Build utilities for flask
check_existance[source]
check_existance()
Check if the data file exists, if not, use torch ember in your pytorch modeling first according to the short tutorial
check_existance()
True
get_ember_list[source]
get_ember_list()
get_ember_list()[:5]
['base_AlexNet_20200215_132629.json', 'base_AlexNet_20200226_002834.json', 'base_AlexNet_20200211_230327.json', 'base_AlexNet_20200211_192928.json', 'base_AlexNet_20200209_183014.json']
unpack_meta[source]
unpack_meta(fname)
get_ember_record[source]
get_ember_record()
df = get_ember_df(get_ember_list())
df.head(5)
| start | user | name | |
|---|---|---|---|
| 0 | 2020-03-07 10:37:37 | salvor | AlexNet_20200307_103737 |
| 1 | 2020-03-07 10:35:52 | salvor | AlexNet_20200307_103552 |
| 2 | 2020-03-05 23:12:37 | salvor | AlexNet_20200305_231237 |
| 3 | 2020-03-05 23:10:04 | salvor | AlexNet_20200305_231004 |
| 4 | 2020-03-03 23:50:54 | salvor | AlexNet_20200303_235054 |
get_ember_record()
[{'start': '2020-03-07 10:37:37',
'user': 'salvor',
'name': 'AlexNet_20200307_103737',
'latest': 0},
{'start': '2020-03-07 10:35:52',
'user': 'salvor',
'name': 'AlexNet_20200307_103552',
'latest': 1},
{'start': '2020-03-05 23:12:37',
'user': 'salvor',
'name': 'AlexNet_20200305_231237',
'latest': 2},
{'start': '2020-03-05 23:10:04',
'user': 'salvor',
'name': 'AlexNet_20200305_231004',
'latest': 3},
{'start': '2020-03-03 23:50:54',
'user': 'salvor',
'name': 'AlexNet_20200303_235054',
'latest': 4},
{'start': '2020-03-03 23:44:40',
'user': 'salvor',
'name': 'AlexNet_20200303_234440',
'latest': 5},
{'start': '2020-03-03 23:41:30',
'user': 'salvor',
'name': 'AlexNet_20200303_234130',
'latest': 6},
{'start': '2020-03-03 23:39:23',
'user': 'salvor',
'name': 'AlexNet_20200303_233923',
'latest': 7},
{'start': '2020-02-26 00:28:34',
'user': 'salvor',
'name': 'AlexNet_20200226_002834',
'latest': 8},
{'start': '2020-02-26 00:25:28',
'user': 'salvor',
'name': 'AlexNet_20200226_002528',
'latest': 9},
{'start': '2020-02-25 08:21:27',
'user': 'salvor',
'name': 'AlexNet_20200225_082127',
'latest': 10},
{'start': '2020-02-24 23:32:07',
'user': 'salvor',
'name': 'AlexNet_20200224_233207',
'latest': 11},
{'start': '2020-02-24 23:28:27',
'user': 'salvor',
'name': 'AlexNet_20200224_232827',
'latest': 12},
{'start': '2020-02-24 22:46:54',
'user': 'salvor',
'name': 'AlexNet_20200224_224654',
'latest': 13},
{'start': '2020-02-24 21:40:30',
'user': 'salvor',
'name': 'AlexNet_20200224_214030',
'latest': 14},
{'start': '2020-02-20 00:32:06',
'user': 'salvor',
'name': 'tinyVGG_20200220_003206',
'latest': 15},
{'start': '2020-02-20 00:30:33',
'user': 'salvor',
'name': 'tinyVGG_20200220_003033',
'latest': 16},
{'start': '2020-02-20 00:28:02',
'user': 'salvor',
'name': 'tinyVGG_20200220_002802',
'latest': 17},
{'start': '2020-02-20 00:16:09',
'user': 'salvor',
'name': 'AlexNet_20200220_001609',
'latest': 18},
{'start': '2020-02-19 23:59:22',
'user': 'salvor',
'name': 'AlexNet_20200219_235922',
'latest': 19},
{'start': '2020-02-19 23:57:57',
'user': 'salvor',
'name': 'AlexNet_20200219_235757',
'latest': 20},
{'start': '2020-02-19 23:56:26',
'user': 'salvor',
'name': 'AlexNet_20200219_235626',
'latest': 21},
{'start': '2020-02-17 22:40:15',
'user': 'salvor',
'name': 'AlexNet_20200217_224015',
'latest': 22},
{'start': '2020-02-16 18:14:43',
