From e3579198f422b2af1382c9fdabf73bebbb57750a Mon Sep 17 00:00:00 2001 From: SevenKingess <2362769214@qq.com> Date: Fri, 18 Oct 2024 11:54:57 +0800 Subject: [PATCH 1/2] update --- summer_ospp/2024/24c6d0468/gpu/cnn.py | 6 +++--- ...\346\272\220\344\271\213\345\244\217.pptx" | Bin 0 -> 2149554 bytes .../24c6d0468/\351\241\271\347\233\256.pdf" | Bin 0 -> 4116720 bytes 3 files changed, 3 insertions(+), 3 deletions(-) create mode 100644 "summer_ospp/2024/24c6d0468/\345\274\200\346\272\220\344\271\213\345\244\217.pptx" create mode 100644 "summer_ospp/2024/24c6d0468/\351\241\271\347\233\256.pdf" diff --git a/summer_ospp/2024/24c6d0468/gpu/cnn.py b/summer_ospp/2024/24c6d0468/gpu/cnn.py index 04759b401..d871d1266 100644 --- a/summer_ospp/2024/24c6d0468/gpu/cnn.py +++ b/summer_ospp/2024/24c6d0468/gpu/cnn.py @@ -172,7 +172,7 @@ class CNN_lsing(torch.nn.Module): # cnn_model[:, :self.img_node_num] = images deta = (torch.mm(cnn_data.T, cnn_data) - torch.mm(cnn_model.T, cnn_model)) / batch_size deta_J_cnn = torch.zeros((self.kenrnal_num, self.kenrnal_size*self.kenrnal_size)) - # deta_J_cnn_num = torch.zeros((self.kenrnal_num, self.kenrnal_size*self.kenrnal_size)) + deta_J_cnn_num = torch.ones((self.kenrnal_num, self.kenrnal_size*self.kenrnal_size)) num = 0 for j, idxs in enumerate(self.cnn_idxs.reshape(self.kenrnal_num, -1, self.kenrnal_size*self.kenrnal_size)): numm = 0 @@ -181,10 +181,10 @@ class CNN_lsing(torch.nn.Module): for i, item in enumerate(idx): # i p num deta_J_cnn[j, i] += deta[self.img_node_num + num, item] - # deta_J_cnn_num[j, i] += 1 + deta_J_cnn_num[j, i] += 1 numm += 1 num += 1 - # deta_J_cnn /= deta_J_cnn_num + deta_J_cnn /= deta_J_cnn_num diff --git "a/summer_ospp/2024/24c6d0468/\345\274\200\346\272\220\344\271\213\345\244\217.pptx" "b/summer_ospp/2024/24c6d0468/\345\274\200\346\272\220\344\271\213\345\244\217.pptx" new file mode 100644 index 0000000000000000000000000000000000000000..8dc3215edf5c4ac885c017ed9dea6628bdc38cfe GIT binary patch literal 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00:00:00 2001 From: SevenKingess <2362769214@qq.com> Date: Thu, 31 Oct 2024 16:23:32 +0800 Subject: [PATCH 2/2] update gen code --- summer_ospp/2024/24c6d0468/gpu/gen.py | 17 ++++++++-------- summer_ospp/2024/24c6d0468/gpu/lsing.py | 27 +++++++++++++++++-------- summer_ospp/2024/24c6d0468/gpu/train.py | 20 +++++++++--------- 3 files changed, 37 insertions(+), 27 deletions(-) diff --git a/summer_ospp/2024/24c6d0468/gpu/gen.py b/summer_ospp/2024/24c6d0468/gpu/gen.py index 3628d5acd..f4c7d4922 100644 --- a/summer_ospp/2024/24c6d0468/gpu/gen.py +++ b/summer_ospp/2024/24c6d0468/gpu/gen.py @@ -1,5 +1,4 @@ import torch -from download import download device="cuda" import matplotlib.pyplot as plt @@ -34,19 +33,21 @@ label_class_num=10 label_node_num=50 all_node_num=4264 -from line_profiler import profile -@profile + def main(): - model = lsing_model(label_class_num=2).to(device) + # model = lsing_model(label_class_num=10).to(device) + model = torch.load("./model.pth") with torch.no_grad(): num = 0 for images_batch, labels_batch in test_dataset_loader: - m = model.create_m(labels_batch) - m = model.construct(m, model.group_gen) + m = model.create_m(labels_batch=labels_batch) + # 退火beta从0到5 + m = model.construct(m, model.group_gen, annealing=True) - m = m[:, model.input_node_num] + m = m[:, :model.input_node_num] for i, image in enumerate(m): - plt.imshow(np.array(image), cmap='Greys') + plt.imshow(np.array(image.reshape(28, 28).cpu()), cmap='Greys') plt.savefig(f'gen/num_{labels_batch[i]}.png') + break main() \ No newline at end of file diff --git a/summer_ospp/2024/24c6d0468/gpu/lsing.py b/summer_ospp/2024/24c6d0468/gpu/lsing.py index 4669f3092..73ad9626b 100644 --- a/summer_ospp/2024/24c6d0468/gpu/lsing.py +++ b/summer_ospp/2024/24c6d0468/gpu/lsing.py @@ -82,7 +82,11 @@ class lsing_model(torch.nn.Module): self.deta_H_all = 0 def create_m(self, images_batch=None, labels_batch=None): - m = torch.randint(0, 2, (images_batch.shape[0], self.all_node_num)).to(device) + if images_batch == None: + batch_size = labels_batch.shape[0] + else: + batch_size = images_batch.shape[0] + m = torch.randint(0, 2, (batch_size, self.all_node_num)).to(device) if images_batch != None: m[:, :self.input_node_num] = images_batch @@ -96,16 +100,23 @@ class lsing_model(torch.nn.Module): return m - from line_profiler import profile - @profile - def construct(self, m, group, sample_num=1e+3): + def construct(self, m, group, sample_num=1e+3, annealing=False): J_group = [self.J[idxs].to_sparse_coo() for idxs in group.values()] H_group = [self.H[idxs] for idxs in group.values()] - for _ in range(int(sample_num)): - for idxs, J, H in zip(group.values(), J_group, H_group): - I = torch.sparse.mm(J, m.T).T + H - m[:, idxs] = torch.sgn(torch.tanh(I) - (torch.rand_like(I)*2-1)) + # 退火beta从0到5 + if annealing: + beta = 0 + for _ in range(int(sample_num)): + beta += (5-0)/int(sample_num) + for idxs, J, H in zip(group.values(), J_group, H_group): + I = torch.sparse.mm(J, m.T).T + H + m[:, idxs] = torch.sgn(beta*torch.tanh(I) - (torch.rand_like(I)*2-1)) + else: + for _ in range(int(sample_num)): + for idxs, J, H in zip(group.values(), J_group, H_group): + I = torch.sparse.mm(J, m.T).T + H + m[:, idxs] = torch.sgn(torch.tanh(I) - (torch.rand_like(I)*2-1)) return m diff --git a/summer_ospp/2024/24c6d0468/gpu/train.py b/summer_ospp/2024/24c6d0468/gpu/train.py index 79a07a63b..936527b1e 100644 --- a/summer_ospp/2024/24c6d0468/gpu/train.py +++ b/summer_ospp/2024/24c6d0468/gpu/train.py @@ -10,19 +10,19 @@ from read_MNIST import decode_idx3_ubyte as read_images from read_MNIST import decode_idx1_ubyte as read_labels images = torch.tensor(read_images("./MNIST_Data/train/train-images-idx3-ubyte")).reshape(60000, 28*28).to(device) labels = torch.tensor(read_labels("./MNIST_Data/train/train-labels-idx1-ubyte")).to(device) -# idxs = torch.where(torch.logical_xor(labels==0, labels==7)) -# images = images[idxs] -# labels = labels[idxs] -# labels = torch.where(labels==7, 1, 0) +idxs = torch.where(torch.logical_xor(labels==0, labels==7)) +images = images[idxs] +labels = labels[idxs] +labels = torch.where(labels==7, 1, 0) train_dataset = torch.utils.data.TensorDataset(images, labels) train_dataset_loader = torch.utils.data.DataLoader(train_dataset, batch_size=50, shuffle=True, pin_memory=False) images = torch.tensor(read_images("./MNIST_Data/test/t10k-images-idx3-ubyte")).reshape(10000, 28*28).to(device) labels = torch.tensor(read_labels("./MNIST_Data/test/t10k-labels-idx1-ubyte")).to(device) -# idxs = torch.where(torch.logical_xor(labels==0, labels==7)) -# images = images[idxs] -# labels = labels[idxs] -# labels = torch.where(labels==7, 1, 0) +idxs = torch.where(torch.logical_xor(labels==0, labels==7)) +images = images[idxs] +labels = labels[idxs] +labels = torch.where(labels==7, 1, 0) test_dataset = torch.utils.data.TensorDataset(images, labels) test_dataset_loader = torch.utils.data.DataLoader(test_dataset, batch_size=50, shuffle=True, pin_memory=False) @@ -36,8 +36,6 @@ label_class_num=10 label_node_num=50 all_node_num=4264 -from line_profiler import profile -@profile def main(): model = lsing_model(label_class_num=10).to(device) @@ -64,11 +62,11 @@ def main(): "acc" : acc.mean().item() }) + acc = torch.tensor([]).cuda() for images_batch, labels_batch in test_dataset_loader: m = model.create_m(images_batch) m = model.construct(m, model.group_clssify) - acc = torch.tensor([]).cuda() logits = m[:, input_node_num:input_node_num+label_node_num].reshape(-1, label_node_num//label_class_num, label_class_num) logits = logits.sum(dim=-2).argmax(dim=-1) logits = torch.where(logits==labels_batch, 1., 0.) -- Gitee

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