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Add ModelZoo Network: Unet.

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pull/6385/head
zhanghuiyao 5 years ago
parent 4d6bbd1218
commit 776eb28e6e
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model_zoo/official/cv/unet/README.md
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model_zoo/official/cv/unet/eval.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
#
# 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)

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model_zoo/official/cv/unet/scripts/run_distribute_train.sh
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#!/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

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model_zoo/official/cv/unet/scripts/run_standalone_eval.sh
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#!/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 &

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model_zoo/official/cv/unet/scripts/run_standalone_train.sh
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#!/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 &

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model_zoo/official/cv/unet/src/config.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
#
# 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,
}

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model_zoo/official/cv/unet/src/data_loader.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
#
# 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

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model_zoo/official/cv/unet/src/loss.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
#
# 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)

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model_zoo/official/cv/unet/src/unet/__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
#
# 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

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model_zoo/official/cv/unet/src/unet/unet_model.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
#
# 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

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model_zoo/official/cv/unet/src/unet/unet_parts.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
#
# 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

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model_zoo/official/cv/unet/src/utils.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
#
# 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))

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model_zoo/official/cv/unet/train.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
#
# 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)
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