From 48cacb144d9ff81506a5c7ca9c9f13fdb8718d0c Mon Sep 17 00:00:00 2001
From: Mazdak Fatahi <mazdak.fatahi@univ-lille.fr>
Date: Thu, 17 Nov 2022 13:40:34 +0100
Subject: [PATCH] Upload New File
---
SpiNNaker/SpiNNakerHelpers.py | 337 ++++++++++++++++++++++++++++++++++
1 file changed, 337 insertions(+)
create mode 100644 SpiNNaker/SpiNNakerHelpers.py
diff --git a/SpiNNaker/SpiNNakerHelpers.py b/SpiNNaker/SpiNNakerHelpers.py
new file mode 100644
index 0000000..0f169b6
--- /dev/null
+++ b/SpiNNaker/SpiNNakerHelpers.py
@@ -0,0 +1,337 @@
+import numpy as np
+import math
+from mpl_toolkits.axes_grid1 import ImageGrid
+import matplotlib.pyplot as plt
+import torch
+import snntorch
+from snntorch import utils, spikegen
+from tqdm import tqdm
+#from pyNN.utility.plotting import Figure, Panel
+#import spynnaker.pyNN as sim
+INPUT_SIZE=784
+# Initialization
+#Neurons Parameters
+excitatory_neuron_parameters = {
+ 'v_rest': -70.0,#V_REST, # Resting membrane potential in mV.
+ 'cm': .3,# 1.0, # Capacity of the membrane in nF
+ 'tau_m': 10.0, #100.0, # Membrane time constant in ms.
+ 'tau_refrac': 10,#4.0,#5.0, # Duration of refractory period in ms.
+ 'tau_syn_E': 1.0, # Decay time of the excitatory synaptic conductance in ms.
+ 'tau_syn_I': 1.0,#2.0, # Decay time of the inhibitory synaptic conductance in ms.
+# 'e_rev_E': 0.0, # Reversal potential for excitatory input in mV
+# 'e_rev_I': -100.0, # Reversal potential for inhibitory input in mV
+ 'v_thresh': -55.4,#-52.0, # Spike threshold in mV.
+ 'v_reset': -65.0,#V_RESET, # Reset potential after a spike in mV.
+ 'i_offset': 0.005# 0.0, # Offset current in nA
+}
+
+
+def img_grid(input_data, nrows_ncols, file_name=None, figsize=(10, 10), cmap="jet"):
+ """
+ Using ImageGrid from mpl_toolkits.axes_grid1 to show multiple images in grids
+ Parameters
+ ----------
+ input_data: An array of images.
+ nrows_ncols: A tuple (number of rows, number of columns)
+ file_name: A string to save the plot as
+ cmap: 'flag', 'prism', 'ocean', 'gist_earth', 'terrain',
+ 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap',
+ 'cubehelix', 'brg', 'gist_rainbow', 'rainbow', 'jet',
+ 'turbo', 'nipy_spectral', 'gist_ncar'
+
+
+ Ex_1:
+ p=np.random.random_integers(0,255,(100,10,10))
+ plot_and_save_grid(p, (10, 10), 'test.svg')
+
+ Ex_2:
+ p=[[[1,2,3],[1,2,3],[1,2,3]] for _ in range(8)]
+ plots.img_grid(p, (3,2), 'test.svg')
+ """
+ fig = plt.figure(figsize=figsize)
+ grid = ImageGrid(fig, 111,
+ nrows_ncols,
+ axes_pad=0.1,
+ )
+
+ for ax, inx in zip(grid, list(range(nrows_ncols[0]*nrows_ncols[1]))):
+ ax.imshow(input_data[inx],cmap=cmap)
+ ax.set_axis_off()
+
+ if file_name:
+ plt.savefig(file_name)
+ return
+
+def multi_titled_imgs(input_data, main_title, row_titles, col_titles, file_name=None, axis_off=True, figsize=(10,15), main_title_fontsize=20, rows_fontsize=15, cols_fontsize=10):
+ """
+ Parameters
+ ----------
+ input_data: An array of images.
