2020-10-26 16:51:25.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1600.jpg
torch.Size([1, 3, 1024, 724])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 506.9 MiB 506.9 MiB 1 @profile
141 def forward(self, x):
142 553.1 MiB 46.1 MiB 1 x = self.conv1(x)
143 598.6 MiB 45.5 MiB 1 x = self.bn1(x)
144 598.6 MiB 0.1 MiB 1 x = self.relu(x)
145 612.9 MiB 14.3 MiB 1 x = self.maxpool(x)
146
147 681.8 MiB 68.9 MiB 1 x = self.layer1(x)
148 707.1 MiB 25.4 MiB 1 x = self.layer2(x)
149 755.9 MiB 48.7 MiB 1 x = self.layer3(x)
150 788.4 MiB 32.5 MiB 1 x = self.layer4(x)
151 788.4 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 788.4 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:29.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1701.jpg
torch.Size([1, 3, 768, 1024])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 685.6 MiB 685.6 MiB 1 @profile
141 def forward(self, x):
142 733.6 MiB 48.0 MiB 1 x = self.conv1(x)
143 781.6 MiB 48.0 MiB 1 x = self.bn1(x)
144 781.6 MiB 0.0 MiB 1 x = self.relu(x)
145 781.6 MiB 0.0 MiB 1 x = self.maxpool(x)
146
147 854.1 MiB 72.4 MiB 1 x = self.layer1(x)
148 940.2 MiB 86.1 MiB 1 x = self.layer2(x)
149 985.1 MiB 45.0 MiB 1 x = self.layer3(x)
150 1022.6 MiB 37.4 MiB 1 x = self.layer4(x)
151 1022.6 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 1022.6 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:32.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1728.jpg
torch.Size([1, 3, 1024, 576])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 817.4 MiB 817.4 MiB 1 @profile
141 def forward(self, x):
142 853.4 MiB 36.0 MiB 1 x = self.conv1(x)
143 889.5 MiB 36.0 MiB 1 x = self.bn1(x)
144 889.5 MiB 0.0 MiB 1 x = self.relu(x)
145 898.5 MiB 9.0 MiB 1 x = self.maxpool(x)
146
147 952.8 MiB 54.3 MiB 1 x = self.layer1(x)
148 1027.0 MiB 74.2 MiB 1 x = self.layer2(x)
149 1034.5 MiB 7.5 MiB 1 x = self.layer3(x)
150 1064.4 MiB 29.9 MiB 1 x = self.layer4(x)
151 1064.4 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 1064.4 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:35.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1729.jpg
torch.Size([1, 3, 1024, 576])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 960.9 MiB 960.9 MiB 1 @profile
141 def forward(self, x):
142 996.9 MiB 36.0 MiB 1 x = self.conv1(x)
143 1032.8 MiB 35.9 MiB 1 x = self.bn1(x)
144 1032.8 MiB 0.0 MiB 1 x = self.relu(x)
145 1041.8 MiB 9.0 MiB 1 x = self.maxpool(x)
146
147 1095.6 MiB 53.7 MiB 1 x = self.layer1(x)
148 1149.4 MiB 53.8 MiB 1 x = self.layer2(x)
149 1149.4 MiB 0.0 MiB 1 x = self.layer3(x)
150 1151.5 MiB 2.1 MiB 1 x = self.layer4(x)
151 1144.8 MiB -6.8 MiB 1 x = self.avgpool(x)
152
153 1144.8 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:37.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1730.jpg
torch.Size([1, 3, 1024, 576])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 960.4 MiB 960.4 MiB 1 @profile
141 def forward(self, x):
142 996.4 MiB 36.0 MiB 1 x = self.conv1(x)
143 1032.3 MiB 35.9 MiB 1 x = self.bn1(x)
144 1032.3 MiB 0.0 MiB 1 x = self.relu(x)
145 1041.3 MiB 9.0 MiB 1 x = self.maxpool(x)
146
147 1095.1 MiB 53.7 MiB 1 x = self.layer1(x)
148 1148.9 MiB 53.8 MiB 1 x = self.layer2(x)
149 1148.9 MiB 0.0 MiB 1 x = self.layer3(x)
150 1151.0 MiB 2.1 MiB 1 x = self.layer4(x)
151 1148.1 MiB -2.9 MiB 1 x = self.avgpool(x)
152
