# pd_weights is a dict with parameter's name as key and a numpy array as value.

for block in fluid.default_main_program().blocks:
    for param in block.all_parameters():
        pd_var = fluid.global_scope().find_var(param.name)
        pd_param = pd_var.get_tensor()
        print("load: {}".format(param.name))
        pd_param.set(pd_weights[param.name], place)

I tried this

print("Parameters")
    for block in fluid.default_main_program().blocks:
        print("Blocks", block)
        for param in block.all_parameters():
            print("Params", param)
            pd_var = fluid.global_scope().find_var(param.name)
            pd_param = pd_var.get_tensor()
            print("load: {}".format(param.name))
            # pd_param.set(pd_weights[param.name], place)

Output ->

Parameters
Blocks idx: 0
parent_idx: -1

I inserted the code snippet above at line 208 in the following file ->
https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/ssd/train.py

What went wrong? I assumed that the output should have been the names of all the parameters present in my model.

https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/ssd/train.py
The network is defined in train_prog but not the fluid.default_main_program(). So you should write:

for block in train_prog.blocks:
...
...

@wanghaoshuang Seems to work!!

Is there a pd_param.get() too? Because I want to see whether the weights were set properly or not

Thank You

You can get parameters from scope as numpy array.

pd_param = numpy.array(fluid.global_scope().find_var(param.name).get_tensor())
print pd_param

Ohh great!! Will try that.

Btw, one final thing. The weights that I have ported are still trainable right? I mean just because I loaded the weights from numpy doesn't mean the default is set to not trainable or something right?

Thank You

Yes. They are still trainable.

@wanghaoshuang what if I want them to not be trainable? How can I control that?

I tried the following ->

for block in train_prog.blocks:
    for param in block.all_parameters():
        pd_var = fluid.global_scope().find_var(param.name)
        pd_param = pd_var.get_tensor()
        print("load: {}".format(param.name))
        pd_param.set(pd_weights[param.name], place)
        param.stop_gradient = True

But this didn't work and weights were still getting updated. Probably because the optimizer is defined and optimizer.minimize is called before this. So I thought I need to do optimizer.minimize later

I tried

# In function build_program

...
                    loss = fluid.layers.reduce_sum(loss)
                    for block in main_prog.blocks:
                        for param in block.all_parameters():
                            param.stop_gradient = True
                    optimizer = optimizer_setting(train_params)
                    optimizer.minimize(loss)
...

But this gave me segmentation fault. Not only that, but after I run this code, my GPU memory is occupied but I can see no python process running and so finally I had to reboot the server to get the memory back.

Then I thought maybe I should isolate the optimizer.minimize part, so I did this ->

train_py_reader, loss, optimizer = build_program(
        main_prog=train_prog,
        startup_prog=startup_prog,
        train_params=train_params,
        is_train=True,
        name_dict=pd_weights)
    # Right after the build_program is run, I set the weights here
    .....
    # Now I try to run the optimizer again. I also tried commenting the optimizer in build_program. Both ways gave the same error
    with fluid.program_guard(train_prog, startup_prog):
        with fluid.unique_name.guard():
            with fluid.unique_name.guard("train"):
                optimizer.minimize(loss)

I got the error

ValueError: Variable train_generated_var_0 has been created before. The previous type is VarType.STEP_SCOPES; the new type is VarType.LOD_TENSOR. They are not matched

I hope this helps... Any idea how I can freeze the weights which I have just loaded and set into the model? @wanghaoshuang