add dataset

model_zoo/deeplabv3/src/__init__.py

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+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================

model_zoo/deeplabv3/src/adapter.py

@@ -0,0 +1,67 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Adapter dataset."""
+import fnmatch
+import io
+import os
+
+import numpy as np
+from PIL import Image
+
+from ..utils import file_io
+
+
+def get_raw_samples(data_url):
+    """
+    Get dataset from raw data.
+
+    Args:
+        data_url (str): Dataset path.
+
+    Returns:
+        list, a file list.
+    """
+    def _list_files(dir_path, pattern):
+        full_files = []
+        _, _, files = next(file_io.walk(dir_path))
+        for f in files:
+            if fnmatch.fnmatch(f.lower(), pattern.lower()):
+                full_files.append(os.path.join(dir_path, f))
+        return full_files
+
+    img_files = _list_files(os.path.join(data_url, "Images"), "*.jpg")
+    seg_files = _list_files(os.path.join(data_url, "SegmentationClassRaw"), "*.png")
+
+    files = []
+    for img_file in img_files:
+        _, file_name = os.path.split(img_file)
+        name, _ = os.path.splitext(file_name)
+        seg_file = os.path.join(data_url, "SegmentationClassRaw", ".".join([name, "png"]))
+        if seg_file in seg_files:
+            files.append([img_file, seg_file])
+    return files
+
+
+def read_image(img_path):
+    """
+    Read image from file.
+
+    Args:
+        img_path (str): image path.
+    """
+    img = file_io.read(img_path.strip(), binary=True)
+    data = io.BytesIO(img)
+    img = Image.open(data)
+    return np.array(img)

model_zoo/deeplabv3/src/custom_transforms.py

@@ -0,0 +1,148 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Random process dataset."""
+import random
+
+import numpy as np
+from PIL import Image, ImageOps, ImageFilter
+
+
+class Normalize(object):
+    """Normalize a tensor image with mean and standard deviation.
+    Args:
+        mean (tuple): means for each channel.
+        std (tuple): standard deviations for each channel.
+    """
+
+    def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, img, mask):
+        img = np.array(img).astype(np.float32)
+        mask = np.array(mask).astype(np.float32)
+
+        return img, mask
+
+
+class RandomHorizontalFlip(object):
+    """Randomly decide whether to horizontal flip."""
+    def __call__(self, img, mask):
+        if random.random() < 0.5:
+            img = img.transpose(Image.FLIP_LEFT_RIGHT)
+            mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
+
+        return img, mask
+
+
+class RandomRotate(object):
+    """
+    Randomly decide whether to rotate.
+
+    Args:
+        degree (float): The degree of rotate.
+    """
+    def __init__(self, degree):
+        self.degree = degree
+
+    def __call__(self, img, mask):
+        rotate_degree = random.uniform(-1 * self.degree, self.degree)
+        img = img.rotate(rotate_degree, Image.BILINEAR)
+        mask = mask.rotate(rotate_degree, Image.NEAREST)
+
+        return img, mask
+
+
+class RandomGaussianBlur(object):
+    """Randomly decide whether to filter image with gaussian blur."""
+    def __call__(self, img, mask):
+        if random.random() < 0.5:
+            img = img.filter(ImageFilter.GaussianBlur(
+                radius=random.random()))
+
+        return img, mask
+
+
+class RandomScaleCrop(object):
+    """Randomly decide whether to scale and crop image."""
+    def __init__(self, base_size, crop_size, fill=0):
+        self.base_size = base_size
+        self.crop_size = crop_size
+        self.fill = fill
+
+    def __call__(self, img, mask):
+        # random scale (short edge)
+        short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0))
+        w, h = img.size
+        if h > w:
+            ow = short_size
+            oh = int(1.0 * h * ow / w)
+        else:
+            oh = short_size
+            ow = int(1.0 * w * oh / h)
+        img = img.resize((ow, oh), Image.BILINEAR)
+        mask = mask.resize((ow, oh), Image.NEAREST)
+        # pad crop
+        if short_size < self.crop_size:
+            padh = self.crop_size - oh if oh < self.crop_size else 0
+            padw = self.crop_size - ow if ow < self.crop_size else 0
+            img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
+            mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill)
+        # random crop crop_size
+        w, h = img.size
+        x1 = random.randint(0, w - self.crop_size)
+        y1 = random.randint(0, h - self.crop_size)
+        img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+        mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+
+        return img, mask
+
+
+class FixScaleCrop(object):
+    """Scale and crop image with fixing size."""
