pixel_std=[70.30930, 69.41244, 72.60676])
dataload = ImageSetLoader(**init_args['image_set_loader']['train'])
model = load_model(
'/tmp/fcn_vgg16_weights.h5',
custom_objects={
'CroppingLike2D':
CroppingLike2D,
#'mean_categorical_crossentropy': mean_categorical_crossentropy})
'flatten_categorical_crossentropy':
flatten_categorical_crossentropy(classes=21)
})
print(model.summary())
for fn in dataload.filenames[:10]:
x = dataload.load_img(fn)
x = datagen.standardize(x)
print(x.min(), x.max())
X = x[np.newaxis, ...]
label = dataload.load_seg(fn)
label = np.squeeze(label, axis=-1).astype('int')
y_enc = np.eye(21)[label]
y_true = y_enc[np.newaxis, ...]
result = model.evaluate(X, y_true)
y_pred = model.predict(X)
print(np.unique(y_true), np.unique(y_pred))
loss = mean_categorical_crossentropy(K.variable(y_true),
K.variable(y_pred))
print(y_true.shape, y_pred.shape)
print(result, K.eval(loss))
datagen = PascalVocGenerator(image_shape=[224, 224, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[115.85100, 110.50989, 102.16182],
pixelwise_std_normalization=True,
pixel_std=[70.30930, 69.41244, 72.60676])
dataload = ImageSetLoader(**init_args['image_set_loader']['train'])
model = load_model('/tmp/fcn_vgg16_weights.h5',
custom_objects={'BilinearUpSampling2D': BilinearUpSampling2D})
print(model.summary())
for fn in dataload.filenames[:10]:
x = dataload.load_img(fn)
x = datagen.standardize(x)
print(x.min(), x.max())
X = x[np.newaxis, ...]
label = dataload.load_seg(fn)
label = np.squeeze(label, axis=-1).astype('int')
y_enc = np.eye(21)[label]
y_true = y_enc[np.newaxis, ...]
result = model.evaluate(X, y_true)
y_pred = model.predict(X)
print(np.unique(y_true), np.unique(y_pred))
loss = keras.losses.categorical_crossentropy(K.variable(y_true), K.variable(y_pred))
print(y_true.shape, y_pred.shape)
print(result, K.eval(loss))
datagen = PascalVocGenerator(image_shape=[224, 224, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[115.85100, 110.50989, 102.16182],
pixelwise_std_normalization=True,
pixel_std=[70.30930, 69.41244, 72.60676])
dataload = ImageSetLoader(**init_args['image_set_loader']['test'])
model = load_model(
'/usr/local/workspace/keras-fcn/voc2011/tmp/fcn_vgg16_weights.h5',
custom_objects={'BilinearUpSampling2D': BilinearUpSampling2D})
print(model.summary())
for fn in dataload.filenames[:10]:
x = dataload.load_img(fn)
x = datagen.standardize(x)
print(x.min(), x.max())
X = x[np.newaxis, ...]
label = dataload.load_seg(fn)
label = np.squeeze(label, axis=-1).astype('int')
y_enc = np.eye(21)[label]
y_true = y_enc[np.newaxis, ...]
result = model.evaluate(X, y_true)
y_pred = model.predict(X)
print(np.unique(y_true), np.unique(y_pred))
loss = keras.losses.categorical_crossentropy(K.variable(y_true),
K.variable(y_pred))
print(y_true.shape, y_pred.shape)
print(result, K.eval(loss))