Add ModelZoo Network: Unet.

4 years ago · 776eb28e6e
parent 4d6bbd1218
commit 776eb28e6e
13 changed files with 1096 additions and 0 deletions
--- a/model_zoo/official/cv/unet/README.md
+++ b/model_zoo/official/cv/unet/README.md
--- a/model_zoo/official/cv/unet/eval.py
+++ b/model_zoo/official/cv/unet/eval.py
@ -0,0 +1,123 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+import os
+import argparse
+import logging
+import numpy as np
+import mindspore
+import mindspore.nn as nn
+import mindspore.ops.operations as F
+from mindspore import context, Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.nn.loss.loss import _Loss
+
+from src.data_loader import create_dataset
+from src.unet import UNet
+from src.config import cfg_unet
+
+from scipy.special import softmax
+
+device_id = int(os.getenv('DEVICE_ID'))
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
+
+
+
+
+class CrossEntropyWithLogits(_Loss):
+    def __init__(self):
+        super(CrossEntropyWithLogits, self).__init__()
+        self.transpose_fn = F.Transpose()
+        self.reshape_fn = F.Reshape()
+        self.softmax_cross_entropy_loss = nn.SoftmaxCrossEntropyWithLogits()
+        self.cast = F.Cast()
+    def construct(self, logits, label):
+        # NCHW->NHWC
+        logits = self.transpose_fn(logits, (0, 2, 3, 1))
+        logits = self.cast(logits, mindspore.float32)
+        label = self.transpose_fn(label, (0, 2, 3, 1))
+
+        loss = self.reduce_mean(self.softmax_cross_entropy_loss(self.reshape_fn(logits, (-1, 2)),
+                                                                self.reshape_fn(label, (-1, 2))))
+        return self.get_loss(loss)
+
+
+class dice_coeff(nn.Metric):
+    def __init__(self):
+        super(dice_coeff, self).__init__()
+        self.clear()
+    def clear(self):
+        self._dice_coeff_sum = 0
+        self._samples_num = 0
+
+    def update(self, *inputs):
+        if len(inputs) != 2:
+            raise ValueError('Mean dice coeffcient need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
+
+        y_pred = self._convert_data(inputs[0])
+        y = self._convert_data(inputs[1])
+        self._samples_num += y.shape[0]
+        y_pred = y_pred.transpose(0, 2, 3, 1)
+        y = y.transpose(0, 2, 3, 1)
+        y_pred = softmax(y_pred, axis=3)
+
+        inter = np.dot(y_pred.flatten(), y.flatten())
+        union = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
+
+        single_dice_coeff = 2*float(inter)/float(union+1e-6)
+        print("single dice coeff is:", single_dice_coeff)
+        self._dice_coeff_sum += single_dice_coeff
+
+    def eval(self):
+        if self._samples_num == 0:
+            raise RuntimeError('Total samples num must not be 0.')
+        return self._dice_coeff_sum / float(self._samples_num)
+
+def test_net(data_dir,
+             ckpt_path,
+             cross_valid_ind=1,
+             cfg=None):
+
+    net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+
+    criterion = CrossEntropyWithLogits()
+    _, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
+    model = Model(net, loss_fn=criterion, metrics={"dice_coeff": dice_coeff()})
+
+    print("============== Starting Evaluating ============")
+    dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
+    print("============== Cross valid dice coeff is:", dice_score)
+
+def get_args():
+    parser = argparse.ArgumentParser(description='Test the UNet on images and target masks',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
+                        help='data directory')
+    parser.add_argument('-p', '--ckpt_path', dest='ckpt_path', type=str, default='ckpt_unet_medical_adam-1_600.ckpt',
+                        help='checkpoint path')
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+    args = get_args()
+    print("Testing setting:", args)
+    test_net(data_dir=args.data_url,
+             ckpt_path=args.ckpt_path,
+             cross_valid_ind=cfg_unet['cross_valid_ind'],
+             cfg=cfg_unet)
--- a/model_zoo/official/cv/unet/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/unet/scripts/run_distribute_train.sh
@ -0,0 +1,50 @@
+#!/bin/bash
+# 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
+#
+# less 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]"