+    def __init__(self, crop_size):
+        self.crop_size = crop_size
+
+    def __call__(self, img, mask):
+        w, h = img.size
+        if w > h:
+            oh = self.crop_size
+            ow = int(1.0 * w * oh / h)
+        else:
+            ow = self.crop_size
+            oh = int(1.0 * h * ow / w)
+        img = img.resize((ow, oh), Image.BILINEAR)
+        mask = mask.resize((ow, oh), Image.NEAREST)
+        # center crop
+        w, h = img.size
+        x1 = int(round((w - self.crop_size) / 2.))
+        y1 = int(round((h - self.crop_size) / 2.))
+        img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+        mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+
+        return img, mask
+
+
+class FixedResize(object):
+    """Resize image with fixing size."""
+    def __init__(self, size):
+        self.size = (size, size)
+
+    def __call__(self, img, mask):
+        assert img.size == mask.size
+
+        img = img.resize(self.size, Image.BILINEAR)
+        mask = mask.resize(self.size, Image.NEAREST)
+        return img, mask

model_zoo/deeplabv3/src/deeplabv3.py

@@ -0,0 +1,457 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""DeepLabv3."""
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from .backbone.resnet_deeplab import _conv_bn_relu, resnet50_dl, _deep_conv_bn_relu, \
+    DepthwiseConv2dNative, SpaceToBatch, BatchToSpace
+
+
+class ASPPSampleBlock(nn.Cell):
+    """ASPP sample block."""
+    def __init__(self, feature_shape, scale_size,output_stride):
+        super(ASPPSampleBlock, self).__init__()
+        sample_h = (feature_shape[0] * scale_size + 1) / output_stride + 1
+        sample_w = (feature_shape[1] * scale_size + 1) / output_stride + 1
+        self.sample = P.ResizeBilinear((int(sample_h),int(sample_w)),align_corners=True)
+
+    def construct(self, x):
+        return self.sample(x)
+
+
+class ASPP(nn.Cell):
+    """
+    ASPP model for DeepLabv3.
+
+    Args:
+        channel (int): Input channel.
+        depth (int): Output channel.
+        feature_shape (list): The shape of feature,[h,w].
+        scale_sizes (list): Input scales for multi-scale feature extraction.
+        atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+        output_stride (int): 'The ratio of input to output spatial resolution.'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ASPP(channel=2048,256,[14,14],[1],[6],16)
+    """
+    def __init__(self, channel, depth, feature_shape, scale_sizes,
+                 atrous_rates, output_stride, fine_tune_batch_norm=False):
+        super(ASPP, self).__init__()
+        self.aspp0 = _conv_bn_relu(channel,
+                                   depth,
+                                   ksize=1,
+                                   stride=1,
+                                   use_batch_statistics=fine_tune_batch_norm)
+        self.atrous_rates = []
+        if atrous_rates is not None:
+            self.atrous_rates = atrous_rates
+            self.aspp_pointwise = _conv_bn_relu(channel,
+                                                depth,
+                                                ksize=1,
+                                                stride=1,
+                                                use_batch_statistics=fine_tune_batch_norm)
+            self.aspp_depth_depthwiseconv = DepthwiseConv2dNative(channel,
+                                                                  channel_multiplier=1,
+                                                                  kernel_size=3,
+                                                                  stride=1,
+                                                                  dilation=1,
+                                                                  pad_mode="valid")
+            self.aspp_depth_bn = nn.BatchNorm2d(1 * channel, use_batch_statistics=fine_tune_batch_norm)
+            self.aspp_depth_relu = nn.ReLU()
+            self.aspp_depths = []
+            self.aspp_depth_spacetobatchs = []
+            self.aspp_depth_batchtospaces = []
+
+            for scale_size in scale_sizes:
+                aspp_scale_depth_size = np.ceil((feature_shape[0]*scale_size)/16)
