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resnet50

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utils/debug.py 0 → 100644
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import os
import sys
from shutil import copy
import numpy as np
import matplotlib.pyplot as plt
from numpy.core.fromnumeric import var
def npy_compare(lhs_path, rhs_path):
lhs = np.load(lhs_path)
rhs = np.load(rhs_path)
# if not np.allclose(lhs, rhs):
# print(lhs)
# print(rhs)
return np.allclose(lhs, rhs)
def npy_diff(lhs_path, rhs_path):
lhs = np.load(lhs_path)
rhs = np.load(rhs_path)
diff = np.absolute(lhs - rhs)
return diff.mean(), diff.std(), diff.max(), diff.min()
def walk_compare_npy(lhs, rhs):
assert os.path.isdir(lhs)
assert os.path.isdir(rhs)
same = 0
diff = 0
ignore = 0
for root, dirs, files in os.walk(lhs):
for name in filter(lambda f: f.endswith(".npy"), files):
lhs_path = os.path.join(root, name)
rhs_path = os.path.join(rhs, os.path.relpath(lhs_path, lhs))
if os.path.exists(rhs_path) and os.path.isfile(rhs_path):
if not npy_compare(lhs_path, rhs_path):
mean, std, max_, min_ = npy_diff(lhs_path, rhs_path)
print(lhs_path, f"mean={mean}, std={std}, max={max_}, min={min_}")
diff += 1
else:
same += 1
else:
print("{} ignore".format(lhs_path))
ignore += 1
print("same:", same)
print("diff:", diff)
print("ignore:", ignore)
import zlib
def crc32(filename):
with open(filename, "rb") as f:
data = f.read()
print(filename, zlib.crc32(data))
return zlib.crc32(data)
def var_compare(lhs_path, rhs_path):
lhs = crc32(lhs_path)
rhs = crc32(rhs_path)
if lhs != rhs:
print(lhs)
print(rhs)
return lhs == rhs
def walk_compare_of_variable(lhs, rhs):
assert os.path.isdir(lhs)
assert os.path.isdir(rhs)
same = 0
diff = 0
ignore = 0
for root, dirs, files in os.walk(lhs):
for name in filter(lambda f: f.endswith("out"), files):
lhs_path = os.path.join(root, name)
rhs_path = os.path.join(rhs, os.path.relpath(lhs_path, lhs))
if os.path.exists(rhs_path) and os.path.isfile(rhs_path):
if not var_compare(lhs_path, rhs_path):
print("{} False".format(lhs_path))
diff += 1
else:
same += 1
else:
print("{} ignore".format(lhs_path))
ignore += 1
print("same:", same)
print("diff:", diff)
print("ignore:", ignore)
def get_varible_name(var_org):
# for item in sys._getframe().f_locals.items():
# print(item[0],item[1])
# for item in sys._getframe(1).f_locals.items():
# print(item[0],item[1])
for item in sys._getframe(2).f_locals.items():
if var_org is item[1]:
return item[0]
def dump_to_npy(tensor, root="./output", sub="", name=""):
if sub != "":
root = os.path.join(root, str(sub))
if not os.path.isdir(root):
os.makedirs(root)
var_org_name = get_varible_name(tensor) if name == "" else name
path = os.path.join(root, f"{var_org_name}.npy")
if not isinstance(tensor, np.ndarray):
tensor = tensor.to_local().numpy()
np.save(path, tensor)
def save_param_npy(module, root="./output"):
for name, param in module.named_parameters():
# if name.endswith('bias'):
dump_to_npy(param.numpy(), root=root, sub=0, name=name)
def param_hist(param, name, root="output"):
print(name, param.shape)
# print(param.flatten())
# the histogram of the data
n, bins, patches = plt.hist(param.flatten(), density=False, facecolor="g")
# plt.xlabel('Smarts')
# plt.ylabel('value')
plt.title(f"Histogram of {name}")
# plt.xlim(40, 160)
# plt.ylim(0, 0.03)
plt.grid(True)
plt.savefig(os.path.join(root, f"{name}.png"))
plt.close()
def save_param_hist_pngs(module, root="output"):
for name, param in module.named_parameters():
# if name.endswith('bias'):
param_hist(param.numpy(), name, root=root)
if __name__ == "__main__":
# walk_compare_of_variable('init_ckpt', '/ssd/xiexuan/OneFlow-Benchmark/Classification/cnns/loaded_init_ckpt')
walk_compare_npy(
"output", "/ssd/xiexuan/OneFlow-Benchmark/Classification/cnns/output"
)
utils/imagenet1000_clsidx_to_labels.py 0 → 100644
View file @ b0f41f60
"""
Copyright 2020 The OneFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
clsidx_2_labels = {
0: "tench, Tinca tinca",
1: "goldfish, Carassius auratus",
2: "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
3: "tiger shark, Galeocerdo cuvieri",
4: "hammerhead, hammerhead shark",
5: "electric ray, crampfish, numbfish, torpedo",
6: "stingray",
7: "cock",
8: "hen",
9: "ostrich, Struthio camelus",
10: "brambling, Fringilla montifringilla",
11: "goldfinch, Carduelis carduelis",
12: "house finch, linnet, Carpodacus mexicanus",
13: "junco, snowbird",
14: "indigo bunting, indigo finch, indigo bird, Passerina cyanea",
15: "robin, American robin, Turdus migratorius",
16: "bulbul",
17: "jay",
18: "magpie",
19: "chickadee",
20: "water ouzel, dipper",
21: "kite",
22: "bald eagle, American eagle, Haliaeetus leucocephalus",
23: "vulture",
24: "great grey owl, great gray owl, Strix nebulosa",
25: "European fire salamander, Salamandra salamandra",
26: "common newt, Triturus vulgaris",
27: "eft",
28: "spotted salamander, Ambystoma maculatum",
29: "axolotl, mud puppy, Ambystoma mexicanum",
30: "bullfrog, Rana catesbeiana",
31: "tree frog, tree-frog",
32: "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
33: "loggerhead, loggerhead turtle, Caretta caretta",
34: "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
35: "mud turtle",
36: "terrapin",
37: "box turtle, box tortoise",
38: "banded gecko",
39: "common iguana, iguana, Iguana iguana",
40: "American chameleon, anole, Anolis carolinensis",
41: "whiptail, whiptail lizard",
42: "agama",
43: "frilled lizard, Chlamydosaurus kingi",
44: "alligator lizard",
45: "Gila monster, Heloderma suspectum",
46: "green lizard, Lacerta viridis",
47: "African chameleon, Chamaeleo chamaeleon",
48: "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
49: "African crocodile, Nile crocodile, Crocodylus niloticus",
50: "American alligator, Alligator mississipiensis",
