Merge pull request bonlime#1 from brianmanderson/tf2_branch

Tf2 branch

model.py → model_tf2.py

@@ -19,27 +19,27 @@
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow.compat.v1 as tf
-
-from tensorflow.python.keras.models import Model
-from tensorflow.python.keras import layers
-from tensorflow.python.keras.layers import Input
-from tensorflow.python.keras.layers import Lambda
-from tensorflow.python.keras.layers import Activation
-from tensorflow.python.keras.layers import Concatenate
-from tensorflow.python.keras.layers import Add
-from tensorflow.python.keras.layers import Dropout
-from tensorflow.python.keras.layers import BatchNormalization
-from tensorflow.python.keras.layers import Conv2D
-from tensorflow.python.keras.layers import DepthwiseConv2D
-from tensorflow.python.keras.layers import ZeroPadding2D
-from tensorflow.python.keras.layers import GlobalAveragePooling2D
-from tensorflow.python.keras.layers import UpSampling2D
-from tensorflow.python.keras.utils.layer_utils import get_source_inputs
-from tensorflow.python.keras.utils.data_utils import get_file
-from tensorflow.python.keras import backend as K
-from tensorflow.python.keras.activations import relu
-from tensorflow.python.keras.applications.imagenet_utils import preprocess_input
+import tensorflow as tf
+from tensorflow.keras.mixed_precision import experimental as mixed_precision
+policy = mixed_precision.Policy('mixed_float16')
+mixed_precision.set_policy(policy)
+from tensorflow.keras.models import Model
+from tensorflow.keras import layers
+from tensorflow.keras.layers import Input
+from tensorflow.keras.layers import Lambda
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import Concatenate
+from tensorflow.keras.layers import Add
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import Conv2D
+from tensorflow.keras.layers import DepthwiseConv2D
+from tensorflow.keras.layers import ZeroPadding2D
+from tensorflow.keras.layers import GlobalAveragePooling2D
+from tensorflow.keras.utils import get_file
+from tensorflow.keras.utils import get_source_inputs
+from tensorflow.keras.applications.imagenet_utils import preprocess_input
+import tensorflow.keras.backend as K
 
 WEIGHTS_PATH_X = "https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5"
 WEIGHTS_PATH_MOBILE = "https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels.h5"
@@ -169,7 +169,7 @@ def _make_divisible(v, divisor, min_value=None):
 
 
 def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id, skip_connection, rate=1):
- in_channels = inputs.shape[-1].value # inputs._keras_shape[-1]
+ in_channels = inputs.shape[-1] # inputs._keras_shape[-1]
  pointwise_conv_filters = int(filters * alpha)
  pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
  x = inputs
@@ -364,8 +364,8 @@ def Deeplabv3(weights='pascal_voc', input_tensor=None, input_shape=(512, 512, 3)
  # Image Feature branch
  b4 = GlobalAveragePooling2D()(x)
  # from (b_size, channels)->(b_size, 1, 1, channels)
- b4 = Lambda(lambda x: K.expand_dims(x, 1))(b4)
- b4 = Lambda(lambda x: K.expand_dims(x, 1))(b4)
+ b4 = Lambda(lambda x: tf.expand_dims(x, 1))(b4)
+ b4 = Lambda(lambda x: tf.expand_dims(x, 1))(b4)
  b4 = Conv2D(256, (1, 1), padding='same',
  use_bias=False, name='image_pooling')(b4)
  b4 = BatchNormalization(name='image_pooling_BN', epsilon=1e-5)(b4)
@@ -374,7 +374,7 @@ def Deeplabv3(weights='pascal_voc', input_tensor=None, input_shape=(512, 512, 3)
  size_before = K.int_shape(x)
 
  b4 = Lambda(lambda x: tf.image.resize(x, size_before[1:3],
- method='bilinear', align_corners=True))(b4)
+ method='bilinear'))(b4)
  # b4 = UpSampling2D(size=(size_before[1],size_before[2]),interpolation='bilinear')(b4)
  # simple 1x1
  b0 = Conv2D(256, (1, 1), padding='same', use_bias=False, name='aspp0')(x)
@@ -401,8 +401,8 @@ def Deeplabv3(weights='pascal_voc', input_tensor=None, input_shape=(512, 512, 3)
  x = Conv2D(256, (1, 1), padding='same',
  use_bias=False, name='concat_projection')(x)
  x = BatchNormalization(name='concat_projection_BN', epsilon=1e-5)(x)
- x = Activation('elu')(x)
  x = Dropout(0.1)(x)
+ x = Activation('elu')(x)
  # DeepLab v.3+ decoder
 
  if backbone == 'xception':
@@ -435,7 +435,7 @@ def Deeplabv3(weights='pascal_voc', input_tensor=None, input_shape=(512, 512, 3)
  # size_out = K.int_shape(img_input)
  # x = UpSampling2D(size=(size_out[1] // size_in[1], size_out[2] // size_in[2]), interpolation='bilinear')(x)
  size_before3 = K.int_shape(img_input)
- x = Lambda(lambda xx: tf.image.resize(xx, size_before3[1:3], method='bilinear', align_corners=True))(x)
+ x = Lambda(lambda xx: tf.image.resize(xx, size_before3[1:3], method='bilinear'))(x)
  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
@@ -445,7 +445,7 @@ def Deeplabv3(weights='pascal_voc', input_tensor=None, input_shape=(512, 512, 3)
 
  if activation in {'softmax', 'sigmoid'}:
  x = Activation(activation)(x)
-
+ x = Activation('linear', dtype='float32')(x)
  model = Model(inputs=inputs, outputs=x, name='deeplabv3plus')
 
  # load weights