diff --git a/paddle2.0_docs/image_segmentation/pets_image_segmentation_U_Net_like.ipynb b/paddle2.0_docs/image_segmentation/pets_image_segmentation_U_Net_like.ipynb index 9dd56ed42fb192d5bd9309c6aa64901e508c7214..bcb2466cd2adc0643ca9e0c2548271bcb339544a 100644 --- a/paddle2.0_docs/image_segmentation/pets_image_segmentation_U_Net_like.ipynb +++ b/paddle2.0_docs/image_segmentation/pets_image_segmentation_U_Net_like.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -43,7 +43,7 @@ "'2.0.0-beta0'" ] }, - "execution_count": 21, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -388,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -464,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", @@ -527,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", @@ -648,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", @@ -724,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -743,28 +743,28 @@ "--------------------------------------------------------------------------------\n", " Layer (type) Input Shape Output Shape Param #\n", "================================================================================\n", - " Conv2d-38 [-1, 3, 160, 160] [-1, 32, 80, 80] 896\n", - " BatchNorm2d-14 [-1, 32, 80, 80] [-1, 32, 80, 80] 128\n", - " ReLU-14 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n", - " ReLU-17 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n", - " Conv2d-49 [-1, 128, 20, 20] [-1, 128, 20, 20] 1,152\n", - " Conv2d-50 [-1, 128, 20, 20] [-1, 256, 20, 20] 33,024\n", - "SeparableConv2d-17 [-1, 128, 20, 20] [-1, 256, 20, 20] 0\n", - " BatchNorm2d-17 [-1, 256, 20, 20] [-1, 256, 20, 20] 1,024\n", - " Conv2d-51 [-1, 256, 20, 20] [-1, 256, 20, 20] 2,304\n", - " Conv2d-52 [-1, 256, 20, 20] [-1, 256, 20, 20] 65,792\n", - "SeparableConv2d-18 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n", - " MaxPool2d-9 [-1, 256, 20, 20] [-1, 256, 10, 10] 0\n", - " Conv2d-53 [-1, 128, 20, 20] [-1, 256, 10, 10] 33,024\n", - " Encoder-9 [-1, 128, 20, 20] [-1, 256, 10, 10] 0\n", - " ReLU-21 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n", - "ConvTranspose2d-17 [-1, 64, 80, 80] [-1, 32, 80, 80] 18,464\n", - " BatchNorm2d-21 [-1, 32, 80, 80] [-1, 32, 80, 80] 128\n", - "ConvTranspose2d-18 [-1, 32, 80, 80] [-1, 32, 80, 80] 9,248\n", - " Upsample-8 [-1, 64, 80, 80] [-1, 64, 160, 160] 0\n", - " Conv2d-57 [-1, 64, 160, 160] [-1, 32, 160, 160] 2,080\n", - " Decoder-9 [-1, 64, 80, 80] [-1, 32, 160, 160] 0\n", - " Conv2d-58 [-1, 32, 160, 160] [-1, 4, 160, 160] 1,156\n", + " Conv2d-1 [-1, 3, 160, 160] [-1, 32, 80, 80] 896\n", + " BatchNorm2d-1 [-1, 32, 80, 80] [-1, 32, 80, 80] 128\n", + " ReLU-1 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n", + " ReLU-4 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n", + " Conv2d-12 [-1, 128, 20, 20] [-1, 128, 20, 20] 1,152\n", + " Conv2d-13 [-1, 128, 20, 20] [-1, 256, 20, 20] 33,024\n", + "SeparableConv2d-5 [-1, 128, 20, 20] [-1, 256, 20, 20] 0\n", + " BatchNorm2d-4 [-1, 256, 20, 20] [-1, 256, 20, 20] 1,024\n", + " Conv2d-14 [-1, 256, 20, 20] [-1, 256, 20, 20] 2,304\n", + " Conv2d-15 [-1, 256, 20, 20] [-1, 256, 20, 20] 65,792\n", + "SeparableConv2d-6 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n", + " MaxPool2d-3 [-1, 256, 20, 20] [-1, 256, 10, 10] 0\n", + " Conv2d-16 [-1, 128, 20, 20] [-1, 256, 10, 10] 33,024\n", + " Encoder-3 [-1, 128, 20, 20] [-1, 256, 10, 10] 0\n", + " ReLU-8 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n", + "ConvTranspose2d-7 [-1, 64, 80, 80] [-1, 32, 80, 80] 18,464\n", + " BatchNorm2d-8 [-1, 32, 80, 80] [-1, 32, 80, 80] 128\n", + "ConvTranspose2d-8 [-1, 32, 80, 80] [-1, 32, 80, 80] 9,248\n", + " Upsample-4 [-1, 64, 80, 80] [-1, 64, 160, 160] 0\n", + " Conv2d-20 [-1, 64, 160, 160] [-1, 32, 160, 160] 2,080\n", + " Decoder-4 [-1, 64, 80, 80] [-1, 32, 160, 160] 0\n", + " Conv2d-21 [-1, 32, 160, 160] [-1, 4, 160, 160] 1,156\n", "================================================================================\n", "Total params: 168,420\n", "Trainable params: 167,140\n", @@ -784,7 +784,7 @@ "{'total_params': 168420, 'trainable_params': 167140}" ] }, - "execution_count": 31, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -822,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": { "colab": {}, "colab_type": "code", @@ -850,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": { "colab": {}, "colab_type": "code", @@ -903,7 +903,7 @@ " epsilon=1e-07, \n", " centered=False,\n", " parameters=model.parameters())\n", - "model = paddle.Model(PetModel(num_classes))\n", + "model = paddle.Model(PetNet(num_classes))\n", "model.prepare(optim, SoftmaxWithCrossEntropy())\n", "model.fit(train_dataset, \n", " val_dataset, \n",