'user': 'salvor',
'name': 'AlexNet_20200216_181443',
'latest': 23},
{'start': '2020-02-16 18:13:06',
'user': 'salvor',
'name': 'AlexNet_20200216_181306',
'latest': 24},
{'start': '2020-02-16 17:45:33',
'user': 'salvor',
'name': 'AlexNet_20200216_174533',
'latest': 25},
{'start': '2020-02-16 12:35:27',
'user': 'salvor',
'name': 'tinyVGG_20200216_123527',
'latest': 26},
{'start': '2020-02-16 12:07:42',
'user': 'salvor',
'name': 'tinyVGG_20200216_120742',
'latest': 27},
{'start': '2020-02-15 13:44:48',
'user': 'salvor',
'name': 'AlexNet_20200215_134448',
'latest': 28},
{'start': '2020-02-15 13:26:29',
'user': 'salvor',
'name': 'AlexNet_20200215_132629',
'latest': 29},
{'start': '2020-02-15 13:25:46',
'user': 'salvor',
'name': 'AlexNet_20200215_132546',
'latest': 30},
{'start': '2020-02-11 23:07:43',
'user': 'salvor',
'name': 'AlexNet_20200211_230743',
'latest': 31},
{'start': '2020-02-11 23:07:40',
'user': 'salvor',
'name': 'AlexNet_20200211_230740',
'latest': 32},
{'start': '2020-02-11 23:06:14',
'user': 'salvor',
'name': 'AlexNet_20200211_230614',
'latest': 33},
{'start': '2020-02-11 23:06:09',
'user': 'salvor',
'name': 'AlexNet_20200211_230609',
'latest': 34},
{'start': '2020-02-11 23:04:37',
'user': 'salvor',
'name': 'AlexNet_20200211_230437',
'latest': 35},
{'start': '2020-02-11 23:04:12',
'user': 'salvor',
'name': 'AlexNet_20200211_230412',
'latest': 36},
{'start': '2020-02-11 23:03:35',
'user': 'salvor',
'name': 'AlexNet_20200211_230335',
'latest': 37},
{'start': '2020-02-11 23:03:27',
'user': 'salvor',
'name': 'AlexNet_20200211_230327',
'latest': 38},
{'start': '2020-02-11 23:03:09',
'user': 'salvor',
'name': 'AlexNet_20200211_230309',
'latest': 39},
{'start': '2020-02-11 22:59:58',
'user': 'salvor',
'name': 'AlexNet_20200211_225958',
'latest': 40},
{'start': '2020-02-11 22:59:27',
'user': 'salvor',
'name': 'AlexNet_20200211_225927',
'latest': 41},
{'start': '2020-02-11 22:52:12',
'user': 'salvor',
'name': 'AlexNet_20200211_225212',
'latest': 42},
{'start': '2020-02-11 22:49:03',
'user': 'salvor',
'name': 'AlexNet_20200211_224903',
'latest': 43},
{'start': '2020-02-11 22:48:14',
'user': 'salvor',
'name': 'AlexNet_20200211_224814',
'latest': 44},
{'start': '2020-02-11 22:46:55',
'user': 'salvor',
'name': 'AlexNet_20200211_224655',
'latest': 45},
{'start': '2020-02-11 22:37:41',
'user': 'salvor',
'name': 'AlexNet_20200211_223741',
'latest': 46},
{'start': '2020-02-11 22:33:28',
'user': 'salvor',
'name': 'AlexNet_20200211_223328',
'latest': 47},
{'start': '2020-02-11 22:30:11',
'user': 'salvor',
'name': 'AlexNet_20200211_223011',
'latest': 48},
{'start': '2020-02-11 20:57:08',
'user': 'salvor',
'name': 'AlexNet_20200211_205708',
'latest': 49},
{'start': '2020-02-11 19:39:34',
'user': 'salvor',
'name': 'AlexNet_20200211_193934',
'latest': 50},
{'start': '2020-02-11 19:36:48',
'user': 'salvor',
'name': 'AlexNet_20200211_193648',
'latest': 51},
{'start': '2020-02-11 19:34:37',
'user': 'salvor',
'name': 'AlexNet_20200211_193437',
'latest': 52},
{'start': '2020-02-11 19:34:11',
'user': 'salvor',
'name': 'AlexNet_20200211_193411',
'latest': 53},
{'start': '2020-02-11 19:32:27',
'user': 'salvor',
'name': 'AlexNet_20200211_193227',
'latest': 54},
{'start': '2020-02-11 19:32:05',
'user': 'salvor',
'name': 'AlexNet_20200211_193205',
'latest': 55},