+echo "for example: bash run_distribute_train.sh /absolute/path/to/RANK_TABLE_FILE /absolute/path/to/data"
+echo "=============================================================================================================="
+
+if [ $# != 2 ]
+then
+    echo "Usage: bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]"
+    exit 1
+fi
+
+export RANK_SIZE=8
+
+for((i=0;i<RANK_SIZE;i++))
+do
+    rm -rf LOG$i
+    mkdir ./LOG$i
+    cp ./*.py ./LOG$i
+    cp -r ./src ./LOG$i
+    cd ./LOG$i || exit
+    export RANK_TABLE_FILE=$1
+    export RANK_SIZE=8
+    export RANK_ID=$i
+    export DEVICE_ID=$i
+    echo "start training for rank $i, device $DEVICE_ID"
+    env > env.log
+
+    python3 train.py \
+    --run_distribute=True \
+    --data_url=$2 > log.txt 2>&1 &
+
+    cd ../
+done
--- a/model_zoo/official/cv/unet/scripts/run_standalone_eval.sh
+++ b/model_zoo/official/cv/unet/scripts/run_standalone_eval.sh
@ -0,0 +1,24 @@
+#!/bin/bash
+# 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
+#
+# less 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]"
+echo "for example: bash run_standalone_eval.sh /path/to/data/ /path/to/checkpoint/"
+echo "=============================================================================================================="
+
+export DEVICE_ID=0
+python eval.py --data_url=$1 --ckpt_path=$2 > eval.log 2>&1 &
--- a/model_zoo/official/cv/unet/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/unet/scripts/run_standalone_train.sh
@ -0,0 +1,24 @@
+#!/bin/bash
+# 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
+#
+# less 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash scripts/run_standalone_train.sh [DATASET]"
+echo "for example: bash run_standalone_train.sh /path/to/data/"
+echo "=============================================================================================================="
+
+export DEVICE_ID=0
+python train.py --data_url=$1 > train.log 2>&1 &
--- a/model_zoo/official/cv/unet/src/config.py
+++ b/model_zoo/official/cv/unet/src/config.py
@ -0,0 +1,30 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+cfg_unet = {
+    'name': 'Unet',
+    'lr': 0.0001,
+    'epochs': 400,
+    'distribute_epochs': 1600,
+    'batchsize': 16,
+    'cross_valid_ind': 1,
+    'num_classes': 2,
+    'num_channels': 1,
+
+    'keep_checkpoint_max': 10,
+    'weight_decay': 0.0005,
+    'loss_scale': 1024.0,
+    'FixedLossScaleManager': 1024.0,
+}
--- a/model_zoo/official/cv/unet/src/data_loader.py
+++ b/model_zoo/official/cv/unet/src/data_loader.py
@ -0,0 +1,159 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+import os
+from collections import deque
+import numpy as np
+from PIL import Image, ImageSequence
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as c_vision
+from mindspore.dataset.vision.utils import Inter
+from mindspore.communication.management import get_rank, get_group_size
+
+
+
+def _load_multipage_tiff(path):
+    """Load tiff images containing many images in the channel dimension"""
+    return np.array([np.array(p) for p in ImageSequence.Iterator(Image.open(path))])
+
+def _get_val_train_indices(length, fold, ratio=0.8):
+    assert 0 < ratio <= 1, "Train/total data ratio must be in range (0.0, 1.0]"
+    np.random.seed(0)
+    indices = np.arange(0, length, 1, dtype=np.int)
+    np.random.shuffle(indices)
+
+    if fold is not None:
+        indices = deque(indices)
+        indices.rotate(fold * round((1.0 - ratio) * length))
+        indices = np.array(indices)
+        train_indices = indices[:round(ratio * len(indices))]
+        val_indices = indices[round(ratio * len(indices)):]
+    else:
+        train_indices = indices
+        val_indices = []
+    return train_indices, val_indices
+
+def data_post_process(img, mask):
+
+    img = np.expand_dims(img, axis=0)
+    mask = (mask > 0.5).astype(np.int)
+    mask = (np.arange(mask.max() + 1) == mask[..., None]).astype(int)
+    mask = mask.transpose(2, 0, 1).astype(np.float32)
+    return img, mask
+
+
+def train_data_augmentation(img, mask):
+
+    h_flip = np.random.random()
+    if h_flip > 0.5:
+        img = np.flipud(img)
+        mask = np.flipud(mask)
+    v_flip = np.random.random()
+    if v_flip > 0.5:
+        img = np.fliplr(img)
+        mask = np.fliplr(mask)
+
+    left = int(np.random.uniform()*0.3*572)
+    right = int((1-np.random.uniform()*0.3)*572)
