From 715009b56497357c22947a0e70b733b062db3829 Mon Sep 17 00:00:00 2001 From: Megvii Engine Team Date: Mon, 14 Sep 2020 18:43:15 +0800 Subject: [PATCH] refactor(mge/api): remove external, dropout fix GitOrigin-RevId: 5e6ff1a372522be2e7af85a55edf038f0520ddab --- imperative/python/megengine/functional/nn.py | 2 +- imperative/python/megengine/module/dropout.py | 2 +- .../python/megengine/module/external.py | 56 ---------- .../test/unit/functional/test_functional.py | 101 ++++++++---------- .../python/test/unit/functional/test_math.py | 90 ---------------- 5 files changed, 48 insertions(+), 203 deletions(-) delete mode 100644 imperative/python/megengine/module/external.py diff --git a/imperative/python/megengine/functional/nn.py b/imperative/python/megengine/functional/nn.py index d4890874f..2282956b1 100644 --- a/imperative/python/megengine/functional/nn.py +++ b/imperative/python/megengine/functional/nn.py @@ -1226,7 +1226,7 @@ def dropout(inp: Tensor, drop_prob: float, training: bool = True) -> Tensor: """ assert 0 <= drop_prob < 1 - rv = uniform(inp.shape) + rv = uniform(size=inp.shape) mask = rv > drop_prob inp *= mask.astype(inp.dtype) if training: diff --git a/imperative/python/megengine/module/dropout.py b/imperative/python/megengine/module/dropout.py index fffc19529..0aac97129 100644 --- a/imperative/python/megengine/module/dropout.py +++ b/imperative/python/megengine/module/dropout.py @@ -25,6 +25,6 @@ class Dropout(Module): def forward(self, inputs): if self.training: - return dropout(inputs, self.drop_prob, rescale=True) + return dropout(inputs, self.drop_prob, training=True) else: return inputs diff --git a/imperative/python/megengine/module/external.py b/imperative/python/megengine/module/external.py deleted file mode 100644 index 387125c41..000000000 --- a/imperative/python/megengine/module/external.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from ..functional import cambricon_subgraph, extern_opr_subgraph -from .module import Module - - -class CambriconSubgraph(Module): - r"""Load a serialized Cambricon subgraph. - - See :func:`~.cambricon_subgraph` for more details. - """ - - def __init__( - self, data, symbol, tensor_dim_mutable, - ): - super(CambriconSubgraph, self).__init__() - self._data = data - self.symbol = symbol - self.tensor_dim_mutable = tensor_dim_mutable - - @property - def data(self): - return self._data.tobytes() - - @data.setter - def data(self, val): - self._data = np.frombuffer(val, dtype=np.uint8) - - def forward(self, inputs): - outputs = cambricon_subgraph( - inputs, self._data, self.symbol, self.tensor_dim_mutable, - ) - return outputs - - -class ExternOprSubgraph(Module): - r"""Load a serialized extern opr subgraph. - """ - - def __init__(self, data, name, output_shapes): - super(ExternOprSubgraph, self).__init__() - self.data = data - self.name = name - self.output_shapes = output_shapes - - def forward(self, inputs): - outputs = extern_opr_subgraph(inputs, self.output_shapes, self.name, self.data,) - return outputs diff --git a/imperative/python/test/unit/functional/test_functional.py b/imperative/python/test/unit/functional/test_functional.py index 133722d2e..5cb7a4de1 100644 --- a/imperative/python/test/unit/functional/test_functional.py +++ b/imperative/python/test/unit/functional/test_functional.py @@ -113,6 +113,52 @@ def test_where(): opr_test(cases, F.where, ref_fn=np.where) +def test_dropout(): + data = tensor(np.ones(10, dtype=np.float32)) + out = F.dropout(data, 1.0 / 3.0, training=False) + + assert out.numpy().sum() >= 0.0 + + +def test_matmul(): + shape1 = 3 + shape2 = 3 + shape3 = (3, 5) + shape4 = (5, 6) + data1 = np.random.random(shape1).astype("float32") + data2 = np.random.random(shape2).astype("float32") + data3 = np.random.random(shape3).astype("float32") + data4 = np.random.random(shape4).astype("float32") + + cases = [ + {"input": [data1, data2]}, + {"input": [data2, data3]}, + {"input": [data3, data4]}, + ] + opr_test(cases, F.matmul, ref_fn=np.matmul) + + batch_size = 10 + shape1 = (batch_size, 2, 3) + shape2 = (batch_size, 3, 4) + shape3 = (batch_size, 10, 4, 5) + data1 = np.random.random(shape1).astype("float32") + data2 = np.random.random(shape2).astype("float32") + data3 = np.random.random(shape3).astype("float32") + + cases = [{"input": [data1, data2]}, {"input": [data2, data3]}] + for i in range(0, batch_size): + + def compare_fn(x, y): + x.numpy()[i, ...] == y + + opr_test( + cases, + F.matmul, + compare_fn=compare_fn, + ref_fn=lambda x, y: np.matmul(x[i, ...], y[i, ...]), + ) + + def test_interpolate(): def linear_interpolate(): inp = tensor(np.arange(1, 3, dtype=np.float32).reshape(1, 1, 2)) @@ -281,48 +327,6 @@ def test_add_update_params(): assertTensorClose(res.numpy(), b + 1) -# def test_cross_entropy_with_softmax(): -# data1_shape = (1, 2) -# label1_shape = (1,) -# data2_shape = (1, 3) -# label2_shape = (1,) - -# data1 = np.array([1, 0.5], dtype=np.float32).reshape(data1_shape) -# label1 = np.array([1], dtype=np.int32).reshape(label1_shape) -# expect1 = F.cross_entropy(F.softmax(tensor(data1)), tensor(label1)).numpy() - -# data2 = np.array([0.3, 0.4, 0.3], dtype=np.float32).reshape(data2_shape) -# label2 = np.array([1], dtype=np.int32).reshape(label2_shape) -# expect2 = F.cross_entropy(F.softmax(tensor(data2)), tensor(label2)).numpy() - -# cases = [ -# {"input": [data1, label1], "output": expect1,}, -# {"input": [data2, label2], "output": expect2,}, -# ] -# opr_test(cases, F.cross_entropy_with_softmax) - - -# def test_cross_entropy(): -# data1_shape = (1, 2) -# label1_shape = (1,) -# data2_shape = (1, 3) -# label2_shape = (1,) - -# data1 = np.array([0.5, 0.5], dtype=np.float32).reshape(data1_shape) -# label1 = np.array([1], dtype=np.int32).reshape(label1_shape) -# expect1 = np.array([-np.log(0.5)], dtype=np.float32) - -# data2 = np.array([0.3, 0.4, 0.3], dtype=np.float32).reshape(data2_shape) -# label2 = np.array([1], dtype=np.int32).reshape(label2_shape) -# expect2 = np.array([-np.log(0.4)], dtype=np.float32) - -# cases = [ -# {"input": [data1, label1], "output": expect1,}, -# {"input": [data2, label2], "output": expect2,}, -# ] -# opr_test(cases, F.cross_entropy) - - def test_binary_cross_entropy(): data1_shape = (2, 2) label1_shape = (2, 2) @@ -413,19 +417,6 @@ def test_batched_nms(): np.testing.assert_equal(results.numpy(), np.array([1, 4, 5], dtype=np.int32)) -# def test_smooth_l1_loss(): -# np.random.seed(123) -# cases = [] -# for shape in [(2, 2), (2, 3)]: -# data = np.random.uniform(size=shape).astype(np.float32) -# label = np.random.uniform(size=shape).astype(np.float32) -# diff = np.abs(data - label) -# expect = np.where(diff < 1, 0.5 * diff ** 2, diff - 0.5).mean() -# cases.append({"input": [data, label], "output": tensor(expect)}) - -# opr_test(cases, F.smooth_l1_loss) - - def test_conv_bias(): inp_scale = 1.5 w_scale = 2.5 diff --git a/imperative/python/test/unit/functional/test_math.py