121 lines
3.4 KiB
Python
121 lines
3.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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import torch
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from torch import nn
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from mmseg.models import BACKBONES, HEADS
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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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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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rng = np.random.RandomState(0)
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imgs = rng.rand(*input_shape)
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segs = rng.randint(
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low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
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img_metas = [{
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'img_shape': (H, W, C),
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'ori_shape': (H, W, C),
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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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mm_inputs = {
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'imgs': torch.FloatTensor(imgs),
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'img_metas': img_metas,
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'gt_semantic_seg': torch.LongTensor(segs)
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}
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return mm_inputs
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@BACKBONES.register_module()
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class ExampleBackbone(nn.Module):
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def __init__(self):
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super(ExampleBackbone, self).__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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@HEADS.register_module()
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class ExampleDecodeHead(BaseDecodeHead):
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def __init__(self):
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super(ExampleDecodeHead, self).__init__(3, 3, num_classes=19)
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def forward(self, inputs):
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return self.cls_seg(inputs[0])
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@HEADS.register_module()
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class ExampleCascadeDecodeHead(BaseCascadeDecodeHead):
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def __init__(self):
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super(ExampleCascadeDecodeHead, self).__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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imgs = mm_inputs.pop('imgs')
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img_metas = mm_inputs.pop('img_metas')
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gt_semantic_seg = mm_inputs['gt_semantic_seg']
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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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imgs = imgs.cuda()
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gt_semantic_seg = gt_semantic_seg.cuda()
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# Test forward train
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losses = segmentor.forward(
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imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True)
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assert isinstance(losses, dict)
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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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# pack into lists
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img_list = [img[None, :] for img in imgs]
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img_meta_list = [[img_meta] for img_meta in img_metas]
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segmentor.forward(img_list, img_meta_list, return_loss=False)
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# Test forward aug test
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with torch.no_grad():
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segmentor.eval()
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# pack into lists
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img_list = [img[None, :] for img in imgs]
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img_list = img_list + img_list
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img_meta_list = [[img_meta] for img_meta in img_metas]
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img_meta_list = img_meta_list + img_meta_list
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segmentor.forward(img_list, img_meta_list, return_loss=False)
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