51: "triceratops",
52: "thunder snake, worm snake, Carphophis amoenus",
53: "ringneck snake, ring-necked snake, ring snake",
54: "hognose snake, puff adder, sand viper",
55: "green snake, grass snake",
56: "king snake, kingsnake",
57: "garter snake, grass snake",
58: "water snake",
59: "vine snake",
60: "night snake, Hypsiglena torquata",
61: "boa constrictor, Constrictor constrictor",
62: "rock python, rock snake, Python sebae",
63: "Indian cobra, Naja naja",
64: "green mamba",
65: "sea snake",
66: "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
67: "diamondback, diamondback rattlesnake, Crotalus adamanteus",
68: "sidewinder, horned rattlesnake, Crotalus cerastes",
69: "trilobite",
70: "harvestman, daddy longlegs, Phalangium opilio",
71: "scorpion",
72: "black and gold garden spider, Argiope aurantia",
73: "barn spider, Araneus cavaticus",
74: "garden spider, Aranea diademata",
75: "black widow, Latrodectus mactans",
76: "tarantula",
77: "wolf spider, hunting spider",
78: "tick",
79: "centipede",
80: "black grouse",
81: "ptarmigan",
82: "ruffed grouse, partridge, Bonasa umbellus",
83: "prairie chicken, prairie grouse, prairie fowl",
84: "peacock",
85: "quail",
86: "partridge",
87: "African grey, African gray, Psittacus erithacus",
88: "macaw",
89: "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
90: "lorikeet",
91: "coucal",
92: "bee eater",
93: "hornbill",
94: "hummingbird",
95: "jacamar",
96: "toucan",
97: "drake",
98: "red-breasted merganser, Mergus serrator",
99: "goose",
100: "black swan, Cygnus atratus",
101: "tusker",
102: "echidna, spiny anteater, anteater",
103: "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
104: "wallaby, brush kangaroo",
105: "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
106: "wombat",
107: "jellyfish",
108: "sea anemone, anemone",
109: "brain coral",
110: "flatworm, platyhelminth",
111: "nematode, nematode worm, roundworm",
112: "conch",
113: "snail",
114: "slug",
115: "sea slug, nudibranch",
116: "chiton, coat-of-mail shell, sea cradle, polyplacophore",
117: "chambered nautilus, pearly nautilus, nautilus",
118: "Dungeness crab, Cancer magister",
119: "rock crab, Cancer irroratus",
120: "fiddler crab",
121: "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
122: "American lobster, Northern lobster, Maine lobster, Homarus americanus",
123: "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
124: "crayfish, crawfish, crawdad, crawdaddy",
125: "hermit crab",
126: "isopod",
127: "white stork, Ciconia ciconia",
128: "black stork, Ciconia nigra",
129: "spoonbill",
130: "flamingo",
131: "little blue heron, Egretta caerulea",
132: "American egret, great white heron, Egretta albus",
133: "bittern",
134: "crane",
135: "limpkin, Aramus pictus",
136: "European gallinule, Porphyrio porphyrio",
137: "American coot, marsh hen, mud hen, water hen, Fulica americana",
138: "bustard",
139: "ruddy turnstone, Arenaria interpres",
140: "red-backed sandpiper, dunlin, Erolia alpina",
141: "redshank, Tringa totanus",
142: "dowitcher",
143: "oystercatcher, oyster catcher",
144: "pelican",
145: "king penguin, Aptenodytes patagonica",
146: "albatross, mollymawk",
147: "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
148: "killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
149: "dugong, Dugong dugon",
150: "sea lion",
151: "Chihuahua",
152: "Japanese spaniel",
153: "Maltese dog, Maltese terrier, Maltese",
154: "Pekinese, Pekingese, Peke",
155: "Shih-Tzu",
156: "Blenheim spaniel",
157: "papillon",
158: "toy terrier",
159: "Rhodesian ridgeback",
160: "Afghan hound, Afghan",
161: "basset, basset hound",
162: "beagle",
163: "bloodhound, sleuthhound",
164: "bluetick",
165: "black-and-tan coonhound",
166: "Walker hound, Walker foxhound",
167: "English foxhound",
168: "redbone",
169: "borzoi, Russian wolfhound",
170: "Irish wolfhound",
171: "Italian greyhound",
172: "whippet",
173: "Ibizan hound, Ibizan Podenco",
174: "Norwegian elkhound, elkhound",
175: "otterhound, otter hound",
176: "Saluki, gazelle hound",
177: "Scottish deerhound, deerhound",
178: "Weimaraner",
179: "Staffordshire bullterrier, Staffordshire bull terrier",
180: "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
181: "Bedlington terrier",
182: "Border terrier",
183: "Kerry blue terrier",
184: "Irish terrier",
185: "Norfolk terrier",
186: "Norwich terrier",
187: "Yorkshire terrier",
188: "wire-haired fox terrier",
189: "Lakeland terrier",
190: "Sealyham terrier, Sealyham",
191: "Airedale, Airedale terrier",
192: "cairn, cairn terrier",
193: "Australian terrier",
194: "Dandie Dinmont, Dandie Dinmont terrier",
195: "Boston bull, Boston terrier",
196: "miniature schnauzer",
197: "giant schnauzer",
198: "standard schnauzer",
199: "Scotch terrier, Scottish terrier, Scottie",
200: "Tibetan terrier, chrysanthemum dog",
201: "silky terrier, Sydney silky",
202: "soft-coated wheaten terrier",
203: "West Highland white terrier",
204: "Lhasa, Lhasa apso",
205: "flat-coated retriever",
206: "curly-coated retriever",
207: "golden retriever",
208: "Labrador retriever",
209: "Chesapeake Bay retriever",
210: "German short-haired pointer",
211: "vizsla, Hungarian pointer",
212: "English setter",
213: "Irish setter, red setter",
214: "Gordon setter",
215: "Brittany spaniel",
216: "clumber, clumber spaniel",
217: "English springer, English springer spaniel",
218: "Welsh springer spaniel",
219: "cocker spaniel, English cocker spaniel, cocker",
220: "Sussex spaniel",
221: "Irish water spaniel",
222: "kuvasz",
223: "schipperke",
224: "groenendael",
225: "malinois",
226: "briard",
227: "kelpie",
228: "komondor",
229: "Old English sheepdog, bobtail",
230: "Shetland sheepdog, Shetland sheep dog, Shetland",
231: "collie",
232: "Border collie",
233: "Bouvier des Flandres, Bouviers des Flandres",
