258 lines
10 KiB
Python
258 lines
10 KiB
Python
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import torch
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from .torch_utils import convert2cpu
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def parse_cfg(cfgfile):
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blocks = []
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fp = open(cfgfile, 'r')
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block = None
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line = fp.readline()
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while line != '':
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line = line.rstrip()
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if line == '' or line[0] == '#':
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line = fp.readline()
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continue
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elif line[0] == '[':
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if block:
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blocks.append(block)
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block = dict()
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block['type'] = line.lstrip('[').rstrip(']')
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# set default value
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if block['type'] == 'convolutional':
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block['batch_normalize'] = 0
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else:
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key, value = line.split('=')
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key = key.strip()
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if key == 'type':
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key = '_type'
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value = value.strip()
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block[key] = value
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line = fp.readline()
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if block:
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blocks.append(block)
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fp.close()
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return blocks
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def print_cfg(blocks):
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print('layer filters size input output');
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prev_width = 416
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prev_height = 416
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prev_filters = 3
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out_filters = []
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out_widths = []
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out_heights = []
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ind = -2
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for block in blocks:
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ind = ind + 1
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if block['type'] == 'net':
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prev_width = int(block['width'])
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prev_height = int(block['height'])
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continue
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elif block['type'] == 'convolutional':
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filters = int(block['filters'])
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kernel_size = int(block['size'])
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stride = int(block['stride'])
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is_pad = int(block['pad'])
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pad = (kernel_size - 1) // 2 if is_pad else 0
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width = (prev_width + 2 * pad - kernel_size) // stride + 1
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height = (prev_height + 2 * pad - kernel_size) // stride + 1
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print('%5d %-6s %4d %d x %d / %d %3d x %3d x%4d -> %3d x %3d x%4d' % (
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ind, 'conv', filters, kernel_size, kernel_size, stride, prev_width, prev_height, prev_filters, width,
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height, filters))
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prev_width = width
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prev_height = height
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prev_filters = filters
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'maxpool':
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pool_size = int(block['size'])
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stride = int(block['stride'])
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width = prev_width // stride
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height = prev_height // stride
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print('%5d %-6s %d x %d / %d %3d x %3d x%4d -> %3d x %3d x%4d' % (
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ind, 'max', pool_size, pool_size, stride, prev_width, prev_height, prev_filters, width, height,
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filters))
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prev_width = width
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prev_height = height
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prev_filters = filters
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'avgpool':
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width = 1
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height = 1
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print('%5d %-6s %3d x %3d x%4d -> %3d' % (
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ind, 'avg', prev_width, prev_height, prev_filters, prev_filters))
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prev_width = width
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prev_height = height
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prev_filters = filters
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'softmax':
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print('%5d %-6s -> %3d' % (ind, 'softmax', prev_filters))
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'cost':
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print('%5d %-6s -> %3d' % (ind, 'cost', prev_filters))
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'reorg':
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stride = int(block['stride'])
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filters = stride * stride * prev_filters
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width = prev_width // stride
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height = prev_height // stride
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print('%5d %-6s / %d %3d x %3d x%4d -> %3d x %3d x%4d' % (
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ind, 'reorg', stride, prev_width, prev_height, prev_filters, width, height, filters))
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prev_width = width
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prev_height = height
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prev_filters = filters
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'upsample':
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stride = int(block['stride'])
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filters = prev_filters
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width = prev_width * stride
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height = prev_height * stride
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print('%5d %-6s * %d %3d x %3d x%4d -> %3d x %3d x%4d' % (
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ind, 'upsample', stride, prev_width, prev_height, prev_filters, width, height, filters))
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prev_width = width
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prev_height = height
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prev_filters = filters
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'route':
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layers = block['layers'].split(',')
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layers = [int(i) if int(i) > 0 else int(i) + ind for i in layers]
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if len(layers) == 1:
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print('%5d %-6s %d' % (ind, 'route', layers[0]))
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prev_width = out_widths[layers[0]]
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prev_height = out_heights[layers[0]]
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prev_filters = out_filters[layers[0]]
