	2020-04-23 11:16:24
m4 t2	a02b9610e868f4ba5e64496dc0c861a269f4cb9f	Thai Thien	2020-04-17 16:01:39
fix url	358f164d558dab393f65c0829d8d9c37b1437ff3	Thai Thien	2020-04-16 14:32:49
increase epoch	03be68a9e02df1ffa245394ea3096990e8f9d44b	Thai Thien	2020-04-16 14:30:15
add load model	044a398d62add2e854b79b0b3c48c961a4a20bb0	Thai Thien	2020-04-16 14:27:43
M4	c960a8e3ddbfb7fc57f3f843fa4184c063cf8cdb	Thai Thien	2020-04-16 14:22:37
typo again	3dbe3ce4634b8d4ca30b012851c5b9690b1d88d7	Thai Thien	2020-04-13 15:49:23
typo	9b9a84ed5bfffc6e8979fe8b9aa2d6411bfd70c2	Thai Thien	2020-04-13 15:47:51
typo	69cdd6f3037ef0357783ad4c3f8cdf8de2258c3b	Thai Thien	2020-04-13 15:38:14
small tall on split branch	36c80eee740df7449c112f4dd4925e0ffbc7ac5a	Thai Thien	2020-04-13 15:23:33
fix load model	68c03563a5aa3acda165eae104ff4d2df83201b2	Thai Thien	2020-04-09 17:30:14
get lr and weight_decay	b43666da2cb8bb5710f30d0f3bfd2c3b1e7b6473	Thai Thien	2020-04-09 17:26:13
1201 epoch (because cur epoch continue when load)	ee303fe3945fcc6cc3c26601039de77a58fa3d60	Thai Thien	2020-04-09 17:23:18
shb load model	062126f959c021577dbf08224aeb442ca308587c	Thai Thien	2020-04-09 17:20:27
4	2f49bfa380e997c177af1de34c8d6882ed7099e9	Thai Thien	2020-04-09 17:06:28
11	331644c623b1bf5a34c4db432e1526f6bc34a398	Thai Thien	2020-04-09 17:04:49
batchsize 24	f6aeba845ee1915cb0aeb8fce298e56a6ba40b3a	Thai Thien	2020-04-09 16:46:25
t10	91d9e83c80a6a533535fd91278b9175c839c9715	Thai Thien	2020-04-09 16:43:38
t9	927b97f2f285000ce7b407496c52da2c61539cb8	Thai Thien	2020-04-09 16:37:39
minor bugfix	cbab75b39a2d3495c1d07a6f5c127ccc6e7cfbf5	Thai Thien	2020-04-09 16:15:24

Commit bb26cec915aa04d68a0dd00911c273542f9b34b5 - best score checkpoint , timer
Author: Thai Thien
Author date (UTC): 2020-04-23 11:16
Committer name: Thai Thien
Committer date (UTC): 2020-04-23 11:16
Parent(s): a02b9610e868f4ba5e64496dc0c861a269f4cb9f
Signing key:
Tree: 7aa4fca70178fd2903aaa64dfdbd4e6e75ed7b8d

File	Lines added	Lines deleted
experiment_meow_main.py	40	2

File experiment_meow_main.py changed (mode: 100644) (index 84b1083..4441b69)
...	...	from args_util import meow_parse
4	4	from data_flow import get_dataloader, create_image_list	from data_flow import get_dataloader, create_image_list
5	5	from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator	from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
6	6	from ignite.metrics import Loss	from ignite.metrics import Loss
7		from ignite.handlers import Checkpoint, DiskSaver
	7		from ignite.handlers import Checkpoint, DiskSaver, Timer
8	8	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError
9	9	from visualize_util import get_readable_time	from visualize_util import get_readable_time
10	10