234: "Rottweiler",
235: "German shepherd, German shepherd dog, German police dog, alsatian",
236: "Doberman, Doberman pinscher",
237: "miniature pinscher",
238: "Greater Swiss Mountain dog",
239: "Bernese mountain dog",
240: "Appenzeller",
241: "EntleBucher",
242: "boxer",
243: "bull mastiff",
244: "Tibetan mastiff",
245: "French bulldog",
246: "Great Dane",
247: "Saint Bernard, St Bernard",
248: "Eskimo dog, husky",
249: "malamute, malemute, Alaskan malamute",
250: "Siberian husky",
251: "dalmatian, coach dog, carriage dog",
252: "affenpinscher, monkey pinscher, monkey dog",
253: "basenji",
254: "pug, pug-dog",
255: "Leonberg",
256: "Newfoundland, Newfoundland dog",
257: "Great Pyrenees",
258: "Samoyed, Samoyede",
259: "Pomeranian",
260: "chow, chow chow",
261: "keeshond",
262: "Brabancon griffon",
263: "Pembroke, Pembroke Welsh corgi",
264: "Cardigan, Cardigan Welsh corgi",
265: "toy poodle",
266: "miniature poodle",
267: "standard poodle",
268: "Mexican hairless",
269: "timber wolf, grey wolf, gray wolf, Canis lupus",
270: "white wolf, Arctic wolf, Canis lupus tundrarum",
271: "red wolf, maned wolf, Canis rufus, Canis niger",
272: "coyote, prairie wolf, brush wolf, Canis latrans",
273: "dingo, warrigal, warragal, Canis dingo",
274: "dhole, Cuon alpinus",
275: "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
276: "hyena, hyaena",
277: "red fox, Vulpes vulpes",
278: "kit fox, Vulpes macrotis",
279: "Arctic fox, white fox, Alopex lagopus",
280: "grey fox, gray fox, Urocyon cinereoargenteus",
281: "tabby, tabby cat",
282: "tiger cat",
283: "Persian cat",
284: "Siamese cat, Siamese",
285: "Egyptian cat",
286: "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
287: "lynx, catamount",
288: "leopard, Panthera pardus",
289: "snow leopard, ounce, Panthera uncia",
290: "jaguar, panther, Panthera onca, Felis onca",
291: "lion, king of beasts, Panthera leo",
292: "tiger, Panthera tigris",
293: "cheetah, chetah, Acinonyx jubatus",
294: "brown bear, bruin, Ursus arctos",
295: "American black bear, black bear, Ursus americanus, Euarctos americanus",
296: "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
297: "sloth bear, Melursus ursinus, Ursus ursinus",
298: "mongoose",
299: "meerkat, mierkat",
300: "tiger beetle",
301: "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
302: "ground beetle, carabid beetle",
303: "long-horned beetle, longicorn, longicorn beetle",
304: "leaf beetle, chrysomelid",
305: "dung beetle",
306: "rhinoceros beetle",
307: "weevil",
308: "fly",
309: "bee",
310: "ant, emmet, pismire",
311: "grasshopper, hopper",
312: "cricket",
313: "walking stick, walkingstick, stick insect",
314: "cockroach, roach",
315: "mantis, mantid",
316: "cicada, cicala",
317: "leafhopper",
318: "lacewing, lacewing fly",
319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
320: "damselfly",
321: "admiral",
322: "ringlet, ringlet butterfly",
323: "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
324: "cabbage butterfly",
325: "sulphur butterfly, sulfur butterfly",
326: "lycaenid, lycaenid butterfly",
327: "starfish, sea star",
328: "sea urchin",
329: "sea cucumber, holothurian",
330: "wood rabbit, cottontail, cottontail rabbit",
331: "hare",
332: "Angora, Angora rabbit",
333: "hamster",
334: "porcupine, hedgehog",
335: "fox squirrel, eastern fox squirrel, Sciurus niger",
336: "marmot",
337: "beaver",
338: "guinea pig, Cavia cobaya",
339: "sorrel",
340: "zebra",
341: "hog, pig, grunter, squealer, Sus scrofa",
342: "wild boar, boar, Sus scrofa",
343: "warthog",
344: "hippopotamus, hippo, river horse, Hippopotamus amphibius",
345: "ox",
346: "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
347: "bison",
348: "ram, tup",
349: "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
350: "ibex, Capra ibex",
351: "hartebeest",
352: "impala, Aepyceros melampus",
353: "gazelle",
354: "Arabian camel, dromedary, Camelus dromedarius",
355: "llama",
356: "weasel",
357: "mink",
358: "polecat, fitch, foulmart, foumart, Mustela putorius",
359: "black-footed ferret, ferret, Mustela nigripes",
360: "otter",
361: "skunk, polecat, wood pussy",
362: "badger",
363: "armadillo",
364: "three-toed sloth, ai, Bradypus tridactylus",
365: "orangutan, orang, orangutang, Pongo pygmaeus",
366: "gorilla, Gorilla gorilla",
367: "chimpanzee, chimp, Pan troglodytes",
368: "gibbon, Hylobates lar",
369: "siamang, Hylobates syndactylus, Symphalangus syndactylus",
370: "guenon, guenon monkey",
371: "patas, hussar monkey, Erythrocebus patas",
372: "baboon",
373: "macaque",
374: "langur",
375: "colobus, colobus monkey",
376: "proboscis monkey, Nasalis larvatus",
377: "marmoset",
378: "capuchin, ringtail, Cebus capucinus",
379: "howler monkey, howler",
380: "titi, titi monkey",
381: "spider monkey, Ateles geoffroyi",
382: "squirrel monkey, Saimiri sciureus",
383: "Madagascar cat, ring-tailed lemur, Lemur catta",
384: "indri, indris, Indri indri, Indri brevicaudatus",
385: "Indian elephant, Elephas maximus",
386: "African elephant, Loxodonta africana",
387: "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
388: "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
389: "barracouta, snoek",
390: "eel",
391: "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
392: "rock beauty, Holocanthus tricolor",
393: "anemone fish",
394: "sturgeon",
395: "gar, garfish, garpike, billfish, Lepisosteus osseus",
396: "lionfish",
397: "puffer, pufferfish, blowfish, globefish",
398: "abacus",
399: "abaya",
400: "academic gown, academic robe, judge's robe",
401: "accordion, piano accordion, squeeze box",
402: "acoustic guitar",
403: "aircraft carrier, carrier, flattop, attack aircraft carrier",
404: "airliner",
405: "airship, dirigible",
406: "altar",
407: "ambulance",
408: "amphibian, amphibious vehicle",
