Closed Mjxkill closed 4 years ago
AIWintermuteAI
commented
4 years ago Thanks for noticing this - there was a bug in segmentation mask generation, which I fixed in the latest commit. DO git pull to get the latest changes and try again. Please, reply if the issue was resolved :)
AIWintermuteAI
commented
4 years ago Oh and by the way currently aXeleRate only supports local training on Linux machines(tested with Ubuntu 18.04) - the training and inference will(should) work on OS X or Windows, but conversion functions will not work, since converters use binaries compiled for specific OS and I only have added binaries for Linux now. You might want to try the examples in Google Colab, which requires no local setup and supports all operations.
Mjxkill
commented
4 years ago Yes now it is working ! thanks !!!
Describe the bug
Hello,
I have install (on OS X) aXelerate to learn how to create a model
conda -n ml python=3.7 conda activate ml pip install git+https://github.com/AIWintermuteAI/aXeleRate git clone https://github.com/AIWintermuteAI/aXeleRate
all is OK for installation
I start the script tests_training_and_inference.py
python tests_training_and_inference.py and this script crash at segmentation part :
Project folder projects/segment already exists. Creating a folder for new training session. Segmentation Failed to load pre-trained weights for the whole model. It might be because you didn't specify any or the weight file cannot be found Current training session folder is projects/segment/2020-09-23_14-06-08
Model: "model_4"
Layer (type) Output Shape Param #
input_4 (InputLayer) (None, 320, 240, 3) 0
conv1_pad (ZeroPadding2D) (None, 322, 242, 3) 0
conv1 (Conv2D) (None, 160, 120, 24) 648
conv1_bn (BatchNormalization (None, 160, 120, 24) 96
conv1_relu (ReLU) (None, 160, 120, 24) 0
conv_dw_1 (DepthwiseConv2D) (None, 160, 120, 24) 216
conv_dw_1_bn (BatchNormaliza (None, 160, 120, 24) 96
conv_dw_1_relu (ReLU) (None, 160, 120, 24) 0
conv_pw_1 (Conv2D) (None, 160, 120, 48) 1152
conv_pw_1_bn (BatchNormaliza (None, 160, 120, 48) 192
conv_pw_1_relu (ReLU) (None, 160, 120, 48) 0
conv_pad_2 (ZeroPadding2D) (None, 162, 122, 48) 0
conv_dw_2 (DepthwiseConv2D) (None, 80, 60, 48) 432
conv_dw_2_bn (BatchNormaliza (None, 80, 60, 48) 192
conv_dw_2_relu (ReLU) (None, 80, 60, 48) 0
conv_pw_2 (Conv2D) (None, 80, 60, 96) 4608
conv_pw_2_bn (BatchNormaliza (None, 80, 60, 96) 384
conv_pw_2_relu (ReLU) (None, 80, 60, 96) 0
conv_dw_3 (DepthwiseConv2D) (None, 80, 60, 96) 864
conv_dw_3_bn (BatchNormaliza (None, 80, 60, 96) 384
conv_dw_3_relu (ReLU) (None, 80, 60, 96) 0
conv_pw_3 (Conv2D) (None, 80, 60, 96) 9216
conv_pw_3_bn (BatchNormaliza (None, 80, 60, 96) 384
conv_pw_3_relu (ReLU) (None, 80, 60, 96) 0
conv_pad_4 (ZeroPadding2D) (None, 82, 62, 96) 0
conv_dw_4 (DepthwiseConv2D) (None, 40, 30, 96) 864
conv_dw_4_bn (BatchNormaliza (None, 40, 30, 96) 384
conv_dw_4_relu (ReLU) (None, 40, 30, 96) 0
conv_pw_4 (Conv2D) (None, 40, 30, 192) 18432
conv_pw_4_bn (BatchNormaliza (None, 40, 30, 192) 768
