Update visualization codees for WS.
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@@ -3,12 +3,12 @@
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#####################################################################################################
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# modified from https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py #
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#####################################################################################################
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# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.01
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# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.01
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# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.01
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# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.01
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# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.01
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# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.01
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#####################################################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np, collections
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@@ -11,7 +11,7 @@ for dataset in ${datasets}
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do
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for search_space in ${search_spaces}
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do
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python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
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python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.01
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python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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@@ -399,6 +399,9 @@ def main(xargs):
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logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
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network.set_drop_path(float(epoch+1) / total_epoch, xargs.drop_path_rate)
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if xargs.algo == 'gdas':
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network.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
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logger.log('[Reset tau as : {:}'.format(network.tau))
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search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
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= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, xargs.algo, logger)
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search_time.update(time.time() - start_time)
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@@ -480,6 +483,9 @@ if __name__ == '__main__':
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parser.add_argument('--dataset' , type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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parser.add_argument('--search_space', type=str, default='tss', choices=['tss'], help='The search space name.')
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parser.add_argument('--algo' , type=str, choices=['darts-v1', 'darts-v2', 'gdas', 'setn', 'random', 'enas'], help='The search space name.')
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# FOR GDAS
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parser.add_argument('--tau_min', type=float, default=0.1, help='The minimum tau for Gumbel Softmax.')
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parser.add_argument('--tau_max', type=float, default=10, help='The maximum tau for Gumbel Softmax.')
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# channels and number-of-cells
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parser.add_argument('--max_nodes' , type=int, default=4, help='The maximum number of nodes.')
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parser.add_argument('--channel' , type=int, default=16, help='The number of channels.')
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