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!20942 Improve performance of EPP-MVSNet 

Merge pull request !20942 from NewMesc/master
This commit is contained in:
i-robot 2021-07-28 01:44:31 +00:00 committed by Gitee
parent e2011134d0 0d9f90ad02
commit 70607366ed
4 changed files with 20 additions and 21 deletions
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12
model_zoo/research/cv/eppmvsnet/README.md
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@ -60,7 +60,7 @@ After installing MindSpore via the official website and Dataset is correctly gen
```python
# run evaluation example with BlendedMVS dataset
sh eval.sh
sh eval.sh [DATA_PATH] [GPU_ID]
```
# [Script Description](#contents)
@ -107,7 +107,7 @@ Parameters for EPP-MVSNet evaluation can be set in validate.py.
- EPP-MVSNet evaluation on GPU
```python
sh eval.sh
sh eval.sh [DATA_PATH] [GPU_ID]
```
Evaluation result will be stored in "./results/blendedmvs/val/metrics.txt". You can find the result like the
@ -117,7 +117,7 @@ Parameters for EPP-MVSNet evaluation can be set in validate.py.
stage3_l1_loss:1.1738
stage3_less1_acc:0.8734
stage3_less3_acc:0.938
mean forward time(s/pic):0.2697
mean forward time(s/pic):0.1259
```
# [Model Description](#contents)
@ -128,16 +128,16 @@ Parameters for EPP-MVSNet evaluation can be set in validate.py.
| Parameter | EPP-MVSNet GPU |
| ------------------------------ | ---------------------------- |
| Model Version | Inception V1 |
| Model Version | Inception V2 |
| Resource | Tesla V100 16GB; Ubuntu16.04 |
| uploaded Date | 06/22/2021(month/day/year) |
| uploaded Date | 07/27/2021(month/day/year) |
| MindSpore Version | 1.3.0 |
| Dataset | BlendedMVS |
| Batch_size | 1 |
| Output | ./results/blendedmvs/val |
| Acc_less_1mm | 0.8734 |
| Acc_less_3mm | 0.938 |
| mean_time(s/pic) | 0.2697 |
| mean_time(s/pic) | 0.1259 |
# [Description of random situation](#contents)

20
model_zoo/research/cv/eppmvsnet/src/eppmvsnet.py
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@ -54,15 +54,15 @@ class SingleStage(nn.Cell):
depth_interval = depth_interval.view(B, 1, 1, 1)
interim_scale = 1
ref_ncdhw = self.expand_dims(ref_feat, 2).view(B, C, 1, -1)
ref_ncdhw = self.tile(ref_ncdhw, (1, 1, D, 1)).view(B, C, D, H, W)
ref_ncdhw = self.transpose(ref_feat, (0, 2, 3, 1)).view(B, 1, -1, C)
ref_ncdhw = self.tile(ref_ncdhw, (1, D, 1, 1)).view(B, D, H, W, C)
pair_results = [] # MVS
weight_sum = self.zeros((ref_ncdhw.shape[0], 1, 1, ref_ncdhw.shape[3] // interim_scale,
ref_ncdhw.shape[4] // interim_scale), mstype.float32)
fused_interim = self.zeros((ref_ncdhw.shape[0], 8, ref_ncdhw.shape[2] // interim_scale, ref_ncdhw.shape[3] //
interim_scale, ref_ncdhw.shape[4] // interim_scale), mstype.float32)
weight_sum = self.zeros((ref_ncdhw.shape[0], 1, 1, ref_ncdhw.shape[2] // interim_scale,
ref_ncdhw.shape[3] // interim_scale), mstype.float32)
fused_interim = self.zeros((ref_ncdhw.shape[0], 8, ref_ncdhw.shape[1] // interim_scale, ref_ncdhw.shape[2] //
interim_scale, ref_ncdhw.shape[3] // interim_scale), mstype.float32)
depth_values = get_depth_values(depth_start, D, depth_interval, False)
@ -221,8 +221,8 @@ class SingleStageP2_S1(nn.Cell):
B, C, H, W = ref_feat.shape
ref_ncdhw = self.expand_dims(ref_feat, 2).view(B, C, 1, -1)
ref_ncdhw = self.tile(ref_ncdhw, (1, 1, 32, 1)).view(B, C, 32, H, W)
ref_ncdhw = self.transpose(ref_feat, (0, 2, 3, 1)).view(B, 1, -1, C)
ref_ncdhw = self.tile(ref_ncdhw, (1, 32, 1, 1)).view(B, 32, H, W, C)
src_feat = src_feats[:, idx]
proj_mat = proj_mats[:, idx]
@ -279,8 +279,8 @@ class SingleStageP2_S3(nn.Cell):
depth_start = depth_start.reshape(B, 1, 1, 1)
depth_end = depth_start + (D - 1) * depth_interval
ref_ncdhw = self.expand_dims(ref_feat, 2).view(B, C, 1, -1)
ref_ncdhw = self.tile(ref_ncdhw, (1, 1, 96, 1)).view(B, C, 96, H, W)
ref_ncdhw = self.transpose(ref_feat, (0, 2, 3, 1)).view(B, 1, -1, C)
ref_ncdhw = self.tile(ref_ncdhw, (1, 96, 1, 1)).view(B, 96, H, W, C)
src_feat = src_feats[:, idx]
proj_mat = proj_mats[:, idx]

5
model_zoo/research/cv/eppmvsnet/src/modules.py
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@ -335,7 +335,6 @@ class HomoWarp(nn.Cell):
x_s = squeeze(x_s)
y_s = squeeze(y_s)
out_fmap = self.bilinear_sampler(input_fmap, x_s, y_s)
out_fmap = trans(out_fmap, (0, 4, 1, 2, 3))
return out_fmap
@ -429,8 +428,8 @@ def entropy_num_based(volume, dim, depth_num, keepdim=False):
def groupwise_correlation(v1, v2, groups, dim):
n, c, d, h, w = v1.shape
reshaped_size = (n, groups, c // groups, d, h, w)
n, d, h, w, c = v1.shape
reshaped_size = (n, d, h, w, groups, c // groups)
v1_reshaped = v1.view(*reshaped_size)
v2_reshaped = v2.view(*reshaped_size)
vc = P.ReduceSum()(v1_reshaped * v2_reshaped, dim + 1)

4
model_zoo/research/cv/eppmvsnet/validate.py
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@ -36,7 +36,7 @@ def get_opts():
parser = ArgumentParser()
parser.add_argument('--gpu_id', type=int, default=0, choices=[0, 1, 2, 3, 4, 5, 6, 7],
help='which gpu used to inference')
## vis
## data
parser.add_argument('--root_dir', type=str,
default='/home/ubuntu/data/DTU/mvs_training/dtu/',
help='root directory of dtu dataset')
@ -73,7 +73,7 @@ def get_opts():
if __name__ == "__main__":
args = get_opts()
context.set_context(mode=0, device_target='GPU', device_id=args.gpu_id, save_graphs=False)
context.set_context(mode=0, device_target='GPU', device_id=args.gpu_id, save_graphs=False, enable_graph_kernel=True)
dataset = BlendedMVSDataset(args.root_dir, args.split, n_views=args.n_views, depth_interval=args.depth_interval,
img_wh=tuple(args.img_wh), levels=args.levels, scan=args.scan)