135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmengine.optim import OptimWrapper
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from mmengine.structures import PixelData
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from torch import nn
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from torch.optim import SGD
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from mmseg.models import SegDataPreProcessor
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from mmseg.models.decode_heads.cascade_decode_head import BaseCascadeDecodeHead
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from mmseg.models.decode_heads.decode_head import BaseDecodeHead
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from mmseg.registry import MODELS
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from mmseg.structures import SegDataSample
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def _demo_mm_inputs(input_shape=(1, 3, 8, 16), num_classes=10):
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"""Create a superset of inputs needed to run test or train batches.
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Args:
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input_shape (tuple):
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input batch dimensions
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num_classes (int):
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number of semantic classes
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"""
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(N, C, H, W) = input_shape
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imgs = torch.randn(*input_shape)
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segs = torch.randint(
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low=0, high=num_classes - 1, size=(N, H, W), dtype=torch.long)
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img_metas = [{
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'img_shape': (H, W),
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'ori_shape': (H, W),
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'pad_shape': (H, W, C),
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'filename': '<demo>.png',
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'scale_factor': 1.0,
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'flip': False,
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'flip_direction': 'horizontal'
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} for _ in range(N)]
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data_samples = [
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SegDataSample(
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gt_sem_seg=PixelData(data=segs[i]), metainfo=img_metas[i])
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for i in range(N)
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]
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mm_inputs = {'imgs': torch.FloatTensor(imgs), 'data_samples': data_samples}
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return mm_inputs
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@MODELS.register_module()
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class ExampleBackbone(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = nn.Conv2d(3, 3, 3)
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def init_weights(self, pretrained=None):
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pass
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def forward(self, x):
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return [self.conv(x)]
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@MODELS.register_module()
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class ExampleDecodeHead(BaseDecodeHead):
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def __init__(self, num_classes=19, out_channels=None, **kwargs):
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super().__init__(
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3, 3, num_classes=num_classes, out_channels=out_channels, **kwargs)
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def forward(self, inputs):
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return self.cls_seg(inputs[0])
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@MODELS.register_module()
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class ExampleCascadeDecodeHead(BaseCascadeDecodeHead):
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def __init__(self):
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super().__init__(3, 3, num_classes=19)
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def forward(self, inputs, prev_out):
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return self.cls_seg(inputs[0])
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def _segmentor_forward_train_test(segmentor):
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if isinstance(segmentor.decode_head, nn.ModuleList):
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num_classes = segmentor.decode_head[-1].num_classes
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else:
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num_classes = segmentor.decode_head.num_classes
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# batch_size=2 for BatchNorm
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mm_inputs = _demo_mm_inputs(num_classes=num_classes)
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# convert to cuda Tensor if applicable
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if torch.cuda.is_available():
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segmentor = segmentor.cuda()
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# check data preprocessor
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if not hasattr(segmentor,
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'data_preprocessor') or segmentor.data_preprocessor is None:
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segmentor.data_preprocessor = SegDataPreProcessor()
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mm_inputs = segmentor.data_preprocessor(mm_inputs, True)
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imgs = mm_inputs.pop('imgs')
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data_samples = mm_inputs.pop('data_samples')
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# create optimizer wrapper
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optimizer = SGD(segmentor.parameters(), lr=0.1)
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optim_wrapper = OptimWrapper(optimizer)
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# Test forward train
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losses = segmentor.forward(imgs, data_samples, mode='loss')
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assert isinstance(losses, dict)
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# Test train_step
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data_batch = dict(inputs=imgs, data_samples=data_samples)
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outputs = segmentor.train_step(data_batch, optim_wrapper)
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assert isinstance(outputs, dict)
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assert 'loss' in outputs
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# Test val_step
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with torch.no_grad():
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segmentor.eval()
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data_batch = dict(inputs=imgs, data_samples=data_samples)
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outputs = segmentor.val_step(data_batch)
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assert isinstance(outputs, list)
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# Test forward simple test
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with torch.no_grad():
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segmentor.eval()
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data_batch = dict(inputs=imgs, data_samples=data_samples)
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results = segmentor.forward(imgs, data_samples, mode='tensor')
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assert isinstance(results, torch.Tensor)
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