e-AI translator (free version) supports microcontrollers with comparatively small ROM/RAM capacity. In order to compress the capacity used by the library, only functions that are often used by neural networks are supported.
Translatable Functions (Inference)
Neural net functions that are used only for learning and not for inference (for example, tf.nn.l2_loss for evaluating loss) are usable.
| Type | PyTorch | Tensorflow (tf-keras) | Keras | Tensorflow Lite (New V2.1.0) | |
|---|---|---|---|---|---|
| C_FP32 | C_FP32 | C_FP32 | API | C_INT8 | |
| 1. Convolution Layers | nn.Conv2d | tf.keras.layers.Conv2D | keras.layers.Conv2D | tfl.conv_2d | support |
| nn.Conv2d | tf.keras.layers.DepthwiseConv2D | keras.layers.DepthwiseConv2D | tfl.depthwise_conv_2d | support | |
| nn.ConvTranspose2d | tf.keras.layers.Conv2DTranspose | keras.layers.Conv2DTranspose | tfl.transpose_conv | support | |
| + | tf.keras.layers.add | keras.layers.add | support | ||
| 2. Pooling layers | nn.MaxPool2d | tf.keras.layers.MaxPool2D | keras.layers.MaxPooling2D | tfl.max_pool_2d | support |
| nn.MaxPool2d | tf.keras.layers.GlobalMaxPooling2D | keras.layers.GlobalMaxPooling2D | tfl.mean | support | |
| nn.AvgPool2d | tf.keras.layers.AveragePooling2D | keras.layers.AveragePooling2D | tfl.average_pool_2d | support | |
| nn.AvgPool2d | tf.keras.layers.GlobalAveragePooling2D | keras.layers.GlobalAveragePooling2D | tfl.reducemax | support | |
| 3. Non-linear Activations (weighted sum, nonlinearity) | nn.Hardsigmoid | tf.keras.activations.hard_sigmoid tf.keras.layers.Activation (“hard_sigmoid”) | keras.activations.hard_sigmoid keras.layers.Activation (“hard_sigmoid”) | None | - |
| nn.LeakyReLU | tf.keras.layers.LeakyReLU | keras.layers.LeakyReLU | tfl.leaky_relu | support | |
| nn.ReLU | tf.keras.activations.relu tf.keras.layers.Activation (“relu”) tf.keras.layers.relu | keras.activations.relu keras.layers.Activation (“relu”) keras.layers.relu | tfl.relu (fused activation) | support | |
| nn.ReLU6 | tf.keras.activations.relu (max_value=6.) tf.keras.layers.relu (max_value=6.) | keras.activations.relu (max_value=6.) keras.layers.relu (max_value=6.) | tfl.relu with max_value = 6 | support | |
| nn.Sigmoid | tf.keras.activations.sigmoid tf.keras.layers.Activation (“sigmoid”) | keras.activations.sigmoid keras.layers.Activation (“sigmoid”) | tfl.logistic | support | |
| nn.Softplus | tf.keras.activations.softplus tf.keras.layers.Activation (“softplus”) | keras.activations.softplus keras.layers.Activation (“softplus”) | None | - | |
| nn.Softsign | tf.keras.activations.softsign tf.keras.layers.Activation (“softsign”) | keras.activations.softsign keras.layers.Activation (“softsign”) | None | - | |
| nn.Tanh | tf.keras.activations.tanh tf.keras.layers.Activation (“tanh”) | keras.activations.tanh keras.layers.Activation (“tanh”) | tfl.tanh | support | |
| 4. Non-linear Activations (other) | nn.Softmax nn.Softmax2d | tf.keras.activations.softmax tf.keras.layers.Activation (“softmax”) tf.keras.layers.Softmax | keras.activations.softmax keras.layers.Activation (“softmax”) keras.layers.Softmax | tfl.softmax | support |
| torch.clip torch.clamp | tf.keras.backend.clip (use lambda) | keras.backend.clip (use lambda) | None | - | |
| 5. Normalization Layers | nn.BatchNorm2d | tf.keras.layers.BatchNormalization | keras.layers.BatchNormalization | tfl.mul, tfl.add | - CBR only |
| 6. Linear Layers | nn.Linear torch.matmul | tf.keras.layers.Dense | keras.layers.Dense | tfl.fully_connected | support |
