I have below implementation which can flatten and unflatten both the net itself.Hopefully you can use some of that.
import numpy as np

#############################################################################
# Flattening the NET
#############################################################################
def flattenNetwork(net):
    flatNet = []
    shapes = []
    for param in net.parameters():
        #if its WEIGHTS
        curr_shape = param.cpu().data.numpy().shape
        shapes.append(curr_shape)
        if len(curr_shape) == 2:
            param = param.cpu().data.numpy().reshape(curr_shape[0]*curr_shape[1])
            flatNet.append(param)
        elif len(curr_shape) == 4:
            param = param.cpu().data.numpy().reshape(curr_shape[0]*curr_shape[1]*curr_shape[2]*curr_shape[3])
            flatNet.append(param)
        else:
            param = param.cpu().data.numpy().reshape(curr_shape[0])
            flatNet.append(param)
    finalNet = []
    for obj in flatNet:
        for x in obj:
            finalNet.append(x)
    finalNet = np.array(finalNet)
    return finalNet,shapes


#############################################################################
# UN-Flattening the NET
#############################################################################
def unFlattenNetwork(weights, shapes):
    #this is how we know how to slice weights

    begin_slice = 0
    end_slice = 0
    finalParams = []
    #print(len(weights))
    for idx,shape in enumerate(shapes):
        if len(shape) == 2:
            end_slice = end_slice+(shape[0]*shape[1])
            curr_slice = weights[begin_slice:end_slice]
            param = np.array(curr_slice).reshape(shape[0], shape[1])
            finalParams.append(param)
            begin_slice = end_slice
        elif len(shape) == 4:
            end_slice = end_slice+(shape[0]*shape[1]*shape[2]*shape[3])
            curr_slice = weights[begin_slice:end_slice]
            #print("shape: "+str(shape))
            #print("curr_slice: "+str(curr_slice.shape))
            param = np.array(curr_slice).reshape(shape[0], shape[1], shape[2], shape[3])
            finalParams.append(param)
            begin_slice = end_slice
        else:
            end_slice = end_slice+shape[0]
            curr_slice = weights[begin_slice:end_slice]
            param = np.array(curr_slice).reshape(shape[0],)
            finalParams.append(param)
            begin_slice = end_slice
    finalArr = np.array(finalParams)
    return np.array(finalArr)

 flat_weights,shapes=flattenNetwork(model)
unFlattenNetwork(flat_weights,shapes) --Gives you Numpy n-dimensional array in your case the image which you can directly assign to a variable