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* WIP initially adding edge presets * WIP el variant working * added all hf timm edge presets * removing irrelevant note * format pass * remove irrelevant old commented code * fix unit test regression * add presets to preset file * WIP starting changes needed for additional presets * WIP 2 variants working * add ConvBNAct Block and tiny variant * adds preset data * updated correct config * resolve merge conflicts * review updates * add projection_activation argument to fused blocks to fix timm discrepancy * additional review updates
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| import keras | ||
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| BN_AXIS = 3 | ||
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| class CBABlock(keras.layers.Layer): | ||
| """ | ||
| Args: | ||
| input_filters: int, the number of input filters | ||
| output_filters: int, the number of output filters | ||
| kernel_size: default 3, the kernel_size to apply to the expansion phase | ||
| convolutions | ||
| strides: default 1, the strides to apply to the expansion phase | ||
| convolutions | ||
| data_format: str, channels_last (default) or channels_first, expects | ||
| tensors to be of shape (N, H, W, C) or (N, C, H, W) respectively | ||
| batch_norm_momentum: default 0.9, the BatchNormalization momentum | ||
| batch_norm_epsilon: default 1e-3, the BatchNormalization epsilon | ||
| activation: default "swish", the activation function used between | ||
| convolution operations | ||
| dropout: float, the optional dropout rate to apply before the output | ||
| convolution, defaults to 0.2 | ||
| nores: bool, default False, forces no residual connection if True, | ||
| otherwise allows it if False. | ||
| Returns: | ||
| A tensor representing a feature map, passed through the ConvBNAct | ||
| block | ||
| Note: | ||
| Not intended to be used outside of the EfficientNet architecture. | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| input_filters, | ||
| output_filters, | ||
| kernel_size=3, | ||
| strides=1, | ||
| data_format="channels_last", | ||
| batch_norm_momentum=0.9, | ||
| batch_norm_epsilon=1e-3, | ||
| activation="swish", | ||
| dropout=0.2, | ||
| nores=False, | ||
| **kwargs | ||
| ): | ||
| super().__init__(**kwargs) | ||
| self.input_filters = input_filters | ||
| self.output_filters = output_filters | ||
| self.kernel_size = kernel_size | ||
| self.strides = strides | ||
| self.data_format = data_format | ||
| self.batch_norm_momentum = batch_norm_momentum | ||
| self.batch_norm_epsilon = batch_norm_epsilon | ||
| self.activation = activation | ||
| self.dropout = dropout | ||
| self.nores = nores | ||
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| padding_pixels = kernel_size // 2 | ||
| self.conv1_pad = keras.layers.ZeroPadding2D( | ||
| padding=(padding_pixels, padding_pixels), | ||
| name=self.name + "conv_pad", | ||
| ) | ||
| self.conv1 = keras.layers.Conv2D( | ||
| filters=self.output_filters, | ||
| kernel_size=kernel_size, | ||
| strides=strides, | ||
| kernel_initializer=self._conv_kernel_initializer(), | ||
| padding="valid", | ||
| data_format=data_format, | ||
| use_bias=False, | ||
| name=self.name + "conv", | ||
| ) | ||
| self.bn1 = keras.layers.BatchNormalization( | ||
| axis=BN_AXIS, | ||
| momentum=self.batch_norm_momentum, | ||
| epsilon=self.batch_norm_epsilon, | ||
| name=self.name + "bn", | ||
| ) | ||
| self.act = keras.layers.Activation( | ||
| self.activation, name=self.name + "activation" | ||
| ) | ||
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| if self.dropout: | ||
| self.dropout_layer = keras.layers.Dropout( | ||
| self.dropout, | ||
| noise_shape=(None, 1, 1, 1), | ||
| name=self.name + "drop", | ||
| ) | ||
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| def _conv_kernel_initializer( | ||
| self, | ||
| scale=2.0, | ||
| mode="fan_out", | ||
| distribution="truncated_normal", | ||
| seed=None, | ||
| ): | ||
| return keras.initializers.VarianceScaling( | ||
| scale=scale, mode=mode, distribution=distribution, seed=seed | ||
| ) | ||
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| def build(self, input_shape): | ||
| if self.name is None: | ||
| self.name = keras.backend.get_uid("block0") | ||
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| def call(self, inputs): | ||
| x = self.conv1_pad(inputs) | ||
| x = self.conv1(x) | ||
| x = self.bn1(x) | ||
| x = self.act(x) | ||
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| # Residual: | ||
| if ( | ||
| self.strides == 1 | ||
| and self.input_filters == self.output_filters | ||
| and not self.nores | ||
| ): | ||
| if self.dropout: | ||
| x = self.dropout_layer(x) | ||
| x = keras.layers.Add(name=self.name + "add")([x, inputs]) | ||
| return x | ||
|
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| def get_config(self): | ||
| config = super().get_config() | ||
| config.update( | ||
| { | ||
| "input_filters": self.input_filters, | ||
| "output_filters": self.output_filters, | ||
| "kernel_size": self.kernel_size, | ||
| "strides": self.strides, | ||
| "data_format": self.data_format, | ||
| "batch_norm_momentum": self.batch_norm_momentum, | ||
| "batch_norm_epsilon": self.batch_norm_epsilon, | ||
| "activation": self.activation, | ||
| "dropout": self.dropout, | ||
| "nores": self.nores, | ||
| } | ||
| ) | ||
|
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| return config |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| import keras | ||
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| from keras_hub.src.models.efficientnet.cba import CBABlock | ||
| from keras_hub.src.tests.test_case import TestCase | ||
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| class CBABlockTest(TestCase): | ||
| def test_same_input_output_shapes(self): | ||
| inputs = keras.random.normal(shape=(1, 64, 64, 32), dtype="float32") | ||
| layer = CBABlock(input_filters=32, output_filters=32) | ||
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| output = layer(inputs) | ||
| self.assertEquals(output.shape, (1, 64, 64, 32)) | ||
| self.assertLen(output, 1) | ||
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| def test_different_input_output_shapes(self): | ||
| inputs = keras.random.normal(shape=(1, 64, 64, 32), dtype="float32") | ||
| layer = CBABlock(input_filters=32, output_filters=48) | ||
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| output = layer(inputs) | ||
| self.assertEquals(output.shape, (1, 64, 64, 48)) | ||
| self.assertLen(output, 1) |
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