409: "analog clock",
410: "apiary, bee house",
411: "apron",
412: "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
413: "assault rifle, assault gun",
414: "backpack, back pack, knapsack, packsack, rucksack, haversack",
415: "bakery, bakeshop, bakehouse",
416: "balance beam, beam",
417: "balloon",
418: "ballpoint, ballpoint pen, ballpen, Biro",
419: "Band Aid",
420: "banjo",
421: "bannister, banister, balustrade, balusters, handrail",
422: "barbell",
423: "barber chair",
424: "barbershop",
425: "barn",
426: "barometer",
427: "barrel, cask",
428: "barrow, garden cart, lawn cart, wheelbarrow",
429: "baseball",
430: "basketball",
431: "bassinet",
432: "bassoon",
433: "bathing cap, swimming cap",
434: "bath towel",
435: "bathtub, bathing tub, bath, tub",
436: "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
437: "beacon, lighthouse, beacon light, pharos",
438: "beaker",
439: "bearskin, busby, shako",
440: "beer bottle",
441: "beer glass",
442: "bell cote, bell cot",
443: "bib",
444: "bicycle-built-for-two, tandem bicycle, tandem",
445: "bikini, two-piece",
446: "binder, ring-binder",
447: "binoculars, field glasses, opera glasses",
448: "birdhouse",
449: "boathouse",
450: "bobsled, bobsleigh, bob",
451: "bolo tie, bolo, bola tie, bola",
452: "bonnet, poke bonnet",
453: "bookcase",
454: "bookshop, bookstore, bookstall",
455: "bottlecap",
456: "bow",
457: "bow tie, bow-tie, bowtie",
458: "brass, memorial tablet, plaque",
459: "brassiere, bra, bandeau",
460: "breakwater, groin, groyne, mole, bulwark, seawall, jetty",
461: "breastplate, aegis, egis",
462: "broom",
463: "bucket, pail",
464: "buckle",
465: "bulletproof vest",
466: "bullet train, bullet",
467: "butcher shop, meat market",
468: "cab, hack, taxi, taxicab",
469: "caldron, cauldron",
470: "candle, taper, wax light",
471: "cannon",
472: "canoe",
473: "can opener, tin opener",
474: "cardigan",
475: "car mirror",
476: "carousel, carrousel, merry-go-round, roundabout, whirligig",
477: "carpenter's kit, tool kit",
478: "carton",
479: "car wheel",
480: "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
481: "cassette",
482: "cassette player",
483: "castle",
484: "catamaran",
485: "CD player",
486: "cello, violoncello",
487: "cellular telephone, cellular phone, cellphone, cell, mobile phone",
488: "chain",
489: "chainlink fence",
490: "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
491: "chain saw, chainsaw",
492: "chest",
493: "chiffonier, commode",
494: "chime, bell, gong",
495: "china cabinet, china closet",
496: "Christmas stocking",
497: "church, church building",
498: "cinema, movie theater, movie theatre, movie house, picture palace",
499: "cleaver, meat cleaver, chopper",
500: "cliff dwelling",
501: "cloak",
502: "clog, geta, patten, sabot",
503: "cocktail shaker",
504: "coffee mug",
505: "coffeepot",
506: "coil, spiral, volute, whorl, helix",
507: "combination lock",
508: "computer keyboard, keypad",
509: "confectionery, confectionary, candy store",
510: "container ship, containership, container vessel",
511: "convertible",
512: "corkscrew, bottle screw",
513: "cornet, horn, trumpet, trump",
514: "cowboy boot",
515: "cowboy hat, ten-gallon hat",
516: "cradle",
517: "crane",
518: "crash helmet",
519: "crate",
520: "crib, cot",
521: "Crock Pot",
522: "croquet ball",
523: "crutch",
524: "cuirass",
525: "dam, dike, dyke",
526: "desk",
527: "desktop computer",
528: "dial telephone, dial phone",
529: "diaper, nappy, napkin",
530: "digital clock",
531: "digital watch",
532: "dining table, board",
533: "dishrag, dishcloth",
534: "dishwasher, dish washer, dishwashing machine",
535: "disk brake, disc brake",
536: "dock, dockage, docking facility",
537: "dogsled, dog sled, dog sleigh",
538: "dome",
539: "doormat, welcome mat",
540: "drilling platform, offshore rig",
541: "drum, membranophone, tympan",
542: "drumstick",
543: "dumbbell",
544: "Dutch oven",
545: "electric fan, blower",
546: "electric guitar",
547: "electric locomotive",
548: "entertainment center",
549: "envelope",
550: "espresso maker",
551: "face powder",
552: "feather boa, boa",
553: "file, file cabinet, filing cabinet",
554: "fireboat",
555: "fire engine, fire truck",
556: "fire screen, fireguard",
557: "flagpole, flagstaff",
558: "flute, transverse flute",
559: "folding chair",
560: "football helmet",
561: "forklift",
562: "fountain",
563: "fountain pen",
564: "four-poster",
565: "freight car",
566: "French horn, horn",
567: "frying pan, frypan, skillet",
568: "fur coat",
569: "garbage truck, dustcart",
570: "gasmask, respirator, gas helmet",
571: "gas pump, gasoline pump, petrol pump, island dispenser",
572: "goblet",
573: "go-kart",
574: "golf ball",
575: "golfcart, golf cart",
576: "gondola",
577: "gong, tam-tam",
578: "gown",
579: "grand piano, grand",
580: "greenhouse, nursery, glasshouse",
581: "grille, radiator grille",
582: "grocery store, grocery, food market, market",
583: "guillotine",
584: "hair slide",
585: "hair spray",
586: "half track",
587: "hammer",
588: "hamper",
589: "hand blower, blow dryer, blow drier, hair dryer, hair drier",
590: "hand-held computer, hand-held microcomputer",
591: "handkerchief, hankie, hanky, hankey",
592: "hard disc, hard disk, fixed disk",
593: "harmonica, mouth organ, harp, mouth harp",
594: "harp",
595: "harvester, reaper",
596: "hatchet",
597: "holster",
598: "home theater, home theatre",
599: "honeycomb",
600: "hook, claw",
601: "hoopskirt, crinoline",
602: "horizontal bar, high bar",
603: "horse cart, horse-cart",
604: "hourglass",
605: "iPod",
606: "iron, smoothing iron",
607: "jack-o'-lantern",
608: "jean, blue jean, denim",
609: "jeep, landrover",
610: "jersey, T-shirt, tee shirt",
611: "jigsaw puzzle",
612: "jinrikisha, ricksha, rickshaw",
613: "joystick",
614: "kimono",