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elif len(layers) == 2:
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print('%5d %-6s %d %d' % (ind, 'route', layers[0], layers[1]))
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prev_width = out_widths[layers[0]]
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prev_height = out_heights[layers[0]]
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assert (prev_width == out_widths[layers[1]])
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assert (prev_height == out_heights[layers[1]])
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prev_filters = out_filters[layers[0]] + out_filters[layers[1]]
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elif len(layers) == 4:
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print('%5d %-6s %d %d %d %d' % (ind, 'route', layers[0], layers[1], layers[2], layers[3]))
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prev_width = out_widths[layers[0]]
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prev_height = out_heights[layers[0]]
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assert (prev_width == out_widths[layers[1]] == out_widths[layers[2]] == out_widths[layers[3]])
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assert (prev_height == out_heights[layers[1]] == out_heights[layers[2]] == out_heights[layers[3]])
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prev_filters = out_filters[layers[0]] + out_filters[layers[1]] + out_filters[layers[2]] + out_filters[
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layers[3]]
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else:
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print("route error !!! {} {} {}".format(sys._getframe().f_code.co_filename,
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sys._getframe().f_code.co_name, sys._getframe().f_lineno))
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] in ['region', 'yolo']:
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print('%5d %-6s' % (ind, 'detection'))
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'shortcut':
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from_id = int(block['from'])
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from_id = from_id if from_id > 0 else from_id + ind
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print('%5d %-6s %d' % (ind, 'shortcut', from_id))
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prev_width = out_widths[from_id]
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prev_height = out_heights[from_id]
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prev_filters = out_filters[from_id]
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out_widths.append(prev_width)
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out_heights.append(prev_height)
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out_filters.append(prev_filters)
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elif block['type'] == 'connected':
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filters = int(block['output'])
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print('%5d %-6s %d -> %3d' % (ind, 'connected', prev_filters, filters))
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prev_filters = filters
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out_widths.append(1)
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out_heights.append(1)
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out_filters.append(prev_filters)
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else:
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print('unknown type %s' % (block['type']))
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def load_conv(buf, start, conv_model):
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num_w = conv_model.weight.numel()
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num_b = conv_model.bias.numel()
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conv_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]));
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start = start + num_b
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conv_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_w]).reshape(conv_model.weight.data.shape));
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start = start + num_w
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return start
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def save_conv(fp, conv_model):
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if conv_model.bias.is_cuda:
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convert2cpu(conv_model.bias.data).numpy().tofile(fp)
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convert2cpu(conv_model.weight.data).numpy().tofile(fp)
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else:
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conv_model.bias.data.numpy().tofile(fp)
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conv_model.weight.data.numpy().tofile(fp)
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def load_conv_bn(buf, start, conv_model, bn_model):
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num_w = conv_model.weight.numel()
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num_b = bn_model.bias.numel()
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bn_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]));
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start = start + num_b
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bn_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_b]));
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start = start + num_b
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bn_model.running_mean.copy_(torch.from_numpy(buf[start:start + num_b]));
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start = start + num_b
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bn_model.running_var.copy_(torch.from_numpy(buf[start:start + num_b]));
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start = start + num_b
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conv_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_w]).reshape(conv_model.weight.data.shape));
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start = start + num_w
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return start
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def save_conv_bn(fp, conv_model, bn_model):
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if bn_model.bias.is_cuda:
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convert2cpu(bn_model.bias.data).numpy().tofile(fp)
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convert2cpu(bn_model.weight.data).numpy().tofile(fp)
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convert2cpu(bn_model.running_mean).numpy().tofile(fp)
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convert2cpu(bn_model.running_var).numpy().tofile(fp)
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convert2cpu(conv_model.weight.data).numpy().tofile(fp)
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else:
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bn_model.bias.data.numpy().tofile(fp)
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bn_model.weight.data.numpy().tofile(fp)
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bn_model.running_mean.numpy().tofile(fp)
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bn_model.running_var.numpy().tofile(fp)
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conv_model.weight.data.numpy().tofile(fp)
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def load_fc(buf, start, fc_model):
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num_w = fc_model.weight.numel()
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num_b = fc_model.bias.numel()
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fc_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]));
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start = start + num_b
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fc_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_w]));
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start = start + num_w
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return start
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def save_fc(fp, fc_model):
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fc_model.bias.data.numpy().tofile(fp)
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fc_model.weight.data.numpy().tofile(fp)
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if __name__ == '__main__':
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import sys
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blocks = parse_cfg('cfg/yolo.cfg')
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if len(sys.argv) == 2:
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blocks = parse_cfg(sys.argv[1])
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print_cfg(blocks)
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