91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
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import numpy as np
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import tensorflow as tf
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from .unet import UNet
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def tf2pytorch(checkpoint_path, num_instrumments):
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tf_vars = {}
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init_vars = tf.train.list_variables(checkpoint_path)
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# print(init_vars)
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for name, shape in init_vars:
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try:
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# print('Loading TF Weight {} with shape {}'.format(name, shape))
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data = tf.train.load_variable(checkpoint_path, name)
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tf_vars[name] = data
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except Exception as e:
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print('Load error')
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conv_idx = 0
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tconv_idx = 0
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bn_idx = 0
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outputs = []
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for i in range(num_instrumments):
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output = {}
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outputs.append(output)
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for j in range(1,7):
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if conv_idx == 0:
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conv_suffix = ""
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else:
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conv_suffix = "_" + str(conv_idx)
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if bn_idx == 0:
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bn_suffix = ""
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else:
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bn_suffix = "_" + str(bn_idx)
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output['down{}_conv.weight'.format(j)] = np.transpose(
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tf_vars["conv2d{}/kernel".format(conv_suffix)], (3, 2, 0, 1))
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# print('conv dtype: ',output['down{}.0.weight'.format(j)].dtype)
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output['down{}_conv.bias'.format(
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j)] = tf_vars["conv2d{}/bias".format(conv_suffix)]
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output['down{}_act.0.weight'.format(
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j)] = tf_vars["batch_normalization{}/gamma".format(bn_suffix)]
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output['down{}_act.0.bias'.format(
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j)] = tf_vars["batch_normalization{}/beta".format(bn_suffix)]
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output['down{}_act.0.running_mean'.format(
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j)] = tf_vars['batch_normalization{}/moving_mean'.format(bn_suffix)]
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output['down{}_act.0.running_var'.format(
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j)] = tf_vars['batch_normalization{}/moving_variance'.format(bn_suffix)]
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conv_idx += 1
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bn_idx += 1
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# up blocks
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for j in range(1, 7):
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if tconv_idx == 0:
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tconv_suffix = ""
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else:
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tconv_suffix = "_" + str(tconv_idx)
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if bn_idx == 0:
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bn_suffix = ""
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else:
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bn_suffix= "_" + str(bn_idx)
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output['up{}.0.weight'.format(j)] = np.transpose(
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tf_vars["conv2d_transpose{}/kernel".format(tconv_suffix)], (3,2,0, 1))
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output['up{}.0.bias'.format(
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j)] = tf_vars["conv2d_transpose{}/bias".format(tconv_suffix)]
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output['up{}.2.weight'.format(
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j)] = tf_vars["batch_normalization{}/gamma".format(bn_suffix)]
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output['up{}.2.bias'.format(
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j)] = tf_vars["batch_normalization{}/beta".format(bn_suffix)]
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output['up{}.2.running_mean'.format(
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j)] = tf_vars['batch_normalization{}/moving_mean'.format(bn_suffix)]
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output['up{}.2.running_var'.format(
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j)] = tf_vars['batch_normalization{}/moving_variance'.format(bn_suffix)]
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tconv_idx += 1
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bn_idx += 1
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if conv_idx == 0:
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suffix = ""
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else:
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suffix = "_" + str(conv_idx)
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output['up7.0.weight'] = np.transpose(
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tf_vars['conv2d{}/kernel'.format(suffix)], (3, 2, 0, 1))
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output['up7.0.bias'] = tf_vars['conv2d{}/bias'.format(suffix)]
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conv_idx += 1
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return outputs
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