{'start': '2020-02-11 19:29:28',
'user': 'salvor',
'name': 'AlexNet_20200211_192928',
'latest': 56},
{'start': '2020-02-09 21:03:01',
'user': 'salvor',
'name': 'AlexNet_20200209_210301',
'latest': 57},
{'start': '2020-02-09 21:01:24',
'user': 'salvor',
'name': 'AlexNet_20200209_210124',
'latest': 58},
{'start': '2020-02-09 20:47:01',
'user': 'salvor',
'name': 'AlexNet_20200209_204701',
'latest': 59},
{'start': '2020-02-09 20:41:29',
'user': 'salvor',
'name': 'AlexNet_20200209_204129',
'latest': 60},
{'start': '2020-02-09 20:39:50',
'user': 'salvor',
'name': 'AlexNet_20200209_203950',
'latest': 61},
{'start': '2020-02-09 20:38:36',
'user': 'salvor',
'name': 'AlexNet_20200209_203836',
'latest': 62},
{'start': '2020-02-09 20:37:21',
'user': 'salvor',
'name': 'AlexNet_20200209_203721',
'latest': 63},
{'start': '2020-02-09 20:36:36',
'user': 'salvor',
'name': 'AlexNet_20200209_203636',
'latest': 64},
{'start': '2020-02-09 20:23:15',
'user': 'salvor',
'name': 'AlexNet_20200209_202315',
'latest': 65},
{'start': '2020-02-09 20:21:21',
'user': 'salvor',
'name': 'AlexNet_20200209_202121',
'latest': 66},
{'start': '2020-02-09 20:15:23',
'user': 'salvor',
'name': 'AlexNet_20200209_201523',
'latest': 67},
{'start': '2020-02-09 20:03:02',
'user': 'salvor',
'name': 'AlexNet_20200209_200302',
'latest': 68},
{'start': '2020-02-09 20:00:03',
'user': 'salvor',
'name': 'AlexNet_20200209_200003',
'latest': 69},
{'start': '2020-02-09 18:47:46',
'user': 'salvor',
'name': 'AlexNet_20200209_184746',
'latest': 70},
{'start': '2020-02-09 18:47:17',
'user': 'salvor',
'name': 'AlexNet_20200209_184717',
'latest': 71},
{'start': '2020-02-09 18:46:51',
'user': 'salvor',
'name': 'AlexNet_20200209_184651',
'latest': 72},
{'start': '2020-02-09 18:31:54',
'user': 'salvor',
'name': 'AlexNet_20200209_183154',
'latest': 73},
{'start': '2020-02-09 18:30:44',
'user': 'salvor',
'name': 'AlexNet_20200209_183044',
'latest': 74},
{'start': '2020-02-09 18:30:14',
'user': 'salvor',
'name': 'AlexNet_20200209_183014',
'latest': 75},
{'start': '2020-02-09 18:29:37',
'user': 'salvor',
'name': 'AlexNet_20200209_182937',
'latest': 76},
{'start': '2020-02-09 18:29:12',
'user': 'salvor',
'name': 'AlexNet_20200209_182912',
'latest': 77},
{'start': '2020-02-09 18:29:02',
'user': 'salvor',
'name': 'AlexNet_20200209_182902',
'latest': 78},
{'start': '2020-02-09 18:28:21',
'user': 'salvor',
'name': 'AlexNet_20200209_182821',
'latest': 79}]
Ember Reader¶
A reading handler to process Torch Ember data
class emberReader[source]
emberReader(name,verbose=False)
er = emberReader("AlexNet_20200307_103737",verbose = True)
['init-00_phase-valid_epoch-0.log', 'init-00_phase-train_epoch-0.log', 'init-00_phase-train_epoch-1.log', 'init-00_phase-valid_epoch-1.log']
er.structure
{'name': 'model(AlexNet)',
'short': 'model(AlexNet)',
'children': [{'name': 'model(AlexNet).features(Sequential)',
'short': 'features(Sequential)',
'children': [{'name': 'model(AlexNet).features(Sequential).0(Conv2d)',
'short': '0(Conv2d)'},
{'name': 'model(AlexNet).features(Sequential).1(ReLU)',
'short': '1(ReLU)'},
{'name': 'model(AlexNet).features(Sequential).2(MaxPool2d)',
'short': '2(MaxPool2d)'},
{'name': 'model(AlexNet).features(Sequential).3(Conv2d)',
'short': '3(Conv2d)'},
{'name': 'model(AlexNet).features(Sequential).4(ReLU)',
'short': '4(ReLU)'},
