From b238341eff90dcec22e2e2b04e2f47346f946920 Mon Sep 17 00:00:00 2001
From: humu789 <88702197+humu789@users.noreply.github.com>
Date: Mon, 17 Apr 2023 06:13:43 +0800
Subject: [PATCH] [Bug] Fix codecov (#509)

* remove codecov in requirements

* try to fix ci

* del adaround loss
---
 .github/workflows/build.yml            |  2 +-
 mmrazor/models/losses/adaround_loss.py | 87 --------------------------
 requirements/tests.txt                 |  2 +-
 3 files changed, 2 insertions(+), 89 deletions(-)
 delete mode 100644 mmrazor/models/losses/adaround_loss.py

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 9ed4cb002..2c2b8ed21 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -120,7 +120,7 @@ jobs:
       # Upload coverage report for python3.8 && pytorch1.12.0 cpu
       - name: Upload coverage to Codecov
         if: ${{matrix.torch == '1.13.0' && matrix.python-version == '3.8'}}
-        uses: codecov/codecov-action@v2
+        uses: codecov/codecov-action@v3
         with:
           file: ./coverage.xml
           flags: unittests
diff --git a/mmrazor/models/losses/adaround_loss.py b/mmrazor/models/losses/adaround_loss.py
deleted file mode 100644
index 76c97977d..000000000
--- a/mmrazor/models/losses/adaround_loss.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from mmengine.logging import print_log
-
-from mmrazor.registry import MODELS
-
-_ADAROUND_SUPPORT_TYPE = (torch.nn.Conv2d, torch.nn.Linear)
-
-
-@MODELS.register_module()
-class AdaRoundLoss(nn.Module):
-    r'''loss function to calculate mse reconstruction loss and relaxation loss
-    use some tempdecay to balance the two losses.
-    '''
-
-    def __init__(self,
-                 weight: float = 1.,
-                 iters: int = 10000,
-                 beta_range: tuple = (20, 2),
-                 warm_up: float = 0.0,
-                 p: float = 2.):
-        self.weight = weight
-        self.loss_start = iters * warm_up
-        self.p = p
-
-        self.temp_decay = LinearTempDecay(
-            iters,
-            warm_up=warm_up,
-            start_beta=beta_range[0],
-            end_beta=beta_range[1])
-        self.count = 0
-
-    def forward(self, subgraph, pred, tgt):
-        """Compute the total loss for adaptive rounding: rec_loss is the
-        quadratic output reconstruction loss, round_loss is a regularization
-        term to optimize the rounding policy.
-
-        :param pred: output from quantized model
-        :param tgt: output from FP model
-        :return: total loss function
-        """
-
-        def lp_loss(pred, tgt, p=2.0):
-            """loss function measured in L_p Norm."""
-            return (pred - tgt).abs().pow(p).sum(1).mean()
-
-        self.count += 1
-        rec_loss = lp_loss(pred, tgt, p=self.p)
-
-        beta = self.temp_decay(self.count)
-        if self.count < self.loss_start:
-            round_loss = 0
-        else:
-            round_loss = 0
-            for layer in subgraph.modules():
-                if isinstance(layer, _ADAROUND_SUPPORT_TYPE):
-                    round_vals = layer.weight_fake_quant.rectified_sigmoid()
-                    round_loss += self.weight * (1 - (
-                        (round_vals - .5).abs() * 2).pow(beta)).sum()
-
-        total_loss = rec_loss + round_loss
-        if self.count % 500 == 0:
-            print_log('Total loss:\t{:.3f} (rec_loss:{:.3f}, '
-                      'round_loss:{:.3f})\tbeta={:.2f}\tcount={}'.format(
-                          float(total_loss), float(rec_loss),
-                          float(round_loss), beta, self.count))
-        return total_loss
-
-
-class LinearTempDecay:
-
-    def __init__(self, t_max=10000, warm_up=0.2, start_beta=20, end_beta=2):
-        self.t_max = t_max
-        self.start_decay = warm_up * t_max
-        self.start_beta = start_beta
-        self.end_beta = end_beta
-
-    def __call__(self, t):
-        if t < self.start_decay:
-            return self.start_beta
-        elif t > self.t_max:
-            return self.end_beta
-        else:
-            rel_t = (t - self.start_decay) / (self.t_max - self.start_decay)
-            return self.end_beta + (self.start_beta - self.end_beta) * \
-                max(0.0, (1 - rel_t))
diff --git a/requirements/tests.txt b/requirements/tests.txt
index e38249fcd..5980dc303 100644
--- a/requirements/tests.txt
+++ b/requirements/tests.txt
@@ -1,4 +1,4 @@
-codecov
+coverage
 flake8
 interrogate
 isort==4.3.21