+                if atrous_rates is None:
+                    break
+                for i in range(len(atrous_rates)):
+                    padding = 0
+                    for j in range(100):
+                        padded_size = atrous_rates[i] * j
+                        if padded_size >= aspp_scale_depth_size + 2 * atrous_rates[i]:
+                            padding = padded_size - aspp_scale_depth_size - 2 * atrous_rates[i]
+                            break
+                    paddings = [[atrous_rates[i], atrous_rates[i] + int(padding)],
+                                [atrous_rates[i], atrous_rates[i] + int(padding)]]
+                    self.aspp_depth_spacetobatch = SpaceToBatch(atrous_rates[i],paddings)
+                    self.aspp_depth_spacetobatchs.append(self.aspp_depth_spacetobatch)
+                    crops =[[0, int(padding)], [0, int(padding)]]
+                    self.aspp_depth_batchtospace = BatchToSpace(atrous_rates[i],crops)
+                    self.aspp_depth_batchtospaces.append(self.aspp_depth_batchtospace)
+            self.aspp_depths = nn.CellList(self.aspp_depths)
+            self.aspp_depth_spacetobatchs = nn.CellList(self.aspp_depth_spacetobatchs)
+            self.aspp_depth_batchtospaces = nn.CellList(self.aspp_depth_batchtospaces)
+
+        self.global_pooling = nn.AvgPool2d(kernel_size=(int(feature_shape[0]),int(feature_shape[1])))
+        self.global_poolings = []
+        for scale_size in scale_sizes:
+            pooling_h = np.ceil((feature_shape[0]*scale_size)/output_stride)
+            pooling_w = np.ceil((feature_shape[0]*scale_size)/output_stride)
+            self.global_poolings.append(nn.AvgPool2d(kernel_size=(int(pooling_h), int(pooling_w))))
+        self.global_poolings = nn.CellList(self.global_poolings)
+        self.conv_bn = _conv_bn_relu(channel,
+                                     depth,
+                                     ksize=1,
+                                     stride=1,
+                                     use_batch_statistics=fine_tune_batch_norm)
+        self.samples = []
+        for scale_size in scale_sizes:
+            self.samples.append(ASPPSampleBlock(feature_shape,scale_size,output_stride))
+        self.samples = nn.CellList(self.samples)
+        self.feature_shape = feature_shape
+        self.concat = P.Concat(axis=1)
+
+    def construct(self, x, scale_index=0):
+        aspp0 = self.aspp0(x)
+        aspp1 = self.global_poolings[scale_index](x)
+        aspp1 = self.conv_bn(aspp1)
+        aspp1 = self.samples[scale_index](aspp1)
+        output = self.concat((aspp1,aspp0))
+
+        for i in range(len(self.atrous_rates)):
+            aspp_i = self.aspp_depth_spacetobatchs[i + scale_index * len(self.atrous_rates)](x)
+            aspp_i = self.aspp_depth_depthwiseconv(aspp_i)
+            aspp_i = self.aspp_depth_batchtospaces[i + scale_index * len(self.atrous_rates)](aspp_i)
+            aspp_i = self.aspp_depth_bn(aspp_i)
+            aspp_i = self.aspp_depth_relu(aspp_i)
+            aspp_i = self.aspp_pointwise(aspp_i)
+            output = self.concat((output,aspp_i))
+        return output
+
+
+class DecoderSampleBlock(nn.Cell):
+    """Decoder sample block."""
+    def __init__(self,feature_shape,scale_size=1.0,decoder_output_stride=4):
+        super(DecoderSampleBlock, self).__init__()
+        sample_h = (feature_shape[0] * scale_size + 1) / decoder_output_stride + 1
+        sample_w = (feature_shape[1] * scale_size + 1) / decoder_output_stride + 1
+        self.sample = P.ResizeBilinear((int(sample_h), int(sample_w)), align_corners=True)
+
+    def construct(self, x):
+        return self.sample(x)
+
+
+class Decoder(nn.Cell):
+    """
+    Decode module for DeepLabv3.
+    Args:
+        low_level_channel (int): Low level input channel
+        channel (int): Input channel.
+        depth (int): Output channel.
+        feature_shape (list): 'Input image shape, [N,C,H,W].'
+        scale_sizes (list): 'Input scales for multi-scale feature extraction.'
+        decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+    Returns:
+        Tensor, output tensor.
