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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)
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
fix padding with p	86c2fa07822d956a34b3b37e14da485a4249f01b	Thai Thien	2019-10-06 02:52:58
pacnn perspective loss	fb673e38a5f24ae9004fe2b7b93c88991e0c2304	Thai Thien	2019-10-06 01:38:28
data_flow shanghaitech_pacnn_with_perspective seem working	91d350a06f358e03223966297d124daee94123d0	Thai Thien	2019-10-06 01:31:11

Commit ae1fdb49ddb9ea77659529dceb7fb87c2790c8dc - add readable timestamp viz
Author: Thai Thien
Author date (UTC): 2020-02-24 03:49
Committer name: Thai Thien
Committer date (UTC): 2020-02-24 03:49
Parent(s): d2a050aa1a77462a24c20a6fb3c122e8c6d46128
Signing key:
Tree: 43525f810a9dcdf948c971730c193e62484caac5

File	Lines added	Lines deleted
train_context_aware_network.py	8	4
train_script/CAN/train_can_server_100epoch_shA.sh	6	0
train_script/CAN/train_can_short.sh	0	0
visualize_util.py	8	0

File train_context_aware_network.py changed (mode: 100644) (index a7404f8..3d5c656)
...	...	from ignite.metrics import Loss, MeanAbsoluteError, MeanSquaredError
5	5	from ignite.engine import Engine	from ignite.engine import Engine
6	6	from ignite.handlers import Checkpoint, DiskSaver	from ignite.handlers import Checkpoint, DiskSaver
7	7	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError
	8		from visualize_util import get_readable_time
8	9
9	10	import torch	import torch
10	11	from torch import nn	from torch import nn

...	...	if __name__ == "__main__":
55	56
56	57	@trainer.on(Events.ITERATION_COMPLETED(every=50))	@trainer.on(Events.ITERATION_COMPLETED(every=50))
57	58	def log_training_loss(trainer):	def log_training_loss(trainer):
58		print("Epoch[{}] Loss: {:.2f}".format(trainer.state.epoch, trainer.state.output))
	59		timestamp = get_readable_time()
	60		print(timestamp + " Epoch[{}] Loss: {:.2f}".format(trainer.state.epoch, trainer.state.output))
59	61
60	62
61	63	@trainer.on(Events.EPOCH_COMPLETED)	@trainer.on(Events.EPOCH_COMPLETED)
62	64	def log_training_results(trainer):	def log_training_results(trainer):
63	65	evaluator.run(train_loader)	evaluator.run(train_loader)
64	66	metrics = evaluator.state.metrics	metrics = evaluator.state.metrics
65		print("Training set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
	67		timestamp = get_readable_time()
	68		print(timestamp + " Training set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
66	69	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))
67	70
68	71

...	...	if __name__ == "__main__":
70	73	def log_validation_results(trainer):	def log_validation_results(trainer):
71	74	evaluator.run(val_loader)	evaluator.run(val_loader)
72	75	metrics = evaluator.state.metrics	metrics = evaluator.state.metrics
73		print("Validation set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
	76		timestamp = get_readable_time()
	77		print(timestamp + " Validation set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
74	78	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))
75	79
76	80	# docs on save and load	# docs on save and load
77	81	to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer}	to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer}
78		save_handler = Checkpoint(to_save, DiskSaver('saved_model/context_aware_network', create_dir=True), filename_prefix=args.task_id)
	82		save_handler = Checkpoint(to_save, DiskSaver('saved_model/' + args.task_id, create_dir=True), filename_prefix=args.task_id)
79	83	trainer.add_event_handler(Events.EPOCH_COMPLETED(every=1), save_handler)	trainer.add_event_handler(Events.EPOCH_COMPLETED(every=1), save_handler)
80	84
81	85	trainer.run(train_loader, max_epochs=args.epochs)	trainer.run(train_loader, max_epochs=args.epochs)

File train_script/CAN/train_can_server_100epoch_shA.sh added (mode: 100644) (index 0000000..c80599b)
	1	CUDA_VISIBLE_DEVICES=4 nohup python train_context_aware_network.py \
	2	--task_id can_default_shtA_100 \
	3	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
	4	--output saved_model/context_aware_network \
	5	--datasetname shanghaitech_keepfull \
	6	--epochs 100 > logs/can_default_shtA_100_nohup.log &

File train_script/CAN/train_can_short.sh renamed from train_script/train_can_short.sh (similarity 100%)

File visualize_util.py changed (mode: 100644) (index 0190315..1b0f845)
...	...	from matplotlib import pyplot as plt
4	4	from matplotlib import cm as CM	from matplotlib import cm as CM
5	5	import os	import os
6	6	import numpy as np	import numpy as np
	7		import time
7	8
8	9	from PIL import Image	from PIL import Image
9	10

...	...	def save_img(imgnp, name):
31	32	# plt.show()	# plt.show()
32	33	# im = Image.fromarray(imgnp[0].permute(1, 2, 0).numpy())	# im = Image.fromarray(imgnp[0].permute(1, 2, 0).numpy())
33	34	# im.save(name)	# im.save(name)
	35
	36		def get_readable_time():
	37		"""
	38		make human readable time with format year-month-day hour-minute
	39		:return: a string of human readable time (ex: '2020-02-24 10:31' )
	40		"""
	41		return time.strftime('%Y-%m-%d %H:%M')

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