58 lines
2.7 KiB
Python
58 lines
2.7 KiB
Python
# This file is for experimental usage
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import torch, random
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import numpy as np
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from copy import deepcopy
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import torch.nn as nn
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# from utils import obtain_accuracy
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from models import CellStructure
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from log_utils import time_string
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def evaluate_one_shot(model, xloader, api, cal_mode, seed=111):
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print ('This is an old version of codes to use NAS-Bench-API, and should be modified to align with the new version. Please contact me for more details if you use this function.')
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weights = deepcopy(model.state_dict())
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model.train(cal_mode)
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with torch.no_grad():
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logits = nn.functional.log_softmax(model.arch_parameters, dim=-1)
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archs = CellStructure.gen_all(model.op_names, model.max_nodes, False)
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probs, accuracies, gt_accs_10_valid, gt_accs_10_test = [], [], [], []
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loader_iter = iter(xloader)
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random.seed(seed)
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random.shuffle(archs)
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for idx, arch in enumerate(archs):
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arch_index = api.query_index_by_arch( arch )
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metrics = api.get_more_info(arch_index, 'cifar10-valid', None, False, False)
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gt_accs_10_valid.append( metrics['valid-accuracy'] )
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metrics = api.get_more_info(arch_index, 'cifar10', None, False, False)
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gt_accs_10_test.append( metrics['test-accuracy'] )
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select_logits = []
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for i, node_info in enumerate(arch.nodes):
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for op, xin in node_info:
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node_str = '{:}<-{:}'.format(i+1, xin)
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op_index = model.op_names.index(op)
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select_logits.append( logits[model.edge2index[node_str], op_index] )
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cur_prob = sum(select_logits).item()
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probs.append( cur_prob )
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cor_prob_valid = np.corrcoef(probs, gt_accs_10_valid)[0,1]
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cor_prob_test = np.corrcoef(probs, gt_accs_10_test )[0,1]
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print ('{:} correlation for probabilities : {:.6f} on CIFAR-10 validation and {:.6f} on CIFAR-10 test'.format(time_string(), cor_prob_valid, cor_prob_test))
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for idx, arch in enumerate(archs):
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model.set_cal_mode('dynamic', arch)
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try:
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inputs, targets = next(loader_iter)
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except:
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loader_iter = iter(xloader)
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inputs, targets = next(loader_iter)
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_, logits = model(inputs.cuda())
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_, preds = torch.max(logits, dim=-1)
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correct = (preds == targets.cuda() ).float()
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accuracies.append( correct.mean().item() )
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if idx != 0 and (idx % 500 == 0 or idx + 1 == len(archs)):
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cor_accs_valid = np.corrcoef(accuracies, gt_accs_10_valid[:idx+1])[0,1]
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cor_accs_test = np.corrcoef(accuracies, gt_accs_10_test [:idx+1])[0,1]
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print ('{:} {:05d}/{:05d} mode={:5s}, correlation : accs={:.5f} for CIFAR-10 valid, {:.5f} for CIFAR-10 test.'.format(time_string(), idx, len(archs), 'Train' if cal_mode else 'Eval', cor_accs_valid, cor_accs_test))
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model.load_state_dict(weights)
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return archs, probs, accuracies
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