diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py index 3721bc3c44..63ae7a94ac 100644 --- a/mindspore/nn/layer/image.py +++ b/mindspore/nn/layer/image.py @@ -21,9 +21,13 @@ from mindspore.ops import functional as F from mindspore.ops.primitive import constexpr from mindspore._checkparam import Validator as validator from mindspore._checkparam import Rel +from .conv import Conv2d +from .container import CellList +from .pooling import AvgPool2d +from .activation import ReLU from ..cell import Cell -__all__ = ['ImageGradients', 'SSIM', 'PSNR', 'CentralCrop'] +__all__ = ['ImageGradients', 'SSIM', 'MSSSIM', 'PSNR', 'CentralCrop'] class ImageGradients(Cell): r""" @@ -83,21 +87,6 @@ def _convert_img_dtype_to_float32(img, max_val): ret = ret * scale return ret - -@constexpr -def _gauss_kernel_helper(filter_size): - """gauss kernel helper""" - filter_size = F.scalar_cast(filter_size, mstype.int32) - coords = () - for i in range(filter_size): - i_cast = F.scalar_cast(i, mstype.float32) - offset = F.scalar_cast(filter_size-1, mstype.float32)/2.0 - element = i_cast-offset - coords = coords+(element,) - g = np.square(coords).astype(np.float32) - g = Tensor(g) - return filter_size, g - @constexpr def _check_input_4d(input_shape, param_name, func_name): if len(input_shape) != 4: @@ -110,9 +99,65 @@ def _check_input_filter_size(input_shape, param_name, filter_size, func_name): validator.check(param_name + " shape[2]", input_shape[2], "filter_size", filter_size, Rel.GE, func_name) validator.check(param_name + " shape[3]", input_shape[3], "filter_size", filter_size, Rel.GE, func_name) -@constexpr -def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name): - validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name) +def _conv2d(in_channels, out_channels, kernel_size, weight, stride=1, padding=0): + return Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, + weight_init=weight, padding=padding, pad_mode="valid") + +def _create_window(size, sigma): + x_data, y_data = np.mgrid[-size // 2 + 1:size // 2 + 1, -size // 2 + 1:size // 2 + 1] + x_data = np.expand_dims(x_data, axis=-1).astype(np.float32) + x_data = np.expand_dims(x_data, axis=-1) ** 2 + y_data = np.expand_dims(y_data, axis=-1).astype(np.float32) + y_data = np.expand_dims(y_data, axis=-1) ** 2 + sigma = 2 * sigma ** 2 + g = np.exp(-(x_data + y_data) / sigma) + return np.transpose(g / np.sum(g), (2, 3, 0, 1)) + +def _split_img(x): + _, c, _, _ = F.shape(x) + img_split = P.Split(1, c) + output = img_split(x) + return output, c + +def _compute_per_channel_loss(c1, c2, img1, img2, conv): + """computes ssim index between img1 and img2 per single channel""" + dot_img = img1 * img2 + mu1 = conv(img1) + mu2 = conv(img2) + mu1_sq = mu1 * mu1 + mu2_sq = mu2 * mu2 + mu1_mu2 = mu1 * mu2 + sigma1_tmp = conv(img1 * img1) + sigma1_sq = sigma1_tmp - mu1_sq + sigma2_tmp = conv(img2 * img2) + sigma2_sq = sigma2_tmp - mu2_sq + sigma12_tmp = conv(dot_img) + sigma12 = sigma12_tmp - mu1_mu2 + a = (2 * mu1_mu2 + c1) + b = (mu1_sq + mu2_sq + c1) + v1 = 2 * sigma12 + c2 + v2 = sigma1_sq + sigma2_sq + c2 + ssim = (a * v1) / (b * v2) + cs = v1 / v2 + return ssim, cs + +def _compute_multi_channel_loss(c1, c2, img1, img2, conv, concat, mean): + """computes ssim index between img1 and img2 per color channel""" + split_img1, c = _split_img(img1) + split_img2, _ = _split_img(img2) + multi_ssim = () + multi_cs = () + for i in range(c): + ssim_per_channel, cs_per_channel = _compute_per_channel_loss(c1, c2, split_img1[i], split_img2[i], conv) + multi_ssim += (ssim_per_channel,) + multi_cs += (cs_per_channel,) + + multi_ssim = concat(multi_ssim) + multi_cs = concat(multi_cs) + + ssim = mean(multi_ssim, (2, 3)) + cs = mean(multi_cs, (2, 3)) + return ssim, cs class SSIM(Cell): r""" @@ -157,67 +202,126 @@ class SSIM(Cell): self.max_val = max_val