modify deeplabv3

model_zoo/deeplabv3/evaluation.py

+#!/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
+#
+# 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.
+# ============================================================================
+"""evaluation."""
+import os, time
+import argparse
+from mindspore import context
+from mindspore import log as logger
+from mindspore.communication.management import init
+import mindspore.nn as nn
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore import Model, ParallelMode
+import argparse
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import Callback,CheckpointConfig, ModelCheckpoint, TimeMonitor
+from src.md_dataset import create_dataset
+from src.losses import OhemLoss
+from src.miou_precision import MiouPrecision 
+from src.deeplabv3 import deeplabv3_resnet50
+from src.config import config
+parser = argparse.ArgumentParser(description="Deeplabv3 evaluation")
+parser.add_argument('--epoch_size', type=int, default=2, help='Epoch size.')
+parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+parser.add_argument('--batch_size', type=int, default=2, help='Batch size.')
+parser.add_argument('--data_url', required=True, default=None, help='Train data url')
+parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
+
+args_opt = parser.parse_args()
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+print(args_opt)
+if __name__ == "__main__":
+    args_opt.crop_size = config.crop_size
+    args_opt.base_size = config.crop_size
+    eval_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="eval")   
+    net =  deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size,3,args_opt.crop_size,args_opt.crop_size],
+                                     infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                                     decoder_output_stride=config.decoder_output_stride, output_stride = config.output_stride,
+                                     fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid = config.image_pyramid)
+    param_dict = load_checkpoint(args_opt.checkpoint_url)
+    load_param_into_net(net, param_dict)
+    mIou = MiouPrecision(config.seg_num_classes)
+    metrics={'mIou':mIou}
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    model = Model(net, loss, metrics=metrics)
+    model.eval(eval_dataset)
\ No newline at end of file

model_zoo/deeplabv3/src/__init__.py

@@ -12,3 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""Init DeepLabv3."""
+from .deeplabv3 import ASPP, DeepLabV3, deeplabv3_resnet50
+from . import backbone
+from .backbone import *
+
+__all__ = [
+    "ASPP", "DeepLabV3", "deeplabv3_resnet50", "Decoder"
+]
+
+__all__.extend(backbone.__all__)

model_zoo/deeplabv3/src/config.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+network config setting, will be used in train.py and evaluation.py
+"""
+from easydict import EasyDict as ed
+
+config = ed({
+    "learning_rate": 0.0014,
+    "weight_decay": 0.00005,
+    "momentum": 0.97,
+    "crop_size": 513,
+    "eval_scales": [0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+    "atrous_rates": None,
+    "image_pyramid": None,
+    "output_stride": 16,
+    "fine_tune_batch_norm": False,
+    "ignore_label": 255,
+    "decoder_output_stride": None,
+    "seg_num_classes": 21
+})
\ No newline at end of file

model_zoo/deeplabv3/src/losses.py

+#!/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
+#
+# 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.
+# ============================================================================
+"""OhemLoss."""
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+ 
+class OhemLoss(nn.Cell):
+    def __init__(self, num, ignore_label):
+        super(OhemLoss, self).__init__()
+        self.mul = P.Mul()
+        self.shape = P.Shape()
+        self.one_hot = nn.OneHot(-1, num, 1.0, 0.0)
+        self.squeeze = P.Squeeze()
+        self.num = num
+        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean()
+        self.select = P.Select()
+        self.reshape = P.Reshape()
+        self.cast = P.Cast()
+        self.not_equal = P.NotEqual()
+        self.equal = P.Equal()
+        self.reduce_sum = P.ReduceSum(keep_dims=False)
+        self.fill = P.Fill()
+        self.transpose = P.Transpose()
+        self.ignore_label = ignore_label
+        self.loss_weight = 1.0
+ 
+    def construct(self, logits, labels):
+        logits = self.transpose(logits,(0,2,3,1))
+        logits = self.reshape(logits, (-1,self.num))
+        labels = F.cast(labels,mstype.int32)
+        labels = self.reshape(labels, (-1,))
+        one_hot_labels = self.one_hot(labels)
+        losses = self.cross_entropy(logits, one_hot_labels)[0]
+        weights = self.cast(self.not_equal(labels,self.ignore_label),mstype.float32) * self.loss_weight
+        weighted_losses = self.mul(losses, weights)
+        loss = self.reduce_sum(weighted_losses,(0,))
+        zeros = self.fill(mstype.float32, self.shape(weights), 0.0)
+        ones = self.fill(mstype.float32, self.shape(weights), 1.0)
+        present = self.select(
+          self.equal(weights, zeros),
+          zeros,
+          ones)
+        present = self.reduce_sum(present,(0,))
+ 
+        zeros = self.fill(mstype.float32, self.shape(present), 0.0)
+        min_control = self.fill(mstype.float32, self.shape(present), 1.0)
+        present = self.select(
+          self.equal(present, zeros),
+          min_control,
+          present)
+        loss = loss / present
+        return loss
\ No newline at end of file