+ main_title: A sting as top title
+ row_titles: An array of n strings for the title of n each row
+ col_titles: An array of 2 strings for the title of each columns
+
+ Ex_1:
+ p=np.random.random_integers(0,255,(6,10,10))# 6 random (10, 10) matrixes
+ main_title='Main Title'
+ row_titles=["A", "B", "C"]
+ col_titles=["First Col", "2nd Col"]
+ plots.multi_titled_imgs(p[:10],main_title,row_titles,col_titles, file_name='a.png', axis_off=False)
+ """
+
+ fig = plt.figure(figsize=figsize,constrained_layout=True)
+ fig.suptitle(main_title, fontsize=main_title_fontsize)
+ subfigs = fig.subfigures(nrows=len(row_titles), ncols=1)
+ for row, subfig in enumerate(subfigs):
+ subfig.suptitle(f'{row_titles[row]}', fontsize=rows_fontsize)
+
+ axs = subfig.subplots(nrows=1, ncols=2)
+ for col, ax in enumerate(axs):
+ ax.imshow(input_data[row*2+col])
+ ax.set_title(f' {col_titles[col]}', fontsize=cols_fontsize)
+ if axis_off:
+ ax.set_axis_off()
+ if file_name:
+ plt.savefig(file_name)
+ return
+
+def Extract_ConnectionFiles(Input_CSNN_Binary_Files=[], ConnectionFile_Names=[], Input_size=784, Output_size=400, n_samples_to_plot=0, Non_Zero=False, Transpoed=False, Inpute_shape={'x':28, 'y':28},):
+ """
+ To generate connection files from the CSNN binary files.
+ ----------
+ NOTE: For each layer you need to call the function separately.
+ ----------
+ Parameters
+ ----------
+ Authors Note: It is recommended to call the function with Transpoed=False and transpose the input before passing it to the SpikeSourceArray in PyNN.
+ -----
+ Input_CSNN_Binary_Files: Python list of binary file names (different binary files from the same layer)
+ ConnectionFile_Names: Python list of output file names (as text files compatible with PyNN FromFileConnector)
+ Input_size: Number of the neurons as the input of the layer
+ Output_size: Number of the neurons as the output of the layer
+ n_samples_to_plot: You can plot n_samples_to_plot weights between one input and all the output as plot
+ Non_Zero: If set to True, the weights with zero values will be removed from the output file.
+ Transpoed: Since CSNN binary file for the trained weights is transposed, is set Transpoed to True, the function will transpose the weights before generating the output file.
+ Inpute_shape: Python dictionary of shape of the input samples
+
+ """
+ extracted_weights=[[] for _ in Input_CSNN_Binary_Files]
+ for i, file_name in enumerate(Input_CSNN_Binary_Files):
+ w = np.fromfile(file_name, dtype=np.dtype(np.float32))
+ print(f'Input file shape = {w.shape}')
+ f=open(ConnectionFile_Names[i], 'w')
+ f.write('# columns = ["i", "j", "weight", "delay"]\n')
+
+ if Transpoed:
+ w=w.reshape(Inpute_shape['x'], Inpute_shape['y'], -1)#(28, 28, 400)
+ w=np.array([w[:, :, j].transpose() for j in range(Output_size) ])#(400, 28, 28)
+
+ w=w.reshape( Output_size, Input_size).transpose()# 400 rows of (28, 28) to 400 rows of 784 then to 784*400
+ X,Y=w.shape
+ print(X,Y)
+ for x in range(X):
+ for y in range(Y):
+ if Non_Zero:
+ if w[x][y]:
+ line=f'{x} {y} {w[x][y]} 1\n'
+ f.write(line)
+ extracted_weights[i].append(w[x][y])
+ else:
+ line=f'{x} {y} {w[x][y]} 1\n'
+ f.write(line)
+ extracted_weights[i].append(w[x][y])
+
+ f.close()
+ if n_samples_to_plot:
+ w_to_plot=[w.reshape(Inpute_shape['x'], Inpute_shape['y'], -1)[ :, :, i] for i in range(n_samples_to_plot)]
+
+ img_grid(w_to_plot,(int(math.sqrt(n_samples_to_plot)),int(math.sqrt(n_samples_to_plot))))
