Update visualization codes

2020-07-04 09:19:24 +00:00
parent a45808b8e6
commit 9659f132be
4 changed files with 74 additions and 231 deletions
--- a/exps/experimental/test-nas-plot.py
+++ b/exps/experimental/test-nas-plot.py
@@ -0,0 +1,114 @@
+# python ./exps/vis/test.py
+import os, sys, random
+from pathlib import Path
+from copy import deepcopy
+import torch
+import numpy as np
+from collections import OrderedDict
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+
+from nas_201_api import NASBench201API as API
+
+def test_nas_api():
+  from nas_201_api import ArchResults
+  xdata   = torch.load('/home/dxy/FOR-RELEASE/NAS-Projects/output/NAS-BENCH-201-4/simplifies/architectures/000157-FULL.pth')
+  for key in ['full', 'less']:
+    print ('\n------------------------- {:} -------------------------'.format(key))
+    archRes = ArchResults.create_from_state_dict(xdata[key])
+    print(archRes)
+    print(archRes.arch_idx_str())
+    print(archRes.get_dataset_names())
+    print(archRes.get_comput_costs('cifar10-valid'))
+    # get the metrics
+    print(archRes.get_metrics('cifar10-valid', 'x-valid', None, False))
+    print(archRes.get_metrics('cifar10-valid', 'x-valid', None,  True))
+    print(archRes.query('cifar10-valid', 777))
+
+
+OPS    = ['skip-connect', 'conv-1x1', 'conv-3x3', 'pool-3x3']
+COLORS = ['chartreuse'  , 'cyan'    , 'navyblue', 'chocolate1']
+
+def plot(filename):
+  from graphviz import Digraph
+  g = Digraph(
+      format='png',
+      edge_attr=dict(fontsize='20', fontname="times"),
+      node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
+      engine='dot')
+  g.body.extend(['rankdir=LR'])
+
+  steps = 5
+  for i in range(0, steps):
+    if i == 0:
+      g.node(str(i), fillcolor='darkseagreen2')
+    elif i+1 == steps:
+      g.node(str(i), fillcolor='palegoldenrod')
+    else: g.node(str(i), fillcolor='lightblue')
+
+  for i in range(1, steps):
+    for xin in range(i):
+      op_i = random.randint(0, len(OPS)-1)
+      #g.edge(str(xin), str(i), label=OPS[op_i], fillcolor=COLORS[op_i])
+      g.edge(str(xin), str(i), label=OPS[op_i], color=COLORS[op_i], fillcolor=COLORS[op_i])
+      #import pdb; pdb.set_trace()
+  g.render(filename, cleanup=True, view=False)
+
+
+def test_auto_grad():
+  class Net(torch.nn.Module):
+    def __init__(self, iS):
+      super(Net, self).__init__()
+      self.layer = torch.nn.Linear(iS, 1)
+    def forward(self, inputs):
+      outputs = self.layer(inputs)
+      outputs = torch.exp(outputs)
+      return outputs.mean()
+  net = Net(10)
+  inputs = torch.rand(256, 10)
+  loss = net(inputs)
+  first_order_grads = torch.autograd.grad(loss, net.parameters(), retain_graph=True, create_graph=True)
+  first_order_grads = torch.cat([x.view(-1) for x in first_order_grads])
+  second_order_grads = []
+  for grads in  first_order_grads:
+    s_grads = torch.autograd.grad(grads, net.parameters())
+    second_order_grads.append( s_grads )
+
+
+def test_one_shot_model(ckpath, use_train):
+  from models import get_cell_based_tiny_net, get_search_spaces
+  from datasets import get_datasets, SearchDataset
+  from config_utils import load_config, dict2config
+  from utils.nas_utils import evaluate_one_shot
+  use_train = int(use_train) > 0
+  #ckpath = 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/seed-11416-basic.pth'
+  #ckpath = 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/seed-28640-basic.pth'
+  print ('ckpath : {:}'.format(ckpath))
+  ckp = torch.load(ckpath)
+  xargs = ckp['args']
+  train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
+  #config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, None)
+  config = load_config('./configs/nas-benchmark/algos/DARTS.config', {'class_num': class_num, 'xshape': xshape}, None)
+  if xargs.dataset == 'cifar10':
+    cifar_split = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
+    xvalid_data = deepcopy(train_data)
+    xvalid_data.transform = valid_data.transform
+    valid_loader= torch.utils.data.DataLoader(xvalid_data, batch_size=2048, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar_split.valid), num_workers=12, pin_memory=True)
+  else: raise ValueError('invalid dataset : {:}'.format(xargs.dataseet))
+  search_space = get_search_spaces('cell', xargs.search_space_name)
+  model_config = dict2config({'name': 'SETN', 'C': xargs.channel, 'N': xargs.num_cells,
+                              'max_nodes': xargs.max_nodes, 'num_classes': class_num,
+                              'space'    : search_space,
+                              'affine'   : False, 'track_running_stats': True}, None)
+  search_model = get_cell_based_tiny_net(model_config)
+  search_model.load_state_dict( ckp['search_model'] )
+  search_model = search_model.cuda()
+  api = API('/home/dxy/.torch/NAS-Bench-201-v1_0-e61699.pth')
+  archs, probs, accuracies = evaluate_one_shot(search_model, valid_loader, api, use_train)
+
+
+if __name__ == '__main__':
+  #test_nas_api()
+  #for i in range(200): plot('{:04d}'.format(i))
+  #test_auto_grad()
+  test_one_shot_model(sys.argv[1], sys.argv[2])