...	...	if __name__ == "__main__":
102	102
103	103	print(args)	print(args)
104	104
	105
	106		# timer
	107		train_timer = Timer() # time to train whole epoch
	108		batch_timer = Timer(average=True) # every batch
	109		evaluate_timer = Timer()
	110
	111		batch_timer.attach(trainer,
	112		start =Events.EPOCH_STARTED,
	113		resume =Events.ITERATION_STARTED,
	114		pause =Events.ITERATION_COMPLETED,
	115		step =Events.ITERATION_COMPLETED)
	116
	117		train_timer.attach(trainer,
	118		start =Events.EPOCH_STARTED,
	119		resume =Events.EPOCH_STARTED,
	120		pause =Events.EPOCH_COMPLETED,
	121		step =Events.EPOCH_COMPLETED)
	122
105	123	if len(args.load_model) > 0:	if len(args.load_model) > 0:
106	124	load_model_path = args.load_model	load_model_path = args.load_model
107	125	print("load mode " + load_model_path)	print("load mode " + load_model_path)

...	...	if __name__ == "__main__":
116	134	print("do not load, keep training")	print("do not load, keep training")
117	135
118	136
119		@trainer.on(Events.ITERATION_COMPLETED(every=50))
	137		@trainer.on(Events.ITERATION_COMPLETED(every=100))
120	138	def log_training_loss(trainer):	def log_training_loss(trainer):
121	139	timestamp = get_readable_time()	timestamp = get_readable_time()
122	140	print(timestamp + " Epoch[{}] Loss: {:.2f}".format(trainer.state.epoch, trainer.state.output))	print(timestamp + " Epoch[{}] Loss: {:.2f}".format(trainer.state.epoch, trainer.state.output))

...	...	if __name__ == "__main__":
135	153	experiment.log_metric("train_loss", metrics['loss'])	experiment.log_metric("train_loss", metrics['loss'])
136	154	experiment.log_metric("lr", get_lr(optimizer))	experiment.log_metric("lr", get_lr(optimizer))
137	155
	156		experiment.log_metric("batch_timer", batch_timer.value())
	157		experiment.log_metric("train_timer", train_timer.value())
	158
138	159	@trainer.on(Events.EPOCH_COMPLETED)	@trainer.on(Events.EPOCH_COMPLETED)
139	160	def log_validation_results(trainer):	def log_validation_results(trainer):
	161		evaluate_timer.resume()
140	162	evaluator.run(test_loader)	evaluator.run(test_loader)
	163		evaluate_timer.pause()
	164		evaluate_timer.step()
	165
141	166	metrics = evaluator.state.metrics	metrics = evaluator.state.metrics
142	167	timestamp = get_readable_time()	timestamp = get_readable_time()
143	168	print(timestamp + " Validation set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"	print(timestamp + " Validation set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"

...	...	if __name__ == "__main__":
146	171	experiment.log_metric("valid_mse", metrics['mse'])	experiment.log_metric("valid_mse", metrics['mse'])
147	172	experiment.log_metric("valid_loss", metrics['loss'])	experiment.log_metric("valid_loss", metrics['loss'])
148	173
	174		# timer
	175		experiment.log_metric("evaluate_timer", evaluate_timer.value())
	176
	177		def checkpoint_valid_mae_score_function(engine):
	178		score = engine.state.metrics['valid_mae']
	179		return score
	180
149	181
150	182	# docs on save and load	# docs on save and load
151	183	to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer}	to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer}

...	...	if __name__ == "__main__":
153	185	filename_prefix=args.task_id,	filename_prefix=args.task_id,
154	186	n_saved=5)	n_saved=5)
155	187
	188		save_handler_best = Checkpoint(to_save, DiskSaver('saved_model_best/' + args.task_id, create_dir=True, atomic=True),
	189		filename_prefix=args.task_id, score_name="valid_mae", score_function=checkpoint_valid_mae_score_function,
	190		n_saved=5)
	191
156	192	trainer.add_event_handler(Events.EPOCH_COMPLETED(every=5), save_handler)	trainer.add_event_handler(Events.EPOCH_COMPLETED(every=5), save_handler)
	193		trainer.add_event_handler(Events.EPOCH_COMPLETED(every=1), save_handler_best)
	194
157	195
158	196	trainer.run(train_loader, max_epochs=args.epochs)	trainer.run(train_loader, max_epochs=args.epochs)

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