+    top = int(np.random.uniform()*0.3*572)
+    bottom = int((1-np.random.uniform()*0.3)*572)
+
+
+    img = img[top:bottom, left:right]
+    mask = mask[top:bottom, left:right]
+
+    #adjust brightness
+    brightness = np.random.uniform(-0.2, 0.2)
+    img = np.float32(img+brightness*np.ones(img.shape))
+    img = np.clip(img, -1.0, 1.0)
+
+    return img, mask
+
+
+def create_dataset(data_dir, repeat=400, train_batch_size=16, augment=False, cross_val_ind=1, run_distribute=False):
+
+    images = _load_multipage_tiff(os.path.join(data_dir, 'train-volume.tif'))
+    masks = _load_multipage_tiff(os.path.join(data_dir, 'train-labels.tif'))
+
+    train_indices, val_indices = _get_val_train_indices(len(images), cross_val_ind)
+    train_images = images[train_indices]
+    train_masks = masks[train_indices]
+    train_images = np.repeat(train_images, repeat, axis=0)
+    train_masks = np.repeat(train_masks, repeat, axis=0)
+    val_images = images[val_indices]
+    val_masks = masks[val_indices]
+
+    train_image_data = {"image": train_images}
+    train_mask_data = {"mask": train_masks}
+    valid_image_data = {"image": val_images}
+    valid_mask_data = {"mask": val_masks}
+
+
+    ds_train_images = ds.NumpySlicesDataset(data=train_image_data, sampler=None, shuffle=False)
+    ds_train_masks = ds.NumpySlicesDataset(data=train_mask_data, sampler=None, shuffle=False)
+
+    if run_distribute:
+        rank_id = get_rank()
+        rank_size = get_group_size()
+        ds_train_images = ds.NumpySlicesDataset(data=train_image_data,
+                                                sampler=None,
+                                                shuffle=False,
+                                                num_shards=rank_size,
+                                                shard_id=rank_id)
+        ds_train_masks = ds.NumpySlicesDataset(data=train_mask_data,
+                                               sampler=None,
+                                               shuffle=False,
+                                               num_shards=rank_size,
+                                               shard_id=rank_id)
+
+    ds_valid_images = ds.NumpySlicesDataset(data=valid_image_data, sampler=None, shuffle=False)
+    ds_valid_masks = ds.NumpySlicesDataset(data=valid_mask_data, sampler=None, shuffle=False)
+
+    c_resize_op = c_vision.Resize(size=(388, 388), interpolation=Inter.BILINEAR)
+    c_pad = c_vision.Pad(padding=92)
+    c_rescale_image = c_vision.Rescale(1.0/127.5, -1)
+    c_rescale_mask = c_vision.Rescale(1.0/255.0, 0)
+
+    c_trans_normalize_img = [c_rescale_image, c_resize_op, c_pad]
+    c_trans_normalize_mask = [c_rescale_mask, c_resize_op, c_pad]
+    c_center_crop = c_vision.CenterCrop(size=388)
+
+    train_image_ds = ds_train_images.map(input_columns="image", operations=c_trans_normalize_img)
+    train_mask_ds = ds_train_masks.map(input_columns="mask", operations=c_trans_normalize_mask)
+    train_ds = ds.zip((train_image_ds, train_mask_ds))
+    train_ds = train_ds.project(columns=["image", "mask"])
+    if augment:
+        augment_process = train_data_augmentation
+        c_resize_op = c_vision.Resize(size=(572, 572), interpolation=Inter.BILINEAR)
+        train_ds = train_ds.map(input_columns=["image", "mask"], operations=augment_process)
+        train_ds = train_ds.map(input_columns="image", operations=c_resize_op)
+        train_ds = train_ds.map(input_columns="mask", operations=c_resize_op)
+
+    train_ds = train_ds.map(input_columns="mask", operations=c_center_crop)
+    post_process = data_post_process
+    train_ds = train_ds.map(input_columns=["image", "mask"], operations=post_process)
+    train_ds = train_ds.shuffle(repeat*24)
+    train_ds = train_ds.batch(batch_size=train_batch_size, drop_remainder=True)
+
+    valid_image_ds = ds_valid_images.map(input_columns="image", operations=c_trans_normalize_img)
+    valid_mask_ds = ds_valid_masks.map(input_columns="mask", operations=c_trans_normalize_mask)
+    valid_ds = ds.zip((valid_image_ds, valid_mask_ds))
+    valid_ds = valid_ds.project(columns=["image", "mask"])
+    valid_ds = valid_ds.map(input_columns="mask", operations=c_center_crop)
+    post_process = data_post_process
+    valid_ds = valid_ds.map(input_columns=["image", "mask"], operations=post_process)
