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78 lines
2.0 KiB
78 lines
2.0 KiB
import os |
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import sys |
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sys.path.append(os.path.abspath('../PaddleRS')) |
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import paddlers as pdrs |
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# 定义训练和验证时的transforms |
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train_transforms = pdrs.datasets.ComposeTrans( |
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input_keys=['lq', 'gt'], |
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output_keys=['lq', 'gt'], |
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pipelines=[{ |
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'name': 'SRPairedRandomCrop', |
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'gt_patch_size': 192, |
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'scale': 4 |
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}, { |
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'name': 'PairedRandomHorizontalFlip' |
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}, { |
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'name': 'PairedRandomVerticalFlip' |
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}, { |
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'name': 'PairedRandomTransposeHW' |
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}, { |
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'name': 'Transpose' |
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}, { |
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'name': 'Normalize', |
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'mean': [0.0, 0.0, 0.0], |
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'std': [255.0, 255.0, 255.0] |
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}]) |
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test_transforms = pdrs.datasets.ComposeTrans( |
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input_keys=['lq', 'gt'], |
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output_keys=['lq', 'gt'], |
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pipelines=[{ |
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'name': 'Transpose' |
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}, { |
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'name': 'Normalize', |
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'mean': [0.0, 0.0, 0.0], |
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'std': [255.0, 255.0, 255.0] |
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}]) |
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# 定义训练集 |
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train_gt_floder = r"../work/RSdata_for_SR/trian_HR" # 高分辨率影像所在路径 |
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train_lq_floder = r"../work/RSdata_for_SR/train_LR/x4" # 低分辨率影像所在路径 |
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num_workers = 4 |
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batch_size = 16 |
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scale = 4 |
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train_dataset = pdrs.datasets.SRdataset( |
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mode='train', |
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gt_floder=train_gt_floder, |
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lq_floder=train_lq_floder, |
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transforms=train_transforms(), |
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scale=scale, |
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num_workers=num_workers, |
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batch_size=batch_size) |
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# 定义测试集 |
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test_gt_floder = r"../work/RSdata_for_SR/test_HR" |
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test_lq_floder = r"../work/RSdata_for_SR/test_LR/x4" |
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test_dataset = pdrs.datasets.SRdataset( |
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mode='test', |
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gt_floder=test_gt_floder, |
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lq_floder=test_lq_floder, |
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transforms=test_transforms(), |
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scale=scale) |
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# 初始化模型，可以对网络结构的参数进行调整 |
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model = pdrs.tasks.LESRCNNet(scale=4, multi_scale=False, group=1) |
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model.train( |
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total_iters=1000000, |
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train_dataset=train_dataset(), |
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test_dataset=test_dataset(), |
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output_dir='output_dir', |
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validate=5000, |
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snapshot=5000, |
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log=100, |
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lr_rate=0.0001, |
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periods=[250000, 250000, 250000, 250000], |
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restart_weights=[1, 1, 1, 1])
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