+
+ else:
+ w=w.reshape(Input_size, Output_size)# 784*400
+ X,Y=w.shape
+ for x in range(X):
+ for y in range(Y):
+ if Non_Zero:
+ if w[x][y]:
+ line=f'{x} {y} {w[x][y]} 1\n'
+ f.write(line)
+ extracted_weights[i].append(w[x][y])
+ else:
+ line=f'{x} {y} {w[x][y]} 1\n'
+ f.write(line)
+ extracted_weights[i].append(w[x][y])
+
+ f.close()
+ if n_samples_to_plot:
+ w_to_plot=[w.reshape(Inpute_shape['x'], Inpute_shape['y'], -1)[ :, :, i] for i in range(n_samples_to_plot)]
+
+ img_grid(w_to_plot,(int(math.sqrt(n_samples_to_plot)),int(math.sqrt(n_samples_to_plot))))
+
+ return extracted_weights
+
+
+def convert_to_latency_code_V4(input_data, tarnsposed=True, samples_intervel=5, time_step=5, num_steps=10):# for CSNN simulation since exracted weights are transposed
+
+ slice_interval=time_step
+ input_interval=samples_intervel
+
+ number_of_digits=len(input_data)
+ coded_digits=[]
+ if tarnsposed:
+ for i in range(number_of_digits):
+ s=spikegen.latency((torch.t(input_data[i][0:28,0:28].T/255)).float(),num_steps=num_steps,normalize=True,linear=True)##spikegen.rate((input_data[i][0:28,0:28]/255).float(), num_steps=num_steps)
+ #print(s)
+ coded_digits.append(s[:-1])
+ else:
+ for i in range(number_of_digits):
+ s=spikegen.latency((torch.t(input_data[i][0:28,0:28]/255)).float(),num_steps=num_steps,normalize=True,linear=True)##spikegen.rate((input_data[i][0:28,0:28]/255).float(), num_steps=num_steps)
+ #print(s)
+ coded_digits.append(s[:-1])
+
+ #print(f'coded_digits ={len(coded_digits)}')
+ current_time=5# 0 will make the strongest vlaue which will spike first to zero
+ INPUT_SIZE=784
+ spikes_time_source_array=[[] for _ in range(INPUT_SIZE)]
+ with tqdm(total=number_of_digits) as pbar:
+ for coded_digit_counter,coded_digit in enumerate(coded_digits):
+ for time_slice_counter, time_slice in enumerate(coded_digit):
+ timed_flatten_time_slice=time_slice.view(-1)*current_time
+ #print(timed_flatten_time_slice.shape)
+ for spike_time_index in torch.nonzero(timed_flatten_time_slice):
+ spikes_time_source_array[spike_time_index].append(timed_flatten_time_slice[spike_time_index].item())
+ current_time+=slice_interval
+ current_time+=input_interval
+ pbar.update(1)
+ return (current_time, spikes_time_source_array)
+
+#========================================================================================================== extract_wieghts
+def extract_wieghts(projection):
+ return (projection.get(["weight"],"list", with_address=False))
+
+#========================================================================================================== extract_synaptic_parameters
+def extract_synaptic_parameters(projection):
+ stored_kernel_connector=projection.get(['weight', 'delay'],"list")
+ connection_list=[]
+ for con in stored_kernel_connector:
+ connection_list.append((con['source'],con['target'],con['weight'],con['delay']))
+ return ( sorted(connection_list, key=lambda x: x[1])) # sort it according to index of neurons in the post population
+
+#========================================================================================================== plot_and_save_grid
+def plot_and_save_grid(input_data,nrows_ncols,save=False,file_name=None):
+ fig = plt.figure(figsize=(10, 10))
+ grid = ImageGrid(fig, 111, # similar to subplot(111)
+ nrows_ncols, # creates nrow x ncol grid of axes
+ axes_pad=0.1, # pad between axes in inch.
