forked from mindspore-Ecosystem/mindspore
387 lines
14 KiB
Python
387 lines
14 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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from mindspore.common.tensor import Tensor
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import mindspore.nn as nn
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from mindspore.ops.operations import _quant_ops as Q
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU', device_id=0)
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class Net(nn.Cell):
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def __init__(self,
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num_bits=8,
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quant_delay=0,
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symmetric=False,
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narrow_range=False,
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training=True):
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super(Net, self).__init__()
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self.fake_quant = Q.FakeQuantPerLayer(num_bits=num_bits,
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quant_delay=quant_delay,
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symmetric=symmetric,
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narrow_range=narrow_range,
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training=training)
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def construct(self, x, minq, maxq):
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return self.fake_quant(x, minq, maxq)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant1():
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# (8, false, 0.0f, 0.0f, TensorShape({2, 3}),
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# {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
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# {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f});
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x = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).reshape(2, 3).astype(np.float32)
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min_val = np.array([0]).reshape(1).astype(np.float32)
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max_val = np.array([0]).reshape(1).astype(np.float32)
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expect = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant2():
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# 8, false, -10.0f, 53.75f, TensorShape({2, 3}),
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# {-10.1f, -10.0f, -9.9f, -9.75f, 53.75f, 53.8f},
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# {-10.0f, -10.0f, -10.0f, -9.75f, 53.75f, 53.75f});
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x = np.array([-10.1, -10.0, -9.9, -9.75, 53.75, 53.8]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-10.0]).reshape(1).astype(np.float32)
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max_val = np.array([53.75]).reshape(1).astype(np.float32)
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expect = np.array([-10.0, -10.0, -10.0, -9.75, 53.75, 53.75]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant3():
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# WithVarsNoNudging_NarrowRange
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x = np.array([-10.1, -10.0, -9.90, -9.75, 53.5, 53.6]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-10.0]).reshape(1).astype(np.float32)
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max_val = np.array([53.5]).reshape(1).astype(np.float32)
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expect = np.array([-10.0, -10.0, -10.0, -9.75, 53.5, 53.5]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant4():
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# WithVarsNudgedDown_RegularRange
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x = np.array([-0.1, 0.0, 0.1, 0.25, 63.75, 63.8]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.1]).reshape(1).astype(np.float32)
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max_val = np.array([63.65]).reshape(1).astype(np.float32)
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expect = np.array([-0.0, 0.0, 0.0, 0.25, 63.75, 63.75]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant5():
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# WithVarsNudgedDown_NarrowRange
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x = np.array([-0.1, 0.0, 0.1, 0.25, 63.5, 63.6]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.1]).reshape(1).astype(np.float32)
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max_val = np.array([63.4]).reshape(1).astype(np.float32)
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expect = np.array([-0.0, 0.0, 0.0, 0.25, 63.5, 63.5]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant6():
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# WithVarsNudgedUp_RegularRange
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x = np.array([-0.26, -0.25, -0.24, 0.0, 63.5, 63.6]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.125]).reshape(1).astype(np.float32)
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max_val = np.array([63.625]).reshape(1).astype(np.float32)
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expect = np.array([-0.25, -0.25, -0.25, 0.0, 63.5, 63.5]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant7():
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# WithVarsNudgedUp_NarrowRange
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x = np.array([-0.26, -0.25, -0.24, 0.0, 63.25, 63.3]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.125]).reshape(1).astype(np.float32)
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max_val = np.array([63.375]).reshape(1).astype(np.float32)
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expect = np.array([-0.25, -0.25, -0.25, 0.0, 63.25, 63.25]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant8():
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# WithVarsNudgedZeroIs255_RegularRange
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x = np.array([-63.80, -63.75, -63.70, -63.5, 0.0, 0.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-63.65]).reshape(1).astype(np.float32)
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max_val = np.array([0.1]).reshape(1).astype(np.float32)
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expect = np.array([-63.75, -63.75, -63.75, -63.5, 0.0, 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant9():
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# WithVarsNudgedZeroIs255_NarrowRange
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x = np.array([-63.6, -63.5, -63.4, -63.25, 0.0, 0.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-63.4]).reshape(1).astype(np.float32)
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max_val = np.array([0.1]).reshape(1).astype(np.float32)
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expect = np.array([-63.5, -63.5, -63.5, -63.25, 0.0, 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant10():
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# WithVarsNoNudging_4Bits_RegularRange
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x = np.array([-6.1, -6.0, -5.9, -5.5, 1.5, 1.6]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-6.0]).reshape(1).astype(np.float32)
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max_val = np.array([1.5]).reshape(1).astype(np.float32)
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expect = np.array([-6.0, -6.0, -6.0, -5.5, 1.5, 1.5]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant11():
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# WithVarsNoNudging_4Bits_NarrowRange
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x = np.array([-6.1, -6.0, -5.9, -5.5, 1.0, 1.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-6.0]).reshape(1).astype(np.float32)
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max_val = np.array([1.0]).reshape(1).astype(np.float32)
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expect = np.array([-6.0, -6.0, -6.0, -5.5, 1.0, 1.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant12():
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# WithVarsNudgedDown_4Bits_RegularRange
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x = np.array([-0.1, 0.0, 0.1, 0.5, 7.5, 7.6]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.1]).reshape(1).astype(np.float32)
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max_val = np.array([7.4]).reshape(1).astype(np.float32)
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expect = np.array([-0.0, 0.0, 0.0, 0.5, 7.5, 7.5]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant13():
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# WithVarsNudgedDown_4Bits_NarrowRange
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x = np.array([-0.1, 0.0, 0.1, 0.5, 7.0, 7.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.1]).reshape(1).astype(np.float32)
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max_val = np.array([6.9]).reshape(1).astype(np.float32)
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expect = np.array([-0.0, 0.0, 0.0, 0.5, 7.0, 7.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant14():
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# WithVarsNudgedUp_4Bits_RegularRange
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x = np.array([-0.6, -0.5, -0.24, 0.0, 7.0, 7.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.4]).reshape(1).astype(np.float32)
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max_val = np.array([7.1]).reshape(1).astype(np.float32)
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expect = np.array([-0.5, -0.5, -0.00, 0.0, 7.0, 7.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant15():
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# WithVarsNudgedUp_4Bits_NarrowRange
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x = np.array([-0.6, -0.5, -0.24, 0.0, 6.5, 6.6]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-0.4]).reshape(1).astype(np.float32)
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max_val = np.array([6.6]).reshape(1).astype(np.float32)
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expect = np.array([-0.5, -0.5, -0.00, 0.0, 6.5, 6.5]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant16():
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# WithVarsNudgedZero15_4Bits_RegularRange
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x = np.array([-7.6, -7.5, -7.4, -7.2, 0.0, 0.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-7.3]).reshape(1).astype(np.float32)
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max_val = np.array([0.2]).reshape(1).astype(np.float32)
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expect = np.array([-7.5, -7.5, -7.5, -7.0, 0.0, 0.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=False)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant17():
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# WithVarsNudgedZero15_4Bits_NarrowRange
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x = np.array([-7.1, -7.0, -6.9, -6.5, 0.0, 0.1]).reshape(2, 3).astype(np.float32)
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min_val = np.array([-6.8]).reshape(1).astype(np.float32)
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max_val = np.array([0.2]).reshape(1).astype(np.float32)
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expect = np.array([-7.0, -7.0, -7.0, -6.5, 0.0, 0.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=True)
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output = net(Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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