def find_closest_with_brute_force(neighbors_df, ref_location): min_distance = 99999999999 closest_neighbor_name = closest_neighbor_coords = None for name, coords in neighbors_df.iterrows(): d = distance(ref_location, tuple(coords)) if d < min_distance: min_distance = d closest_neighbor_name = name closest_neighbor_coords = tuple(coords) return closest_neighbor_name, closest_neighbor_coords
'--result_npy',
default='./models/resnet50v1b-sparse-0.npy',
help='result numpy to compare reconstructor result')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
with open(args.graph, 'r') as f:
graph = json.load(f)
params = np.load(args.params, allow_pickle=True)['arr_0'][()]
trc = src.TorchReconstructor(graph, params)
trc.load_weights()
trc.eval()
print(trc)
print("spec_name, nnz, sparsity")
for module in trc.model:
print(module.spec_name, module.nnz, module.sparsity)
if args.input_npy:
x = torch.from_numpy(np.load(args.input_npy))
else:
x = torch.randn((1, 3, 224, 224))
total_params, dense_flops, sparse_flops = src.compute_model_complexity(
trc, x, verbose=True)
if args.result_npy and args.input_npy:
result = np.load(args.result_npy)
y = trc(x)
print('distance', src.distance(y.detach().numpy(), result))
dummpy_input = np.random.randn(1, 224, 224, 3)
g = tf.Graph()
with g.as_default():
trc_inference = src.TFReconstructor(graph, params)
result = trc_inference(dummpy_input)
new_g = tf.Graph()
with new_g.as_default():
trc = src.TFReconstructorTrain(graph, params)
trc._execute()
with tf.variable_scope('test', reuse=tf.AUTO_REUSE):
featmap = trc.model(is_training=True)
featmap_test = trc.model(is_training=False)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# frozen_graph_def = trc.frozen()
# tf.import_graph_def(frozen_graph_def, name='')
# tf.io.write_graph(frozen_graph_def, os.path.dirname(args.save_path),
# os.path.basename(args.save_path), as_text=True)
input_tsr = new_g.get_tensor_by_name('test/0:0')
new_train_result = sess.run(featmap,
feed_dict={input_tsr: dummpy_input})
print(src.distance(result, new_train_result['506']))
input_tsr = new_g.get_tensor_by_name('test/0_1:0')
new_test_result = sess.run(featmap_test,
feed_dict={input_tsr: dummpy_input})
print(src.distance(result, new_test_result['506']))
help='save pb path')
parser.add_argument('--post', default='', help='postprocess key')
parser.add_argument('--shape', default=224, help='', type=int)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
with open(args.graph, 'r') as f:
graph = json.load(f)
params = np.load(args.params, allow_pickle=True)['arr_0'][()]
params = src.transform_weight_from_mxnet_to_tensorflow(params)
trc = src.TFReconstructor(graph, params)
# import pdb; pdb.set_trace()
if args.post:
trc.set_postprocessor(args.post)
if args.input_npy:
x = np.load(args.input_npy)
else:
shape = [1, 3, args.shape, args.shape]
x = np.random.uniform(0, 1, shape)
x = x.transpose((0, 2, 3, 1))
y = trc(x)
if args.result_npy and args.input_npy:
result = np.load(args.result_npy)
print('distance', src.distance(y, result))
if args.save_path:
trc.save_graph(args.save_path)
help='quantization strategy')
parser.add_argument('-wq',
'--weight-quan',
default='',
help='per layer or per channel')
args = parser.parse_args()
with open(args.config_file, 'r') as f:
config = easydict.EasyDict(yaml.load(f.read()))
merge_args_to_config(config, args)
with open(config.MODEL.graph, 'r') as f:
graph = json.load(f)
params = np.load(config.MODEL.params, allow_pickle=True)['arr_0'][()]
print(config)
out_quan, quan_act_list = run(graph, params, config.QUAN,
config.EVALUATION, config.SAVE_PATH)
config.QUAN.strategy = 'null'
print(config)
out, act_list = run(graph, params, config.QUAN, config.EVALUATION, '')
assert len(quan_act_list) == len(act_list)
for idx in range(len(act_list)):
try:
print(
act_list[idx]['node'],
src.distance(quan_act_list[idx]['act'], act_list[idx]['act']))
except Exception as err:
import pdb
pdb.set_trace()
print(act_list[idx])
print('final distance', src.distance(out_quan, out))