Remove assert statement from non-test files (#3745)

* Remove assert statement from non-test files

* [MONAI] python code formatting

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Co-authored-by: monai-bot <monai.miccai2019@gmail.com>

monai/config/deviceconfig.py

@@ -161,9 +161,9 @@ def get_system_info() -> OrderedDict:
                 ),
             )
             mem = psutil.virtual_memory()
-            _dict_append(output, "Total physical memory (GB)", lambda: round(mem.total / 1024 ** 3, 1))
-            _dict_append(output, "Available memory (GB)", lambda: round(mem.available / 1024 ** 3, 1))
-            _dict_append(output, "Used memory (GB)", lambda: round(mem.used / 1024 ** 3, 1))
+            _dict_append(output, "Total physical memory (GB)", lambda: round(mem.total / 1024**3, 1))
+            _dict_append(output, "Available memory (GB)", lambda: round(mem.available / 1024**3, 1))
+            _dict_append(output, "Used memory (GB)", lambda: round(mem.used / 1024**3, 1))
 
     return output
 
@@ -209,7 +209,7 @@ def get_gpu_info() -> OrderedDict:
         _dict_append(output, f"GPU {gpu} Is integrated", lambda: bool(gpu_info.is_integrated))
         _dict_append(output, f"GPU {gpu} Is multi GPU board", lambda: bool(gpu_info.is_multi_gpu_board))
         _dict_append(output, f"GPU {gpu} Multi processor count", lambda: gpu_info.multi_processor_count)
-        _dict_append(output, f"GPU {gpu} Total memory (GB)", lambda: round(gpu_info.total_memory / 1024 ** 3, 1))
+        _dict_append(output, f"GPU {gpu} Total memory (GB)", lambda: round(gpu_info.total_memory / 1024**3, 1))
         _dict_append(output, f"GPU {gpu} CUDA capability (maj.min)", lambda: f"{gpu_info.major}.{gpu_info.minor}")
 
     return output

monai/data/dataset.py

@@ -513,7 +513,7 @@ def __init__(
         self.db_file = self.cache_dir / f"{db_name}.lmdb"
         self.lmdb_kwargs = lmdb_kwargs or {}
         if not self.lmdb_kwargs.get("map_size", 0):
-            self.lmdb_kwargs["map_size"] = 1024 ** 4  # default map_size
+            self.lmdb_kwargs["map_size"] = 1024**4  # default map_size
         # lmdb is single-writer multi-reader by default
         # the cache is created without multi-threading
         self._read_env = None

monai/data/dataset_summary.py

@@ -150,7 +150,7 @@ def calculate_statistics(self, foreground_threshold: int = 0):
 
         self.data_max, self.data_min = max(voxel_max), min(voxel_min)
         self.data_mean = (voxel_sum / voxel_ct).item()
-        self.data_std = (torch.sqrt(voxel_square_sum / voxel_ct - self.data_mean ** 2)).item()
+        self.data_std = (torch.sqrt(voxel_square_sum / voxel_ct - self.data_mean**2)).item()
 
     def calculate_percentiles(
         self,

monai/handlers/parameter_scheduler.py

@@ -134,7 +134,7 @@ def _exponential(initial_value: float, gamma: float, current_step: int) -> float
         Returns:
             float: new parameter value
         """
-        return initial_value * gamma ** current_step
+        return initial_value * gamma**current_step
 
     @staticmethod
     def _step(initial_value: float, gamma: float, step_size: int, current_step: int) -> float:

monai/losses/image_dissimilarity.py

@@ -126,7 +126,7 @@ def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         if target.shape != pred.shape:
             raise ValueError(f"ground truth has differing shape ({target.shape}) from pred ({pred.shape})")
 
-        t2, p2, tp = target ** 2, pred ** 2, target * pred
+        t2, p2, tp = target**2, pred**2, target * pred
         kernel, kernel_vol = self.kernel.to(pred), self.kernel_vol.to(pred)
         # sum over kernel
         t_sum = separable_filtering(target, kernels=[kernel.to(pred)] * self.ndim)
@@ -217,7 +217,7 @@ def __init__(
         self.num_bins = num_bins
         self.kernel_type = kernel_type
         if self.kernel_type == "gaussian":
-            self.preterm = 1 / (2 * sigma ** 2)
+            self.preterm = 1 / (2 * sigma**2)
             self.bin_centers = bin_centers[None, None, ...]
         self.smooth_nr = float(smooth_nr)
         self.smooth_dr = float(smooth_dr)
@@ -280,7 +280,7 @@ def parzen_windowing_b_spline(self, img: torch.Tensor, order: int) -> Tuple[torc
             weight = weight + (sample_bin_matrix < 0.5) + (sample_bin_matrix == 0.5) * 0.5
         elif order == 3:
             weight = (
-                weight + (4 - 6 * sample_bin_matrix ** 2 + 3 * sample_bin_matrix ** 3) * (sample_bin_matrix < 1) / 6
+                weight + (4 - 6 * sample_bin_matrix**2 + 3 * sample_bin_matrix**3) * (sample_bin_matrix < 1) / 6
             )
             weight = weight + (2 - sample_bin_matrix) ** 3 * (sample_bin_matrix >= 1) * (sample_bin_matrix < 2) / 6
         else:

