chore: import upstream snapshot with attribution

tools/pytorch-quantization/tests/quant_instancenorm_test.py

@@ -0,0 +1,199 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""tests of QuantInstanceNorm module.
+Mose tests check the functionality of all the combinations in Quant instancenorm against the corresponding functionalities in
+tensor_quant. There are tests for all the three QuantInstaceNorm1D, QuantInstanceNorm2D, and QuantInstanceNorm3D
+"""
+import pytest
+import numpy as np
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from pytorch_quantization import tensor_quant
+from pytorch_quantization.tensor_quant import QuantDescriptor
+from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer
+from pytorch_quantization import utils as quant_utils
+from pytorch_quantization.nn.modules import quant_instancenorm
+#import tests.utils as test_utils
+
+# make everything run on the GPU
+torch.set_default_tensor_type('torch.cuda.FloatTensor')
+
+torch.backends.cudnn.deterministic = True
+
+np.random.seed(1234)
+
+# pylint:disable=missing-docstring, no-self-use
+NUM_CHANNELS = 15
+
+class TestQuantInstanceNorm1D():
+
+    def test_no_quant(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True)
+        quant_instancenorm_object.input_quantizer.disable()
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128)
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(test_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+    def test_fake_quant_per_tensor(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True,
+                                                                           quant_desc_input=QuantDescriptor())
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128)
+        quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(quant_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+    def test_fake_quant_per_channel(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True,
+                                                                           quant_desc_input=QuantDescriptor(axis=(1)))
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128)
+        quant_input = tensor_quant.fake_tensor_quant(test_input,
+                                                     torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0])
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(quant_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+
+class TestQuantInstanceNorm2D():
+
+    def test_no_quant(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True)
+        quant_instancenorm_object.input_quantizer.disable()
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128, 128)
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(test_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+    def test_fake_quant_per_tensor(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True,
+                                                                           quant_desc_input=QuantDescriptor())
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128, 128)
+        quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(quant_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+    def test_fake_quant_per_channel(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True,
+                                                                           quant_desc_input=QuantDescriptor(axis=(1)))
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128, 128)
+        quant_input = tensor_quant.fake_tensor_quant(test_input,
+                                                     torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0].max(3, keepdim=True)[0])
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(quant_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+
+
+class TestQuantInstanceNorm3D():
+
+    def test_no_quant(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True)
+        quant_instancenorm_object.input_quantizer.disable()
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128)
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(test_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+    def test_fake_quant_per_tensor(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True,
+                                                                           quant_desc_input=QuantDescriptor())
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128)
+        quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(quant_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
+
+    def test_fake_quant_per_channel(self):
+
+        quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True,
+                                                                           quant_desc_input=QuantDescriptor(axis=(1)))
+
+        test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128)
+        quant_input = tensor_quant.fake_tensor_quant(test_input,
+                                                     torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0]
+                                                          .max(3, keepdim=True)[0].max(4, keepdim=True)[0])
+
+        out1 = quant_instancenorm_object(test_input)
+        out2 = F.instance_norm(quant_input,
+                               quant_instancenorm_object.running_mean,
+                               quant_instancenorm_object.running_var,
+                               quant_instancenorm_object.weight,
+                               quant_instancenorm_object.bias)
+        np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())