configs/edgenext/edgenext_small_ascend.yaml

@@ -51,7 +51,7 @@ ckpt_path: ''
 keep_checkpoint_max: 10
 val_interval: 2
 ckpt_save_dir: './ckpt'
-epoch_size: 400
+epoch_size: 350
 dataset_sink_mode: True
 amp_level: 'O3'
 drop_path_rate: 0.1

configs/edgenext/edgenext_small_bn_hs_ascend.yaml

@@ -0,0 +1,77 @@
+# Copyright 2021 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.
+# ============================================================================
+
+# system config
+mode: 0
+distribute: True
+num_parallel_workers: 8
+val_while_train: True
+
+# dataset config
+dataset: 'imagenet'
+data_dir: ''
+shuffle: True
+dataset_download: False
+batch_size: 256
+drop_remainder: True
+val_split: val
+
+# Augmentation config
+image_resize: 256
+scale: [0.08, 1.0]
+ratio: [0.75, 1.333]
+hflip: 0.5
+interpolation: 'bicubic'
+crop_pct: 0.875
+color_jitter: 0.4
+re_prob: 0.0
+cutmix: 1.0
+cutmix_prob: 0.0
+auto_augment: 'randaug-m9-mstd0.5-inc1'
+use_ema: True
+ema_decay: 0.9995
+
+# model config
+model: 'edgenext_small_bn_hs'
+num_classes: 1000
+pretrained: False
+ckpt_path: ''
+keep_checkpoint_max: 10
+val_interval: 2
+ckpt_save_dir: './ckpt'
+epoch_size: 350
+dataset_sink_mode: True
+amp_level: 'O3'
+drop_path_rate: 0.1
+
+# loss config
+loss: 'CE'
+label_smoothing: 0.1
+
+# lr scheduler config
+scheduler: 'cosine_decay'
+min_lr: 1e-6
+lr: 4e-3
+warmup_epochs: 20
+decay_rate: 0.1
+decay_epochs: 330
+
+# optimizer config
+opt: 'adamw'
+filter_bias_and_bn: True
+momentum: 0.9
+weight_decay: 0.05
+loss_scale: 1024
+use_nesterov: False

configs/edgenext/edgenext_x_small_ascend.yaml

@@ -33,25 +33,25 @@ image_resize: 256
 scale: [0.08, 1.0]
 ratio: [0.75, 1.333]
 hflip: 0.5
-interpolation: 'bilinear'
+interpolation: 'bicubic'
 crop_pct: 0.875
 color_jitter: 0.4
 re_prob: 0.0
-cutmix: 0.875
+cutmix: 1.0
 cutmix_prob: 0.0
 auto_augment: 'randaug-m9-mstd0.5-inc1'
 use_ema: True
 ema_decay: 0.9995
 
 # model config
-model: 'edgenext_small'
+model: 'edgenext_x_small'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''
 keep_checkpoint_max: 10
 val_interval: 2
 ckpt_save_dir: './ckpt'
-epoch_size: 400
+epoch_size: 350
 dataset_sink_mode: True
 amp_level: 'O3'
 drop_path_rate: 0.1
@@ -63,10 +63,10 @@ label_smoothing: 0.1
 # lr scheduler config
 scheduler: 'cosine_decay'
 min_lr: 1e-6
-lr: 4.5e-3
+lr: 4e-3
 warmup_epochs: 20
 decay_rate: 0.1
-decay_epochs: 380
+decay_epochs: 330
 
 # optimizer config
 opt: 'adamw'

configs/edgenext/edgenext_xx_small_ascend.yaml

@@ -33,25 +33,25 @@ image_resize: 256
 scale: [0.08, 1.0]
 ratio: [0.75, 1.333]
 hflip: 0.5
-interpolation: 'bilinear'
+interpolation: 'bicubic'
 crop_pct: 0.875
 color_jitter: 0.4
 re_prob: 0.0
-cutmix: 0.875
+cutmix: 1.0
 cutmix_prob: 0.0
 auto_augment: 'randaug-m9-mstd0.5-inc1'
 use_ema: True
 ema_decay: 0.9995
 
 # model config
-model: 'edgenext_small_bn_hs'
+model: 'edgenext_xx_small'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''
 keep_checkpoint_max: 10
 val_interval: 2
 ckpt_save_dir: './ckpt'
-epoch_size: 300
+epoch_size: 350
 dataset_sink_mode: True
 amp_level: 'O3'
 drop_path_rate: 0.1
@@ -66,7 +66,7 @@ min_lr: 1e-6
 lr: 4e-3
 warmup_epochs: 20
 decay_rate: 0.1
-decay_epochs: 380
+decay_epochs: 330
 
 # optimizer config
 opt: 'adamw'

mindcv/models/edgenext.py

@@ -472,7 +472,7 @@ class EdgeNeXt(nn.Cell):
     def forward_features(self, x):
         x = self.downsample_layers[0](x)
         x = self.stages[0](x)
-        if self.pos_embd:
+        if self.pos_embd is not None:
             B, C, H, W = x.shape
             x = x + self.pos_embd(B, H, W)
         for i in range(1, 4):

transfer.py

@@ -0,0 +1,3 @@
+x = 3
+if x is not None:
+    print(1)