{'name': 'model(AlexNet).features(Sequential).5(MaxPool2d)',
'short': '5(MaxPool2d)'},
{'name': 'model(AlexNet).features(Sequential).6(Conv2d)',
'short': '6(Conv2d)'},
{'name': 'model(AlexNet).features(Sequential).7(ReLU)',
'short': '7(ReLU)'},
{'name': 'model(AlexNet).features(Sequential).8(Conv2d)',
'short': '8(Conv2d)'},
{'name': 'model(AlexNet).features(Sequential).9(ReLU)',
'short': '9(ReLU)'},
{'name': 'model(AlexNet).features(Sequential).10(Conv2d)',
'short': '10(Conv2d)'},
{'name': 'model(AlexNet).features(Sequential).11(ReLU)',
'short': '11(ReLU)'},
{'name': 'model(AlexNet).features(Sequential).12(MaxPool2d)',
'short': '12(MaxPool2d)'}]},
{'name': 'model(AlexNet).avgpool(AdaptiveAvgPool2d)',
'short': 'avgpool(AdaptiveAvgPool2d)'},
{'name': 'model(AlexNet).classifier(Sequential)',
'short': 'classifier(Sequential)',
'children': [{'name': 'model(AlexNet).classifier(Sequential).0(Dropout)',
'short': '0(Dropout)'},
{'name': 'model(AlexNet).classifier(Sequential).1(Linear)',
'short': '1(Linear)'},
{'name': 'model(AlexNet).classifier(Sequential).2(ReLU)',
'short': '2(ReLU)'},
{'name': 'model(AlexNet).classifier(Sequential).3(Dropout)',
'short': '3(Dropout)'},
{'name': 'model(AlexNet).classifier(Sequential).4(Linear)',
'short': '4(Linear)'},
{'name': 'model(AlexNet).classifier(Sequential).5(ReLU)',
'short': '5(ReLU)'},
{'name': 'model(AlexNet).classifier(Sequential).6(Linear)',
'short': '6(Linear)'}]}]}
Basical information
er.base
{'start': '2020-03-07 10:37:37', 'user': 'salvor'}
List the log files under log directory on this module task
er.t.log_files
['init-00_phase-valid_epoch-0.log', 'init-00_phase-train_epoch-0.log', 'init-00_phase-train_epoch-1.log', 'init-00_phase-valid_epoch-1.log']
Latest record as dataframe
er.t.latest_df
| shape | mean | std | max | min | cnt_zero | zero_pct | module | ts | ttype | tname | n_batch | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | [2, 3, 224, 224] | -1.001885 | 0.577536 | -0.000016 | -1.999997 | 0 | 0.0 | model(AlexNet) | 2020-03-07 10:37:52 | input | x | 1 |
| 1 | [2, 3, 224, 224] | -1.001885 | 0.577536 | -0.000016 | -1.999997 | 0 | 0.0 | model(AlexNet).features(Sequential) | 2020-03-07 10:37:52 | input | input | 1 |
| 2 | [2, 3, 224, 224] | -1.001885 | 0.577536 | -0.000016 | -1.999997 | 0 | 0.0 | model(AlexNet).features(Sequential).0(Conv2d) | 2020-03-07 10:37:52 | input | input | 1 |
| 3 | [64, 3, 11, 11] | -0.000152 | 0.030288 | 0.052461 | -0.052483 | 0 | 0.0 | model(AlexNet).features(Sequential).0(Conv2d) | 2020-03-07 10:37:52 | weight | weight_0 | 1 |
| 4 | [64] | 0.003515 | 0.027836 | 0.051731 | -0.044360 | 0 | 0.0 | model(AlexNet).features(Sequential).0(Conv2d) | 2020-03-07 10:37:52 | weight | weight_1 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 59 | [1000, 4096] | 0.000008 | 0.009020 | 0.015625 | -0.015625 | 0 | 0.0 | model(AlexNet).classifier(Sequential).6(Linear) | 2020-03-07 10:37:53 | weight | weight_0 | 1 |
| 60 | [1000] | 0.000555 | 0.008901 | 0.015613 | -0.015598 | 0 | 0.0 | model(AlexNet).classifier(Sequential).6(Linear) | 2020-03-07 10:37:53 | weight | weight_1 | 1 |
| 61 | [2, 1000] | 0.000637 | 0.011510 | 0.033282 | -0.033744 | 0 | 0.0 | model(AlexNet).classifier(Sequential).6(Linear) | 2020-03-07 10:37:53 | output | output_0 | 1 |
