网络模型为：
Model Summary:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
img (InputLayer) (None, 427, 561, 3) 0
__________________________________________________________________________________________________
dmap (InputLayer) (None, 427, 561, 3) 0
__________________________________________________________________________________________________
padding1_1 (ZeroPadding2D) (None, 429, 563, 3) 0 img[0][0]
__________________________________________________________________________________________________
padding1_1d (ZeroPadding2D) (None, 429, 563, 3) 0 dmap[0][0]
__________________________________________________________________________________________________
conv1_1 (Conv2D) (None, 427, 561, 64) 1792 padding1_1[0][0]
__________________________________________________________________________________________________
conv1_1d (Conv2D) (None, 427, 561, 64) 1792 padding1_1d[0][0]
__________________________________________________________________________________________________
padding1_2 (ZeroPadding2D) (None, 429, 563, 64) 0 conv1_1[0][0]
__________________________________________________________________________________________________
padding1_2d (ZeroPadding2D) (None, 429, 563, 64) 0 conv1_1d[0][0]
__________________________________________________________________________________________________
conv1_2 (Conv2D) (None, 427, 561, 64) 36928 padding1_2[0][0]
__________________________________________________________________________________________________
conv1_2d (Conv2D) (None, 427, 561, 64) 36928 padding1_2d[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, 214, 281, 64) 0 conv1_2[0][0]
__________________________________________________________________________________________________
pool1d (MaxPooling2D) (None, 214, 281, 64) 0 conv1_2d[0][0]
__________________________________________________________________________________________________
padding2_1 (ZeroPadding2D) (None, 216, 283, 64) 0 pool1[0][0]
__________________________________________________________________________________________________
padding2_1d (ZeroPadding2D) (None, 216, 283, 64) 0 pool1d[0][0]
__________________________________________________________________________________________________
conv2_1 (Conv2D) (None, 214, 281, 128 73856 padding2_1[0][0]
__________________________________________________________________________________________________
conv2_1d (Conv2D) (None, 214, 281, 128 73856 padding2_1d[0][0]
__________________________________________________________________________________________________
padding2_2 (ZeroPadding2D) (None, 216, 283, 128 0 conv2_1[0][0]
__________________________________________________________________________________________________
padding2_2d (ZeroPadding2D) (None, 216, 283, 128 0 conv2_1d[0][0]
__________________________________________________________________________________________________
conv2_2 (Conv2D) (None, 214, 281, 128 147584 padding2_2[0][0]
__________________________________________________________________________________________________
conv2_2d (Conv2D) (None, 214, 281, 128 147584 padding2_2d[0][0]
__________________________________________________________________________________________________
pool2 (MaxPooling2D) (None, 107, 141, 128 0 conv2_2[0][0]
__________________________________________________________________________________________________
pool2d (MaxPooling2D) (None, 107, 141, 128 0 conv2_2d[0][0]
__________________________________________________________________________________________________
padding3_1 (ZeroPadding2D) (None, 109, 143, 128 0 pool2[0][0]
__________________________________________________________________________________________________
padding3_1d (ZeroPadding2D) (None, 109, 143, 128 0 pool2d[0][0]
__________________________________________________________________________________________________
conv3_1 (Conv2D) (None, 107, 141, 256 295168 padding3_1[0][0]
__________________________________________________________________________________________________
conv3_1d (Conv2D) (None, 107, 141, 256 295168 padding3_1d[0][0]
__________________________________________________________________________________________________
bn_conv3_1 (BatchNormalization) (None, 107, 141, 256 1024 conv3_1[0][0]
__________________________________________________________________________________________________
bn_conv3_1d (BatchNormalization (None, 107, 141, 256 1024 conv3_1d[0][0]
__________________________________________________________________________________________________
relu3_1 (Activation) (None, 107, 141, 256 0 bn_conv3_1[0][0]
__________________________________________________________________________________________________
relu3_1d (Activation) (None, 107, 141, 256 0 bn_conv3_1d[0][0]
__________________________________________________________________________________________________
padding3_2 (ZeroPadding2D) (None, 109, 143, 256 0 relu3_1[0][0]
__________________________________________________________________________________________________
padding3_2d (ZeroPadding2D) (None, 109, 143, 256 0 relu3_1d[0][0]
__________________________________________________________________________________________________
conv3_2 (Conv2D) (None, 107, 141, 256 590080 padding3_2[0][0]