+ )
+ for ax, inx in zip(grid, list(range(nrows_ncols[0]*nrows_ncols[1]))):
+ ax.imshow(input_data[inx],cmap="jet")
+ ax.set_axis_off()
+ if save:
+ plt.savefig(file_name)
+ return
+
+#========================================================================================================== make_population
+def make_populations(inupu_spike_train, EXC_POP_SIZE, INH_POP_SIZE):
+ INPUT_POP = sim.Population(int(INPUT_SIZE), sim.SpikeSourceArray(inupu_spike_train), label="input")
+ EXC_POP=[]
+ INH_POP=[]
+ for exp_pop_number in range(len(EXC_POP_SIZE)):
+ print(f'exp_pop_number_{exp_pop_number} has {EXC_POP_SIZE[exp_pop_number]} neurons')
+ EXC_POP.append(sim.Population(int(EXC_POP_SIZE[exp_pop_number]),sim.IF_curr_exp(**excitatory_neuron_parameters), initial_values={'v': excitatory_neuron_parameters["v_rest"]}, label=f"EXC_POP_{exp_pop_number}"))
+ for inh_pop_number in range(len(INH_POP_SIZE)):
+ print(f'inh_pop_number_{inh_pop_number} has {INH_POP_SIZE[inh_pop_number]} neurons')
+ INH_POP.append(sim.Population(int(INH_POP_SIZE[inh_pop_number]),sim.IF_curr_exp(**inhibitory_neuron_parameters), initial_values={'v': inhibitory_neuron_parameters["v_rest"]}, label=f"INH_POP_{inh_pop_number}"))
+ return INPUT_POP, EXC_POP, INH_POP
+
+#========================================================================================================== Make Network
+def make_projections_from_file(INPUT_POP, EXC_POPs, INH_POPs, FILES):
+ EXC_EXC_PRJs=[]
+ EXC_INH_PRJs=[]
+ INH_EXC_PRJs=[]
+
+ connections_list_from_file=sim.FromFileConnector(FILES[0])
+ INPUT_EXC_PRJ=sim.Projection(INPUT_POP, EXC_POPs[0], connections_list_from_file, receptor_type = 'excitatory')
+ print(f'Porjection from Input ot Excitatory pop_0 is created')
+ print('============================================================')
+
+ connections_list_from_file=[]
+ for exc_pop_number, file in enumerate(FILES[1:]):
+ connections_list_from_file=sim.FromFileConnector(file)
+ EXC_EXC_PRJs.append(sim.Projection(EXC_POPs[exc_pop_number], EXC_POPs[exc_pop_number+1], connections_list_from_file, receptor_type = 'excitatory'))
+ print(f'Porjection from Excitatory pop_{exc_pop_number} ot Excitatory pop_{exc_pop_number+1} is created')
+ print('============================================================')
+ for inh_pop_number in range(len(INH_POPs)):
+ EXC_INH_PRJs.append(sim.Projection(EXC_POPs[inh_pop_number], INH_POPs[inh_pop_number], sim.OneToOneConnector(), synapse_type = sim.StaticSynapse(weight=2, delay=1.0),receptor_type = 'excitatory') )
+ print(f'Porjection from Excitatory pop_{inh_pop_number} ot Inhibatory pop_{inh_pop_number} is created')
+
+ inhibitory_excitatory_connection_list = []
+ for i in range(INH_POP_SIZE[inh_pop_number]):
+ for j in range(EXC_POP_SIZE[inh_pop_number]):
+ if not i==j:
+ inhibitory_excitatory_connection_list.append((i,j))
+ print(f'Connection list from Inhibatory_Pop_{inh_pop_number} to Exctatory_Pop_{inh_pop_number} = {INH_POP_SIZE[inh_pop_number]}*{EXC_POP_SIZE[inh_pop_number]}')
+ INH_EXC_PRJs.append(sim.Projection(INH_POPs[inh_pop_number], EXC_POPs[inh_pop_number],
+ sim.FromListConnector(inhibitory_excitatory_connection_list),
+ synapse_type = sim.StaticSynapse(weight=25,delay=1.0),
