I don't seem to be able to run the code correctly

[PandaK404]

Hello, I have the following problem when running the code and I need your help very much!

1. After I clone the code and install the dependencies according to the tutorial (i.e. complete the following steps), I open jupyternotebook and then how to execute the code?
   

2. I tried to run directly in vscode python . /datagen/gen_data.py, but the following error is reported
   

3. After encountering the above error, I tried to create an hdf5 file using the command: touch /xxx/test/deep-route/data/layout_data.hdf5, after executing python . /datagen/gen_data.py still reported the same error, and I created the hdf5 file disappeared.

I've tried many things and still can't solve the problem, hope to get your help!

[emillal]

There was an issue with the genData function. An issue related with the directory

The rectified function code is as below:

###########################################

def genData(N=10, H=32, W=32, pin_range=(2, 6)):

# 8 layout layers for now
C = 8

data_dir = os.getcwd() + "/data/"
# Ensure the directory exists
if not os.path.exists(data_dir):
    os.makedirs(data_dir)

if os.path.exists(data_dir):
    for file in os.listdir(data_dir):
        if file.endswith(".hdf5"):
            os.remove(data_dir+file)
else:
    os.makedirs(data_dir)

data = h5py.File(data_dir + "layout_data.hdf5", 'w')

# numpy arrays no longer needed; use HDF5 instead
#X = np.zeros([N, 1, H, W], dtype = np.int8)
#Y = np.zeros([N, C, H, W], dtype = np.int8)

X = data.create_dataset("X", shape=(N, 1, H, W), dtype='uint8', compression='lzf', chunks=(1, 1, H, W))
Y = data.create_dataset("Y", shape=(N, C, H, W), dtype='uint8', compression='lzf', chunks=(1, 1, H, W))

# Set physical size represented by HxW pixels
microns = 11.0    # To have balanced dataset covering from m3 to m6 (based on resistance plots from resistance_vs_distance.ipynb)
microns_per_xpixel = microns/W
microns_per_ypixel = microns/H

# Layer map
l_map = {
    # Pins
    'pin' : 0,
    
    # Vias
    'via3' : 2,
    'via4' : 4,
    'via5' : 6,
    
    # Vertical tracks
    'm3' : 1,
    'm5' : 5,
  
    # Horizontal tracks
    'm4' : 3,
    'm6' : 7
}

#m3_m4 = m5_m4 = m5_m6 = 0

n = 0
print_every = 5000

while n < N:
    # Randomly select number of pins from given range
    # Uniform distribution over pin range
    nPins = np.random.randint(*pin_range)
    # Non-uniform distribution (skewed exponentially towards smaller number of pins)
    #p_range = np.array(range(*pin_range))
    #p = np.exp(-p_range) / np.sum(np.exp(-p_range))
    #nPins = np.random.choice(p_range, p=p)
    
    # Randomly pick x and y co-ords for nPins from [0, W) and [0, H) pixels
    x_pins = np.random.randint(W, size=nPins)
    y_pins = np.random.randint(H, size=nPins)
    
    max_xlen = (max(x_pins) - min(x_pins)) * microns_per_xpixel   # length in um
    max_ylen = (max(y_pins) - min(y_pins)) * microns_per_ypixel   # length in um
    
    # Corner case when pins overlap each other (invalid case)
    # Bug fix for https://github.com/sjain-stanford/RouteAI/issues/4
    if (max_xlen == 0) and (max_ylen == 0):
        continue

    # Draw pins on layer 'pin (m2)' of both X (data) and Y (labels)
    X[n] = drawPins(X[n], x_pins, y_pins, l_map)
    Y[n] = drawPins(Y[n], x_pins, y_pins, l_map)
    
    # Add routes to Y (labels)
    x_wire, y_wire = selectWireClass(max_xlen, max_ylen)
    Y[n] = drawRoutes(Y[n], x_pins, y_pins, max_xlen, max_ylen, x_wire, y_wire, l_map)
    
    n += 1
    
    if (n % print_every == 0):
        print("Finished generating %d samples." %(n))
    
    #if x_wire == 'm4' and y_wire == 'm3':
    #    m3_m4 += 1
    #elif x_wire == 'm4' and y_wire == 'm5':
    #    m5_m4 += 1
    #elif x_wire == 'm6' and y_wire == 'm5':
    #    m5_m6 += 1
    #else:
    #    print(x_wire, y_wire)
    
#print(m3_m4, m5_m4, m5_m6)

# Storing as .npy using np.save -> Issue: RAM out of memory, disk memory limitation   
#data_dir = os.getcwd() + '/data/'    
#if os.path.exists(data_dir):
#    for file in os.listdir(data_dir):
#        if file.endswith(".npy"):
#            os.remove(data_dir+file)
#else:
#    os.makedirs(data_dir)
#X_save = data_dir + 'X_save.npy'
#Y_save = data_dir + 'Y_save.npy'    
#np.save(X_save, X, allow_pickle=False)
#np.save(Y_save, Y, allow_pickle=False)

print("Dataset generated as follows:")
for ds in data:
    print(ds, data[ds])

#############################################