615: "knee pad",
616: "knot",
617: "lab coat, laboratory coat",
618: "ladle",
619: "lampshade, lamp shade",
620: "laptop, laptop computer",
621: "lawn mower, mower",
622: "lens cap, lens cover",
623: "letter opener, paper knife, paperknife",
624: "library",
625: "lifeboat",
626: "lighter, light, igniter, ignitor",
627: "limousine, limo",
628: "liner, ocean liner",
629: "lipstick, lip rouge",
630: "Loafer",
631: "lotion",
632: "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
633: "loupe, jeweler's loupe",
634: "lumbermill, sawmill",
635: "magnetic compass",
636: "mailbag, postbag",
637: "mailbox, letter box",
638: "maillot",
639: "maillot, tank suit",
640: "manhole cover",
641: "maraca",
642: "marimba, xylophone",
643: "mask",
644: "matchstick",
645: "maypole",
646: "maze, labyrinth",
647: "measuring cup",
648: "medicine chest, medicine cabinet",
649: "megalith, megalithic structure",
650: "microphone, mike",
651: "microwave, microwave oven",
652: "military uniform",
653: "milk can",
654: "minibus",
655: "miniskirt, mini",
656: "minivan",
657: "missile",
658: "mitten",
659: "mixing bowl",
660: "mobile home, manufactured home",
661: "Model T",
662: "modem",
663: "monastery",
664: "monitor",
665: "moped",
666: "mortar",
667: "mortarboard",
668: "mosque",
669: "mosquito net",
670: "motor scooter, scooter",
671: "mountain bike, all-terrain bike, off-roader",
672: "mountain tent",
673: "mouse, computer mouse",
674: "mousetrap",
675: "moving van",
676: "muzzle",
677: "nail",
678: "neck brace",
679: "necklace",
680: "nipple",
681: "notebook, notebook computer",
682: "obelisk",
683: "oboe, hautboy, hautbois",
684: "ocarina, sweet potato",
685: "odometer, hodometer, mileometer, milometer",
686: "oil filter",
687: "organ, pipe organ",
688: "oscilloscope, scope, cathode-ray oscilloscope, CRO",
689: "overskirt",
690: "oxcart",
691: "oxygen mask",
692: "packet",
693: "paddle, boat paddle",
694: "paddlewheel, paddle wheel",
695: "padlock",
696: "paintbrush",
697: "pajama, pyjama, pj's, jammies",
698: "palace",
699: "panpipe, pandean pipe, syrinx",
700: "paper towel",
701: "parachute, chute",
702: "parallel bars, bars",
703: "park bench",
704: "parking meter",
705: "passenger car, coach, carriage",
706: "patio, terrace",
707: "pay-phone, pay-station",
708: "pedestal, plinth, footstall",
709: "pencil box, pencil case",
710: "pencil sharpener",
711: "perfume, essence",
712: "Petri dish",
713: "photocopier",
714: "pick, plectrum, plectron",
715: "pickelhaube",
716: "picket fence, paling",
717: "pickup, pickup truck",
718: "pier",
719: "piggy bank, penny bank",
720: "pill bottle",
721: "pillow",
722: "ping-pong ball",
723: "pinwheel",
724: "pirate, pirate ship",
725: "pitcher, ewer",
726: "plane, carpenter's plane, woodworking plane",
727: "planetarium",
728: "plastic bag",
729: "plate rack",
730: "plow, plough",
731: "plunger, plumber's helper",
732: "Polaroid camera, Polaroid Land camera",
733: "pole",
734: "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
735: "poncho",
736: "pool table, billiard table, snooker table",
737: "pop bottle, soda bottle",
738: "pot, flowerpot",
739: "potter's wheel",
740: "power drill",
741: "prayer rug, prayer mat",
742: "printer",
743: "prison, prison house",
744: "projectile, missile",
745: "projector",
746: "puck, hockey puck",
747: "punching bag, punch bag, punching ball, punchball",
748: "purse",
749: "quill, quill pen",
750: "quilt, comforter, comfort, puff",
751: "racer, race car, racing car",
752: "racket, racquet",
753: "radiator",
754: "radio, wireless",
755: "radio telescope, radio reflector",
756: "rain barrel",
757: "recreational vehicle, RV, R.V.",
758: "reel",
759: "reflex camera",
760: "refrigerator, icebox",
761: "remote control, remote",
762: "restaurant, eating house, eating place, eatery",
763: "revolver, six-gun, six-shooter",
764: "rifle",
765: "rocking chair, rocker",
766: "rotisserie",
767: "rubber eraser, rubber, pencil eraser",
768: "rugby ball",
769: "rule, ruler",
770: "running shoe",
771: "safe",
772: "safety pin",
773: "saltshaker, salt shaker",
774: "sandal",
775: "sarong",
776: "sax, saxophone",
777: "scabbard",
778: "scale, weighing machine",
779: "school bus",
780: "schooner",
781: "scoreboard",
782: "screen, CRT screen",
783: "screw",
784: "screwdriver",
785: "seat belt, seatbelt",
786: "sewing machine",
787: "shield, buckler",
788: "shoe shop, shoe-shop, shoe store",
789: "shoji",
790: "shopping basket",
791: "shopping cart",
792: "shovel",
793: "shower cap",
794: "shower curtain",
795: "ski",
796: "ski mask",
797: "sleeping bag",
798: "slide rule, slipstick",
799: "sliding door",
800: "slot, one-armed bandit",
801: "snorkel",
802: "snowmobile",
803: "snowplow, snowplough",
804: "soap dispenser",
805: "soccer ball",
806: "sock",
807: "solar dish, solar collector, solar furnace",
808: "sombrero",
809: "soup bowl",
810: "space bar",
811: "space heater",
812: "space shuttle",
813: "spatula",
814: "speedboat",
815: "spider web, spider's web",
816: "spindle",
817: "sports car, sport car",
818: "spotlight, spot",
819: "stage",
820: "steam locomotive",
821: "steel arch bridge",
822: "steel drum",
823: "stethoscope",
824: "stole",
825: "stone wall",
826: "stopwatch, stop watch",
827: "stove",
828: "strainer",
829: "streetcar, tram, tramcar, trolley, trolley car",
830: "stretcher",
831: "studio couch, day bed",
832: "stupa, tope",
833: "submarine, pigboat, sub, U-boat",
834: "suit, suit of clothes",
835: "sundial",
836: "sunglass",
837: "sunglasses, dark glasses, shades",
838: "sunscreen, sunblock, sun blocker",
839: "suspension bridge",
840: "swab, swob, mop",
841: "sweatshirt",
842: "swimming trunks, bathing trunks",
843: "swing",
844: "switch, electric switch, electrical switch",
845: "syringe",
846: "table lamp",
