def load_weights(model, cfg_file, weight_file):
# Open the weights file
fp = open(weight_file, 'rb')
# The first 5 in values are header information
# 1. Major version number
# 2. Minor Version Number
# 3. Subversion number
# 4,5. Images seen by the network (during training)
header = np.fromfile(fp, dtype=np.int32, count=5)
blocks = parse_cfg(cfg_file)
for i, block in enumerate(blocks[1:]):
if block['type'] == 'convolutional':
conv_layer = model.get_layer('conv_' + str(i))
print(f"Layer: {i + 1} {conv_layer}")
filters = conv_layer.filters
k_size = conv_layer.kernel_size[0]
in_dim = conv_layer.input_shape[-1]
if "batch_normalize" in block:
norm_layer = model.get_layer('batch_normalize_' + str(i))
print(f"Layer: {i + 1} {norm_layer}")
size = np.prod(norm_layer.get_weights()[0].shape)
bn_weights = np.fromfile(fp,
dtype=np.float32,
count=4 * filters)
# tf [gama, beta, mean, variance]
bn_weights = bn_weights.reshape((4, filters))
bn_weights = bn_weights[[1, 0, 2, 3]]
else:
conv_bias = np.fromfile(fp, dtype=np.float32, count=filters)
# darknet shape (out_dim, in_dim, height, width)
conv_shape = (filters, in_dim, k_size, k_size)
conv_weights = np.fromfile(fp,
dtype=np.float32,
count=np.product(conv_shape))
# tf shape (height, width, in_dim, out_dim)
conv_weights = conv_weights.reshape(conv_shape).transpose(
[2, 3, 1, 0])
if "batch_normalize" in block:
norm_layer.set_weights(bn_weights)
conv_layer.set_weights([conv_weights])
else:
conv_layer.set_weights([conv_weights, conv_bias])
assert len(fp.read()) == 0, 'failed to read all data'
fp.close()
def load_weights(model, cfgfile, weightfile):
# Open the weights file
fp = open(weightfile, "rb")
# Skip 5 header values
np.fromfile(fp, dtype=np.int32, count=5)
# The rest of the values are the weights
blocks = parse_cfg(cfgfile)
for i, block in enumerate(blocks[1:]):
if (block["type"] == "convolutional"):
conv_layer = model.get_layer('conv_' + str(i))
print("layer: ", i + 1, conv_layer)
filters = conv_layer.filters
k_size = conv_layer.kernel_size[0]
in_dim = conv_layer.input_shape[-1]
if "batch_normalize" in block:
norm_layer = model.get_layer('bnorm_' + str(i))
print("layer: ", i + 1, norm_layer)
size = np.prod(norm_layer.get_weights()[0].shape)
bn_weights = np.fromfile(fp,
dtype=np.float32,
count=4 * filters)
# tf [gamma, beta, mean, variance]
bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]
else:
conv_bias = np.fromfile(fp, dtype=np.float32, count=filters)
# darknet shape (out_dim, in_dim, height, width)
conv_shape = (filters, in_dim, k_size, k_size)
conv_weights = np.fromfile(fp,
dtype=np.float32,
count=np.product(conv_shape))
# tf shape (height, width, in_dim, out_dim)
conv_weights = conv_weights.reshape(conv_shape).transpose(
[2, 3, 1, 0])
if "batch_normalize" in block:
norm_layer.set_weights(bn_weights)
conv_layer.set_weights([conv_weights])
else:
conv_layer.set_weights([conv_weights, conv_bias])
assert len(fp.read()) == 0, 'failed to read all data'
fp.close()
def load_weights(model, cfgfile, weightfile):
fp = open(weightfile, 'rb')
#header information
np.fromfile(fp, dtype=np.int32, count=5)
blocks = parse_cfg(cfgfile)
for i, block in enumerate(blocks[1:]):
if block['type'] == 'convolutional':
conv_layer = model.get_layer('conv_' + str(i))
print('layer: ', i + 1, conv_layer)
filters = conv_layer.filters
k_size = conv_layer.kernel_size[0]
in_dim = conv_layer.input_shape[-1]
if 'batch_normalize' in block:
norm_layer = model.get_layer('bnorm_' + str(i))
print('layer: ', i + 1, norm_layer)
size = np.prod(norm_layer.get_weights()[0].shape)
#[gamma, beta, mean, variance]
bn_weights = np.fromfile(fp,
dtype=np.float32,
count=4 * filters)
bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]
else:
conv_bias = np.fromfile(fp, dtype=np.float32, count=filters)
#darknet shape (out_dim, in_dim, height, width)
conv_shape = (filters, in_dim, k_size, k_size)
conv_weights = np.fromfile(fp,
dtype=np.float32,
count=np.product(conv_shape))
conv_weights = conv_weights.reshape(conv_shape).transpose(
[2, 3, 1, 0])
if 'batch_normalize' in block:
norm_layer.set_weights(bn_weights)
conv_layer.set_weights([conv_weights])
else:
conv_layer.set_weights([conv_weights, conv_bias])
assert len(fp.read()) == 0, 'failed to read all data'
fp.close()
def load_weights(model, cfgfile, weightfile):
fp = open(weightfile, 'rb')
np.fromfile(fp, dtype=np.int32, count=5)
blocks = parse_cfg(cfgfile)
for i, block in enumerate(blocks[1:]):
if block["type"] == "convolutional":
conv_layer = model.get_layer('conv_' + str(i))
print("layer: ", i + 1, conv_layer)
filters = conv_layer.filters
k_size = conv_layer.kernel_size[0]
in_dim = conv_layer.input_shape[-1]
if "batch_normalize" in block:
norm_layer = model.get_layer('bnorm_' + str(i))
print("layer: ", i + 1, norm_layer)
bn_weights = np.fromfile(fp,
dtype=np.float32,
count=4 * filters)
bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]
else:
conv_bias = np.fromfile(fp, dtype=np.float32, count=filters)
conv_shape = (filters, in_dim, k_size, k_size)
conv_weights = np.fromfile(fp,
dtype=np.float32,
count=np.product(conv_shape))
conv_weights = conv_weights.reshape(conv_shape).transpose(
[2, 3, 1, 0])
if "batch_normalize" in block:
norm_layer.set_weights(bn_weights)
conv_layer.set_weights([conv_weights])
else:
conv_layer.set_weights([conv_weights, conv_bias])
#assert len(fp.read()) == 0, 'failed to read data'
fp.close()