__________________________________________________________________________________________________
conv3_2d (Conv2D) (None, 107, 141, 256 590080 padding3_2d[0][0]
__________________________________________________________________________________________________
bn_conv3_2 (BatchNormalization) (None, 107, 141, 256 1024 conv3_2[0][0]
__________________________________________________________________________________________________
bn_conv3_2d (BatchNormalization (None, 107, 141, 256 1024 conv3_2d[0][0]
__________________________________________________________________________________________________
relu3_2 (Activation) (None, 107, 141, 256 0 bn_conv3_2[0][0]
__________________________________________________________________________________________________
relu3_2d (Activation) (None, 107, 141, 256 0 bn_conv3_2d[0][0]
__________________________________________________________________________________________________
padding3_3 (ZeroPadding2D) (None, 109, 143, 256 0 relu3_2[0][0]
__________________________________________________________________________________________________
padding3_3d (ZeroPadding2D) (None, 109, 143, 256 0 relu3_2d[0][0]
__________________________________________________________________________________________________
conv3_3 (Conv2D) (None, 107, 141, 256 590080 padding3_3[0][0]
__________________________________________________________________________________________________
conv3_3d (Conv2D) (None, 107, 141, 256 590080 padding3_3d[0][0]
__________________________________________________________________________________________________
bn_conv3_3 (BatchNormalization) (None, 107, 141, 256 1024 conv3_3[0][0]
__________________________________________________________________________________________________
bn_conv3_3d (BatchNormalization (None, 107, 141, 256 1024 conv3_3d[0][0]
__________________________________________________________________________________________________
relu3_3 (Activation) (None, 107, 141, 256 0 bn_conv3_3[0][0]
__________________________________________________________________________________________________
relu3_3d (Activation) (None, 107, 141, 256 0 bn_conv3_3d[0][0]
__________________________________________________________________________________________________
pool3 (MaxPooling2D) (None, 54, 71, 256) 0 relu3_3[0][0]
__________________________________________________________________________________________________
pool3d (MaxPooling2D) (None, 54, 71, 256) 0 relu3_3d[0][0]
__________________________________________________________________________________________________
padding4_1 (ZeroPadding2D) (None, 56, 73, 256) 0 pool3[0][0]
__________________________________________________________________________________________________
padding4_1d (ZeroPadding2D) (None, 56, 73, 256) 0 pool3d[0][0]
__________________________________________________________________________________________________
conv4_1 (Conv2D) (None, 54, 71, 512) 1180160 padding4_1[0][0]
__________________________________________________________________________________________________
conv4_1d (Conv2D) (None, 54, 71, 512) 1180160 padding4_1d[0][0]
__________________________________________________________________________________________________
bn_conv4_1 (BatchNormalization) (None, 54, 71, 512) 2048 conv4_1[0][0]
__________________________________________________________________________________________________
bn_conv4_1d (BatchNormalization (None, 54, 71, 512) 2048 conv4_1d[0][0]
__________________________________________________________________________________________________
relu4_1 (Activation) (None, 54, 71, 512) 0 bn_conv4_1[0][0]
__________________________________________________________________________________________________
relu4_1d (Activation) (None, 54, 71, 512) 0 bn_conv4_1d[0][0]
__________________________________________________________________________________________________
padding4_2 (ZeroPadding2D) (None, 56, 73, 512) 0 relu4_1[0][0]
__________________________________________________________________________________________________
padding4_2d (ZeroPadding2D) (None, 56, 73, 512) 0 relu4_1d[0][0]
__________________________________________________________________________________________________
conv4_2 (Conv2D) (None, 54, 71, 512) 2359808 padding4_2[0][0]
__________________________________________________________________________________________________
conv4_2d (Conv2D) (None, 54, 71, 512) 2359808 padding4_2d[0][0]
__________________________________________________________________________________________________
bn_conv4_2 (BatchNormalization) (None, 54, 71, 512) 2048 conv4_2[0][0]
__________________________________________________________________________________________________
bn_conv4_2d (BatchNormalization (None, 54, 71, 512) 2048 conv4_2d[0][0]
__________________________________________________________________________________________________
relu4_2 (Activation) (None, 54, 71, 512) 0 bn_conv4_2[0][0]
__________________________________________________________________________________________________
relu4_2d (Activation) (None, 54, 71, 512) 0 bn_conv4_2d[0][0]
__________________________________________________________________________________________________
padding4_3 (ZeroPadding2D) (None, 56, 73, 512) 0 relu4_2[0][0]