self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE, self.cls_name) self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma, self.cls_name) - validator.check_value_type('k1', k1, [float], self.cls_name) - self.k1 = validator.check_number_range('k1', k1, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name) - validator.check_value_type('k2', k2, [float], self.cls_name) - self.k2 = validator.check_number_range('k2', k2, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name) - self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size) + self.k1 = validator.check_value_type('k1', k1, [float], self.cls_name) + self.k2 = validator.check_value_type('k2', k2, [float], self.cls_name) + window = _create_window(filter_size, filter_sigma) + self.conv = _conv2d(1, 1, filter_size, Tensor(window)) + self.conv.weight.requires_grad = False + self.reduce_mean = P.ReduceMean() + self.concat = P.Concat(axis=1) def construct(self, img1, img2): - _check_input_dtype(F.dtype(img1), "img1", [mstype.float32, mstype.float16], self.cls_name) _check_input_filter_size(F.shape(img1), "img1", self.filter_size, self.cls_name) P.SameTypeShape()(img1, img2) max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val) img1 = _convert_img_dtype_to_float32(img1, self.max_val) img2 = _convert_img_dtype_to_float32(img2, self.max_val) - kernel = self._fspecial_gauss(self.filter_size, self.filter_sigma) - kernel = P.Tile()(kernel, (1, P.Shape()(img1)[1], 1, 1)) + c1 = (self.k1 * max_val) ** 2 + c2 = (self.k2 * max_val) ** 2 + + ssim_ave_channel, _ = _compute_multi_channel_loss(c1, c2, img1, img2, self.conv, self.concat, self.reduce_mean) + loss = self.reduce_mean(ssim_ave_channel, -1) + + return loss + +def _downsample(img1, img2, op): + a = op(img1) + b = op(img2) + return a, b + +class MSSSIM(Cell): + r""" + Returns MS-SSIM index between img1 and img2. + + Its implementation is based on Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. `Multiscale structural similarity + for image quality assessment `_. + Signals, Systems and Computers, 2004. - mean_ssim = self._calculate_mean_ssim(img1, img2, kernel, max_val, self.k1, self.k2) + .. math:: - return mean_ssim + l(x,y)&=\frac{2\mu_x\mu_y+C_1}{\mu_x^2+\mu_y^2+C_1}, C_1=(K_1L)^2.\\ + c(x,y)&=\frac{2\sigma_x\sigma_y+C_2}{\sigma_x^2+\sigma_y^2+C_2}, C_2=(K_2L)^2.\\ + s(x,y)&=\frac{\sigma_{xy}+C_3}{\sigma_x\sigma_y+C_3}, C_3=C_2/2.\\ + MSSSIM(x,y)&=l^alpha_M*{\prod_{1\leq j\leq M} (c^beta_j*s^gamma_j)}. - def _calculate_mean_ssim(self, x, y, kernel, max_val, k1, k2): - """calculate mean ssim""" - c1 = (k1 * max_val) * (k1 * max_val) - c2 = (k2 * max_val) * (k2 * max_val) + Args: + max_val (Union[int, float]): The dynamic range of the pixel values (255 for 8-bit grayscale images). + Default: 1.0. + power_factors (Union[tuple, list]): Iterable of weights for each of the scales. + Default: (0.0448, 0.2856, 0.3001, 0.2363, 0.1333). Default values obtained by Wang et al. + filter_size (int): The size of the Gaussian filter. Default: 11. + filter_sigma (float): The standard deviation of Gaussian kernel. Default: 1.5. + k1 (float): The constant used to generate c1 in the luminance comparison function. Default: 0.01. + k2 (float): The constant used to generate c2 in the contrast comparison function. Default: 0.03. - # SSIM luminance formula - # (2 * mean_{x} * mean_{y} + c1) / (mean_{x}**2 + mean_{y}**2 + c1) - mean_x = self.mean(x, kernel) - mean_y = self.mean(y, kernel) - square_sum = F.square(mean_x)+F.square(mean_y) - luminance = (2*mean_x*mean_y+c1)/(square_sum+c1) + Inputs: + - **img1** (Tensor) - The first image batch with format 'NCHW'. It should be the same shape and dtype as img2. + - **img2** (Tensor) - The second image batch with format 'NCHW'. It should be the same shape and dtype as img1. - # SSIM contrast*structure formula (when c3 = c2/2) - # (2 * conv_{xy} + c2) / (conv_{xx} + conv_{yy} + c2), equals to - # (2 * (mean_{xy} - mean_{x}*mean_{y}) + c2) / (mean_{xx}-mean_{x}**2 + mean_{yy}-mean_{y}**2 + c2) - mean_xy = self.mean(x*y, kernel) - mean_square_add = self.mean(F.square(x)+F.square(y), kernel) + Outputs: + Tensor, has the same dtype as img1. It is a 1-D tensor with shape N, where N is the batch num of img1. - cs = (2*(mean_xy-mean_x*mean_y)+c2)/(mean_square_add-square_sum+c2) + Examples: + >>> net = nn.MSSSIM(power_factors=(0.033, 0.033, 0.033)) + >>> img1 = Tensor(np.random.random((1,3,128,128))) + >>> img2 = Tensor(np.random.random((1,3,128,128))) + >>> msssim = net(img1, img2) + """ + def __init__(self, max_val=1.0, power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), filter_size=11, + filter_sigma=1.5, k1=0.01, k2=0.03): + super(MSSSIM, self).__init__() + validator.check_value_type('max_val', max_val, [int, float], self.cls_name) + validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name) + self.max_val = max_val + validator.check_value_type('power_factors', power_factors, [tuple, list], self.cls_name) + self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE, self.cls_name) + self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma, self.cls_name) + self.k1 = validator.check_value_type('k1', k1, [float], self.cls_name) + self.k2 = validator.check_value_type('k2', k2, [float], self.cls_name) + window = _create_window(filter_size, filter_sigma) + self.level = len(power_factors) + self.conv = [] + for i in range(self.level): + self.conv.append(_conv2d(1, 1, filter_size, Tensor(window))) + self.conv[i].weight.requires_grad = False + self.multi_convs_list = CellList(self.conv) + self.weight_tensor = Tensor(power_factors, mstype.float32) + self.avg_pool = AvgPool2d(kernel_size=2, stride=2, pad_mode='valid') + self.relu = ReLU() + self.reduce_mean = P.ReduceMean() + self.prod = P.ReduceProd() + self.pow = P.Pow() + self.pack = P.Pack(axis=-1) + self.concat = P.Concat(axis=1) - # SSIM formula - # luminance * cs - ssim = luminance*cs + def construct(self, img1, img2): + _check_input_4d(F.shape(img1), "img1", self.cls_name) + _check_input_4d(F.shape(img2), "img2", self.cls_name) + P.SameTypeShape()(img1, img2) + max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val) + img1 = _convert_img_dtype_to_float32(img1, self.max_val) + img2 = _convert_img_dtype_to_float32(img2, self.max_val) - mean_ssim = P.ReduceMean()(ssim, (-3, -2, -1)) + c1 = (self.k1 * max_val) ** 2 + c2 = (self.k2 * max_val) ** 2 - return mean_ssim + sim = () + mcs = () - def _fspecial_gauss(self, filter_size, filter_sigma): - """get gauss kernel""" - filter_size, g = _gauss_kernel_helper(filter_size) + for i in range(self.level): + sim, cs = _compute_multi_channel_loss(c1, c2, img1, img2, + self.multi_convs_list[i], self.concat, self.reduce_mean) + mcs += (self.relu(cs),) + img1, img2 = _downsample(img1, img2, self.avg_pool) - square_sigma_scale = -0.5/(filter_sigma * filter_sigma) - g = g*square_sigma_scale - g = F.reshape(g, (1, -1))+F.reshape(g, (-1, 1)) - g = F.reshape(g, (1, -1)) - g = P.Softmax()(g) - ret = F.reshape(g, (1, 1, filter_size, filter_size)) - return ret + mcs = mcs[0:-1:1] + mcs_and_ssim = self.pack(mcs + (self.relu(sim),)) + mcs_and_ssim = self.pow(mcs_and_ssim, self.weight_tensor) + ms_ssim = self.prod(mcs_and_ssim, -1) + loss = self.reduce_mean(ms_ssim, -1) + return loss class PSNR(Cell): r""" diff --git a/tests/ut/python/nn/test_msssim.py b/tests/ut/python/nn/test_msssim.py new file mode 100644 index 0000000000..b85d13c927 --- /dev/null +++ b/tests/ut/python/nn/test_msssim.py @@ -0,0 +1,135 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +""" +test msssim +""" +import numpy as np +import pytest + +import mindspore.common.dtype as mstype +import mindspore.nn as nn +from mindspore import Tensor +from mindspore.common.api import _executor + +_MSSSIM_WEIGHTS = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333) + +class MSSSIMNet(nn.Cell): + def __init__(self, max_val=1.0, power_factors=_MSSSIM_WEIGHTS, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03): + super(MSSSIMNet, self).