First run problem

[gestefane]

Hello!
I think it maybe a little problem around understanding overall execution process.
I follow instructions to prepare and run data on README file.
I am using the parameters:
export DATASET=mnist
export SEED=10
export K=5
export B=4
export NET=2nn
export T=5
export E=5

and receiving this result:

python3 main.py --dataset $DATASET --clients_per_round $K --num_round $T --num_epoch $E --batch_size $B --seed $SEED --model $NET   --noaverage --noprint
>>> Arguments:
	             algo : fedavg4
	       batch_size : 4
	clients_per_round : 5
	          dataset : mnist
	           device : 0
	              dis : 
	       eval_every : 5
	              gpu : False
	      input_shape : 784
	               lr : 0.1
	            model : 2nn
	        noaverage : True
	          noprint : True
	        num_class : 10
	        num_epoch : 5
	        num_round : 5
	             seed : 10
	               wd : 0.001
>>> Read data from:
     ./data/mnist/data/train/all_data_0_equal_niid.pkl
     ./data/mnist/data/train/all_data_1_random_niid.pkl
     ./data/mnist/data/test/all_data_0_equal_niid.pkl
     ./data/mnist/data/test/all_data_1_random_niid.pkl
>>> Don not use gpu
>>> Model statistic per layer
TwoHiddenLayerFc(
  198.8 KMac, 100.000% MACs, 
  (fc1): Linear(156.8 KMac, 78.873% MACs, in_features=784, out_features=200, bias=True)
  (fc2): Linear(40.0 KMac, 20.121% MACs, in_features=200, out_features=200, bias=True)
  (fc3): Linear(2.0 KMac, 1.006% MACs, in_features=200, out_features=10, bias=True)
)
>>> Activate a worker for training
>>> Initialize 100 clients in total
>>> Weigh updates by sample numbers
>>> Select 5 clients per round 

Traceback (most recent call last):
  File "/home/user/fedavgpy/main.py", line 131, in <module>
    main()
  File "/home/user/fedavgpy/main.py", line 127, in main
    trainer.train()
  File "/home/user/fedavgpy/src/trainers/fedavg4.py", line 35, in train
    self.test_latest_model_on_traindata(round_i)
  File "/home/user/fedavgpy/src/trainers/base.py", line 155, in test_latest_model_on_traindata
    stats_from_train_data = self.local_test(use_eval_data=False)
  File "/home/user/fedavgpy/src/trainers/base.py", line 211, in local_test
    tot_correct, num_sample, loss = c.local_test(use_eval_data=use_eval_data)
  File "/home/user/fedavgpy/src/models/client.py", line 89, in local_test
    tot_correct, loss = self.worker.local_test(dataloader)
  File "/home/user/fedavgpy/src/models/worker.py", line 131, in local_test
    pred = self.model(x)
  File "/home/user/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/fedavgpy/src/models/model.py", line 25, in forward
    out = F.relu(self.fc1(x))
  File "/home/user/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/.local/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (112x28 and 784x200)

[lx10077]

The solution is to flatten the input data before we feed data into networks.

For MNIST, the data batch is of the size [batch_size, 1, 28, 28], while the input dimension for the used networks is 784.