__________________________________________________________________________________________________
padding4_3d (ZeroPadding2D) (None, 56, 73, 512) 0 relu4_2d[0][0]
__________________________________________________________________________________________________
conv4_3 (Conv2D) (None, 54, 71, 512) 2359808 padding4_3[0][0]
__________________________________________________________________________________________________
conv4_3d (Conv2D) (None, 54, 71, 512) 2359808 padding4_3d[0][0]
__________________________________________________________________________________________________
bn_conv4_3 (BatchNormalization) (None, 54, 71, 512) 2048 conv4_3[0][0]
__________________________________________________________________________________________________
bn_conv4_3d (BatchNormalization (None, 54, 71, 512) 2048 conv4_3d[0][0]
__________________________________________________________________________________________________
relu4_3 (Activation) (None, 54, 71, 512) 0 bn_conv4_3[0][0]
__________________________________________________________________________________________________
relu4_3d (Activation) (None, 54, 71, 512) 0 bn_conv4_3d[0][0]
__________________________________________________________________________________________________
pool4 (MaxPooling2D) (None, 27, 36, 512) 0 relu4_3[0][0]
__________________________________________________________________________________________________
pool4d (MaxPooling2D) (None, 27, 36, 512) 0 relu4_3d[0][0]
__________________________________________________________________________________________________
padding5_1 (ZeroPadding2D) (None, 29, 38, 512) 0 pool4[0][0]
__________________________________________________________________________________________________
padding5_1d (ZeroPadding2D) (None, 29, 38, 512) 0 pool4d[0][0]
__________________________________________________________________________________________________
conv5_1 (Conv2D) (None, 27, 36, 512) 2359808 padding5_1[0][0]
__________________________________________________________________________________________________
conv5_1d (Conv2D) (None, 27, 36, 512) 2359808 padding5_1d[0][0]
__________________________________________________________________________________________________
bn_conv5_1 (BatchNormalization) (None, 27, 36, 512) 2048 conv5_1[0][0]
__________________________________________________________________________________________________
bn_conv5_1d (BatchNormalization (None, 27, 36, 512) 2048 conv5_1d[0][0]
__________________________________________________________________________________________________
relu5_1 (Activation) (None, 27, 36, 512) 0 bn_conv5_1[0][0]
__________________________________________________________________________________________________
relu5_1d (Activation) (None, 27, 36, 512) 0 bn_conv5_1d[0][0]
__________________________________________________________________________________________________
padding5_2 (ZeroPadding2D) (None, 29, 38, 512) 0 relu5_1[0][0]
__________________________________________________________________________________________________
padding5_2d (ZeroPadding2D) (None, 29, 38, 512) 0 relu5_1d[0][0]
__________________________________________________________________________________________________
conv5_2 (Conv2D) (None, 27, 36, 512) 2359808 padding5_2[0][0]
__________________________________________________________________________________________________
conv5_2d (Conv2D) (None, 27, 36, 512) 2359808 padding5_2d[0][0]
__________________________________________________________________________________________________
bn_conv5_2 (BatchNormalization) (None, 27, 36, 512) 2048 conv5_2[0][0]
__________________________________________________________________________________________________
bn_conv5_2d (BatchNormalization (None, 27, 36, 512) 2048 conv5_2d[0][0]
__________________________________________________________________________________________________
relu5_2 (Activation) (None, 27, 36, 512) 0 bn_conv5_2[0][0]
__________________________________________________________________________________________________
relu5_2d (Activation) (None, 27, 36, 512) 0 bn_conv5_2d[0][0]
__________________________________________________________________________________________________
padding5_3 (ZeroPadding2D) (None, 29, 38, 512) 0 relu5_2[0][0]
__________________________________________________________________________________________________
padding5_3d (ZeroPadding2D) (None, 29, 38, 512) 0 relu5_2d[0][0]
__________________________________________________________________________________________________
conv5_3 (Conv2D) (None, 27, 36, 512) 2359808 padding5_3[0][0]
__________________________________________________________________________________________________
conv5_3d (Conv2D) (None, 27, 36, 512) 2359808 padding5_3d[0][0]
__________________________________________________________________________________________________
bn_conv5_3 (BatchNormalization) (None, 27, 36, 512) 2048 conv5_3[0][0]
__________________________________________________________________________________________________
bn_conv5_3d (BatchNormalization (None, 27, 36, 512) 2048 conv5_3d[0][0]
__________________________________________________________________________________________________
relu5_3 (Activation) (None, 27, 36, 512) 0 bn_conv5_3[0][0]