847: "tank, army tank, armored combat vehicle, armoured combat vehicle",
848: "tape player",
849: "teapot",
850: "teddy, teddy bear",
851: "television, television system",
852: "tennis ball",
853: "thatch, thatched roof",
854: "theater curtain, theatre curtain",
855: "thimble",
856: "thresher, thrasher, threshing machine",
857: "throne",
858: "tile roof",
859: "toaster",
860: "tobacco shop, tobacconist shop, tobacconist",
861: "toilet seat",
862: "torch",
863: "totem pole",
864: "tow truck, tow car, wrecker",
865: "toyshop",
866: "tractor",
867: "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
868: "tray",
869: "trench coat",
870: "tricycle, trike, velocipede",
871: "trimaran",
872: "tripod",
873: "triumphal arch",
874: "trolleybus, trolley coach, trackless trolley",
875: "trombone",
876: "tub, vat",
877: "turnstile",
878: "typewriter keyboard",
879: "umbrella",
880: "unicycle, monocycle",
881: "upright, upright piano",
882: "vacuum, vacuum cleaner",
883: "vase",
884: "vault",
885: "velvet",
886: "vending machine",
887: "vestment",
888: "viaduct",
889: "violin, fiddle",
890: "volleyball",
891: "waffle iron",
892: "wall clock",
893: "wallet, billfold, notecase, pocketbook",
894: "wardrobe, closet, press",
895: "warplane, military plane",
896: "washbasin, handbasin, washbowl, lavabo, wash-hand basin",
897: "washer, automatic washer, washing machine",
898: "water bottle",
899: "water jug",
900: "water tower",
901: "whiskey jug",
902: "whistle",
903: "wig",
904: "window screen",
905: "window shade",
906: "Windsor tie",
907: "wine bottle",
908: "wing",
909: "wok",
910: "wooden spoon",
911: "wool, woolen, woollen",
912: "worm fence, snake fence, snake-rail fence, Virginia fence",
913: "wreck",
914: "yawl",
915: "yurt",
916: "web site, website, internet site, site",
917: "comic book",
918: "crossword puzzle, crossword",
919: "street sign",
920: "traffic light, traffic signal, stoplight",
921: "book jacket, dust cover, dust jacket, dust wrapper",
922: "menu",
923: "plate",
924: "guacamole",
925: "consomme",
926: "hot pot, hotpot",
927: "trifle",
928: "ice cream, icecream",
929: "ice lolly, lolly, lollipop, popsicle",
930: "French loaf",
931: "bagel, beigel",
932: "pretzel",
933: "cheeseburger",
934: "hotdog, hot dog, red hot",
935: "mashed potato",
936: "head cabbage",
937: "broccoli",
938: "cauliflower",
939: "zucchini, courgette",
940: "spaghetti squash",
941: "acorn squash",
942: "butternut squash",
943: "cucumber, cuke",
944: "artichoke, globe artichoke",
945: "bell pepper",
946: "cardoon",
947: "mushroom",
948: "Granny Smith",
949: "strawberry",
950: "orange",
951: "lemon",
952: "fig",
953: "pineapple, ananas",
954: "banana",
955: "jackfruit, jak, jack",
956: "custard apple",
957: "pomegranate",
958: "hay",
959: "carbonara",
960: "chocolate sauce, chocolate syrup",
961: "dough",
962: "meat loaf, meatloaf",
963: "pizza, pizza pie",
964: "potpie",
965: "burrito",
966: "red wine",
967: "espresso",
968: "cup",
969: "eggnog",
970: "alp",
971: "bubble",
972: "cliff, drop, drop-off",
973: "coral reef",
974: "geyser",
975: "lakeside, lakeshore",
976: "promontory, headland, head, foreland",
977: "sandbar, sand bar",
978: "seashore, coast, seacoast, sea-coast",
979: "valley, vale",
980: "volcano",
981: "ballplayer, baseball player",
982: "groom, bridegroom",
983: "scuba diver",
984: "rapeseed",
985: "daisy",
986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
987: "corn",
988: "acorn",
989: "hip, rose hip, rosehip",
990: "buckeye, horse chestnut, conker",
991: "coral fungus",
992: "agaric",
993: "gyromitra",
994: "stinkhorn, carrion fungus",
995: "earthstar",
996: "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
997: "bolete",
998: "ear, spike, capitulum",
999: "toilet tissue, toilet paper, bathroom tissue",
}
utils/logger.py 0 → 100644
View file @ b0f41f60
import time
from datetime import datetime
import numpy as np
import oneflow as flow
_GLOBAL_LOGGER = None
def get_logger(rank, print_ranks):
global _GLOBAL_LOGGER
if _GLOBAL_LOGGER is None:
_GLOBAL_LOGGER = Logger(rank, print_ranks)
return _GLOBAL_LOGGER
class Logger(object):
def __init__(self, rank, print_ranks):
self.rank = rank
self.print_ranks = print_ranks
# self.verbose = verbose
# self.b = backend
self.step = 0
self.m = dict()
def register_metric(
self, metric_key, meter, print_format=None, reset_after_print=False
):
assert metric_key not in self.m
self.m[metric_key] = {
"meter": meter,
"print_format": print_format or (metric_key + ": {}"),
"reset_after_print": reset_after_print,
}
def metric(self, mkey):
if mkey not in self.m:
return None
return self.m[mkey]["meter"]
def meter(self, mkey, *args):
assert mkey in self.m
self.m[mkey]["meter"].record(*args)
def print_metrics(self, print_ranks=None):
fields = []
for m in self.m.values():
meter = m["meter"]
print_format = m["print_format"]
result = meter.get()
if isinstance(result, (list, tuple)):
field = print_format.format(*result)
else:
field = print_format.format(result)
fields.append(field)
if m["reset_after_print"]:
meter.reset()
do_print = self.rank in (print_ranks or self.print_ranks)
if do_print:
print(
"[rank:{}] {}".format(self.rank, ", ".join(fields)),
datetime.now().strftime("| %Y-%m-%d %H:%M:%S.%f")[:-3],
)
def print(self, *args, print_ranks=None):
do_print = self.rank in (print_ranks or self.print_ranks)
if do_print:
print(*args)
class IterationMeter(object):
def __init__(self):
self.val = 0
def record(self, val):
self.val = val
def get(self):
return self.val
def _zeros_by_val(val):
ret = 0
if isinstance(val, flow.Tensor):
ret = flow.zeros_like(val)
elif isinstance(val, np.ndarray):
ret = np.zeros_like(val)
elif isinstance(val, int):
ret = 0
elif isinstance(val, float):
ret = 0.0
else:
raise ValueError
return ret
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.sum = None