153 1148.1 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:40.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1873
torch.Size([1, 3, 835, 1024])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 969.1 MiB 969.1 MiB 1 @profile
141 def forward(self, x):
142 1021.4 MiB 52.3 MiB 1 x = self.conv1(x)
143 1073.6 MiB 52.3 MiB 1 x = self.bn1(x)
144 1073.6 MiB 0.0 MiB 1 x = self.relu(x)
145 1089.9 MiB 16.3 MiB 1 x = self.maxpool(x)
146
147 1168.6 MiB 78.7 MiB 1 x = self.layer1(x)
148 1223.2 MiB 54.6 MiB 1 x = self.layer2(x)
149 1272.3 MiB 49.1 MiB 1 x = self.layer3(x)
150 1280.7 MiB 8.4 MiB 1 x = self.layer4(x)
151 1280.7 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 1280.7 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:43.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1874
torch.Size([1, 3, 961, 1024])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 1127.2 MiB 1127.2 MiB 1 @profile
141 def forward(self, x):
142 1187.4 MiB 60.2 MiB 1 x = self.conv1(x)
143 1247.5 MiB 60.1 MiB 1 x = self.bn1(x)
144 1247.5 MiB 0.0 MiB 1 x = self.relu(x)
145 1262.5 MiB 15.0 MiB 1 x = self.maxpool(x)
146
147 1413.0 MiB 150.4 MiB 1 x = self.layer1(x)
148 1542.6 MiB 129.6 MiB 1 x = self.layer2(x)
149 1568.0 MiB 25.5 MiB 1 x = self.layer3(x)
150 1613.7 MiB 45.7 MiB 1 x = self.layer4(x)
151 1613.7 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 1613.7 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:48.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1875
torch.Size([1, 3, 788, 1024])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 1424.6 MiB 1424.6 MiB 1 @profile
141 def forward(self, x):
142 1473.8 MiB 49.3 MiB 1 x = self.conv1(x)
143 1523.1 MiB 49.3 MiB 1 x = self.bn1(x)
144 1523.1 MiB 0.0 MiB 1 x = self.relu(x)
145 1535.4 MiB 12.3 MiB 1 x = self.maxpool(x)
146
147 1609.6 MiB 74.2 MiB 1 x = self.layer1(x)
148 1661.2 MiB 51.6 MiB 1 x = self.layer2(x)
149 1707.8 MiB 46.6 MiB 1 x = self.layer3(x)
150 1727.7 MiB 19.8 MiB 1 x = self.layer4(x)
151 1727.7 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 1727.7 MiB 0.0 MiB 1 return x
torch.Size([2048])
2020-10-26 16:51:51.000 INFO 6976 --- [ MainThread] __init__ : Extract: /Users/admin/Downloads/1602302351103/test/1876
torch.Size([1, 3, 744, 1024])
Filename: /Users/admin/code/tezign/cbir-feature-extract/core/resnet.py
Line # Mem usage Increment Occurences Line Contents
============================================================
140 1561.1 MiB 1561.1 MiB 1 @profile
141 def forward(self, x):
142 1607.6 MiB 46.5 MiB 1 x = self.conv1(x)
143 1654.1 MiB 46.5 MiB 1 x = self.bn1(x)
144 1654.1 MiB 0.0 MiB 1 x = self.relu(x)
145 1665.7 MiB 11.6 MiB 1 x = self.maxpool(x)
146
147 1735.8 MiB 70.1 MiB 1 x = self.layer1(x)
148 1836.8 MiB 100.9 MiB 1 x = self.layer2(x)
149 1869.1 MiB 32.3 MiB 1 x = self.layer3(x)
150 1869.2 MiB 0.1 MiB 1 x = self.layer4(x)
151 1869.2 MiB 0.0 MiB 1 x = self.avgpool(x)
152
153 1869.2 MiB 0.0 MiB 1 return x
This is memory usage log, tracked with “Memory-Profile” python package
I have tried "self.net.eval() " and “with torch.no_grad()”, but It was not worked.
And I find a weird phenomenon, When input varied size images, that Memory increasing is bigger than same size input.
Does anyone have encountered the same problem?
Main environment:
Mac OS
Python 3.7
PyTorch 1.2(also tried with PyTorch 1.5)