+    valid_ds = valid_ds.batch(batch_size=1, drop_remainder=True)
+
+    return train_ds, valid_ds
--- a/model_zoo/official/cv/unet/src/loss.py
+++ b/model_zoo/official/cv/unet/src/loss.py
@ -0,0 +1,38 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+import mindspore
+import mindspore.nn as nn
+import mindspore.ops.operations as F
+from mindspore.nn.loss.loss import _Loss
+
+
+class CrossEntropyWithLogits(_Loss):
+    def __init__(self):
+        super(CrossEntropyWithLogits, self).__init__()
+        self.transpose_fn = F.Transpose()
+        self.reshape_fn = F.Reshape()
+        self.softmax_cross_entropy_loss = nn.SoftmaxCrossEntropyWithLogits()
+        self.cast = F.Cast()
+
+    def construct(self, logits, label):
+        # NCHW->NHWC
+        logits = self.transpose_fn(logits, (0, 2, 3, 1))
+        logits = self.cast(logits, mindspore.float32)
+        label = self.transpose_fn(label, (0, 2, 3, 1))
+
+        loss = self.reduce_mean(
+            self.softmax_cross_entropy_loss(self.reshape_fn(logits, (-1, 2)), self.reshape_fn(label, (-1, 2))))
+        return self.get_loss(loss)
--- a/model_zoo/official/cv/unet/src/unet/init.py
+++ b/model_zoo/official/cv/unet/src/unet/init.py
@ -0,0 +1,16 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+from .unet_model import UNet
--- a/model_zoo/official/cv/unet/src/unet/unet_model.py
+++ b/model_zoo/official/cv/unet/src/unet/unet_model.py
@ -0,0 +1,47 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+from src.unet.unet_parts import DoubleConv, Down, Up1, Up2, Up3, Up4, OutConv
+import mindspore.nn as nn
+
+class UNet(nn.Cell):
+    def __init__(self, n_channels, n_classes):
+        super(UNet, self).__init__()
+        self.n_channels = n_channels
+        self.n_classes = n_classes
+        self.inc = DoubleConv(n_channels, 64)
+        self.down1 = Down(64, 128)
+        self.down2 = Down(128, 256)
+        self.down3 = Down(256, 512)
+        self.down4 = Down(512, 1024)
+        self.up1 = Up1(1024, 512)
+        self.up2 = Up2(512, 256)
+        self.up3 = Up3(256, 128)
+        self.up4 = Up4(128, 64)
+        self.outc = OutConv(64, n_classes)
+
+    def construct(self, x):
+
+        x1 = self.inc(x)
+        x2 = self.down1(x1)
+        x3 = self.down2(x2)
+        x4 = self.down3(x3)
+        x5 = self.down4(x4)
+        x = self.up1(x5, x4)
+        x = self.up2(x, x3)
+        x = self.up3(x, x2)
+        x = self.up4(x, x1)
+        logits = self.outc(x)
+        return logits
--- a/model_zoo/official/cv/unet/src/unet/unet_parts.py
+++ b/model_zoo/official/cv/unet/src/unet/unet_parts.py
@ -0,0 +1,150 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+""" Parts of the U-Net model """
+
+import mindspore.nn as nn
+import mindspore.ops.operations as F
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.nn import CentralCrop
+
+
+class DoubleConv(nn.Cell):
+
+    def __init__(self, in_channels, out_channels, mid_channels=None):
+        super().__init__()
+        init_value_0 = TruncatedNormal(0.06)
+        init_value_1 = TruncatedNormal(0.06)
+        if not mid_channels:
+            mid_channels = out_channels
+        self.double_conv = nn.SequentialCell(
+            [nn.Conv2d(in_channels, mid_channels, kernel_size=3, has_bias=True,
+                       weight_init=init_value_0, pad_mode="valid"),
+             nn.ReLU(),
+             nn.Conv2d(mid_channels, out_channels, kernel_size=3, has_bias=True,
+                       weight_init=init_value_1, pad_mode="valid"),
+             nn.ReLU()]
+        )
+
+    def construct(self, x):
+        return self.double_conv(x)
+
+
+class Down(nn.Cell):
+    """Downscaling with maxpool then double conv"""
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+
+        self.maxpool_conv = nn.SequentialCell(
+            [nn.MaxPool2d(kernel_size=2, stride=2),
+             DoubleConv(in_channels, out_channels)]
+        )
+
+    def construct(self, x):
+        return self.maxpool_conv(x)
+
+
+class Up1(nn.Cell):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+        self.concat = F.Concat(axis=1)
+        self.factor = 56.0 / 64.0
+        self.center_crop = CentralCrop(central_fraction=self.factor)
+        self.print_fn = F.Print()
+        self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        self.up = nn.Conv2dTranspose(in_channels, in_channels // 2, kernel_size=2, stride=2)
