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deploy/slim/prune/export_prune_model.py

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+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, "..", "..", ".."))
+sys.path.append(os.path.join(__dir__, "..", "..", "..", "tools"))
+
+import paddle
+from ppocr.data import build_dataloader, set_signal_handlers
+from ppocr.modeling.architectures import build_model
+
+from ppocr.postprocess import build_post_process
+from ppocr.metrics import build_metric
+from ppocr.utils.save_load import load_model
+import tools.program as program
+
+
+def main(config, device, logger, vdl_writer):
+    global_config = config["Global"]
+
+    # build dataloader
+    set_signal_handlers()
+    valid_dataloader = build_dataloader(config, "Eval", device, logger)
+
+    # build post process
+    post_process_class = build_post_process(config["PostProcess"], global_config)
+
+    # build model
+    # for rec algorithm
+    if hasattr(post_process_class, "character"):
+        char_num = len(getattr(post_process_class, "character"))
+        config["Architecture"]["Head"]["out_channels"] = char_num
+    model = build_model(config["Architecture"])
+
+    if config["Architecture"]["model_type"] == "det":
+        input_shape = [1, 3, 640, 640]
+    elif config["Architecture"]["model_type"] == "rec":
+        input_shape = [1, 3, 32, 320]
+
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs before pruning: {}".format(flops))
+
+    from paddleslim.dygraph import FPGMFilterPruner
+
+    model.train()
+    pruner = FPGMFilterPruner(model, input_shape)
+
+    # build metric
+    eval_class = build_metric(config["Metric"])
+
+    def eval_fn():
+        metric = program.eval(model, valid_dataloader, post_process_class, eval_class)
+        if config["Architecture"]["model_type"] == "det":
+            main_indicator = "hmean"
+        else:
+            main_indicator = "acc"
+        logger.info("metric[{}]: {}".format(main_indicator, metric[main_indicator]))
+        return metric[main_indicator]
+
+    params_sensitive = pruner.sensitive(
+        eval_func=eval_fn,
+        sen_file="./sen.pickle",
+        skip_vars=["conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0"],
+    )
+
+    logger.info(
+        "The sensitivity analysis results of model parameters saved in sen.pickle"
+    )
+    # calculate pruned params's ratio
+    params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
+    for key in params_sensitive.keys():
+        logger.info("{}, {}".format(key, params_sensitive[key]))
+
+    plan = pruner.prune_vars(params_sensitive, [0])
+
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs after pruning: {}".format(flops))
+
+    # load pretrain model
+    load_model(config, model)
+    metric = program.eval(model, valid_dataloader, post_process_class, eval_class)
+    if config["Architecture"]["model_type"] == "det":
+        main_indicator = "hmean"
+    else:
+        main_indicator = "acc"
+    logger.info("metric['']: {}".format(main_indicator, metric[main_indicator]))
+
+    # start export model
+    from paddle.jit import to_static
+
+    infer_shape = [3, -1, -1]
+    if config["Architecture"]["model_type"] == "rec":
+        infer_shape = [3, 32, -1]  # for rec model, H must be 32
+
+        if (
+            "Transform" in config["Architecture"]
+            and config["Architecture"]["Transform"] is not None
+            and config["Architecture"]["Transform"]["name"] == "TPS"
+        ):
+            logger.info(
+                "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training"
+            )
+            infer_shape[-1] = 100
+    model = to_static(
+        model,
+        input_spec=[
+            paddle.static.InputSpec(shape=[None] + infer_shape, dtype="float32")
+        ],
+    )
+
+    save_path = "{}/inference".format(config["Global"]["save_inference_dir"])
+    paddle.jit.save(model, save_path)
+    logger.info("inference model is saved to {}".format(save_path))
+
+
+if __name__ == "__main__":
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
+    main(config, device, logger, vdl_writer)