Exception concat mode

[AlexunderMicrocontrol]

Hi Marko, I'am trying to use your code. My images are .png files. data.py runs without exceptions. but train.py fails in that line 51 up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1) with that exception message: Exception: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 512, 42, 78), (None, 256, 43, 79)] I know the shapes doesn't match, but why? during competion your code runs well with original ultrasound images(.tiff). Could you help me please to customize your code that i can train your model on my own images?

[jocicmarko]

@AlexUnderMicrocontRoll could you provide your code (or a link to it), this way I can't figure it out.

[AlexunderMicrocontrol]

code changes from data.py

`def create_train_data(): train_data_path = os.path.join(data_path, 'train') images = os.listdir(train_data_path) total = len(images) / 2

imgs = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8)
imgs_mask = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8)

i = 0
print('-'*30)
print('Creating training images...')
print('-'*30)
for image_name in images:
    if 'mask' in image_name or '.DS_Store' in image_name:
        continue
    image_mask_name = image_name.split('.')[0] + '_mask.png'
    img = cv2.imread(os.path.join(train_data_path, image_name), cv2.IMREAD_GRAYSCALE)
    img_mask = cv2.imread(os.path.join(train_data_path, image_mask_name), cv2.IMREAD_GRAYSCALE)

    img = np.array([img])
    img_mask = np.array([img_mask])

    imgs[i] = img
    imgs_mask[i] = img_mask

    if i % 100 == 0:
        print('Done: {0}/{1} images'.format(i, total))
    i += 1
print('Loading done.')

np.save('imgs_train.npy', imgs)
np.save('imgs_mask_train.npy', imgs_mask)
print('Saving to .npy files done.')`

`def create_test_data(): train_data_path = os.path.join(data_path, 'test') images = os.listdir(train_data_path) total = len(images)

imgs = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8)
imgs_id = np.ndarray((total, ), dtype=np.int32)

i = 0
print('-'*30)
print('Creating test images...')
print('-'*30)
for image_name in images:
    if 'mask' in image_name or '.DS_Store' in image_name:
        continue
    img_id = int(image_name.split('.')[0])
    img = cv2.imread(os.path.join(train_data_path, image_name), cv2.IMREAD_GRAYSCALE)

    img = np.array([img])

    imgs[i] = img
    imgs_id[i] = img_id

    if i % 100 == 0:
        print('Done: {0}/{1} images'.format(i, total))
    i += 1
print('Loading done.')

np.save('imgs_test.npy', imgs)
np.save('imgs_id_test.npy', imgs_id)
print('Saving to .npy files done.')`

and inside your data.py i just changed the first lines:

img_rows = 351 img_cols = 638

images are from here i extract only the cars from the labeled images. Here is an example of my mask:

i hope it will possible to recognize cars with your model as start code :)

[AlexunderMicrocontrol]

and here is the original image

[jocicmarko]

The problem is in img_rows = 351 img_cols = 638. Since there are 4 2x2 pooling layers, you should make sure your input dimensions are divisible by 2^4=16. So, I guess you can try by resizing the images to 384x640.

[AlexunderMicrocontrol]

Ok that works. THX