def main():
########################################################################
# Loads the configuration for the experiment from the configuration file
#config, learning_rate, batch_size, num_epochs, target_classes = load_config('configuration.json')
########################################################################
# Obtain the PyTorch data loader objects to load batches of the datasets
#train_loader, val_loader, test_loader, classes = get_data_loader(target_classes, batch_size)
# Load the model
net = torch.load("./saved_models/final.pt")
val_images = np.load("./data/test_images_2.npy")
val_label = np.load("./data/test_label_2.npy")
val_dataset = MyDataset(val_images, val_label)
val_loader = DataLoader(val_dataset, batch_size=1024, shuffle=False)
#model_path = get_model_name(config)
#net.load_state_dict(torch.load(model_path))
# Evaluate the model for the confusion matrix
#evaluate_confusion_matrix(model_path, net, test_loader, target_classes)
# Visualize examples for false positive / true negative
#evaluate_visual_confusion_matrix('Plot6', net, val_loader, ['no landing pad','landing pad'])
#cm=evaluate_confusion_matrix('Plot4', net, val_loader, ['no landing pad','landing pad'])
#plot_confusion_matrix('Plot5', cm, ['no landing pad','landing pad'],normalize=False,title='Confusion matrix',cmap=plt.cm.Blues)
print(evaluate(net, val_loader))
def load_data(batch_size):
train_path = "data/rand_flowers_40/train/"
train_path_masked = "data/rand_flowers_40_masked/train/"
val_path = "data/rand_flowers_40/val/"
val_path_masked = "data/rand_flowers_40_masked/val/"
train_dataset = MyDataset(train_path, train_path_masked)
val_dataset = MyDataset(val_path, val_path_masked)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
return train_loader, val_loader
def load_data(batch_size):
train_images = np.load("./data/train_images.npy")
val_images = np.load("./data/val_images.npy")
#test_images=np.load("./data/test_images.npy")
train_label = np.load("./data/train_label.npy")
val_label = np.load("./data/val_label.npy")
#test_label =np.load("./data/test_label.npy")
train_dataset = MyDataset(train_images, train_label)
val_dataset = MyDataset(val_images, val_label)
#test_dataset = MyDataset(test_images, test_label)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
#test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return train_loader, val_loader #, test_loader
def load_data(batch_size):
test_path = "data/rand_flowers_40/test/"
test_path_masked = "data/rand_flowers_40_masked/test/"
test_path = "data/brick_40/test/"
test_path_masked = "data/brick_40_masked/test/"
test_dataset = MyDataset(test_path, test_path_masked)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False)
return test_loader
c = np.transpose(c, (1, 0, 2))
plt.imshow(c)
plt.gray()
plt.show()
# print(img.size) # (4032, 2268)
model = torch.load("./data/model2.pt")
feat_valid = np.load("./data/images_test.npy")
label_valid = np.load("./data/labels_test.npy")
stride = 252
# feat_valid = feat_valid.transpose((0,2,1))
batch_size = 1
valid_dataset = MyDataset(X=feat_valid, y=label_valid)
val_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
total_corr = 0
matrix = np.zeros(img.size)
for i, data in enumerate(val_loader):
images, label = data
images = images.float()
# print(label)
# print("data: ",inputs.shape)
# print("label",label.detach().numpy()[0][0])
# print("label",label.detach().numpy()[0][1])
outputs = model(images)
# print("outputs:{}".format(outputs.item()))
# cv2.destroyAllWindows()
# cv2.imwrite(newdir + "/" + filename, frame)
print("size:",new_img.shape)
crop_image.append(new_img)
index.append((i, j))
j += stride
i += stride
images_zeros = np.array(crop_image)
label_zeros = np.array(index)
batch_size = 1
valid_dataset = MyDataset(X=images_zeros, y=label_zeros)
val_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
total_corr = 0
matrix_0 = np.zeros((newframe.shape[0] // stride , newframe.shape[1] // stride)) ### 这里
# matrix_0 = np.zeros((newframe.shape[0], newframe.shape[1] )) ### 这里
matrix = np.zeros((newframe.shape[0] // stride , newframe.shape[1] // stride ))
# matrix = np.zeros((newframe.shape[0], newframe.shape[1]))
for i, data in enumerate(val_loader):
images, label = data
print("label", label)
images = images.float()
print(images.shape)
outputs = model(images)