Skip to content
This repository has been archived by the owner on Sep 18, 2024. It is now read-only.

Fix compressor unit test #1997

Merged
merged 1 commit into from
Feb 5, 2020
Merged

Fix compressor unit test #1997

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 12 additions & 12 deletions src/sdk/pynni/tests/test_compressor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -135,12 +135,12 @@ def test_torch_fpgm_pruner(self):

model.conv2.weight.data = torch.tensor(w).float()
layer = torch_compressor.compressor.LayerInfo('conv2', model.conv2)
masks = pruner.calc_mask(layer, config_list[0])
masks = pruner.calc_mask(layer, config_list[0], if_calculated=torch.tensor(0))
assert all(torch.sum(masks['weight'], (1, 2, 3)).numpy() == np.array([45., 45., 45., 45., 0., 0., 45., 45., 45., 45.]))

pruner.update_epoch(1)
model.conv2.weight.data = torch.tensor(w).float()
masks = pruner.calc_mask(layer, config_list[1])
masks = pruner.calc_mask(layer, config_list[1], if_calculated=torch.tensor(0))
assert all(torch.sum(masks['weight'], (1, 2, 3)).numpy() == np.array([45., 45., 0., 0., 0., 0., 0., 0., 45., 45.]))

@tf2
Expand Down Expand Up @@ -187,9 +187,9 @@ def test_torch_l1filter_pruner(self):
model.conv1.weight.data = torch.tensor(w).float()
model.conv2.weight.data = torch.tensor(w).float()
layer1 = torch_compressor.compressor.LayerInfo('conv1', model.conv1)
mask1 = pruner.calc_mask(layer1, config_list[0])
mask1 = pruner.calc_mask(layer1, config_list[0], if_calculated=torch.tensor(0))
layer2 = torch_compressor.compressor.LayerInfo('conv2', model.conv2)
mask2 = pruner.calc_mask(layer2, config_list[1])
mask2 = pruner.calc_mask(layer2, config_list[1], if_calculated=torch.tensor(0))
assert all(torch.sum(mask1['weight'], (1, 2, 3)).numpy() == np.array([0., 27., 27., 27., 27.]))
assert all(torch.sum(mask2['weight'], (1, 2, 3)).numpy() == np.array([0., 0., 0., 27., 27.]))

Expand All @@ -215,9 +215,9 @@ def test_torch_slim_pruner(self):
pruner = torch_compressor.SlimPruner(model, config_list)

layer1 = torch_compressor.compressor.LayerInfo('bn1', model.bn1)
mask1 = pruner.calc_mask(layer1, config_list[0])
mask1 = pruner.calc_mask(layer1, config_list[0], if_calculated=torch.tensor(0))
layer2 = torch_compressor.compressor.LayerInfo('bn2', model.bn2)
mask2 = pruner.calc_mask(layer2, config_list[0])
mask2 = pruner.calc_mask(layer2, config_list[0], if_calculated=torch.tensor(0))
assert all(mask1['weight'].numpy() == np.array([0., 1., 1., 1., 1.]))
assert all(mask2['weight'].numpy() == np.array([0., 1., 1., 1., 1.]))
assert all(mask1['bias'].numpy() == np.array([0., 1., 1., 1., 1.]))
Expand All @@ -229,9 +229,9 @@ def test_torch_slim_pruner(self):
pruner = torch_compressor.SlimPruner(model, config_list)

layer1 = torch_compressor.compressor.LayerInfo('bn1', model.bn1)
mask1 = pruner.calc_mask(layer1, config_list[0])
mask1 = pruner.calc_mask(layer1, config_list[0], if_calculated=torch.tensor(0))
layer2 = torch_compressor.compressor.LayerInfo('bn2', model.bn2)
mask2 = pruner.calc_mask(layer2, config_list[0])
mask2 = pruner.calc_mask(layer2, config_list[0], if_calculated=torch.tensor(0))
assert all(mask1['weight'].numpy() == np.array([0., 0., 0., 1., 1.]))
assert all(mask2['weight'].numpy() == np.array([0., 0., 0., 1., 1.]))
assert all(mask1['bias'].numpy() == np.array([0., 0., 0., 1., 1.]))
Expand Down Expand Up @@ -268,14 +268,14 @@ def test_torch_QAT_quantizer(self):
# test ema
x = torch.tensor([[-0.2, 0], [0.1, 0.2]])
out = model.relu(x)
assert math.isclose(model.relu.tracked_min_biased, 0, abs_tol=eps)
assert math.isclose(model.relu.tracked_max_biased, 0.002, abs_tol=eps)
assert math.isclose(model.relu.module.tracked_min_biased, 0, abs_tol=eps)
assert math.isclose(model.relu.module.tracked_max_biased, 0.002, abs_tol=eps)

quantizer.step()
x = torch.tensor([[0.2, 0.4], [0.6, 0.8]])
out = model.relu(x)
assert math.isclose(model.relu.tracked_min_biased, 0.002, abs_tol=eps)
assert math.isclose(model.relu.tracked_max_biased, 0.00998, abs_tol=eps)
assert math.isclose(model.relu.module.tracked_min_biased, 0.002, abs_tol=eps)
assert math.isclose(model.relu.module.tracked_max_biased, 0.00998, abs_tol=eps)


if __name__ == '__main__':
Expand Down