77 lines
3.3 KiB
Python
77 lines
3.3 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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# modules in AutoDL
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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(
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"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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)
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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(
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"{:} correlation for probabilities : {:.6f} on CIFAR-10 validation and {:.6f} on CIFAR-10 test".format(
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time_string(), cor_prob_valid, cor_prob_test
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)
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)
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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])[
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0, 1
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]
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cor_accs_test = np.corrcoef(accuracies, gt_accs_10_test[: idx + 1])[
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0, 1
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]
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print(
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"{:} {:05d}/{:05d} mode={:5s}, correlation : accs={:.5f} for CIFAR-10 valid, {:.5f} for CIFAR-10 test.".format(
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time_string(),
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idx,
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len(archs),
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"Train" if cal_mode else "Eval",
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cor_accs_valid,
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cor_accs_test,
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)
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)
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model.load_state_dict(weights)
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return archs, probs, accuracies
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