+        self.relu = nn.ReLU()
+
+    def construct(self, x1, x2):
+        x1 = self.up(x1)
+        x1 = self.relu(x1)
+        x2 = self.center_crop(x2)
+        x = self.concat((x1, x2))
+        return self.conv(x)
+
+
+class Up2(nn.Cell):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+        self.concat = F.Concat(axis=1)
+        self.factor = 104.0 / 136.0
+        self.center_crop = CentralCrop(central_fraction=self.factor)
+        self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        self.up = nn.Conv2dTranspose(in_channels, in_channels // 2, kernel_size=2, stride=2)
+        self.relu = nn.ReLU()
+
+    def construct(self, x1, x2):
+        x1 = self.up(x1)
+        x1 = self.relu(x1)
+        x2 = self.center_crop(x2)
+        x = self.concat((x1, x2))
+        return self.conv(x)
+
+
+class Up3(nn.Cell):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+        self.concat = F.Concat(axis=1)
+        self.factor = 200 / 280
+        self.center_crop = CentralCrop(central_fraction=self.factor)
+        self.print_fn = F.Print()
+        self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        self.up = nn.Conv2dTranspose(in_channels, in_channels // 2, kernel_size=2, stride=2)
+        self.relu = nn.ReLU()
+
+    def construct(self, x1, x2):
+        x1 = self.up(x1)
+        x1 = self.relu(x1)
+        x2 = self.center_crop(x2)
+        x = self.concat((x1, x2))
+        return self.conv(x)
+
+
+class Up4(nn.Cell):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+        self.concat = F.Concat(axis=1)
+        self.factor = 392 / 568
+        self.center_crop = CentralCrop(central_fraction=self.factor)
+        self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        self.up = nn.Conv2dTranspose(in_channels, in_channels // 2, kernel_size=2, stride=2)
+        self.relu = nn.ReLU()
+
+    def construct(self, x1, x2):
+        x1 = self.up(x1)
+        x1 = self.relu(x1)
+        x2 = self.center_crop(x2)
+        x = self.concat((x1, x2))
+        return self.conv(x)
+
+
+class OutConv(nn.Cell):
+    def __init__(self, in_channels, out_channels):
+        super(OutConv, self).__init__()
+        init_value = TruncatedNormal(0.06)
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, has_bias=True, weight_init=init_value)
+
+    def construct(self, x):
+        x = self.conv(x)
+        return x
--- a/model_zoo/official/cv/unet/src/utils.py
+++ b/model_zoo/official/cv/unet/src/utils.py
@ -0,0 +1,56 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+import time
+import numpy as np
+from mindspore.train.callback import Callback
+from mindspore.common.tensor import Tensor
+
+class StepLossTimeMonitor(Callback):
+
+    def __init__(self, batch_size, per_print_times=1):
+        super(StepLossTimeMonitor, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self._per_print_times = per_print_times
+        self.batch_size = batch_size
+
+    def step_begin(self, run_context):
+        self.step_time = time.time()
+
+    def step_end(self, run_context):
+
+        step_seconds = time.time() - self.step_time
+        step_fps = self.batch_size*1.0/step_seconds
+
+        cb_params = run_context.original_args()
+        loss = cb_params.net_outputs
+
+        if isinstance(loss, (tuple, list)):
+            if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
+                loss = loss[0]
+
+        if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
+            loss = np.mean(loss.asnumpy())
+
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+
+        if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
+            raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
+                cb_params.cur_epoch_num, cur_step_in_epoch))
+        if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
+            # TEST
+            print("step: %s, loss is %s, fps is %s" % (cur_step_in_epoch, loss, step_fps), flush=True)
+            # print("step: %s, loss is %s, fps is %s" % ( cur_step_in_epoch, loss, step_fps))
--- a/model_zoo/official/cv/unet/train.py
+++ b/model_zoo/official/cv/unet/train.py
@ -0,0 +1,106 @@
+# 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
+#
+# less 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.