+    Examples:
+        >>> Decoder(256, 100, [56,56])
+    """
+    def __init__(self,
+                 low_level_channel,
+                 channel,
+                 depth,
+                 feature_shape,
+                 scale_sizes,
+                 decoder_output_stride,
+                 fine_tune_batch_norm):
+        super(Decoder, self).__init__()
+        self.feature_projection = _conv_bn_relu(low_level_channel, 48, ksize=1, stride=1,
+                                                pad_mode="same", use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_depth0 = _deep_conv_bn_relu(channel + 48,
+                                                 channel_multiplier=1,
+                                                 ksize=3,
+                                                 stride=1,
+                                                 pad_mode="same",
+                                                 dilation=1,
+                                                 use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_pointwise0 = _conv_bn_relu(channel + 48,
+                                                depth,
+                                                ksize=1,
+                                                stride=1,
+                                                use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_depth1 = _deep_conv_bn_relu(depth,
+                                                 channel_multiplier=1,
+                                                 ksize=3,
+                                                 stride=1,
+                                                 pad_mode="same",
+                                                 dilation=1,
+                                                 use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_pointwise1 = _conv_bn_relu(depth,
+                                                depth,
+                                                ksize=1,
+                                                stride=1,
+                                                use_batch_statistics=fine_tune_batch_norm)
+        self.depth = depth
+        self.concat = P.Concat(axis=1)
+        self.samples = []
+        for scale_size in scale_sizes:
+            self.samples.append(DecoderSampleBlock(feature_shape,scale_size,decoder_output_stride))
+        self.samples = nn.CellList(self.samples)
+
+    def construct(self, x, low_level_feature, scale_index):
+        low_level_feature = self.feature_projection(low_level_feature)
+        low_level_feature = self.samples[scale_index](low_level_feature)
+        x = self.samples[scale_index](x)
+        output = self.concat((x, low_level_feature))
+        output = self.decoder_depth0(output)
+        output = self.decoder_pointwise0(output)
+        output = self.decoder_depth1(output)
+        output = self.decoder_pointwise1(output)
+        return output
+
+
+class SingleDeepLabV3(nn.Cell):
+    """
+    DeepLabv3 Network.
+    Args:
+        num_classes (int): Class number.
+        feature_shape (list): Input image shape, [N,C,H,W].
+        backbone (Cell): Backbone Network.
+        channel (int): Resnet output channel.
+        depth (int): ASPP block depth.
+        scale_sizes (list): Input scales for multi-scale feature extraction.
+        atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+        decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+        output_stride (int): 'The ratio of input to output spatial resolution.'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+    Returns:
+        Tensor, output tensor.
+    Examples:
+        >>> SingleDeepLabV3(num_classes=10,
+            >>>           feature_shape=[1,3,224,224],
+            >>>           backbone=resnet50_dl(),
+            >>>           channel=2048,
+            >>>           depth=256)
+            >>>           scale_sizes=[1.0])
+            >>>           atrous_rates=[6])
+            >>>           decoder_output_stride=4)
+            >>>           output_stride=16)
+        """
+
+    def __init__(self,
+                 num_classes,
+                 feature_shape,
+                 backbone,
+                 channel,
+                 depth,
+                 scale_sizes,
+                 atrous_rates,
+                 decoder_output_stride,
+                 output_stride,
+                 fine_tune_batch_norm=False):
+        super(SingleDeepLabV3, self).__init__()
+        self.num_classes = num_classes
+        self.channel = channel
+        self.depth = depth
+        self.scale_sizes = []
+        for scale_size in np.sort(scale_sizes):
+            self.scale_sizes.append(scale_size)
+        self.net = backbone
+        self.aspp = ASPP(channel=self.channel,
+                         depth=self.depth,
+                         feature_shape=[feature_shape[2],
+                                        feature_shape[3]],
+                         scale_sizes=self.scale_sizes,
