Created
February 15, 2017 11:47
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| def add_dilated(**kwargs): | |
| # print kwargs['key'] | |
| return "net['{0}'] = DilatedConv2DLayer(net['{1}'], num_filters={2}, filter_size={3}, dilation={4}, name='{0}')".format( | |
| kwargs['curr_layer_name'], kwargs['prev_layer_name'], kwargs['num_filters'], kwargs['filter_size'], kwargs['dilation']) | |
| def add_vanilla(**kwargs): | |
| # print kwargs['key'] | |
| return "net['{0}'] = ConvLayer(net['{1}'], num_filters={2}, filter_size={3}, pad={4}, name='{0}')".format( | |
| kwargs['curr_layer_name'], kwargs['prev_layer_name'], kwargs['num_filters'], kwargs['filter_size'], kwargs['pad']) | |
| def add_pad(**kwargs): | |
| # print kwargs['key'] | |
| return "net['{0}'] = PadLayer(net['{1}'], width={2}, val=0, name='{0}')".format( | |
| kwargs['curr_layer_name'], kwargs['prev_layer_name'], kwargs['width']) | |
| def add_pool(**kwargs): | |
| return "net['{0}'] = PoolLayer(net['{1}'], 2, name='{0}')".format( | |
| kwargs['curr_layer_name'], kwargs['prev_layer_name']) | |
| def make_net(architecture_string, dilated_channels): | |
| layers = architecture_string.split('+') | |
| prev_layer_name = None | |
| meta_string = [] | |
| for (layer_idx, curr_layer) in enumerate(layers): | |
| # details = curr_layer.split('_') | |
| # num_filters = int([each_param[1:] for each_param in details if 'k' in each_param][0]) | |
| # filter_size = int([each_param[1:] for each_param in details if 'f' in each_param][0]) | |
| if layer_idx==0: | |
| prev_layer_name = 'input' | |
| if curr_layer[0] == 'k': | |
| if 'f' in curr_layer: | |
| params = curr_layer.split('_') | |
| filter_size = int([each_param[1:] for each_param in params if 'f' in each_param][0]) | |
| num_filters = int([each_param[1:] for each_param in params if 'k' in each_param][0]) | |
| else: | |
| filter_size = 3 | |
| num_filters = int(curr_layer[1:]) | |
| meta_string.append( | |
| add_vanilla(curr_layer_name='conv{}'.format(layer_idx+1), | |
| prev_layer_name=prev_layer_name, | |
| num_filters=num_filters, | |
| filter_size=filter_size, | |
| pad="'same'")) | |
| prev_layer_name = 'conv{}'.format(layer_idx+1) | |
| if curr_layer[0] == 'p': | |
| meta_string.append( | |
| add_pool(curr_layer_name='pool{}'.format(layer_idx+1), prev_layer_name=prev_layer_name)) | |
| prev_layer_name = 'pool{}'.format(layer_idx+1) | |
| if curr_layer[0] == 'd': | |
| meta_string.append( | |
| add_pad(curr_layer_name='pad{}'.format(layer_idx+1), | |
| prev_layer_name=prev_layer_name, | |
| width=int(curr_layer[1:]))) | |
| prev_layer_name = 'pad{}'.format(layer_idx+1) | |
| meta_string.append( | |
| add_dilated(curr_layer_name='dil_conv{}'.format(layer_idx+1), | |
| prev_layer_name=prev_layer_name, | |
| num_filters=dilated_channels, | |
| filter_size=3, | |
| dilation=int(curr_layer[1:]))) | |
| prev_layer_name = 'dil_conv{}'.format(layer_idx+1) | |
| outfile = open('temp_'+architecture_string+'.py', 'w') | |
| outfile.write("\n".join(meta_string)) | |
| return meta_string | |
| if __name__ =="__main__": | |
| # b = add_dilated(curr_layer_name="conv2", prev_layer_name="conv1", num_filters=32, filter_size=3, dilation=1) | |
| # c = add_pad(curr_layer_name="conv2", prev_layer_name="conv1", width=1, val=0) | |
| # a = add_vanilla(curr_layer_name="conv2", prev_layer_name="conv1", num_filters=32, filter_size=3, pad='same') | |
| # d = add_pool(curr_layer_name="conv2", prev_layer_name="conv1") | |
| # print a+b+c+d | |
| # from pprint import pprint | |
| # vgg like | |
| make_net('k64+k64+p2+k128+k128+d1', 128) | |
| make_net('k64+k64+p2+k128+k128+d1+d1', 128) | |
| make_net('k64+k64+p2+k128+k128+d1+d1+d1', 128) | |
| make_net('k64+k64+p2+k128+k128+d1+d1+d2+d1', 128) | |
| make_net('k64+k64+p2+k128+k128+d1+d1+d2+d4+d1', 128) | |
| make_net('k64+k64+p2+k128+k128+d1+d1+d2+d4+d8+d1', 128) | |
| make_net('k64+k64+p2+k128+k128+d1+d1+d2+d4+d8+d16+d1', 128) | |
| make_net('k32+k32', 32) | |
| make_net('k32+k32+d1', 32) | |
| make_net('k32+k32+d1+d1', 32) | |
| make_net('k32+k32+d1+d1+d1', 32) | |
| make_net('k32+k32+d1+d1+d2+d1', 32) | |
| make_net('k32+k32+d1+d1+d2+d4+d1', 32) | |
| make_net('k32+k32+d1+d1+d2+d4+d8+d1', 32) | |
| make_net('k32+k32+d1+d1+d2+d4+d8+d16+d1', 32) | |
| make_net('k32+k32+k32+k32', 32) | |
| make_net('k32+k32+k32+k32+d1', 32) | |
| make_net('k32+k32+k32+k32+d1+d1', 32) | |
| make_net('k32+k32+k32+k32+d1+d1+d1', 32) | |
| make_net('k32+k32+k32+k32+d1+d1+d2+d1', 32) | |
| make_net('k32+k32+k32+k32+d1+d1+d2+d4+d1', 32) | |
| make_net('k32+k32+k32+k32+d1+d1+d2+d4+d8+d1', 32) | |
| make_net('k32+k32+k32+k32+d1+d1+d2+d4+d8+d16+d1', 32) | |
| # icra like | |
| make_net('k32+k32+k32+k32+k32+k32', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1+d1', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1+d1+d1', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1+d1+d2+d1', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1+d1+d2+d4+d1', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1+d1+d2+d4+d8+d1', 32) | |
| make_net('k32+k32+k32+k32+k32+k32+d1+d1+d2+d4+d8+d16+d1', 32) | |
| make_net('k32+k32+k64+k64', 64) | |
| make_net('k32+k32+k64+k64+d1', 64) | |
| make_net('k32+k32+k64+k64+d1+d1', 64) | |
| make_net('k32+k32+k64+k64+d1+d1+d1', 64) | |
| make_net('k32+k32+k64+k64+d1+d1+d2+d1', 64) | |
| make_net('k32+k32+k64+k64+d1+d1+d2+d4+d1', 64) | |
| make_net('k32+k32+k64+k64+d1+d1+d2+d4+d8+d1', 64) | |
| make_net('k32+k32+k64+k64+d1+d1+d2+d4+d8+d16+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1+d1+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1+d1+d2+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1+d1+d2+d4+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1+d1+d2+d4+d8+d1', 64) | |
| make_net('k32+k32+k64+k64+k64+k64+d1+d1+d2+d4+d8+d16+d1', 64) |
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