__init__() + self.net = nn.MSSSIM(max_val, power_factors, filter_size, filter_sigma, k1, k2) + + def construct(self, img1, img2): + return self.net(img1, img2) + + +def test_compile(): + factors = (0.033, 0.033, 0.033) + net = MSSSIMNet(power_factors=factors) + img1 = Tensor(np.random.random((8, 3, 128, 128))) + img2 = Tensor(np.random.random((8, 3, 128, 128))) + _executor.compile(net, img1, img2) + + +def test_compile_grayscale(): + max_val = 255 + factors = (0.033, 0.033, 0.033) + net = MSSSIMNet(max_val=max_val, power_factors=factors) + img1 = Tensor(np.random.randint(0, 256, (8, 3, 128, 128), np.uint8)) + img2 = Tensor(np.random.randint(0, 256, (8, 3, 128, 128), np.uint8)) + _executor.compile(net, img1, img2) + + +def test_msssim_max_val_negative(): + max_val = -1 + with pytest.raises(ValueError): + _ = MSSSIMNet(max_val) + + +def test_msssim_max_val_bool(): + max_val = True + with pytest.raises(TypeError): + _ = MSSSIMNet(max_val) + + +def test_msssim_max_val_zero(): + max_val = 0 + with pytest.raises(ValueError): + _ = MSSSIMNet(max_val) + + +def test_msssim_power_factors_set(): + with pytest.raises(TypeError): + _ = MSSSIMNet(power_factors={0.033, 0.033, 0.033}) + + +def test_msssim_filter_size_float(): + with pytest.raises(TypeError): + _ = MSSSIMNet(filter_size=1.1) + + +def test_msssim_filter_size_zero(): + with pytest.raises(ValueError): + _ = MSSSIMNet(filter_size=0) + + +def test_msssim_filter_sigma_zero(): + with pytest.raises(ValueError): + _ = MSSSIMNet(filter_sigma=0.0) + + +def test_msssim_filter_sigma_negative(): + with pytest.raises(ValueError): + _ = MSSSIMNet(filter_sigma=-0.1) + + +def test_msssim_different_shape(): + shape_1 = (8, 3, 128, 128) + shape_2 = (8, 3, 256, 256) + factors = (0.033, 0.033, 0.033) + img1 = Tensor(np.random.random(shape_1)) + img2 = Tensor(np.random.random(shape_2)) + net = MSSSIMNet(power_factors=factors) + with pytest.raises(ValueError): + _executor.compile(net, img1, img2) + + +def test_msssim_different_dtype(): + dtype_1 = mstype.float32 + dtype_2 = mstype.float16 + factors = (0.033, 0.033, 0.033) + img1 = Tensor(np.random.random((8, 3, 128, 128)), dtype=dtype_1) + img2 = Tensor(np.random.random((8, 3, 128, 128)), dtype=dtype_2) + net = MSSSIMNet(power_factors=factors) + with pytest.raises(TypeError): + _executor.compile(net, img1, img2) + + +def test_msssim_invalid_5d_input(): + shape_1 = (8, 3, 128, 128) + shape_2 = (8, 3, 256, 256) + invalid_shape = (8, 3, 128, 128, 1) + factors = (0.033, 0.033, 0.033) + img1 = Tensor(np.random.random(shape_1)) + invalid_img1 = Tensor(np.random.random(invalid_shape)) + img2 = Tensor(np.random.random(shape_2)) + invalid_img2 = Tensor(np.random.random(invalid_shape)) + + net = MSSSIMNet(power_factors=factors) + with pytest.raises(ValueError): + _executor.compile(net, invalid_img1, img2) + with pytest.raises(ValueError): + _executor.compile(net, img1, invalid_img2) + with pytest.raises(ValueError): + _executor.compile(net, invalid_img1, invalid_img2) diff --git a/tests/ut/python/nn/test_ssim.py b/tests/ut/python/nn/test_ssim.py index 5cf1b0c94c..8b7e441014 100644 --- a/tests/ut/python/nn/test_ssim.py +++ b/tests/ut/python/nn/test_ssim.py @@ -78,26 +78,6 @@ def test_ssim_filter_sigma_negative(): _ = SSIMNet(filter_sigma=-0.1) -def test_ssim_k1_k2_wrong_value(): - with pytest.raises(ValueError): - _ = SSIMNet(k1=1.1) - with pytest.raises(ValueError): - _ = SSIMNet(k1=1.0) - with pytest.raises(ValueError): - _ = SSIMNet(k1=0.0) - with pytest.raises(ValueError): - _ = SSIMNet(k1=-1.0) - - with pytest.raises(ValueError): - _ = SSIMNet(k2=1.1) - with pytest.raises(ValueError): - _ = SSIMNet(k2=1.0) - with pytest.raises(ValueError): - _ = SSIMNet(k2=0.0) - with pytest.raises(ValueError): - _ = SSIMNet(k2=-1.0) - - def test_ssim_different_shape(): shape_1 = (8, 3, 16, 16) shape_2 = (8, 3, 8, 8)