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detection.py

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+import torch
+import numpy as np
+import scipy.io as sio
+import matplotlib.pyplot as plt
+import time
+
+from vit_pytorch import VisionTransformer as vits
+from utils import plot_roc_curve
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics.pairwise import laplacian_kernel, sigmoid_kernel
+from sklearn.metrics.pairwise import pairwise_kernels
+from sklearn.metrics import pairwise_distances
+
+
+import moco.builder
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Sandiego2 189bands AVIRIS
+data_name = 'Sandiego2'
+# data_name = 'Sandiego100'
+# data_name = 'MUUFL'
+# data_name = 'Beach4'
+
+# Sandiego2
+hyperdata_path = './data/' + data_name + '/sandiego.mat'
+gdt_path = './data/' + data_name + '/groundtruth.mat'
+prior_path = './data/' + data_name + '/prior_target.mat'
+hyperdata = sio.loadmat(hyperdata_path)['data']
+hyperdata = hyperdata.reshape(120*120,-1)
+hyperdata = hyperdata/1.0
+gdt = sio.loadmat(gdt_path)['gt']
+gdt = gdt.reshape(-1)
+prior = sio.loadmat(prior_path)['prior_target'].T
+prior = prior/1.0
+
+# Sandiego100
+# hyperdata_path = './data/' + data_name + '/sandiego.mat'
+# gdt_path = './data/' + data_name + '/groundtruth.mat'
+# prior_path = './data/' + data_name + '/prior_target.mat'
+# hyperdata = sio.loadmat(hyperdata_path)['data']
+# hyperdata = hyperdata.reshape(100*100,-1)
+# hyperdata = hyperdata/1.0
+# gdt = sio.loadmat(gdt_path)['groundtruth']
+# gdt = gdt.reshape(-1)
+# prior = sio.loadmat(prior_path)['prior_target'].T
+# prior = prior/1.0
+
+# MUUFL
+# hyperdata_path = './data/' + data_name + '/data.mat'
+# gdt_path = './data/' + data_name + '/groundtruth.mat'
+# prior_path = './data/' + data_name + '/prior_target.mat'
+# hyperdata = sio.loadmat(hyperdata_path)['data']
+# hyperdata = hyperdata.reshape(325*220,-1)
+# hyperdata = hyperdata/1.0
+# gdt = sio.loadmat(gdt_path)['groundtruth']
+# gdt = gdt.reshape(-1)
+# prior = sio.loadmat(prior_path)['prior_target'].T
+# prior = prior/1.0
+
+# Beach4
+# hyperdata_path = './data/' + data_name + '/data.mat'
+# gdt_path = './data/' + data_name + '/groundtruth.mat'
+# prior_path = './data/' + data_name + '/prior_target.mat'
+# hyperdata = sio.loadmat(hyperdata_path)['data']
+# hyperdata = hyperdata.reshape(100*120,-1)
+# hyperdata = hyperdata/1.0
+# gdt = sio.loadmat(gdt_path)['groundtruth']
+# gdt = gdt.reshape(-1)
+# prior = sio.loadmat(prior_path)['prior_target'].T
+# prior = prior/1.0
+
+def cos_sim(vector_1, vector_2):
+    return (9*(10**49))**(np.inner(vector_1, vector_2) / (np.linalg.norm(vector_1) * (np.linalg.norm(vector_2))))
+
+#Sandiego2 model
+v = vits(
+        img_size=189,
+        patch_size=9,
+        embed_dim=128,
+        depth=2,
+        num_heads=8,
+        representation_size=None,
+        num_classes=128
+    )
+model = moco.builder.MoCo_ViT(v, dim=128, mlp_dim=256, T=0.07, K=14400, m=0.999)
+
+
+# Sandiego100
+# v = vits(
+#         img_size=189,
+#         patch_size=9,
+#         embed_dim=128,
+#         depth=2,
+#         num_heads=8,
+#         representation_size=None,
+#         num_classes=128
+#     )
+# model = moco.builder.MoCo_ViT(v, dim=128, mlp_dim=256, T=0.07, K=10000, m=0.999)
+
+
+# MUUFL
+# v = vits(
+#         img_size=64,
+#         patch_size=4,
+#         embed_dim=128,
+#         depth=2,
+#         num_heads=8,
+#         representation_size=None,
+#         num_classes=128
+#     )
+# model = moco.builder.MoCo_ViT(v, dim=128, mlp_dim=256, T=0.07, K=13000, m=0.999)
+
+# Beach4
+# v = vits(
+#         img_size=102,
+#         patch_size=6,
+#         embed_dim=128,
+#         depth=2,
+#         num_heads=8,
+#         representation_size=None,
+#         num_classes=128
+#     )
+# model = moco.builder.MoCo_ViT(v, dim=128, mlp_dim=256, T=0.07, K=12000, m=0.999)
+
+#Sandiego2
+ckpt = torch.load('./save/MCLT/Sandiego2_models/Sandiego2_lr_0.05_bsz_480_temp_0.07_trial_0/ckpt_epoch_50.pth', map_location='cpu')
+
+state_dict = ckpt['model']
+if torch.cuda.is_available():
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        k = k.replace("module.", "")
+        new_state_dict[k] = v
+    state_dict = new_state_dict
+model = model.cuda()
+model.load_state_dict(state_dict)
+
+start = time.time()
+model.eval()
+target_detector = []
+feature = []
+prior = torch.FloatTensor(prior)
+dataset = torch.FloatTensor(hyperdata)
+with torch.no_grad():
+    prior = prior.to(device)
+    dataset = dataset.to(device)
+    prior_output = model.base_encoder.forward_features(prior)
+    dataset_out = model.base_encoder.forward_features(dataset)
+    prior_output = prior_output.cuda().data.cpu().numpy()
+    dataset_out = dataset_out.cuda().data.cpu().numpy()
+    target_detector = (9*(10**49))**cosine_similarity(prior_output, dataset_out)
+
+target_detector = np.array(target_detector).T
+max3 = np.amax(target_detector)
+min3 = np.amin(target_detector)
+target_detector = (target_detector - min3)/(max3 - min3)
+
+target_detector = (target_detector)**60  # Sandiego100=60; Sandiego2=20; Beach4=20; MUUFL=60
+
+end = time.time()
+print('running time:', end - start)
+
+plot_roc_curve(gdt, target_detector, data_name)
+
+# Sandiego2
+target_detector = np.reshape(target_detector, (120,120))
+
+target_detector = target_detector.tolist()
+
+plt.figure(2)
+plt.imshow(target_detector, cmap='afmhot')
+plt.axis('off')
+pathfigure = './result/' + data_name + '.jpg'
+plt.savefig(pathfigure, bbox_inches='tight', pad_inches=0, dpi=600)
+plt.show()
+
+path_target_detector = './result/' + data_name + '.mat'
+sio.savemat(path_target_detector, {'detect': target_detector})