import numpy as np
import torch
from meta.matching import *
import dlutil as dl
shots = 2
ways = 5
G = Embedding(1, 10)
context_embedding_network = MatchingNetwork.build_context_embedding_network(10, 64, 1)
network = MatchingNetwork(G, context_embedding_network, ways).cuda(0)
optimizer = torch.optim.Adam(network.parameters())
ckpt = dl.Checkpoint('results/matching/omniglot1', max_to_keep=10, device=0, save_best_only=True, saving_metric='test_acc')
acc = dl.MetricAccuracy(name='acc', device=0)
root = '/data/examples/omniglot'
batch_size = 32
def trans(bxs, bys):
bx = bxs[0]
by = bys[0]
bx = bx.astype(np.float32) / 255.
bx = np.expand_dims(bx, axis=1)
by = np.squeeze(by.astype(np.int64))
classes = sorted(list(set(by.tolist())))
for i, c in enumerate(classes):
by[by==c] = i
inp_x = bx[:ways]
sup_x = bx[ways:]
def input_trans(bxs, bys):
bx, = bxs
by, = bys
bx = bx.astype(np.float32).transpose([2, 0, 1])
by = by.astype(np.int64).squeeze()
return ((bx, ), by)
num_rounds = 5
num_ops = 11
stem = nn.Sequential(ConvOp(3, 64, kernel_size=1), nn.ReLU())
arch = ArchBuilder(stem, 10, 64, [2, 2, 2], num_rounds=num_rounds).cuda(0)
optimizer = torch.optim.Adam(arch.parameters())
loss_func = nn.CrossEntropyLoss()
ckpt = dl.Checkpoint('temp/evo/e1', max_to_keep=10, device=0)
acc = dl.MetricAccuracy(device=0, name='acc')
batch_size = 32
ds = dl.DataReader('/data/testdata/cifar10/cifar10_test.h5',
transform_func=input_trans)
gntr = ds.common_cls_reader(batch_size, selected_classes=['tr'])
gnte = ds.common_cls_reader(batch_size * 3,
selected_classes=['te'],
shuffle=False)
listeners = [EvoListener('test', gnte, [acc])]
emodel = EvoModel(arch, ckpt, num_ops, num_rounds, device=0)
warmup_num_epochs = 10
emodel.warm_up(gntr,
loss_func,
optimizer,
num_epochs=warmup_num_epochs,