| 8. Utilities | nn.Flatten | tf.keras.layers.Flatten | keras.layers.Flatten | None | support (skip) |
| view torch.reshape | tf.keras.layers.Reshape | keras.layers.Reshape | None | support (skip) | |
| 9. Mearge layers (structure check) | + | tf.keras.layers.add | keras.layers.add | tfl.add | support |
| - | tf.keras.layers.subtract | keras.layers.subtract | tfl.sub | support | |
| * | tf.keras.layers.multiply | keras.layers.multiply | tfl.mul | support | |
| torch.cat | tf.keras.layers.concatenate | keras.layers.concatenate | tfl.concatenation | support | |
Supported Neural Network Structures (e-AI Translator V2.1.0)
| Neural network name | model name | Status | Note |
|---|---|---|---|
| AutoEncoder | support | Support Conv2D-Fully Connected-Deconv2D structure, too. | |
| Convolutional Neural Network (CNN) Branch structures are not included. | LeNet | support | |
| AlexNet | support | ||
| VGG | support | If the channel numbers of fully connected layers are big, huge memory capacity is required. | |
| Convolutional Neural Network (CNN) Branch structures are included. (skip connection, etc.) | Network in Network GoogleNet | support | RAM usage reduction function is available (New V2.1.0) |
| MobileNet | support | ||
| ResNet | support | ||
| SqueezeNet | support | ||
| SENet | support | ||
| ContextNet | - | Need to replace activation function from Swish to ReLU | |
| Self Attention Net | - | Support SENe only. | |
| Recurrent Neural Network (RNN) | RNNoise | - | Not supported. Consider to utilize SE module instead of RNN. |
| Long Short Term Memory (LSTM) | - | Not supported. Consider to utilize SE module instead of LSTM. | |
| Gated Recurrent Unit (GRU) | - | Not supported. Consider to utilize SE module instead of GRU. |
Translatable Functions (e-AI Translator V1.6.0: inference)
| Type | TensorFlow | tf-keras | Keras | Caffe |
|---|---|---|---|---|
| 1. Convolution Layers | tf.nn.conv2d tf.contrib.layers.conv2d tf.layers.conv2d | tf.keras.layers.Conv2D | keras.layers.Conv2D | Convolution |
| tf.nn.depthwise_conv2d | tf.keras.layers.DepthwiseConv2D | keras.layers.DepthwiseConv2D | None | |
| tf.nn.conv2d_transpose tf.contrib.layers.conv2d_transpose tf.layers.conv2d_transpose | tf.keras.layers.Conv2DTranspose | keras.layers.Conv2DTranspose | Deconvolution | |
| tf.add tf.nn.bias_add tf.contrib.layers.bias_add + | tf.keras.layers.add | keras.layers.add | None | |
| 2. Pooling layers | tf.nn.max_pool tf.contrib.layers.max_pool2d tf.layers.max_pooling2d | tf.keras.layers.MaxPool2D | keras.layers.MaxPooling2D | Pooling with pooling_param {pool: MAX} |
| None | tf.keras.layers.GlobalMaxPooling2D | keras.layers.GlobalMaxPooling2D | None | |
| tf.nn.avg_pool tf.contrib.layers.avg_pool2d tf.layers.average_pooling2d | tf.keras.layers.AveragePooling2D | keras.layers.AveragePooling2D | Pooling with pooling_param {pool: AVE} | |
| None | tf.keras.layers.GlobalAveragePooling2D | keras.layers.GlobalAveragePooling2D | None | |
| 3. Non-linear Activations (weighted sum, nonlinearity) | None | tf.keras.activations.hard_sigmoid tf.keras.layers.Activation (“hard_sigmoid”) | keras.activations.hard_sigmoid keras.layers.Activation (“hard_sigmoid”) | None |
| tf.nn.leaky_relu | tf.keras.layers.LeakyReLU | keras.layers.LeakyReLU | None | |
| tf.nn.relu | tf.keras.activations.relu tf.keras.layers.Activation ( “relu”) tf.keras.layers.relu | keras.activations.relu keras.layers.Activation (“relu”) keras.layers.relu | ReLU | |