model_zoo/deeplabv3/src/miou_precision.py

+#!/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
+#
+# 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.
+# ============================================================================
+"""mIou."""
+import numpy as np
+from mindspore.nn.metrics.metric import Metric
+def confuse_matrix(target, pred, n):
+    k = (target >= 0) & (target < n)
+    return np.bincount(n * target[k].astype(int) + pred[k], minlength=n ** 2).reshape(n, n)
+def iou(hist):
+    denominator = hist.sum(1) + hist.sum(0) - np.diag(hist)
+    res = np.diag(hist) / np.where(denominator > 0, denominator, 1) 
+    res = np.sum(res) / np.count_nonzero(denominator)
+    return res
+class MiouPrecision(Metric):
+    def __init__(self, num_class=21):
+        super(MiouPrecision, self).__init__()
+        if not isinstance(num_class, int):
+            raise TypeError('num_class should be integer type, but got {}'.format(type(num_class)))
+        if num_class < 1:
+            raise ValueError('num_class must be at least 1, but got {}'.format(num_class))
+        self._num_class = num_class
+        self._mIoU=[]
+        self.clear()
+    def clear(self):
+        self._hist = np.zeros((self._num_class, self._num_class))
+        self._mIoU=[]
+    def update(self, *inputs):
+        if len(inputs) != 2:
+            raise ValueError('Need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
+        predict_in = self._convert_data(inputs[0])
+        label_in = self._convert_data(inputs[1])
+        if predict_in.shape[1] != self._num_class:
+            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
+                             'classes'.format(self._num_class, predict_in.shape[1]))
+        batch_size = predict_in.shape[0]
+        pred = np.argmax(predict_in,axis=1)
+        label = label_in
+        if len(label.flatten()) != len(pred.flatten()):
+            print('Skipping: len(gt) = {:d}, len(pred) = {:d}'.format(len(label.flatten()), len(pred.flatten())))
+            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
+                             'classes'.format(self._num_class, predict_in.shape[1]))
+        self._hist = confuse_matrix(label.flatten(), pred.flatten(), self._num_class)
+        mIoUs = iou(self._hist)
+        self._mIoU.append(mIoUs)
+       
+    def eval(self):
+        """
+        Computes the mIoU categorical accuracy.
+        """
+        mIoU=np.nanmean(self._mIoU)
+        print('mIoU = {}'.format(mIoU))
+        return mIoU
\ No newline at end of file

model_zoo/deeplabv3/src/utils/__init__.py

+#!/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
+#
+# 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.
+# ============================================================================
\ No newline at end of file

model_zoo/deeplabv3/src/utils/file_io.py

+#!/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
+#
+# 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.
+# ============================================================================
+import os
+
+
+def _is_obs(url):
+    return url.startswith("obs://") or url.startswith("s3://")
+
+
+def read(url, binary=False):
+    if _is_obs(url):
+        # TODO read cloud file.
+        return None
+
+    with open(url, "rb" if binary else "r") as f:
+        return f.read()
+
+
+def walk(url):
+    if _is_obs(url):
+        # TODO read cloud file.
+        return None
+    return os.walk(url)
\ No newline at end of file

model_zoo/deeplabv3/train.py

+#!/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
+#
+# 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.
+# ============================================================================
+"""train."""
+import os, time
+import argparse
+from mindspore import context
+from mindspore import log as logger
+from mindspore.communication.management import init
+import mindspore.nn as nn
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore import Model, ParallelMode
+import argparse
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import Callback,CheckpointConfig, ModelCheckpoint, TimeMonitor
+from src.md_dataset import create_dataset
+from src.losses import OhemLoss
+from src.deeplabv3 import deeplabv3_resnet50
+from src.config import config
+
+parser = argparse.ArgumentParser(description="Deeplabv3 training")
+parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
+parser.add_argument('--epoch_size', type=int, default=2, help='Epoch size.')
+parser.add_argument('--batch_size', type=int, default=2, help='Batch size.')
+parser.add_argument('--data_url', required=True, default=None, help='Train data url')
+parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
+parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
+parser.add_argument('--max_checkpoint_num', type=int, default=5, help='Max checkpoint number.')
+parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
+                                                                                "default is 1000.")
+parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
+args_opt = parser.parse_args()
+print(args_opt)
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+    Note:
+        if per_print_times is 0 do not print loss.
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+    def __init__(self, per_print_times=1):
+        super(LossCallBack, 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
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
+                                                           str(cb_params.net_outputs)))
+def model_fine_tune(flags, net, fix_weight_layer):
+    checkpoint_path = flags.checkpoint_url
+    if checkpoint_path is None:
+        return
+    param_dict = load_checkpoint(checkpoint_path)
+    load_param_into_net(net, param_dict)
+    for para in net.trainable_params():
+        if fix_weight_layer in para.name:
+            para.requires_grad=False
+if __name__ == "__main__":
+    if args_opt.distribute == "true":
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
+        init()
+    args_opt.base_size = config.crop_size
+    args_opt.crop_size = config.crop_size
+    train_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="train")   
+    dataset_size = train_dataset.get_dataset_size()
+    time_cb = TimeMonitor(data_size=dataset_size)
+    callback = [time_cb, LossCallBack()]
+    if args_opt.enable_save_ckpt == "true":
+        config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
+                                     keep_checkpoint_max=args_opt.save_checkpoint_num)
+        ckpoint_cb = ModelCheckpoint(prefix='checkpoint_deeplabv3', config=config_ck)
+        callback.append(ckpoint_cb)
+    net =  deeplabv3_resnet50(crop_size.seg_num_classes, [args_opt.batch_size,3,args_opt.crop_size,args_opt.crop_size],
+                                     infer_scale_sizes=crop_size.eval_scales, atrous_rates=crop_size.atrous_rates,
+                                     decoder_output_stride=crop_size.decoder_output_stride, output_stride = crop_size.output_stride,
+                                     fine_tune_batch_norm=crop_size.fine_tune_batch_norm, image_pyramid = crop_size.image_pyramid)
+    net.set_train()
+    model_fine_tune(args_opt, net, 'layer')
+    loss = OhemLoss(crop_size.seg_num_classes, crop_size.ignore_label)
+    opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=args_opt.learning_rate, momentum=args_opt.momentum, weight_decay=args_opt.weight_decay)
+    model = Model(net, loss, opt)
+    model.train(args_opt.epoch_size, train_dataset, callback)
\ No newline at end of file