conv_pw_4_relu (ReLU) (None, 40, 30, 192) 0
conv_dw_5 (DepthwiseConv2D) (None, 40, 30, 192) 1728
conv_dw_5_bn (BatchNormaliza (None, 40, 30, 192) 768
conv_dw_5_relu (ReLU) (None, 40, 30, 192) 0
conv_pw_5 (Conv2D) (None, 40, 30, 192) 36864
conv_pw_5_bn (BatchNormaliza (None, 40, 30, 192) 768
conv_pw_5_relu (ReLU) (None, 40, 30, 192) 0
conv_pad_6 (ZeroPadding2D) (None, 42, 32, 192) 0
conv_dw_6 (DepthwiseConv2D) (None, 20, 15, 192) 1728
conv_dw_6_bn (BatchNormaliza (None, 20, 15, 192) 768
conv_dw_6_relu (ReLU) (None, 20, 15, 192) 0
conv_pw_6 (Conv2D) (None, 20, 15, 384) 73728
conv_pw_6_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_pw_6_relu (ReLU) (None, 20, 15, 384) 0
conv_dw_7 (DepthwiseConv2D) (None, 20, 15, 384) 3456
conv_dw_7_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_dw_7_relu (ReLU) (None, 20, 15, 384) 0
conv_pw_7 (Conv2D) (None, 20, 15, 384) 147456
conv_pw_7_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_pw_7_relu (ReLU) (None, 20, 15, 384) 0
conv_dw_8 (DepthwiseConv2D) (None, 20, 15, 384) 3456
conv_dw_8_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_dw_8_relu (ReLU) (None, 20, 15, 384) 0
conv_pw_8 (Conv2D) (None, 20, 15, 384) 147456
conv_pw_8_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_pw_8_relu (ReLU) (None, 20, 15, 384) 0
conv_dw_9 (DepthwiseConv2D) (None, 20, 15, 384) 3456
conv_dw_9_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_dw_9_relu (ReLU) (None, 20, 15, 384) 0
conv_pw_9 (Conv2D) (None, 20, 15, 384) 147456
conv_pw_9_bn (BatchNormaliza (None, 20, 15, 384) 1536
conv_pw_9_relu (ReLU) (None, 20, 15, 384) 0
conv_dw_10 (DepthwiseConv2D) (None, 20, 15, 384) 3456
conv_dw_10_bn (BatchNormaliz (None, 20, 15, 384) 1536
conv_dw_10_relu (ReLU) (None, 20, 15, 384) 0
conv_pw_10 (Conv2D) (None, 20, 15, 384) 147456
conv_pw_10_bn (BatchNormaliz (None, 20, 15, 384) 1536
conv_pw_10_relu (ReLU) (None, 20, 15, 384) 0
conv_dw_11 (DepthwiseConv2D) (None, 20, 15, 384) 3456
conv_dw_11_bn (BatchNormaliz (None, 20, 15, 384) 1536
conv_dw_11_relu (ReLU) (None, 20, 15, 384) 0
conv_pw_11 (Conv2D) (None, 20, 15, 384) 147456
conv_pw_11_bn (BatchNormaliz (None, 20, 15, 384) 1536
conv_pw_11_relu (ReLU) (None, 20, 15, 384) 0
zero_padding2d_1 (ZeroPaddin (None, 22, 17, 384) 0
conv2d_1 (Conv2D) (None, 20, 15, 256) 884992
batch_normalization_1 (Batch (None, 20, 15, 256) 1024
up_sampling2d_1 (UpSampling2 (None, 40, 30, 256) 0
zero_padding2d_2 (ZeroPaddin (None, 42, 32, 256) 0
conv2d_2 (Conv2D) (None, 40, 30, 128) 295040
batch_normalization_2 (Batch (None, 40, 30, 128) 512
up_sampling2d_2 (UpSampling2 (None, 80, 60, 128) 0
zero_padding2d_3 (ZeroPaddin (None, 82, 62, 128) 0
conv2d_3 (Conv2D) (None, 80, 60, 64) 73792
batch_normalization_3 (Batch (None, 80, 60, 64) 256
up_sampling2d_3 (UpSampling2 (None, 160, 120, 64) 0
zero_padding2d_4 (ZeroPaddin (None, 162, 122, 64) 0
conv2d_4 (Conv2D) (None, 160, 120, 32) 18464
batch_normalization_4 (Batch (None, 160, 120, 32) 128
conv2d_5 (Conv2D) (None, 160, 120, 20) 5780
activation_1 (Activation) (None, 160, 120, 20) 0