__________________________________________________________________________________________________
rois (InputLayer) (None, 5) 0
__________________________________________________________________________________________________
relu5_3d (Activation) (None, 27, 36, 512) 0 bn_conv5_3d[0][0]
__________________________________________________________________________________________________
rois_context (InputLayer) (None, 5) 0
__________________________________________________________________________________________________
pool5 (RoiPoolingConvSingle) (None, 7, 7, 512) 0 relu5_3[0][0]
rois[0][0]
__________________________________________________________________________________________________
pool5d (RoiPoolingConvSingle) (None, 7, 7, 512) 0 relu5_3d[0][0]
rois[0][0]
__________________________________________________________________________________________________
pool5_context (RoiPoolingConvSi (None, 7, 7, 512) 0 relu5_3[0][0]
rois_context[0][0]
__________________________________________________________________________________________________
pool5d_context (RoiPoolingConvS (None, 7, 7, 512) 0 relu5_3d[0][0]
rois_context[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 25088) 0 pool5[0][0]
__________________________________________________________________________________________________
flatten_d (Flatten) (None, 25088) 0 pool5d[0][0]
__________________________________________________________________________________________________
flatten_context (Flatten) (None, 25088) 0 pool5_context[0][0]
__________________________________________________________________________________________________
flatten_d_context (Flatten) (None, 25088) 0 pool5d_context[0][0]
__________________________________________________________________________________________________
concat (Concatenate) (None, 100352) 0 flatten[0][0]
flatten_d[0][0]
flatten_context[0][0]
flatten_d_context[0][0]
__________________________________________________________________________________________________
fc6 (Dense) (None, 4096) 411045888 concat[0][0]
__________________________________________________________________________________________________
drop6 (Dropout) (None, 4096) 0 fc6[0][0]
__________________________________________________________________________________________________
fc7 (Dense) (None, 4096) 16781312 drop6[0][0]
__________________________________________________________________________________________________
drop7 (Dropout) (None, 4096) 0 fc7[0][0]
__________________________________________________________________________________________________
cls_score (Dense) (None, 20) 81940 drop7[0][0]
__________________________________________________________________________________________________
bbox_pred_3d (Dense) (None, 140) 573580 drop7[0][0]
==================================================================================================
Total params: 457,942,816
Trainable params: 457,927,456
Non-trainable params: 15,360
模型定义的输入输出为：
tf_model = Model(
inputs=[img, dmap, rois, rois_context],
outputs=[cls_score, bbox_pred_3d]
)
我在predict时采用的格式为：
roi2d = twod_Proposal('test1_rgb.jpg', 'q') #获得roi proposal
print('------------------------------------------')
# for roi in roi2d:
# print(roi)
# print('------------------------------------------')
#TODO: Set input to the model
img = cv2.imread('test1_rgb.jpg')
dmap = cv2.imread('test1_depth.jpg')
roi2d_context = get_roi_context(roi2d)
tf_model = make_deng_tf_test()
show_model_info(tf_model)
tf_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
[score, result_predict] = tf_model.predict([img, dmap, roi2d, roi2d_context])
报错信息为
Using TensorFlow backend.
Total Number of Region Proposals: 5012
------------------------------------------
WARNING:tensorflow:From /Users/anaconda2/envs/python3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-07-01 09:51:26.089446: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-07-01 09:51:26.090541: I tensorflow/core/common_runtime/process_util.cc:71] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.
WARNING:tensorflow:From /Users/anaconda2/envs/python3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
Traceback (most recent call last):
File "/Users/Documents/PycharmProjects/Amodal3Det_TF/tfmodel/model.py", line 375, in <module>
[score, result_predict] = tf_model.predict([img, dmap, roi2d, roi2d_context])
File "/Users/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training.py", line 1149, in predict
x, _, _ = self._standardize_user_data(x)
File "/Users/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training.py", line 751, in _standardize_user_data
exception_prefix='input')
File "/Users/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 92, in standardize_input_data
data = [standardize_single_array(x) for x in data]
File "/Users/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 92, in <listcomp>
data = [standardize_single_array(x) for x in data]
File "/Users/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 27, in standardize_single_array
elif x.ndim == 1:
AttributeError: 'list' object has no attribute 'ndim'
Process finished with exit code 1
请问各位问题大概出在哪里？