b/imperative/python/test/unit/functional/test_math.py index 4cd0045e6..64e0b10fd 100644 --- a/imperative/python/test/unit/functional/test_math.py +++ b/imperative/python/test/unit/functional/test_math.py @@ -203,93 +203,3 @@ def test_normalize(): cases[0]["input"][0, 0, 0, :] = 0 cases[1]["input"][0, 0, 0, :] = 0 opr_test(cases, partial(F.normalize, axis=3), ref_fn=partial(np_normalize, axis=3)) - - -def test_matmul(): - shape1 = 3 - shape2 = 3 - shape3 = (3, 5) - shape4 = (5, 6) - data1 = np.random.random(shape1).astype("float32") - data2 = np.random.random(shape2).astype("float32") - data3 = np.random.random(shape3).astype("float32") - data4 = np.random.random(shape4).astype("float32") - - cases = [ - {"input": [data1, data2]}, - {"input": [data2, data3]}, - {"input": [data3, data4]}, - ] - opr_test(cases, F.matmul, ref_fn=np.matmul) - - batch_size = 10 - shape1 = (batch_size, 2, 3) - shape2 = (batch_size, 3, 4) - shape3 = (batch_size, 10, 4, 5) - data1 = np.random.random(shape1).astype("float32") - data2 = np.random.random(shape2).astype("float32") - data3 = np.random.random(shape3).astype("float32") - - cases = [{"input": [data1, data2]}, {"input": [data2, data3]}] - for i in range(0, batch_size): - - def compare_fn(x, y): - x.numpy()[i, ...] == y - - opr_test( - cases, - F.matmul, - compare_fn=compare_fn, - ref_fn=lambda x, y: np.matmul(x[i, ...], y[i, ...]), - ) - - -# def test_logsumexp(): -# x = np.arange(10).astype(np.float32) -# expected = np.log(np.sum(np.exp(x))) -# cases = [{"input": x, "output": expected}] -# compare_fn = partial(assertTensorClose, allow_special_values=True) -# # large value check -# n = 100 -# x = np.full(n, 10000, dtype=np.float32) -# expected = 10000 + np.log(n) -# cases.append({"input": x, "output": expected.astype(np.float32)}) -# opr_test(cases, F.logsumexp, axis=0, compare_fn=compare_fn) - -# # special value check -# x = np.array([np.inf], dtype=np.float32) -# expected = x -# cases = [{"input": x, "output": expected}] - -# x = np.array([-np.inf, 0.0], dtype=np.float32) -# expected = np.zeros(1).astype(np.float32) -# cases.append({"input": x, "output": expected}) -# opr_test(cases, F.logsumexp, axis=0, compare_fn=compare_fn) - -# x = np.array([np.nan], dtype=np.float32) -# expected = x -# cases = [{"input": x, "output": expected}] - -# x = np.array([-np.inf, 1], dtype=np.float32) -# expected = np.array([1.0], dtype=np.float32) -# cases.append({"input": x, "output": expected}) - -# opr_test(cases, F.logsumexp, axis=0, compare_fn=compare_fn) - -# # keepdims check -# x = np.array([[1e10, 1e-10], [-1e10, -np.inf]], dtype=np.float32) -# expected = np.array([[1e10], [-1e10]], dtype=np.float32) -# cases = [{"input": x, "output": expected}] -# x = np.array([[1e10, -1e-10, 1e-10], [1e10, 1e-10, np.inf]], dtype=np.float32) -# expected = np.array([[1e10], [np.inf]], dtype=np.float32) -# cases.append({"input": x, "output": expected}) -# opr_test(cases, F.logsumexp, axis=1, keepdims=True, compare_fn=compare_fn) - -# # multiple axes check -# x = np.array([[1e10, 1e-10], [-1e10, -np.inf]], dtype=np.float32) -# expected = np.array([1e10], dtype=np.float32) -# cases = [{"input": x, "output": expected}] -# x = np.array([[1e10, -1e-10, 1e-10], [1e10, 1e-10, np.inf]], dtype=np.float32) -# expected = np.array([np.inf], dtype=np.float32) -# cases.append({"input": x, "output": expected}) -# opr_test(cases, F.logsumexp, axis=(0, 1), keepdims=False, compare_fn=compare_fn) -- GitLab