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hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)

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List of commits:

Subject	Hash	Author	Date (UTC)
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
test if ShanghaiTech datapath is correct	97ee84944a4393ec3732879b24f614826f8e7798	Thai Thien	2020-02-01 03:57:31
refactor and test ShanghaiTech datapath	9542ebc00f257edc38690180b7a4353794be4019	Thai Thien	2020-02-01 03:53:49
fix the unzip flow	b53c5989935335377eb6a88c942713d3eccc5df7	Thai Thien	2020-02-01 03:53:13
data_script run seem ok	67420c08fc1c10a66404d3698994865726a106cd	Thai Thien	2020-02-01 03:33:18
add perspective	642d6fff8c9f31e510fda85a7fb631fb855d8a6d	Thai Thien	2019-10-06 16:54:44

Commit 66dda0858561897cd5f81e10077459adb39d86dd - get ready for short training run with 30 epochs
Author: Thai Thien
Author date (UTC): 2020-02-27 15:22
Committer name: Thai Thien
Committer date (UTC): 2020-02-27 15:22
Parent(s): ffd38664a43d861c20cdc225746b9ce2a00260c7
Signing key:
Tree: 6ed176e6056b0c4196fd98ba03ac5489bc444081

File	Lines added	Lines deleted
models/__init__.py	2	1
train_attn_can_adcrowdnet.py	17	4
train_script/attn_can_adcrowdnet/train_server_31_epoch_shA.sh	6	0

File models/__init__.py changed (mode: 100644) (index 556c781..af62baf)
1	1	from .csrnet import CSRNet	from .csrnet import CSRNet
2	2	from .pacnn import PACNN, PACNNWithPerspectiveMap	from .pacnn import PACNN, PACNNWithPerspectiveMap
3	3	from .context_aware_network import CANNet	from .context_aware_network import CANNet
4		from .deform_conv_v2 import DeformConv2d
	4		from .deform_conv_v2 import DeformConv2d
	5		from .attn_can_adcrowdnet import AttnCanAdcrowdNet

File train_attn_can_adcrowdnet.py copied from file train_context_aware_network.py (similarity 85%) (mode: 100644) (index 3d5c656..e489e9c)
...	...	from visualize_util import get_readable_time
9	9
10	10	import torch	import torch
11	11	from torch import nn	from torch import nn
12		from models import CANNet
	12		from models import AttnCanAdcrowdNet
13	13	import os	import os
14	14
15	15

...	...	if __name__ == "__main__":
32	32
33	33
34	34	# model	# model
35		model = CANNet()
	35		model = AttnCanAdcrowdNet()
36	36	model = model.to(device)	model = model.to(device)
37	37
38	38	# loss function	# loss function

...	...	if __name__ == "__main__":
77	77	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}"
78	78	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))
79	79
	80		def score_function(engine):
	81		"""
	82		saver score function
	83		:param engine:
	84		:return:
	85		"""
	86		engine.state.metrics['mae']
	87
80	88	# docs on save and load	# docs on save and load
81	89	to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer}	to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer}
82		save_handler = Checkpoint(to_save, DiskSaver('saved_model/' + args.task_id, create_dir=True), filename_prefix=args.task_id)
83		trainer.add_event_handler(Events.EPOCH_COMPLETED(every=1), save_handler)
	90		save_handler = Checkpoint(to_save, DiskSaver('saved_model/' + args.task_id, create_dir=True, atomic=True),
	91		filename_prefix=args.task_id,
	92		n_saved=5,
	93		score_function=score_function,
	94		score_name="mae")
	95
	96		trainer.add_event_handler(Events.EPOCH_COMPLETED(every=2), save_handler)
84	97
85	98	trainer.run(train_loader, max_epochs=args.epochs)	trainer.run(train_loader, max_epochs=args.epochs)

File train_script/attn_can_adcrowdnet/train_server_31_epoch_shA.sh added (mode: 100644) (index 0000000..49459f1)
	1	CUDA_VISIBLE_DEVICES=4 nohup python train_context_aware_network.py \
	2	--task_id attn_can_adcrowdnet_default_shtA_31 \
	3	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
	4	--output saved_model/attn_can_adcrowdnet_default_shtA_31 \
	5	--datasetname shanghaitech_keepfull \
	6	--epochs 31 > logs/attn_can_adcrowdnet_default_shtA_31_nohup.log &

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