update vis
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@ -18,6 +18,7 @@ The benchmark file of NAS-Bench-102 can be downloaded from [Google Drive](https:
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You can move it to anywhere you want and send its path to our API for initialization.
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- v1.0: `NAS-Bench-102-v1_0-e61699.pth`, where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
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- v1.0: The full data of each architecture can be download from [Google Drive](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
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- v1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
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The training and evaluation data used in NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan (code:4fg7)](https://pan.baidu.com/s/1XAzavPKq3zcat1yBA1L2tQ).
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It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). If you want to generate NAS-Bench-102 or similar NAS datasets or training models by yourself, you need these data.
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@ -464,18 +464,17 @@ def just_show(api):
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print ('[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}'.format(dataset, metric_on_set, arch_index, highest_acc))
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def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims):
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def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims, x_maxs):
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color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
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dpi, width, height = 300, 3400, 2600
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LabelSize, LegendFontsize = 28, 28
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figsize = width / float(dpi), height / float(dpi)
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fig = plt.figure(figsize=figsize)
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x_maxs = 250
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x_axis = np.arange(0, x_maxs)
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plt.xlim(0, x_maxs)
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#x_maxs = 250
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plt.xlim(0, x_maxs+1)
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plt.ylim(y_lims[0], y_lims[1])
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interval_x, interval_y = x_maxs // 5, y_lims[2]
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plt.xticks(np.arange(0, x_maxs, interval_x), fontsize=LegendFontsize)
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plt.xticks(np.arange(0, x_maxs+1, interval_x), fontsize=LegendFontsize)
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plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
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plt.grid()
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plt.xlabel('The searching epoch', fontsize=LabelSize)
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@ -505,17 +504,24 @@ def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims):
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xresults.append( metrics['accuracy'] )
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return xresults
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for idx, method in enumerate(['RSPS', 'GDAS', 'SETN', 'ENAS']):
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if x_maxs == 50:
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xox, xxxstrs = 'v2', ['DARTS-V1', 'DARTS-V2']
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elif x_maxs == 250:
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xox, xxxstrs = 'v1', ['RSPS', 'GDAS', 'SETN', 'ENAS']
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else: raise ValueError('invalid x_maxs={:}'.format(x_maxs))
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for idx, method in enumerate(xxxstrs):
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xkey = method
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all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
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all_datas = [torch.load(xpath) for xpath in all_paths]
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all_datas = [torch.load(xpath, map_location='cpu') for xpath in all_paths]
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accyss = [get_accs(xdatas) for xdatas in all_datas]
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accyss = np.array( accyss )
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epochs = list(range(accyss.shape[1]))
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plt.plot(epochs, [accyss[:,i].mean() for i in epochs], color=color_set[idx], linestyle='-', label='{:}'.format(method), lw=2)
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plt.fill_between(epochs, [accyss[:,i].mean()-accyss[:,i].std() for i in epochs], [accyss[:,i].mean()+accyss[:,i].std() for i in epochs], alpha=0.2, color=color_set[idx])
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plt.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / '{:}-{:}-{:}'.format(dataset, subset, file_name)
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#plt.legend(loc=4, fontsize=LegendFontsize)
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plt.legend(loc=0, fontsize=LegendFontsize)
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save_path = vis_save_dir / '{:}-{:}-{:}-{:}'.format(xox, dataset, subset, file_name)
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print('save figure into {:}\n'.format(save_path))
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fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
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@ -540,7 +546,13 @@ if __name__ == '__main__':
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#visualize_relative_ranking(vis_save_dir)
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api = API(args.api_path)
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show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (5,95,10))
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for x_maxs in [50, 250]:
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show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
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show_nas_sharing_w(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
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show_nas_sharing_w(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
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show_nas_sharing_w(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
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show_nas_sharing_w(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
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show_nas_sharing_w(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
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"""
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just_show(api)
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plot_results_nas(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (85,95, 1))
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@ -1,11 +1,12 @@
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# python ./exps/vis/test.py
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import os, sys
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import os, sys, random
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from pathlib import Path
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import torch
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import numpy as np
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from collections import OrderedDict
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from graphviz import Digraph
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def test_nas_api():
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@ -23,5 +24,35 @@ def test_nas_api():
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print(archRes.get_metrics('cifar10-valid', 'x-valid', None, True))
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print(archRes.query('cifar10-valid', 777))
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OPS = ['skip-connect', 'conv-1x1', 'conv-3x3', 'pool-3x3']
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COLORS = ['chartreuse' , 'cyan' , 'navyblue', 'chocolate1']
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def plot(filename):
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g = Digraph(
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format='png',
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edge_attr=dict(fontsize='20', fontname="times"),
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node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
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engine='dot')
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g.body.extend(['rankdir=LR'])
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steps = 5
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for i in range(0, steps):
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if i == 0:
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g.node(str(i), fillcolor='darkseagreen2')
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elif i+1 == steps:
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g.node(str(i), fillcolor='palegoldenrod')
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else: g.node(str(i), fillcolor='lightblue')
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for i in range(1, steps):
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for xin in range(i):
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op_i = random.randint(0, len(OPS)-1)
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#g.edge(str(xin), str(i), label=OPS[op_i], fillcolor=COLORS[op_i])
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g.edge(str(xin), str(i), label=OPS[op_i], color=COLORS[op_i], fillcolor=COLORS[op_i])
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#import pdb; pdb.set_trace()
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g.render(filename, cleanup=True, view=False)
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if __name__ == '__main__':
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test_nas_api()
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for i in range(200): plot('{:04d}'.format(i))
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