| 62 | [2, 1000] | 0.000637 | 0.011510 | 0.033282 | -0.033744 | 0 | 0.0 | model(AlexNet).classifier(Sequential) | 2020-03-07 10:37:53 | output | output_0 | 1 |
| 63 | [2, 1000] | 0.000637 | 0.011510 | 0.033282 | -0.033744 | 0 | 0.0 | model(AlexNet) | 2020-03-07 10:37:53 | output | output_0 | 1 |
64 rows × 12 columns
Load a log file as a long list of dictionary
json.loads(er.read_log('init-00_phase-valid_epoch-1.log'))
[{'shape': [2, 3, 224, 224],
'mean': -1.001885175704956,
'std': 0.5775364637374878,
'max': -1.5735626220703125e-05,
'min': -1.9999974966049194,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet)',
'ts': '2020-03-07 10:37:50',
'ttype': 'input',
'tname': 'x',
'n_batch': 0},
{'shape': [2, 3, 224, 224],
'mean': -1.001885175704956,
'std': 0.5775364637374878,
'max': -1.5735626220703125e-05,
'min': -1.9999974966049194,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet).features(Sequential)',
'ts': '2020-03-07 10:37:50',
'ttype': 'input',
'tname': 'input',
'n_batch': 0},
{'shape': [2, 3, 224, 224],
'mean': -1.001885175704956,
'std': 0.5775364637374878,
'max': -1.5735626220703125e-05,
'min': -1.9999974966049194,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet).features(Sequential).0(Conv2d)',
'ts': '2020-03-07 10:37:50',
'ttype': 'input',
'tname': 'input',
'n_batch': 0},
{'shape': [64, 3, 11, 11],
'mean': -0.000151715605170466,
'std': 0.03028801828622818,
'max': 0.05246129631996155,
'min': -0.05248286575078964,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet).features(Sequential).0(Conv2d)',
'ts': '2020-03-07 10:37:50',
'ttype': 'weight',
'tname': 'weight_0',
'n_batch': 0},
{'shape': [64],
'mean': 0.003515456337481737,
'std': 0.027836013585329056,
'max': 0.0517314150929451,
'min': -0.04436026141047478,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet).features(Sequential).0(Conv2d)',
'ts': '2020-03-07 10:37:50',
'ttype': 'weight',
'tname': 'weight_1',
'n_batch': 0},
{'shape': [2, 64, 55, 55],
'mean': 0.058029815554618835,
'std': 0.6799391508102417,
'max': 2.8890106678009033,
'min': -2.7371437549591064,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet).features(Sequential).0(Conv2d)',
'ts': '2020-03-07 10:37:50',
'ttype': 'output',
'tname': 'output_0',
'n_batch': 0},
{'shape': [2, 64, 55, 55],
'mean': 0.058029815554618835,
'std': 0.6799391508102417,
'max': 2.8890106678009033,
'min': -2.7371437549591064,
'cnt_zero': 0,
'zero_pct': 0.0,
'module': 'model(AlexNet).features(Sequential).1(ReLU)',
'ts': '2020-03-07 10:37:50',
'ttype': 'input',
'tname': 'input',
'n_batch': 0},
{'shape': [2, 64, 55, 55],
'mean': 0.2949860095977783,
'std': 0.4212140142917633,
'max': 2.8890106678009033,
'min': 0.0,
'cnt_zero': 179115,
'zero_pct': 0.4625903925619835,
'module': 'model(AlexNet).features(Sequential).1(ReLU)',
'ts': '2020-03-07 10:37:50',
'ttype': 'output',
'tname': 'output_0',
'n_batch': 0},
{'shape': [2, 64, 55, 55],
'mean': 0.2949860095977783,
'std': 0.4212140142917633,
'max': 2.8890106678009033,
'min': 0.0,
'cnt_zero': 179115,
'zero_pct': 0.4625903925619835,
'module': 'model(AlexNet).features(Sequential).2(MaxPool2d)',
'ts': '2020-03-07 10:37:50',
'ttype': 'input',
'tname': 'input',
'n_batch': 0},
{'shape': [2, 64, 27, 27],
'mean': 0.6178204417228699,
'std': 0.5235710144042969,
'max': 2.8890106678009033,
'min': 0.0,
'cnt_zero': 16626,
