Month: June 2022

I am a beginner. I have installed Tensorflow version 2.6.0 and cuda version 11.7. However, tensorflow is not utilizing GPU. When I use tf.config.list_physical_devices(‘GPU’), it gives an empty object. Could anyone help me with this?

Hi all,

My question is linked to a question I asked recently: post

I need to loop over individual samples when training due to too large a batch size to hold in memory. I have had good success generating reproducible losses and and accumulated gradients with one of the training loops I am carrying out – and, applied gradients to weights are accurate (plus floating point errors) –

another custom loop I am carrying out on a batch is the mean squared error between a predicted label and the real label. Again, I need to iterate over the batch of samples manually due to a large batch size. To confirm it works, and I get the same losses and gradients, I am comparing my custom loop on a batch of 100 samples so i can compare both methods using ‘GradientTape()’

My code snippet is as follows: for batch training:

with tf.GradientTape() as tape:

value_loss = tf.reduce_mean((return_buffer – critic_model([degree_buffer, graph_adj_buffer, action_vect_buffer])) ** 2)

value_grads = tape.gradient(value_loss, critic_model.trainable_variables)

value_optimizer.apply_gradients(zip(value_grads, critic_model.trainable_variables))

for individual samples:

value_loss_tracking = []total_loss = 0train_vars_val = critic_model_individual.trainable_variablesaccum_gradient_val = [tf.zeros_like(this_var) for this_var in train_vars_val]for adj_ind, degree_ind, action_vect_ind, return_ind in zip(graph_adj_buffer, degree_buffer, action_vect_buffer, return_buffer_):adj_ind = adjacency_normed_tensor(adj_ind)degree_ind = tf.expand_dims(degree_ind, 0)action_vect_ind = tf.expand_dims(action_vect_ind, 0)

with tf.GradientTape() as tape:

ind_value_loss = tf.square(return_ind – critic_model_individual([degree_ind, adj_ind, action_vect_ind]))

value_loss_tracking.append(ind_value_loss)

total_loss += ind_value_lossgradients = tape.gradient(ind_value_loss,train_vars_val)

accum_gradient_val = [(acum_grad + grad) for acum_grad, grad in zip(accum_gradient_val, gradients)]

accum_gradient_vals_final = [this_grad / steps_per_epoch for this_grad in accum_gradient_val]policy_optimizer_ind.apply_gradients(zip(accum_gradient_vals_final, train_vars_val))

mean_loss = tf.reduce_mean(value_loss_tracking)

forgive the lack of indentation, but both loops work fine (in bold is the loss) – however, when I look at the loss in my custom loop relative to the mean squared error in the batch loop, the values are different starting sometimes from one decimal place – and they do not look like floating point errors to me. i.e. 0.43429542 and 0.4318762 – these seem really different to me to be floating point errors – in the other custom loop, i see floating points changing after about 5 decimal places… this is not the case here. sometime i will even see losses like 0.39 compared 0.40 – this seems not right to me. does anybody if this makes sense, or agree that this does not look right? I have tried np.mean and np.square also – I have looked at source code and cannot see exactly how Tensorflow does this under the hood!

any help is appreciated!

This is my code:

#include <iostream> #include <cstdio> #include <iomanip> #include "src/VideoProcessing.h" #include <opencv2/opencv.hpp> #include <opencv2/videoio.hpp> #include <opencv2/highgui.hpp> #include <interpreter.h> #include "tensorflow/lite/interpreter.h" #include "tensorflow/lite/kernels/register.h" #include "tensorflow/lite/model.h" #include "tensorflow/lite/model_builder.h" #include "tensorflow/lite/interpreter_builder.h" #include "tensorflow/lite/optional_debug_tools.h" #include "tensorflow/lite/tools/gen_op_registration.h" typedef cv::Point3_<float> Pixel; void normalize(Pixel &pixel) {...} int main() { ... auto model = tflite::FlatBufferModel::BuildFromFile("/home/me/tensorflow_src/tensorflow/lite/examples/model-verification/pose_landmark_full.tflite"); if(!model){ printf("Failed to mmap modeln"); exit(0); } tflite::ops::builtin::BuiltinOpResolver resolver; std::unique_ptr<tflite::Interpreter> interpreter; ... 

The last line std::unique_ptr<tflite::Interpreter> interpreter; is throwing an error, suggesting that interpreter, and associated classes, are undefined. This is the error:

/usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(interpreter.cc.o): in function `tflite::Interpreter::SetProfilerImpl(std::unique_ptr<tflite::Profiler, std::default_delete<tflite::Profiler> >)': interpreter.cc:(.text+0x2a66): undefined reference to `tflite::profiling::RootProfiler::RemoveChildProfilers()' /usr/bin/ld: interpreter.cc:(.text+0x2a75): undefined reference to `tflite::profiling::RootProfiler::AddProfiler(std::unique_ptr<tflite::Profiler, std::default_delete<tflite::Profiler> >&&)' /usr/bin/ld: interpreter.cc:(.text+0x2ab2): undefined reference to `vtable for tflite::profiling::RootProfiler' /usr/bin/ld: interpreter.cc:(.text+0x2b19): undefined reference to `vtable for tflite::profiling::RootProfiler' /usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(interpreter.cc.o): in function `tflite::Interpreter::~Interpreter()': interpreter.cc:(.text+0x307e): undefined reference to `vtable for tflite::profiling::RootProfiler' /usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(interpreter.cc.o): in function `tflite::profiling::RootProfiler::~RootProfiler()': interpreter.cc:(.text._ZN6tflite9profiling12RootProfilerD0Ev[_ZN6tflite9profiling12RootProfilerD5Ev]+0x7): undefined reference to `vtable for tflite::profiling::RootProfiler' /usr/bin/ld: tensorflow-lite/libtensorflow-lite.a(interpreter.cc.o): in function `tflite::profiling::RootProfiler::~RootProfiler()': interpreter.cc:(.text._ZN6tflite9profiling12RootProfilerD2Ev[_ZN6tflite9profiling12RootProfilerD5Ev]+0x7): undefined reference to `vtable for tflite::profiling::RootProfiler' collect2: error: ld returned 1 exit status make[2]: *** [CMakeFiles/model-verification.dir/build.make:247: model-verification] Error 1 make[1]: *** [CMakeFiles/Makefile2:1374: CMakeFiles/model-verification.dir/all] Error 2 make: *** [Makefile:149: all] Error 2 

And I only get this error when I use `tflite::interpreter` despite having the correct `interpreter.h` file.

​

This is how I compile:

cmake ../tensorflow/lite/examples/model-verification/ make ./model-verification 

This is my Cmake output:

cmake ../tensorflow/lite/examples/model-verification/ -- Setting build type to Release, for debug builds use'-DCMAKE_BUILD_TYPE=Debug'. CMake Warning at /home/me/tensorflow_src/build/abseil-cpp/CMakeLists.txt:74 (message): A future Abseil release will default ABSL_PROPAGATE_CXX_STD to ON for CMake 3.8 and up. We recommend enabling this option to ensure your project still builds correctly. -- Standard libraries to link to explicitly: none -- The Fortran compiler identification is GNU 11.2.0 -- Could NOT find CLANG_FORMAT: Found unsuitable version "0.0", but required is exact version "9" (found CLANG_FORMAT_EXECUTABLE-NOTFOUND) -- -- Configured Eigen 3.4.90 -- -- Proceeding with version: 2.0.6.v2.0.6 -- CMAKE_CXX_FLAGS: -std=c++0x -Wall -pedantic -Werror -Wextra -Werror=shadow -faligned-new -Werror=implicit-fallthrough=2 -Wunused-result -Werror=unused-result -Wunused-parameter -Werror=unused-parameter -fsigned-char -- Configuring done -- Generating done -- Build files have been written to: /home/onur/tensorflow_src/build 

For a simple TF2 Object detection CNN architecture defined using Keras’s functional API, a batch of data is obtained as:

 example, label = next(data_generator(batch_size = 32)) example.keys() # dict_keys(['image']) image = example['image'] image.shape # (32, 144, 144, 3) label.keys() # dict_keys(['class_out', 'box_out']) label['class_out'].shape, label['box_out'].shape # ((32, 9), (32, 2)) 

The CNN architecture defined using Keras’s functional API is:

 input_ = Input(shape = (144, 144, 3), name = 'image') # name - An optional name string for the Input layer. Should be unique in # a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. # Here 'image' is the Python3 dict's key used to map the data to one of the layer in the model. x = input_ # Define a conv block- x = Conv2D(filters = 64, kernel_size = 3, activation = 'relu')(x) x = BatchNormalization()(x) x = MaxPool2D(pool_size = 2)(x) x = Flatten()(x) # flatten the last pooling layer's output volume x = Dense(256, activation='relu')(x) # We are using a data generator which yields dictionaries. Using 'name' argument makes it # possible to map the correct data generator's output to the appropriate layer class_out = Dense(units = 9, activation = 'softmax', name = 'class_out')(x) # classification output box_out = Dense(units = 2, activation = 'linear', name = 'box_out')(x) # regression output # Define the CNN model- model = tf.keras.models.Model(input_, [class_out, box_out]) # since we have 2 outputs, we use a list 

I am attempting to define it using Model sub-classing as:

 class OD(Model): def __init__(self): super(OD, self).__init__() self.conv1 = Conv2D(filters = 64, kernel_size = 3, activation = None) self.bn = BatchNormalization() self.pool = MaxPool2D(pool_size = 2) self.flatten = Flatten() self.dense = Dense(256, activation = None) self.class_out = Dense(units = 9, activation = None, name = 'class_out') self.box_out = Dense(units = 2, activation = 'linear', name = 'box_out') def call(self, x): x = tf.nn.relu(self.bn(self.conv1(x))) x = self.pool(x) x = self.flatten(x) x = tf.nn.relu(self.dense(x)) x = [tf.nn.softmax(self.class_out(x)), self.box_out(x)] return x A batch of training data is obtained as: example, label = next(data_generator(batch_size = 32)) example.keys() # dict_keys(['image']) image = example['image'] image.shape # (32, 144, 144, 3) label.keys() # dict_keys(['class_out', 'box_out']) label['class_out'].shape, label['box_out'].shape # ((32, 9), (32, 2)) 

Is my Model sub-classing architecture equivalent to Keras’s functional API?