+                         atrous_rates=atrous_rates,
+                         output_stride=output_stride,
+                         fine_tune_batch_norm=fine_tune_batch_norm)
+        self.aspp.add_flags(loop_can_unroll=True)
+        atrous_rates_len = 0
+        if atrous_rates is not None:
+            atrous_rates_len = len(atrous_rates)
+        self.fc1 = _conv_bn_relu(depth * (2 + atrous_rates_len), depth,
+                                 ksize=1,
+                                 stride=1,
+                                 use_batch_statistics=fine_tune_batch_norm)
+        self.fc2 = nn.Conv2d(depth,
+                             num_classes,
+                             kernel_size=1,
+                             stride=1,
+                             has_bias=True)
+        self.upsample = P.ResizeBilinear((int(feature_shape[2]),
+                                          int(feature_shape[3])),
+                                         align_corners=True)
+        self.samples = []
+        for scale_size in self.scale_sizes:
+            self.samples.append(SampleBlock(feature_shape, scale_size))
+        self.samples = nn.CellList(self.samples)
+        self.feature_shape = [float(feature_shape[0]), float(feature_shape[1]), float(feature_shape[2]),
+                              float(feature_shape[3])]
+
+        self.pad = P.Pad(((0, 0), (0, 0), (1, 1), (1, 1)))
+        self.dropout = nn.Dropout(keep_prob=0.9)
+        self.shape = P.Shape()
+        self.decoder_output_stride = decoder_output_stride
+        if decoder_output_stride is not None:
+            self.decoder = Decoder(low_level_channel=depth,
+                                   channel=depth,
+                                   depth=depth,
+                                   feature_shape=[feature_shape[2],
+                                                  feature_shape[3]],
+                                   scale_sizes=self.scale_sizes,
+                                   decoder_output_stride=decoder_output_stride,
+                                   fine_tune_batch_norm=fine_tune_batch_norm)
+
+    def construct(self, x, scale_index=0):
+        x = (2.0 / 255.0) * x - 1.0
+        x = self.pad(x)
+        low_level_feature, feature_map = self.net(x)
+        for scale_size in self.scale_sizes:
+            if scale_size * self.feature_shape[2] + 1.0 >= self.shape(x)[2] - 2:
+                output = self.aspp(feature_map, scale_index)
+                output = self.fc1(output)
+                if self.decoder_output_stride is not None:
+                    output = self.decoder(output, low_level_feature, scale_index)
+                output = self.fc2(output)
+                output = self.samples[scale_index](output)
+                return output
+            scale_index += 1
+        return feature_map
+
+
+class SampleBlock(nn.Cell):
+    """Sample block."""
+    def __init__(self,
+                 feature_shape,
+                 scale_size=1.0):
+        super(SampleBlock, self).__init__()
+        sample_h = np.ceil(float(feature_shape[2]) * scale_size)
+        sample_w = np.ceil(float(feature_shape[3]) * scale_size)
+        self.sample = P.ResizeBilinear((int(sample_h), int(sample_w)), align_corners=True)
+
+    def construct(self, x):
+        return self.sample(x)
+
+
+class DeepLabV3(nn.Cell):
+    """DeepLabV3 model."""
+    def __init__(self, num_classes, feature_shape, backbone, channel, depth, infer_scale_sizes, atrous_rates,
+                 decoder_output_stride, output_stride, fine_tune_batch_norm, image_pyramid):
+        super(DeepLabV3, self).__init__()
+        self.infer_scale_sizes = []
+        if infer_scale_sizes is not None:
+            self.infer_scale_sizes = infer_scale_sizes
+
+        self.infer_scale_sizes = infer_scale_sizes
+        if image_pyramid is None:
+            image_pyramid = [1.0]
+
+        self.image_pyramid = image_pyramid
+        scale_sizes = []
+        for i in range(len(image_pyramid)):
+            scale_sizes.append(image_pyramid[i])
+        for i in range(len(infer_scale_sizes)):
+            scale_sizes.append(infer_scale_sizes[i])
+        self.samples = []
+        for scale_size in scale_sizes:
+            self.samples.append(SampleBlock(feature_shape, scale_size))
+        self.samples = nn.CellList(self.samples)
+        self.deeplabv3 = SingleDeepLabV3(num_classes=num_classes,
+                                         feature_shape=feature_shape,
+                                         backbone=resnet50_dl(fine_tune_batch_norm),
+                                         channel=channel,
+                                         depth=depth,
+                                         scale_sizes=scale_sizes,
+                                         atrous_rates=atrous_rates,
+                                         decoder_output_stride=decoder_output_stride,
+                                         output_stride=output_stride,
+                                         fine_tune_batch_norm=fine_tune_batch_norm)