+ receptor_type = 'inhibitory'))
+
+ print('============================================================')
+
+
+ return INPUT_EXC_PRJ, EXC_EXC_PRJs
+
+def make_projections_from_file_with_one_shut_inhibitory(INPUT_POP, EXC_POPs, INH_POPs, FILES):
+ EXC_EXC_PRJs=[]
+ EXC_INH_PRJs=[]
+ INH_EXC_PRJs=[]
+
+ connections_list_from_file=sim.FromFileConnector(FILES[0])
+ INPUT_EXC_PRJ=sim.Projection(INPUT_POP, EXC_POPs[0], connections_list_from_file, receptor_type = 'excitatory')
+ print(f'Porjection from Input ot Excitatory pop_0 is created')
+ print('============================================================')
+
+ connections_list_from_file=[]
+ for exc_pop_number, file in enumerate(FILES[1:]):
+ connections_list_from_file=sim.FromFileConnector(file)
+ EXC_EXC_PRJs.append(sim.Projection(EXC_POPs[exc_pop_number], EXC_POPs[exc_pop_number+1], connections_list_from_file, receptor_type = 'excitatory'))
+ print(f'Porjection from Excitatory pop_{exc_pop_number} ot Excitatory pop_{exc_pop_number+1} is created')
+ print('============================================================')
+ for inh_pop_number in range(len(INH_POPs)):
+ EXC_INH_PRJs.append(sim.Projection(EXC_POPs[inh_pop_number], INH_POPs[inh_pop_number], sim.OneToOneConnector(), synapse_type = sim.StaticSynapse(weight=50, delay=1.0),receptor_type = 'excitatory') )
+ print(f'Porjection from Excitatory pop_{inh_pop_number} ot Inhibatory pop_{inh_pop_number} is created')
+
+ print(f'Connecting ONE SHUT Inhibatory_Pop_{inh_pop_number} to all Exctatory_Pop_{inh_pop_number} = {INH_POP_SIZE[inh_pop_number]}*{EXC_POP_SIZE[inh_pop_number]}')
+ INH_EXC_PRJs.append(sim.Projection(INH_POPs[inh_pop_number], EXC_POPs[inh_pop_number],
+ sim.AllToAllConnector(),
+ synapse_type = sim.StaticSynapse(weight=100,delay=1.0),
+ receptor_type = 'inhibitory'))
+
+ print('============================================================')
+
+
+ return INPUT_EXC_PRJ, EXC_EXC_PRJs
+
+
+#========================================================================================================== Make Network
+# run simulator for 'time' and feed SpikeSourceArray with 'spike_list_flatten'
+def init_sim(timestep, time_scale_factor, neuron_tpye, number_of_neurons_per_core):
+ sim.end()
+ sim.setup(timestep=timestep,time_scale_factor=time_scale_factor)#sim.setup(timestep=1,time_scale_factor=10)
+ sim.set_number_of_neurons_per_core(neuron_tpye, number_of_neurons_per_core)
+ return
+
+def make_network(spikes_list_flatten, EXC_POP_SIZE, INH_POP_SIZE):
+ INPUT_POP, EXC_POPs, INH_POPs = make_populations(spikes_list_flatten, EXC_POP_SIZE, INH_POP_SIZE)
+ INPUT_EXC_PRJ, EXC_EXC_PRJs = make_projections_from_file(INPUT_POP, EXC_POPs, INH_POPs, weights_files)#make_projections_2(INPUT_POP)
+ return (INPUT_POP, EXC_POPs, INH_POPs, INPUT_EXC_PRJ, EXC_EXC_PRJs)
+
+def record_activities(activities, INPUT_POP, EXC_POPs, INH_POPs):
+ if INPUT_POP:
+ INPUT_POP.record(activities[1])
+ for i in range(len(EXC_POPs)):
+ EXC_POPs[i].record(activities)
+ for i in range(len(INH_POPs)):
+ INH_POPs[i].record(activities)
+ return
+#==================================================
--
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