add effnet_small_eval

ppcls/data/imaug/operators.py

@@ -22,7 +22,6 @@ from __future__ import unicode_literals
 import six
 import math
 import random
-import functools
 import cv2
 import numpy as np
 
@@ -38,8 +37,8 @@ class DecodeImage(object):
 
     def __init__(self, to_rgb=True, to_np=False, channel_first=False):
         self.to_rgb = to_rgb
-        self.to_np = to_np  #to numpy
-        self.channel_first = channel_first  #only enabled when to_np is True
+        self.to_np = to_np  # to numpy
+        self.channel_first = channel_first  # only enabled when to_np is True
 
     def __call__(self, img):
         if six.PY2:
@@ -64,7 +63,8 @@ class DecodeImage(object):
 class ResizeImage(object):
     """ resize image """
 
-    def __init__(self, size=None, resize_short=None):
+    def __init__(self, size=None, resize_short=None, interpolation=-1):
+        self.interpolation = interpolation if interpolation >= 0 else None
         if resize_short is not None and resize_short > 0:
             self.resize_short = resize_short
             self.w = None
@@ -86,8 +86,10 @@ class ResizeImage(object):
         else:
             w = self.w
             h = self.h
-
+        if self.interpolation is None:
             return cv2.resize(img, (w, h))
+        else:
+            return cv2.resize(img, (w, h), interpolation=self.interpolation)
 
 
 class CropImage(object):
@@ -138,8 +140,7 @@ class RandCropImage(object):
         scale_max = min(scale[1], bound)
         scale_min = min(scale[0], bound)
 
-        target_area = img_w * img_h * random.uniform(\
-            scale_min, scale_max)
+        target_area = img_w * img_h * random.uniform(scale_min, scale_max)
         target_size = math.sqrt(target_area)
         w = int(target_size * w)
         h = int(target_size * h)
@@ -176,7 +177,8 @@ class NormalizeImage(object):
     """
 
     def __init__(self, scale=None, mean=None, std=None, order='chw'):
-        if isinstance(scale, str): scale = eval(scale)
+        if isinstance(scale, str):
+            scale = eval(scale)
         self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
         mean = mean if mean is not None else [0.485, 0.456, 0.406]
         std = std if std is not None else [0.229, 0.224, 0.225]

ppcls/modeling/architectures/__init__.py

@@ -36,7 +36,7 @@ from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseN
 from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
 from .darknet import DarkNet53
 from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
-from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
+from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB0_small, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
 from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s
 from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s
 from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C

ppcls/modeling/architectures/efficientnet.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
 #
-#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
+# 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.
+# 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.
 
 from __future__ import absolute_import
 from __future__ import division
@@ -192,7 +192,8 @@ class EfficientNet():
         if is_test:
             return inputs
         keep_prob = 1.0 - prob
-        random_tensor = keep_prob + fluid.layers.uniform_random_batch_size_like(
+        random_tensor = keep_prob + \
+            fluid.layers.uniform_random_batch_size_like(
                 inputs, [-1, 1, 1, 1], min=0., max=1.)
         binary_tensor = fluid.layers.floor(random_tensor)
         output = inputs / keep_prob * binary_tensor
@@ -200,7 +201,8 @@ class EfficientNet():
 
     def _expand_conv_norm(self, inputs, block_args, is_test, name=None):
         # Expansion phase
-        oup = block_args.input_filters * block_args.expand_ratio  # number of output channels
+        oup = block_args.input_filters * \
+            block_args.expand_ratio  # number of output channels
 
         if block_args.expand_ratio != 1:
             conv = self.conv_bn_layer(
@@ -222,7 +224,8 @@ class EfficientNet():
         s = block_args.stride
         if isinstance(s, list) or isinstance(s, tuple):
             s = s[0]
-        oup = block_args.input_filters * block_args.expand_ratio  # number of output channels
+        oup = block_args.input_filters * \
+            block_args.expand_ratio  # number of output channels
 
         conv = self.conv_bn_layer(
             inputs,
@@ -285,7 +288,7 @@ class EfficientNet():
             name=conv_name,
             use_bias=use_bias)
 