self.n = 0
def record(self, val, n=1):
self.n += n
if self.sum is None:
self.sum = _zeros_by_val(val)
if n == 1:
self.sum += val
else:
self.sum += val * n
def get(self):
if self.n == 0:
return 0
avg = self.sum / self.n
if isinstance(avg, flow.Tensor):
# NOTE(zwx): sync happen here
return avg.numpy().item()
elif isinstance(avg, np.ndarray):
return avg.item()
else:
return avg
class RunningMeter(object):
pass
class ProgressMeter(object):
def __init__(self, total):
self.cur = 0
self.total = total
def record(self, cur, total=None):
self.cur = cur
if total is not None:
self.total = total
def get(self):
return self.cur, self.total
class TimeMeter(object):
def __init__(self, return_timestamp=False):
self.return_timestamp = return_timestamp
self.n = 0
self.ets = None
self.bts = None
self.reset()
def reset(self):
self.n = 0
if self.ets is None:
self.bts = time.perf_counter()
else:
self.bts = self.ets
self.ets = None
def record(self, n):
self.n += n
def get(self):
self.ets = time.perf_counter()
assert self.ets > self.bts, f"{self.ets} > {self.bts}"
throughput = self.n / (self.ets - self.bts)
if self.return_timestamp:
return throughput, self.ets
else:
return throughput
utils/numpy_data_utils.py 0 → 100644
View file @ b0f41f60
from PIL import Image
import numpy as np
import os
import random
def load_image(image_path="data/fish.jpg"):
rgb_mean = [123.68, 116.779, 103.939]
rgb_std = [58.393, 57.12, 57.375]
im = Image.open(image_path)
im = im.resize((224, 224))
im = im.convert("RGB")
im = np.array(im).astype("float32")
im = (im - rgb_mean) / rgb_std
im = np.transpose(im, (2, 0, 1))
im = np.expand_dims(im, axis=0)
return np.ascontiguousarray(im, "float32")
class NumpyDataLoader(object):
def __init__(self, dataset_root: str, batch_size: int = 1):
self.dataset_root = dataset_root
sub_folders = os.listdir(self.dataset_root)
self.image_2_class_label_list = []
self.label_2_class_name = {}
self.batch_size = batch_size
label = -1
for sf in sub_folders:
label += 1
self.label_2_class_name[label] = sf
sub_root = os.path.join(self.dataset_root, sf)
image_names = os.listdir(sub_root)
for name in image_names:
self.image_2_class_label_list.append(
(os.path.join(sub_root, name), label)
)
self.curr_idx = 0
self.shuffle_data()
def shuffle_data(self):
random.shuffle(self.image_2_class_label_list)
self.curr_idx = 0
def __getitem__(self, index):
batch_datas = []
batch_labels = []
for i in range(self.batch_size):
image_path, label = self.image_2_class_label_list[self.curr_idx]
batch_datas.append(load_image(image_path))
batch_labels.append(int(label))
self.curr_idx += 1
np_datas = np.concatenate(tuple(batch_datas), axis=0)
np_labels = np.array(batch_labels, dtype=np.int32)
return np.ascontiguousarray(np_datas, "float32"), np_labels
def __len__(self):
return len(self.image_2_class_label_list) // self.batch_size
utils/ofrecord_data_utils.py 0 → 100644
View file @ b0f41f60
from typing import List, Union
import oneflow as flow
import oneflow.nn as nn
import os
class OFRecordDataLoader(nn.Module):
def __init__(
self,
ofrecord_root: str = "./ofrecord",
mode: str = "train", # "val"
dataset_size: int = 9469,
batch_size: int = 1,
total_batch_size: int = 1,
ofrecord_part_num: int = 1,
placement: flow.placement = None,
sbp: Union[flow.sbp.sbp, List[flow.sbp.sbp]] = None,
):
super().__init__()
channel_last = False
output_layout = "NHWC" if channel_last else "NCHW"
self.train_record_reader = flow.nn.OfrecordReader(
os.path.join(ofrecord_root, mode),
batch_size=batch_size,
data_part_num=ofrecord_part_num,
part_name_suffix_length=5,
random_shuffle=True if mode == "train" else False,
shuffle_after_epoch=True if mode == "train" else False,
placement=placement,
sbp=sbp,
)
self.record_label_decoder = flow.nn.OFRecordRawDecoder(
"class/label", shape=(), dtype=flow.int32
)
color_space = "RGB"
height = 224
width = 224
self.record_image_decoder = (
flow.nn.OFRecordImageDecoderRandomCrop("encoded", color_space=color_space)
if mode == "train"
else flow.nn.OFRecordImageDecoder("encoded", color_space=color_space)
)
self.resize = (
flow.nn.image.Resize(target_size=[height, width])
if mode == "train"
else flow.nn.image.Resize(
resize_side="shorter", keep_aspect_ratio=True, target_size=256
)
)
self.flip = (
flow.nn.CoinFlip(batch_size=batch_size, placement=placement, sbp=sbp)
if mode == "train"
else None
)
rgb_mean = [123.68, 116.779, 103.939]
rgb_std = [58.393, 57.12, 57.375]
self.crop_mirror_norm = (
flow.nn.CropMirrorNormalize(
color_space=color_space,
output_layout=output_layout,
mean=rgb_mean,
std=rgb_std,
output_dtype=flow.float,
)
if mode == "train"
else flow.nn.CropMirrorNormalize(
color_space=color_space,
output_layout=output_layout,
crop_h=height,
crop_w=width,
crop_pos_y=0.5,
crop_pos_x=0.5,
mean=rgb_mean,
std=rgb_std,
output_dtype=flow.float,
)
)
self.batch_size = batch_size
self.total_batch_size = total_batch_size
self.dataset_size = dataset_size
def __len__(self):
return self.dataset_size // self.total_batch_size
def forward(self):
train_record = self.train_record_reader()
label = self.record_label_decoder(train_record)
image_raw_buffer = self.record_image_decoder(train_record)
image = self.resize(image_raw_buffer)[0]
rng = self.flip() if self.flip != None else None
image = self.crop_mirror_norm(image, rng)
return image, label
utils/plot.py 0 → 100644
View file @ b0f41f60
import matplotlib.pyplot as plt
of_losses = []
torch_losses = []
with open("of_losses.txt", "r") as lines:
for line in lines:
line = line.strip()
of_losses.append(float(line))
with open("torch_losses.txt", "r") as lines:
for line in lines:
line = line.strip()
torch_losses.append(float(line))
indes = [i for i in range(len(of_losses))]
plt.plot(indes, of_losses, label="oneflow")
plt.plot(indes, torch_losses, label="pytorch")
plt.xlabel("iter - axis")
