AntoineH/smart_augmentation
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LR scheduler + Resolution pb ResNet50/WRN

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Harle, Antoine (Contracteur) 2020-02-12 13:43:44 -05:00
parent 383f63c7b8
commit 79de0191a8
4 changed files with 78 additions and 21 deletions
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25
higher/smart_aug/test_dataug.py
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@ -82,7 +82,7 @@ if __name__ == "__main__":
}
#Parameters
n_inner_iter = 1
epochs = 150
epochs = 200
dataug_epoch_start=0
optim_param={
'Meta':{
@ -91,10 +91,11 @@ if __name__ == "__main__":
},
'Inner':{
'optim': 'SGD',
'lr':1e-2, #1e-2
'lr':1e-1, #1e-2/1e-1
'momentum':0.9, #0.9
'decay':0.0001,
'decay':0.0005, #0.0005
'nesterov':True,
'scheduler':'exponential', #None, 'cosine', 'multiStep', 'exponential'
}
}
@ -103,21 +104,26 @@ if __name__ == "__main__":
#model = ResNet(num_classes=10)
import torchvision.models as models
#model=models.resnet18()
model_name = 'resnet50' #'wide_resnet50_2' #'resnet18' #str(model)
model = getattr(models.resnet, model_name)(pretrained=False)
model_name = 'resnet18' #'wide_resnet50_2' #'resnet18' #str(model)
model = getattr(models.resnet, model_name)(pretrained=False, num_classes=len(dl_train.dataset.classes))
#### Classic ####
if 'classic' in tasks:
torch.cuda.reset_max_memory_allocated() #reset_peak_stats
torch.cuda.reset_max_memory_cached() #reset_peak_stats
t0 = time.perf_counter()
model = model.to(device)
print("{} on {} for {} epochs".format(model_name, device_name, epochs))
#print("RandAugment(N{}-M{:.2f})-{} on {} for {} epochs".format(rand_aug['N'],rand_aug['M'],model_name, device_name, epochs))
log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=10)
#log= train_classic_higher(model=model, epochs=epochs)
exec_time=time.perf_counter() - t0
max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved()
max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
####
print('-'*9)
times = [x["time"] for x in log]
@ -125,10 +131,13 @@ if __name__ == "__main__":
"Time": (np.mean(times),np.std(times), exec_time),
'Optimizer': optim_param['Inner'],
"Device": device_name,
"Memory": max_cached,
"Memory": [max_allocated, max_cached],
#"Rand_Aug": rand_aug,
"Log": log}
print(model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{} epochs".format(model_name,epochs)
#print("RandAugment-",model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
#filename = "RandAugment(N{}-M{:.2f})-{}-{} epochs".format(rand_aug['N'],rand_aug['M'],model_name,epochs)
with open("../res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)
@ -163,7 +172,7 @@ if __name__ == "__main__":
inner_it=n_inner_iter,
dataug_epoch_start=dataug_epoch_start,
opt_param=optim_param,
print_freq=10,
print_freq=1,
unsup_loss=1,
hp_opt=False,
save_sample_freq=None)