I've been assigned a task in my company to try to hydrate a model that was trained for a previous project, and while I can load it again, I'm failing to try it and I don't know why.
The model follows a U-Net architecture, and here's the output of the summary() method after calling load_weights().
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 640, 512, 1 0 []
)]
conv2d (Conv2D) (None, 640, 512, 64 640 ['input_1[0][0]']
)
conv2d_1 (Conv2D) (None, 640, 512, 64 36928 ['conv2d[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 320, 256, 64 0 ['conv2d_1[0][0]']
)
conv2d_2 (Conv2D) (None, 320, 256, 12 73856 ['max_pooling2d[0][0]']
8)
conv2d_3 (Conv2D) (None, 320, 256, 12 147584 ['conv2d_2[0][0]']
8)
max_pooling2d_1 (MaxPooling2D) (None, 160, 128, 12 0 ['conv2d_3[0][0]']
8)
conv2d_4 (Conv2D) (None, 160, 128, 25 295168 ['max_pooling2d_1[0][0]']
6)
conv2d_5 (Conv2D) (None, 160, 128, 25 590080 ['conv2d_4[0][0]']
6)
max_pooling2d_2 (MaxPooling2D) (None, 80, 64, 256) 0 ['conv2d_5[0][0]']
conv2d_6 (Conv2D) (None, 80, 64, 512) 1180160 ['max_pooling2d_2[0][0]']
conv2d_7 (Conv2D) (None, 80, 64, 512) 2359808 ['conv2d_6[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 40, 32, 512) 0 ['conv2d_7[0][0]']
conv2d_8 (Conv2D) (None, 40, 32, 1024 4719616 ['max_pooling2d_3[0][0]']
)
conv2d_9 (Conv2D) (None, 40, 32, 1024 9438208 ['conv2d_8[0][0]']
)
up_sampling2d (UpSampling2D) (None, 80, 64, 1024 0 ['conv2d_9[0][0]']
)
concatenate (Concatenate) (None, 80, 64, 1536 0 ['up_sampling2d[0][0]',
) 'conv2d_7[0][0]']
conv2d_10 (Conv2D) (None, 80, 64, 512) 7078400 ['concatenate[0][0]']
conv2d_11 (Conv2D) (None, 80, 64, 512) 2359808 ['conv2d_10[0][0]']
up_sampling2d_1 (UpSampling2D) (None, 160, 128, 51 0 ['conv2d_11[0][0]']
2)
concatenate_1 (Concatenate) (None, 160, 128, 76 0 ['up_sampling2d_1[0][0]',
8) 'conv2d_5[0][0]']
conv2d_12 (Conv2D) (None, 160, 128, 25 1769728 ['concatenate_1[0][0]']
6)
conv2d_13 (Conv2D) (None, 160, 128, 25 590080 ['conv2d_12[0][0]']
6)
up_sampling2d_2 (UpSampling2D) (None, 320, 256, 25 0 ['conv2d_13[0][0]']
6)
concatenate_2 (Concatenate) (None, 320, 256, 38 0 ['up_sampling2d_2[0][0]',
4) 'conv2d_3[0][0]']
conv2d_14 (Conv2D) (None, 320, 256, 12 442496 ['concatenate_2[0][0]']
8)
conv2d_15 (Conv2D) (None, 320, 256, 12 147584 ['conv2d_14[0][0]']
8)
up_sampling2d_3 (UpSampling2D) (None, 640, 512, 12 0 ['conv2d_15[0][0]']
8)
concatenate_3 (Concatenate) (None, 640, 512, 19 0 ['up_sampling2d_3[0][0]',
2) 'conv2d_1[0][0]']
conv2d_16 (Conv2D) (None, 640, 512, 64 110656 ['concatenate_3[0][0]']
)
conv2d_17 (Conv2D) (None, 640, 512, 64 36928 ['conv2d_16[0][0]']
)
conv2d_18 (Conv2D) (None, 640, 512, 1) 65 ['conv2d_17[0][0]']
==================================================================================================
Total params: 31,377,793
Trainable params: 31,377,793
Non-trainable params: 0
__________________________________________________________________________________________________
My main concern is that when I load a picture as an numpy array, ending up with an input shaped (640, 512, 1), just like the first layer, I get the following error.
from tensorflow.keras.preprocessing.image import load_img, img_to_array
img_size = (640,512)
color_mode = "grayscale"
image = img_to_array(load_img(image_path, target_size=self.image_size, color_mode=self.color_mode))
image = image/255.0
print(image.shape)
#(640, 512, 1)
#unet is a wrapper class which contains the model described above
#unet.load_weights('../../models/unet_model_i_04.h5')
unet.model.predict(image)
This snippet produces this error:
ValueError: Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 640, 512, 1), found shape=(32, 512, 1)
I tried changing the shape of the input, and hence the image size, (I suspect, based on an outdated notebook I was granted access to, that when the model was initially trained and exported, it was done using 320x256 images, but that only changes the error to
ValueError: Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 320, 256, 1), found shape=(32, 256, 1)
As someone suggested in the comments, try expanding the dimension of the input tensor. The model expect a batch of images as input, and for one image, the input shape should be 1 x H x W x 1:
from tensorflow.keras.preprocessing.image import load_img, img_to_array
img_size = (640,512)
color_mode = "grayscale"
image = img_to_array(load_img(image_path, target_size=imge_size, color_mode=color_mode))
image = image/255.0
image = tf.expand_dims(image, 0)
print(image.shape)
#(1, 640, 512, 1)
#unet is a wrapper class which contains the model described above
#unet.load_weights('../../models/unet_model_i_04.h5')
unet.model.predict(image)