# Set the y axis label of the current axis.
plt.ylabel("loss - axis")
# Set a title of the current axes.
plt.title("compare ")
# show a legend on the plot
plt.legend()
# Display a figure.
plt.show()
utils/printer.py 0 → 100644
View file @ b0f41f60
import pandas as pd
from collections import namedtuple
class Printer(object):
def __init__(self, field_names, print_format="table", persistent_file=None):
assert print_format in ("table", "normal")
self.field_names_ = field_names
self.format_ = print_format
self.records_ = []
self.handlers_ = dict()
self.str_lens_ = dict()
self.title_printed_ = False
if persistent_file is not None:
self.csv_ = open(persistent_file, "a")
else:
self.csv_ = None
self.Record = None
def __def__(self):
if self.csv_ is not None:
self.csv_.close()
def finish(self):
err = f"{len(self.field_names_)} vs. {len(self.handlers_)}"
assert len(self.field_names_) == len(self.handlers_), err
err = f"{len(self.field_names_)} vs. {len(self.str_lens_)}"
assert len(self.field_names_) == len(self.str_lens_), err
for fname in self.field_names_:
assert fname in self.handlers_, f"{fname} handler not register"
assert fname in self.str_lens_, f"{fname} str_len not register"
self.Record = namedtuple("Record", self.field_names_)
# DEBUG(zwx):
# dummy = self.Record(*(["-"] * len(self.field_names_)))
# df = pd.DataFrame(dummy)
# if self.persistent_file_ is not None:
# df.to_csv(self.persistent_file_, mode='a', header=True)
def record(self, *args, **kwargs):
assert self.Record is not None
r = self.Record(*args, **kwargs)
self.records_.append(r)
def register_handler(self, field, handler):
assert callable(handler)
self.handlers_[field] = handler
def register_str_len(self, field, str_len):
assert isinstance(str_len, int)
self.str_lens_[field] = str_len
def reset_records(self):
self.records_ = []
def print_table_title(self):
fields = ""
sep = ""
for fname in self.field_names_:
str_len = self.str_lens_[fname]
fields += "| {} ".format(fname.ljust(str_len))
sep += f"| {'-' * str_len} "
fields += "|"
sep += "|"
print(fields)
print(sep)
self.title_printed_ = True
def reset_title_printed(self):
self.title_printed_ = False
def print(self):
df = pd.DataFrame(self.records_)
fields = []
for fname in self.field_names_:
assert fname in self.handlers_
handler = self.handlers_[fname]
field_value = handler(df[fname])
fields.append(field_value)
if self.format_ == "table":
if not self.title_printed_:
self.print_table_title()
record = ""
for i, str_len in enumerate(self.str_lens_.values()):
record += "| {} ".format(str(fields[i]).ljust(str_len))
record += "|"
print(record)
elif self.format_ == "normal":
record = ""
for i, fval in enumerate(fields):
fname = self.field_names_[i]
record += f"{fname}: {fval}, "
print(record)
else:
raise ValueError
if self.csv_ is not None:
df.to_csv(self.csv_, header=False)
self.reset_records()
utils/stat.py 0 → 100644
View file @ b0f41f60
import sys
import signal
import subprocess
import threading
class CudaUtilMemStat:
def __init__(
self, stat_file_path, stat_util=True, stat_mem=True, only_ordinal=None
):
self.stat_file = open(stat_file_path, "wt")
self.stat_util = stat_util
self.stat_mem = stat_mem
self.only_ordinal = only_ordinal
self._write_titles()
def __del__(self):
self.stat_file.close()
def _write_titles(self):
proc = subprocess.Popen(
["nvidia-smi", "-L"], stdout=subprocess.PIPE, stderr=subprocess.PIPE,
)
stdout, stderr = proc.communicate()
if stderr != b"":
raise RuntimeError(stderr)
gpus = []
lines = stdout.decode("utf-8").split("\n")
for line in lines:
if line.strip() == "":
continue
gpus.append(line.split(":")[0])
util_titles, mem_titles = [], []
for gpu in gpus:
if self.stat_util:
util_titles.append(gpu + " utilization")
if self.stat_mem:
mem_titles.append(gpu + " memory used")
if self.only_ordinal is None:
self.stat_file.write(",".join(util_titles + mem_titles) + "\n")
else:
titles = (
util_titles[self.only_ordinal] + "," + mem_titles[self.only_ordinal]
)
self.stat_file.write(titles + "\n")
self.stat_file.flush()
def stat(self):
# command: nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv
proc = subprocess.Popen(
["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
stdout, stderr = proc.communicate()
if stderr != b"":
raise RuntimeError(stderr)
lines = stdout.decode("utf-8").split("\n")
util_vals = []
mem_vals = []
for line in lines[1:]:
if line.strip() == "":
continue
util_mem = line.split(",")
assert len(util_mem) == 2, lines
util = util_mem[0].strip()
mem = util_mem[1].strip()
assert "%" in util
assert "MiB" in mem
util = util.split(" ")
mem = mem.split(" ")
assert len(util) == 2
assert len(mem) == 2
util_val = util[0].strip()
mem_val = mem[0].strip()
if self.stat_util:
util_vals.append(util_val)
if self.stat_mem:
mem_vals.append(mem_val)
if self.only_ordinal is None:
self.stat_file.write(",".join(util_vals + mem_vals) + "\n")
else:
vals = util_vals[self.only_ordinal] + "," + mem_vals[self.only_ordinal]
self.stat_file.write(vals + "\n")
self.stat_file.flush()
def start(self, interval):
stop = threading.Event()
stat_thrd = StatThread(self.stat, interval, stop)
stat_thrd.start()
def close(signum, frame):
print("Closing...")
stop.set()
signal.signal(signal.SIGTERM, close)
signal.signal(signal.SIGINT, close)
print("Start stat")
print("Print Ctrl+C to stop")
stop.wait()
class StatThread(threading.Thread):
def __init__(self, handler, interval, stop_event):
super().__init__()
self.handler = handler
self.interval = interval
self.stopped = stop_event
self.count = 0
def run(self):
while not self.stopped.wait(self.interval):
print(f"{self.count} th run stat")
self.handler()
self.count += 1
if __name__ == "__main__":
stat_file_path = sys.argv[1] if len(sys.argv) > 1 else "gpu_stat.log"
interval = sys.argv[2] if len(sys.argv) > 2 else 1
stat = CudaUtilMemStat(stat_file_path)
stat.start(interval)
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