+# ============================================================================
+
+import os
+import argparse
+import logging
+import ast
+
+import mindspore
+import mindspore.nn as nn
+from mindspore import Model, context
+from mindspore.communication.management import init, get_group_size
+from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
+from mindspore.context import ParallelMode
+
+from src.unet import UNet
+from src.data_loader import create_dataset
+from src.loss import CrossEntropyWithLogits
+from src.utils import StepLossTimeMonitor
+from src.config import cfg_unet
+
+device_id = int(os.getenv('DEVICE_ID'))
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
+
+mindspore.set_seed(1)
+
+def train_net(data_dir,
+              cross_valid_ind=1,
+              epochs=400,
+              batch_size=16,
+              lr=0.0001,
+              run_distribute=False,
+              cfg=None):
+
+
+    if run_distribute:
+        init()
+        group_size = get_group_size()
+        parallel_mode = ParallelMode.DATA_PARALLEL
+        context.set_auto_parallel_context(parallel_mode=parallel_mode,
+                                          device_num=group_size,
+                                          parameter_broadcast=True,
+                                          gradients_mean=False)
+    net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
+
+    criterion = CrossEntropyWithLogits()
+    train_dataset, _ = create_dataset(data_dir, epochs, batch_size, True, cross_valid_ind, run_distribute)
+    train_data_size = train_dataset.get_dataset_size()
+    print("dataset length is:", train_data_size)
+    ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
+                                   keep_checkpoint_max=cfg['keep_checkpoint_max'])
+    ckpoint_cb = ModelCheckpoint(prefix='ckpt_unet_medical_adam',
+                                 directory='./ckpt_{}/'.format(device_id),
+                                 config=ckpt_config)
+
+    optimizer = nn.Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=cfg['weight_decay'],
+                        loss_scale=cfg['loss_scale'])
+
+    loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(cfg['FixedLossScaleManager'], False)
+
+    model = Model(net, loss_fn=criterion, loss_scale_manager=loss_scale_manager, optimizer=optimizer, amp_level="O3")
+
+    print("============== Starting Training ==============")
+    model.train(1, train_dataset, callbacks=[StepLossTimeMonitor(batch_size=batch_size), ckpoint_cb],
+                dataset_sink_mode=False)
+    print("============== End Training ==============")
+
+
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='Train the UNet on images and target masks',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
+                        help='data directory')
+    parser.add_argument('-t', '--run_distribute', type=ast.literal_eval,
+                        default=False, help='Run distribute, default: false.')
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+    args = get_args()
+    print("Training setting:", args)
+
+    epoch_size = cfg_unet['epochs'] if not args.run_distribute else cfg_unet['distribute_epochs']
+    train_net(data_dir=args.data_url,
+              cross_valid_ind=cfg_unet['cross_valid_ind'],
+              epochs=epoch_size,
+              batch_size=cfg_unet['batchsize'],
+              lr=cfg_unet['lr'],
+              run_distribute=args.run_distribute,
+              cfg=cfg_unet)