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deploy/slim/prune/sensitivity_anal.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
+import paddle.distributed as dist
+from ppocr.data import build_dataloader, set_signal_handlers
+from ppocr.modeling.architectures import build_model
+from ppocr.losses import build_loss
+from ppocr.optimizer import build_optimizer
+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
+
+dist.get_world_size()
+
+
+def get_pruned_params(parameters):
+    params = []
+
+    for param in parameters:
+        if (
+            len(param.shape) == 4
+            and "depthwise" not in param.name
+            and "transpose" not in param.name
+            and "conv2d_57" not in param.name
+            and "conv2d_56" not in param.name
+        ):
+            params.append(param.name)
+    return params
+
+
+def main(config, device, logger, vdl_writer):
+    # init dist environment
+    if config["Global"]["distributed"]:
+        dist.init_parallel_env()
+
+    global_config = config["Global"]
+
+    # build dataloader
+    set_signal_handlers()
+    train_dataloader = build_dataloader(config, "Train", device, logger)
+    if config["Eval"]:
+        valid_dataloader = build_dataloader(config, "Eval", device, logger)
+    else:
+        valid_dataloader = None
+
+    # 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 loss
+    loss_class = build_loss(config["Loss"])
+
+    # build optim
+    optimizer, lr_scheduler = build_optimizer(
+        config["Optimizer"],
+        epochs=config["Global"]["epoch_num"],
+        step_each_epoch=len(train_dataloader),
+        model=model,
+    )
+
+    # build metric
+    eval_class = build_metric(config["Metric"])
+    # load pretrain model
+    pre_best_model_dict = load_model(config, model, optimizer)
+
+    logger.info(
+        "train dataloader has {} iters, valid dataloader has {} iters".format(
+            len(train_dataloader), len(valid_dataloader)
+        )
+    )
+    # build metric
+    eval_class = build_metric(config["Metric"])
+
+    logger.info(
+        "train dataloader has {} iters, valid dataloader has {} iters".format(
+            len(train_dataloader), len(valid_dataloader)
+        )
+    )
+
+    def eval_fn():
+        metric = program.eval(
+            model, valid_dataloader, post_process_class, eval_class, False
+        )
+        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]
+
+    run_sensitive_analysis = False
+    """
+    run_sensitive_analysis=True:
+        Automatically compute the sensitivities of convolutions in a model.
+        The sensitivity of a convolution is the losses of accuracy on test dataset in
+        different pruned ratios. The sensitivities can be used to get a group of best
+        ratios with some condition.
+
+    run_sensitive_analysis=False:
+        Set prune trim ratio to a fixed value, such as 10%. The larger the value,
+        the more convolution weights will be cropped.
+
+    """
+
+    if run_sensitive_analysis:
+        params_sensitive = pruner.sensitive(
+            eval_func=eval_fn,
+            sen_file="./deploy/slim/prune/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]))
+    else:
+        params_sensitive = {}
+        for param in model.parameters():
+            if "transpose" not in param.name and "linear" not in param.name:
+                # set prune ratio as 10%. The larger the value, the more convolution weights will be cropped
+                params_sensitive[param.name] = 0.1
+
+    plan = pruner.prune_vars(params_sensitive, [0])
+
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs after pruning: {}".format(flops))
+
+    # start train
+
+    program.train(
+        config,
+        train_dataloader,
+        valid_dataloader,
+        device,
+        model,
+        loss_class,
+        optimizer,
+        lr_scheduler,
+        post_process_class,
+        eval_class,
+        pre_best_model_dict,
+        logger,
+        vdl_writer,
+    )
+
+
+if __name__ == "__main__":
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
+    main(config, device, logger, vdl_writer)