-        if use_bn == False:
+        if use_bn is False:
             return conv
         else:
             bn_name = name + bn_name
@@ -325,7 +328,8 @@ class EfficientNet():
                       drop_connect_rate=None,
                       name=None):
         # Expansion and Depthwise Convolution
-        oup = block_args.input_filters * block_args.expand_ratio  # number of output channels
+        oup = block_args.input_filters * \
+            block_args.expand_ratio  # number of output channels
         has_se = self.use_se and (block_args.se_ratio is not None) and (
             0 < block_args.se_ratio <= 1)
         id_skip = block_args.id_skip  # skip connection and drop connect
@@ -346,8 +350,11 @@ class EfficientNet():
         conv = self._project_conv_norm(conv, block_args, is_test, name)
 
         # Skip connection and drop connect
-        input_filters, output_filters = block_args.input_filters, block_args.output_filters
-        if id_skip and block_args.stride == 1 and input_filters == output_filters:
+        input_filters = block_args.input_filters
+        output_filters = block_args.output_filters
+        if id_skip and \
+                block_args.stride == 1 and \
+                input_filters == output_filters:
             if drop_connect_rate:
                 conv = self._drop_connect(conv, drop_connect_rate,
                                           self.is_test)
@@ -412,7 +419,8 @@ class EfficientNet():
                 num_repeat=round_repeats(block_args.num_repeat,
                                          self._global_params))
 
-            # The first block needs to take care of stride and filter size increase.
+            # The first block needs to take care of stride,
+            # and filter size increase.
             drop_connect_rate = self._global_params.drop_connect_rate
             if drop_connect_rate:
                 drop_connect_rate *= float(idx) / block_size
@@ -440,7 +448,9 @@ class EfficientNet():
 
 
 class BlockDecoder(object):
-    """ Block Decoder for readability, straight from the official TensorFlow repository """
+    """
+    Block Decoder, straight from the official TensorFlow repository.
+    """
 
     @staticmethod
     def _decode_block_string(block_string):
@@ -456,9 +466,10 @@ class BlockDecoder(object):
                 options[key] = value
 
         # Check stride
-        assert (
-            ('s' in options and len(options['s']) == 1) or
-            (len(options['s']) == 2 and options['s'][0] == options['s'][1]))
+        cond_1 = ('s' in options and len(options['s']) == 1)
+        cond_2 = ((len(options['s']) == 2)
+                  and (options['s'][0] == options['s'][1]))
+        assert (cond_1 or cond_2)
 
         return BlockArgs(
             kernel_size=int(options['k']),
@@ -487,10 +498,11 @@ class BlockDecoder(object):
     @staticmethod
     def decode(string_list):
         """
-        Decodes a list of string notations to specify blocks inside the network.
+        Decode a list of string notations to specify blocks in the network.
 
-        :param string_list: a list of strings, each string is a notation of block
-        :return: a list of BlockArgs namedtuples of block args
+        string_list: list of strings, each string is a notation of block
+        return
+            list of BlockArgs namedtuples of block args
         """
         assert isinstance(string_list, list)
         blocks_args = []
@@ -525,6 +537,19 @@ def EfficientNetB0(is_test=False,
     return model
 
 
+def EfficientNetB0_small(is_test=False,
+                         padding_type='DYNAMIC',
+                         override_params=None,
+                         use_se=False):
+    model = EfficientNet(
+        name='b0',
+        is_test=is_test,
+        padding_type=padding_type,
+        override_params=override_params,
+        use_se=use_se)
+    return model
+
+
 def EfficientNetB1(is_test=False,
                    padding_type='SAME',
                    override_params=None,