+        self.softmax = P.Softmax(axis=1)
+        self.concat = P.Concat(axis=2)
+        self.expand_dims = P.ExpandDims()
+        self.reduce_mean = P.ReduceMean()
+        self.sample_common = P.ResizeBilinear((int(feature_shape[2]),
+                                               int(feature_shape[3])),
+                                              align_corners=True)
+
+    def construct(self, x):
+        logits = ()
+        if self.training:
+            if len(self.image_pyramid) >= 1:
+                if self.image_pyramid[0] == 1:
+                    logits = self.deeplabv3(x)
+                else:
+                    x1 = self.samples[0](x)
+                    logits = self.deeplabv3(x1)
+                    logits = self.sample_common(logits)
+                logits = self.expand_dims(logits, 2)
+                for i in range(len(self.image_pyramid) - 1):
+                    x_i = self.samples[i + 1](x)
+                    logits_i = self.deeplabv3(x_i)
+                    logits_i = self.sample_common(logits_i)
+                    logits_i = self.expand_dims(logits_i, 2)
+                    logits = self.concat((logits, logits_i))
+            logits = self.reduce_mean(logits, 2)
+            return logits
+        if len(self.infer_scale_sizes) >= 1:
+            infer_index = len(self.image_pyramid)
+            x1 = self.samples[infer_index](x)
+            logits = self.deeplabv3(x1)
+            logits = self.sample_common(logits)
+            logits = self.softmax(logits)
+            logits = self.expand_dims(logits, 2)
+            for i in range(len(self.infer_scale_sizes) - 1):
+                x_i = self.samples[i + 1 + infer_index](x)
+                logits_i = self.deeplabv3(x_i)
+                logits_i = self.sample_common(logits_i)
+                logits_i = self.softmax(logits_i)
+                logits_i = self.expand_dims(logits_i, 2)
+                logits = self.concat((logits, logits_i))
+        logits = self.reduce_mean(logits, 2)
+        return logits
+
+
+def deeplabv3_resnet50(num_classes, feature_shape, image_pyramid,
+                       infer_scale_sizes, atrous_rates=None, decoder_output_stride=None,
+                       output_stride=16, fine_tune_batch_norm=False):
+    """
+    ResNet50 based DeepLabv3 network.
+
+    Args:
+        num_classes (int): Class number.
+        feature_shape (list): Input image shape, [N,C,H,W].
+        image_pyramid (list): Input scales for multi-scale feature extraction.
+        atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+        infer_scale_sizes (list): 'The scales to resize images for inference.
+        decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+        output_stride (int): 'The ratio of input to output spatial resolution.'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+
+    Returns:
+        Cell, cell instance of ResNet50 based DeepLabv3 neural network.
+
+    Examples:
+        >>> deeplabv3_resnet50(100, [1,3,224,224],[1.0],[1.0])
+    """
+    return DeepLabV3(num_classes=num_classes,
+                     feature_shape=feature_shape,
+                     backbone=resnet50_dl(fine_tune_batch_norm),
+                     channel=2048,
+                     depth=256,
+                     infer_scale_sizes=infer_scale_sizes,
+                     atrous_rates=atrous_rates,
+                     decoder_output_stride=decoder_output_stride,
+                     output_stride=output_stride,
+                     fine_tune_batch_norm=fine_tune_batch_norm,
+                     image_pyramid=image_pyramid)

model_zoo/deeplabv3/src/ei_datasest.py

@@ -0,0 +1,107 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Process Dataset."""
+import abc
+import os
+import time
+
+from .utils.adapter import get_manifest_samples, get_raw_samples, read_image
+
+
+class BaseDataset(object):
+    """
+    Create dataset.
+
+    Args:
+        data_url (str): The path of data.
+        usage (str): Whether to use train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+    def __init__(self, data_url, usage):
+        self.data_url = data_url
+        self.usage = usage
+        self.cur_index = 0
+        self.samples = []
+        _s_time = time.time()
+        self._load_samples()
+        _e_time = time.time()
+        print(f"load samples success~, time cost = {_e_time - _s_time}")
+
+    def __getitem__(self, item):
+        sample = self.samples[item]
+        return self._next_data(sample)
+
+    def __len__(self):
+        return len(self.samples)
+
+    @staticmethod
+    def _next_data(sample):
+        image_path = sample[0]
+        mask_image_path = sample[1]
+
+        image = read_image(image_path)
+        mask_image = read_image(mask_image_path)
+        return [image, mask_image]
+
+    @abc.abstractmethod
+    def _load_samples(self):
+        pass
+
+
+class HwVocManifestDataset(BaseDataset):
+    """
+    Create dataset with manifest data.