Total params: 2,207,108 Trainable params: 2,195,108 Non-trainable params: 12,000
Epoch 1/5 4/4 [==============================] - 5s 1s/step - loss: 3.4831 - val_loss: 2.9801
Epoch 00001: val_loss improved from inf to 2.98010, saving model to projects/segment/2020-09-23_14-06-08/Segnet_best_val_loss.h5 /Users/michael/aXeleRate/axelerate/networks/common_utils/fit.py:165: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. plt.show(block=False) /Users/michael/aXeleRate/axelerate/networks/common_utils/fit.py:166: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. plt.pause(1) Epoch 2/5 4/4 [==============================] - 2s 533ms/step - loss: 3.2988 - val_loss: 2.9247
Epoch 00002: val_loss improved from 2.98010 to 2.92473, saving model to projects/segment/2020-09-23_14-06-08/Segnet_best_val_loss.h5 Epoch 3/5 4/4 [==============================] - 2s 531ms/step - loss: 3.0916 - val_loss: 2.7481
Epoch 00003: val_loss improved from 2.92473 to 2.74812, saving model to projects/segment/2020-09-23_14-06-08/Segnet_best_val_loss.h5 Epoch 4/5 4/4 [==============================] - 2s 539ms/step - loss: 2.9650 - val_loss: 2.5661
Epoch 00004: val_loss improved from 2.74812 to 2.56610, saving model to projects/segment/2020-09-23_14-06-08/Segnet_best_val_loss.h5 Epoch 5/5 4/4 [==============================] - 2s 542ms/step - loss: 2.9098 - val_loss: 2.5182
Epoch 00005: val_loss improved from 2.56610 to 2.51816, saving model to projects/segment/2020-09-23_14-06-08/Segnet_best_val_loss.h5 39-seconds to train Folder projects/segment/2020-09-23_14-06-08/Inference_results is created. Segmentation Loading pre-trained weights for the whole model: projects/segment/2020-09-23_14-06-08/Segnet_best_val_loss.h5 Found the following classes in the segmentation image: [ 0 1 6 7 8 9 12 16 17] Traceback (most recent call last): File "tests_training_and_inference.py", line 160, in
setup_inference(item, model_path)
File "/Users/michael/aXeleRate/axelerate/infer.py", line 100, in setup_inference
predict(model=segnet._network, inp=input_arr, image = orig_image, out_fname=out_fname)
File "/Users/michael/aXeleRate/axelerate/networks/segnet/predict.py", line 136, in predict
seg_img = visualize_segmentation(pr, inp_img=image, n_classes=n_classes, overlay_img=True, colors=colors)
File "/Users/michael/aXeleRate/axelerate/networks/segnet/predict.py", line 102, in visualize_segmentation
seg_img = get_colored_segmentation_image(seg_arr, n_classes , colors=colors)
File "/Users/michael/aXeleRate/axelerate/networks/segnet/predict.py", line 52, in get_colored_segmentation_image
seg_img[:, :, 0] += ((seg_arr[:, :] == c)*(colors[c][0])).astype('uint8')
ValueError: operands could not be broadcast together with shapes (120,160) (160,120) (120,160)
operands could not be broadcast together with shapes (120,160) (160,120) (120,160)
['SegNet MobileNet7_5 operands could not be broadcast together with shapes (120,160) (160,120) (120,160) ']
Do you know why ?
thanks a lot in avance !