235 lines
7.6 KiB
Python
235 lines
7.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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import numpy as np
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import pytest
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import torch
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import torch.nn as nn
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from mmcv import ConfigDict
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from mmseg.models import BACKBONES, HEADS
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from mmseg.models.decode_heads.decode_head import BaseDecodeHead
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Backend, Codebase
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from mmdeploy.utils.test import (WrapModel, check_backend, get_model_outputs,
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get_rewrite_outputs)
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import_codebase(Codebase.MMSEG)
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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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def get_model(type='EncoderDecoder',
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backbone='ExampleBackbone',
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decode_head='ExampleDecodeHead'):
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from mmseg.models import build_segmentor
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cfg = ConfigDict(
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type=type,
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backbone=dict(type=backbone),
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decode_head=dict(type=decode_head),
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train_cfg=None,
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test_cfg=dict(mode='whole'))
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segmentor = build_segmentor(cfg)
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return segmentor
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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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@pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME, Backend.OPENVINO])
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def test_encoderdecoder_simple_test(backend):
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check_backend(backend)
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segmentor = get_model()
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segmentor.cpu().eval()
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type=backend.value),
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onnx_config=dict(output_names=['result'], input_shape=None),
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codebase_config=dict(type='mmseg', task='Segmentation')))
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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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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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model_inputs = {'img': imgs, 'img_meta': img_metas}
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model_outputs = get_model_outputs(segmentor, 'simple_test', model_inputs)
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img_meta = {
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'img_shape':
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(img_metas[0]['img_shape'][0], img_metas[0]['img_shape'][1])
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}
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wrapped_model = WrapModel(segmentor, 'simple_test', img_meta=img_meta)
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rewrite_inputs = {
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'img': imgs,
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}
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rewrite_outputs, is_backend_output = get_rewrite_outputs(
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wrapped_model=wrapped_model,
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model_inputs=rewrite_inputs,
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deploy_cfg=deploy_cfg)
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model_outputs = torch.tensor(model_outputs[0])
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rewrite_outputs = rewrite_outputs[0].to(model_outputs).reshape(
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model_outputs.shape)
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assert torch.allclose(rewrite_outputs, model_outputs)
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@pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME, Backend.OPENVINO])
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def test_basesegmentor_forward(backend):
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check_backend(backend)
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segmentor = get_model()
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segmentor.cpu().eval()
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type=backend.value),
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onnx_config=dict(output_names=['result'], input_shape=None),
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codebase_config=dict(type='mmseg', task='Segmentation')))
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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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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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model_inputs = {
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'img': [imgs],
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'img_metas': [img_metas],
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'return_loss': False
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}
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model_outputs = get_model_outputs(segmentor, 'forward', model_inputs)
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wrapped_model = WrapModel(segmentor, 'forward')
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rewrite_inputs = {'img': imgs}
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rewrite_outputs, is_backend_output = get_rewrite_outputs(
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wrapped_model=wrapped_model,
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model_inputs=rewrite_inputs,
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deploy_cfg=deploy_cfg)
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model_outputs = torch.tensor(model_outputs[0])
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rewrite_outputs = rewrite_outputs[0].to(model_outputs).reshape(
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model_outputs.shape)
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assert torch.allclose(rewrite_outputs, model_outputs)
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@pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME, Backend.OPENVINO])
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def test_aspphead_forward(backend):
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check_backend(backend)
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from mmseg.models.decode_heads import ASPPHead
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head = ASPPHead(
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in_channels=32, channels=16, num_classes=19,
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dilations=(1, 12, 24)).eval()
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type=backend.value),
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onnx_config=dict(
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output_names=['result'], input_shape=(1, 32, 45, 45)),
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codebase_config=dict(type='mmseg', task='Segmentation')))
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inputs = [torch.randn(1, 32, 45, 45)]
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model_inputs = {'inputs': inputs}
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with torch.no_grad():
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model_outputs = get_model_outputs(head, 'forward', model_inputs)
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wrapped_model = WrapModel(head, 'forward')
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rewrite_inputs = {'inputs': inputs}
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rewrite_outputs, is_backend_output = get_rewrite_outputs(
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wrapped_model=wrapped_model,
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model_inputs=rewrite_inputs,
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deploy_cfg=deploy_cfg)
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if is_backend_output:
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rewrite_outputs = rewrite_outputs[0]
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rewrite_outputs = rewrite_outputs.to(model_outputs).reshape(
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model_outputs.shape)
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assert torch.allclose(
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rewrite_outputs, model_outputs, rtol=1e-03, atol=1e-05)
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@pytest.mark.parametrize('backend',
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[Backend.ONNXRUNTIME, Backend.OPENVINO, Backend.NCNN])
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def test_psphead_forward(backend):
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check_backend(backend)
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from mmseg.models.decode_heads import PSPHead
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head = PSPHead(in_channels=32, channels=16, num_classes=19).eval()
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type=backend.value),
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onnx_config=dict(output_names=['result'], input_shape=None),
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codebase_config=dict(type='mmseg', task='Segmentation')))
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inputs = [torch.randn(1, 32, 45, 45)]
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model_inputs = {'inputs': inputs}
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with torch.no_grad():
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model_outputs = get_model_outputs(head, 'forward', model_inputs)
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wrapped_model = WrapModel(head, 'forward')
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rewrite_inputs = {'inputs': inputs}
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rewrite_outputs, is_backend_output = get_rewrite_outputs(
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wrapped_model=wrapped_model,
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model_inputs=rewrite_inputs,
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deploy_cfg=deploy_cfg)
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if is_backend_output:
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rewrite_outputs = rewrite_outputs[0]
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rewrite_outputs = rewrite_outputs.to(model_outputs).reshape(
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model_outputs.shape)
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assert torch.allclose(rewrite_outputs, model_outputs, rtol=1, atol=1)
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