'zero_pct': 0.17817644032921812,
'module': 'model(AlexNet).features(Sequential).2(MaxPool2d)',
'ts': '2020-03-07 10:37:51',
'ttype': 'output',
'tname': 'output_0',
'n_batch': 0},
{'shape': [2, 64, 27, 27],
'mean': 0.6178204417228699,
'std': 0.5235710144042969,
'max': 2.8890106678009033,
'min': 0.0,
'cnt_zero': 16626,
'zero_pct': 0.17817644032921812,
'module': 'model(AlexNet).features(Sequential).3(Conv2d)',
'ts': '2020-03-07 10:37:51',
'ttype': 'input',
'tname': 'input',
'n_batch': 0},
{'shape': [192, 64, 5, 5],
'mean': 2.920041879406199e-05,
'std': 0.014425805769860744,
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Return log file as dataframe
er.json_df('init-00_phase-valid_epoch-1.log')
| shape | mean | std | max | min | cnt_zero | zero_pct | module | ts | ttype | tname | n_batch | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | [2, 3, 224, 224] | -1.001885 | 0.577536 | -0.000016 | -1.999997 | 0 | 0.0 | model(AlexNet) | 2020-03-07 10:37:50 | input | x | 0 |
| 1 | [2, 3, 224, 224] | -1.001885 | 0.577536 | -0.000016 | -1.999997 | 0 | 0.0 | model(AlexNet).features(Sequential) | 2020-03-07 10:37:50 | input | input | 0 |
| 2 | [2, 3, 224, 224] | -1.001885 | 0.577536 | -0.000016 | -1.999997 | 0 | 0.0 | model(AlexNet).features(Sequential).0(Conv2d) | 2020-03-07 10:37:50 | input | input | 0 |
| 3 | [64, 3, 11, 11] | -0.000152 | 0.030288 | 0.052461 | -0.052483 | 0 | 0.0 | model(AlexNet).features(Sequential).0(Conv2d) | 2020-03-07 10:37:50 | weight | weight_0 | 0 |
| 4 | [64] | 0.003515 | 0.027836 | 0.051731 | -0.044360 | 0 | 0.0 | model(AlexNet).features(Sequential).0(Conv2d) | 2020-03-07 10:37:50 | weight | weight_1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 123 | [1000, 4096] | 0.000008 | 0.009020 | 0.015625 | -0.015625 | 0 | 0.0 | model(AlexNet).classifier(Sequential).6(Linear) | 2020-03-07 10:37:53 | weight | weight_0 | 1 |
| 124 | [1000] | 0.000555 | 0.008901 | 0.015613 | -0.015598 | 0 | 0.0 | model(AlexNet).classifier(Sequential).6(Linear) | 2020-03-07 10:37:53 | weight | weight_1 | 1 |
| 125 | [2, 1000] | 0.000637 | 0.011510 | 0.033282 | -0.033744 | 0 | 0.0 | model(AlexNet).classifier(Sequential).6(Linear) | 2020-03-07 10:37:53 | output | output_0 | 1 |
| 126 | [2, 1000] | 0.000637 | 0.011510 | 0.033282 | -0.033744 | 0 | 0.0 | model(AlexNet).classifier(Sequential) | 2020-03-07 10:37:53 | output | output_0 | 1 |
| 127 | [2, 1000] | 0.000637 | 0.011510 | 0.033282 | -0.033744 | 0 | 0.0 | model(AlexNet) | 2020-03-07 10:37:53 | output | output_0 | 1 |
128 rows × 12 columns
Latest JSON data
er.latest
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io cleaner¶
import torch
clean_kv[source]
clean_kv(k,v)
io_cleaner[source]
io_cleaner(**kwargs)
Cleaning up the tensor input/output to a uniformed format of k,v
- k is the tensor name
- v is a tensor The inherent idea is to break down list,tuple,set, dictionary ,at any level return dictionary
Test for a very bizarre case of input/output
a = torch.rand(2,2)
b = torch.rand(2,2)
c = torch.rand(2,2)
d = torch.rand(2,2)
e = torch.rand(2,2)
result = io_cleaner(a = a,
b= (b,c),
d = {"f":d},
e = {"e1":[a,b,{"e2":e}]})
result.keys(), list(is_tensor(v) for v in result.values())
(dict_keys(['a', 'b.tsr0', 'b.tsr1', 'd.f', 'e.e1.tsr0', 'e.e1.tsr1', 'e.e1.tsr2.e2']), [True, True, True, True, True, True, True])