| tf.nn.relu6 | tf.keras.activations.relu (max_value=6.) tf.keras.layers.relu (max_value=6.) | keras.activations.relu (max_value=6.) keras.layers.relu (max_value=6.) | None | |
| tf.sigmoid | tf.keras.activations.sigmoid tf.keras.layers.Activation (“sigmoid”) | keras.activations.sigmoid keras.layers.Activation (“sigmoid”) | Sigmoid | |
| tf.nn.softplus tf.math.softplus | tf.keras.activations.softplus tf.keras.layers.Activation (“softplus”) | keras.activations.softplus keras.layers.Activation (“softplus”) | None | |
| tf.nn.softsign tf.math.softsign | tf.keras.activations.softsign tf.keras.layers.Activation (“softsign”) | keras.activations.softsign keras.layers.Activation (“softsign”) | None | |
| tf.nn.tanh tf.tanh | tf.keras.activations.tanh tf.keras.layers.Activation (“tanh”) | keras.activations.tanh keras.layers.Activation (“tanh”) | TanH | |
| 4. Non-linear Activations (other) | tf.nn.softmax tf.contrib.layers.softmax tf.nn.softmax_cross_entropy_with_logits tf.nn.softmax_cross_entropy_with_logits_v2 | tf.keras.activations.softmax tf.keras.layers.Activation (“softmax”) tf.keras.layers.Softmax | keras.activations.softmax keras.layers.Activation (“softmax”) keras.layers.Softmax | Softmax |
| tf.clip_by_average_norm | None | None | None | |
| tf.clip_by_global_norm | None | None | None | |
| tf.clip_by_norm | None | None | None | |
| tf.clip_by_value | tf.keras.backend.clip (use lambda) | keras.backend.clip (use lambda) | None | |
| 5. Normalization Layers | tf.layers.batch_normalization | tf.keras.layers.BatchNormalization | keras.layers.BatchNormalization | None |
| tf.nn.lrn tf.nn.local_response_normalization | None | None | LRN | |
| 6. Linear Layers | tf.matmul tf.contrib.layers.fully_connected tf.layers.dense | tf.keras.layers.Dense | keras.layers.Dense | InnerProduct |
| 8. Utilities | tf.layers.flatten tf.contrib.layers.flatten | tf.keras.layers.Flatten | keras.layers.Flatten | None |
| tf.reshape | tf.keras.layers.Reshape | keras.layers.Reshape | Reshape Split | |
| 9. Mearge layers (structure check) | tf.add tf.math.add + | tf.keras.layers.add | keras.layers.add | None |
| tf.substruct tf.math.substruct - | tf.keras.layers.subtract | keras.layers.subtract | None | |
| tf.math.multiply * | tf.keras.layers.multiply | keras.layers.multiply | None | |
| tf.concat | tf.keras.layers.concatenate | keras.layers.concatenate | None |
Supported Neural Network Structures (e-AI Translator V1.6.0)
| Neural network name | model name | Status | Note |
|---|---|---|---|
| AutoEncoder | support | Support Conv2D-Fully Connected-Deconv2D structure, too. | |
| Convolutional Neural Network (CNN) Branch structures are not included. | LeNet | support | |
| AlexNet | support | ||
| VGG | support | If the channel numbers of fully connected layers are big, huge memory capacity is required. | |
| Convolutional Neural Network (CNN) Branch structures are included. (skip connection, etc.) | Network in Network GoogleNet | support | RAM usage reduction function is not available. |
| MobileNet | support | RAM usage reduction function is not available. | |
| ResNet | support | RAM usage reduction function is not available. | |
| SENet | support | RAM usage reduction function is not available. | |
| Recurrent Neural Network (RNN) | - | Not supported. Consider to utilize SE module instead of RNN. | |
| Long Short Term Memory (LSTM) | - | Not supported. Consider to utilize SE module instead of LSTM. |