+
+    Args:
+        data_url (str): The path of data.
+        usage (str): Whether to use train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+
+    def __init__(self, data_url, usage="train"):
+        super().__init__(data_url, usage)
+
+    def _load_samples(self):
+        try:
+            self.samples = get_manifest_samples(self.data_url, self.usage)
+        except Exception as e:
+            print("load HwVocManifestDataset samples failed!!!")
+            raise e
+
+
+class HwVocRawDataset(BaseDataset):
+    """
+    Create dataset with raw data.
+
+    Args:
+        data_url (str): The path of data.
+        usage (str): Whether to use train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+    def __init__(self, data_url, usage="train"):
+        super().__init__(data_url, usage)
+
+    def _load_samples(self):
+        try:
+            self.samples = get_raw_samples(os.path.join(self.data_url, self.usage))
+        except Exception as e:
+            print("load HwVocRawDataset failed!!!")
+            raise e

model_zoo/deeplabv3/src/md_dataset.py

@@ -0,0 +1,104 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Dataset module."""
+from PIL import Image
+import mindspore.dataset as de
+import mindspore.dataset.transforms.vision.c_transforms as C
+
+from .ei_dataset import HwVocManifestDataset, HwVocRawDataset
+from .utils import custom_transforms as tr
+
+
+class DataTransform(object):
+    """Transform dataset for DeepLabV3."""
+
+    def __init__(self, args, usage):
+        self.args = args
+        self.usage = usage
+
+    def __call__(self, image, label):
+        if "train" == self.usage:
+            return self._train(image, label)
+        elif "eval" == self.usage:
+            return self._eval(image, label)
+
+    def _train(self, image, label):
+        image = Image.fromarray(image)
+        label = Image.fromarray(label)
+
+        rsc_tr = tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size)
+        image, label = rsc_tr(image, label)
+
+        rhf_tr = tr.RandomHorizontalFlip()
+        image, label = rhf_tr(image, label)
+
+        nor_tr = tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+        image, label = nor_tr(image, label)
+
+        return image, label
+
+    def _eval(self, image, label):
+        image = Image.fromarray(image)
+        label = Image.fromarray(label)
+
+        fsc_tr = tr.FixScaleCrop(crop_size=self.args.crop_size)
+        image, label = fsc_tr(image, label)
+
+        nor_tr = tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+        image, label = nor_tr(image, label)
+
+        return image, label
+
+
+def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train"):
+    """
+    Create Dataset for DeepLabV3.
+
+    Args:
+        args (dict): Train parameters.
+        data_url (str): Dataset path.
+        epoch_num (int): Epoch of dataset (default=1).
+        batch_size (int): Batch size of dataset (default=1).
+        usage (str): Whether is use to train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+    # create iter dataset
+    if data_url.endswith(".manifest"):
+        dataset = HwVocManifestDataset(data_url, usage=usage)
+    else:
+        dataset = HwVocRawDataset(data_url, usage=usage)
+    dataset_len = len(dataset)
+
+    # wrapped with GeneratorDataset
+    dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=None)
+    dataset.set_dataset_size(dataset_len)
+    dataset = dataset.map(input_columns=["image", "label"], operations=DataTransform(args, usage=usage))
+
+    channelswap_op = C.HWC2CHW()
+    dataset = dataset.map(input_columns="image", operations=channelswap_op)
+
+    # 1464 samples / batch_size 8 = 183 batches
+    # epoch_num is num of steps
+    # 3658 steps / 183 = 20 epochs
+    if usage == "train":
+        dataset = dataset.shuffle(1464)
+    dataset = dataset.batch(batch_size, drop_remainder=(usage == usage))
+    dataset = dataset.repeat(count=epoch_num)
+    dataset.map_model = 4
+
+    dataset.__loop_size__ = 1
+    return dataset