monai/networks/blocks/selfattention.py

@@ -46,7 +46,7 @@ def __init__(self, hidden_size: int, num_heads: int, dropout_rate: float = 0.0)
         self.drop_output = nn.Dropout(dropout_rate)
         self.drop_weights = nn.Dropout(dropout_rate)
         self.head_dim = hidden_size // num_heads
-        self.scale = self.head_dim ** -0.5
+        self.scale = self.head_dim**-0.5
 
     def forward(self, x):
         q, k, v = einops.rearrange(self.qkv(x), "b h (qkv l d) -> qkv b l h d", qkv=3, l=self.num_heads)

monai/networks/blocks/upsample.py

@@ -219,7 +219,7 @@ def __init__(
             out_channels = out_channels or in_channels
             if not out_channels:
                 raise ValueError("in_channels need to be specified.")
-            conv_out_channels = out_channels * (scale_factor ** self.dimensions)
+            conv_out_channels = out_channels * (scale_factor**self.dimensions)
             self.conv_block = Conv[Conv.CONV, self.dimensions](
                 in_channels=in_channels, out_channels=conv_out_channels, kernel_size=3, stride=1, padding=1, bias=bias
             )
@@ -247,7 +247,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             x: Tensor in shape (batch, channel, spatial_1[, spatial_2, ...).
         """
         x = self.conv_block(x)
-        if x.shape[1] % (self.scale_factor ** self.dimensions) != 0:
+        if x.shape[1] % (self.scale_factor**self.dimensions) != 0:
             raise ValueError(
                 f"Number of channels after `conv_block` ({x.shape[1]}) must be evenly "
                 "divisible by scale_factor ** dimensions "

monai/networks/blocks/warp.py

@@ -150,7 +150,7 @@ def forward(self, dvf):
         Returns:
             a dense displacement field
         """
-        ddf: torch.Tensor = dvf / (2 ** self.num_steps)
+        ddf: torch.Tensor = dvf / (2**self.num_steps)
         for _ in range(self.num_steps):
             ddf = ddf + self.warp_layer(image=ddf, ddf=ddf)
         return ddf

monai/networks/layers/convutils.py

@@ -115,7 +115,7 @@ def gaussian_1d(
         out = out.clamp(min=0)
     elif approx.lower() == "sampled":
         x = torch.arange(-tail, tail + 1, dtype=torch.float, device=sigma.device)
-        out = torch.exp(-0.5 / (sigma * sigma) * x ** 2)
+        out = torch.exp(-0.5 / (sigma * sigma) * x**2)
         if not normalize:  # compute the normalizer
             out = out / (2.5066282 * sigma)
     elif approx.lower() == "scalespace":

monai/networks/nets/dints.py

@@ -124,7 +124,7 @@ def __init__(self, in_channel: int, out_channel: int, spatial_dims: int = 3):
         # s0 is upsampled 2x from s1, representing feature sizes at two resolutions.
         # in_channel * s0 (activation) + 3 * out_channel * s1 (convolution, concatenation, normalization)
         # s0 = s1 * 2^(spatial_dims) = output_size / out_channel * 2^(spatial_dims)
-        self.ram_cost = in_channel / out_channel * 2 ** self._spatial_dims + 3
+        self.ram_cost = in_channel / out_channel * 2**self._spatial_dims + 3
 
 
 class MixedOp(nn.Module):
@@ -330,7 +330,7 @@ def __init__(
             # define downsample stems before DiNTS search
             if use_downsample:
                 self.stem_down[str(res_idx)] = StemTS(
-                    nn.Upsample(scale_factor=1 / (2 ** res_idx), mode=mode, align_corners=True),
+                    nn.Upsample(scale_factor=1 / (2**res_idx), mode=mode, align_corners=True),
                     conv_type(
                         in_channels=in_channels,
                         out_channels=self.filter_nums[res_idx],
@@ -373,7 +373,7 @@ def __init__(
 
             else:
                 self.stem_down[str(res_idx)] = StemTS(
-                    nn.Upsample(scale_factor=1 / (2 ** res_idx), mode=mode, align_corners=True),
+                    nn.Upsample(scale_factor=1 / (2**res_idx), mode=mode, align_corners=True),
                     conv_type(
                         in_channels=in_channels,
                         out_channels=self.filter_nums[res_idx],
@@ -789,7 +789,7 @@ def get_ram_cost_usage(self, in_size, full: bool = False):
         image_size = np.array(in_size[-self._spatial_dims :])
         sizes = []
         for res_idx in range(self.num_depths):
-            sizes.append(batch_size * self.filter_nums[res_idx] * (image_size // (2 ** res_idx)).prod())
+            sizes.append(batch_size * self.filter_nums[res_idx] * (image_size // (2**res_idx)).prod())
         sizes = torch.tensor(sizes).to(torch.float32).to(self.device) / (2 ** (int(self.use_downsample)))
         probs_a, arch_code_prob_a = self.get_prob_a(child=False)
         cell_prob = F.softmax(self.log_alpha_c, dim=-1)
@@ -807,7 +807,7 @@ def get_ram_cost_usage(self, in_size, full: bool = False):
                     * (1 + (ram_cost[blk_idx, path_idx] * cell_prob[blk_idx, path_idx]).sum())
                     * sizes[self.arch_code2out[path_idx]]
                 )
-        return usage * 32 / 8 / 1024 ** 2
+        return usage * 32 / 8 / 1024**2
 
     def get_topology_entropy(self, probs):
         """

monai/networks/nets/highresnet.py

@@ -168,7 +168,7 @@ def __init__(
         # residual blocks
         for (idx, params) in enumerate(layer_params[1:-2]):  # res blocks except the 1st and last two conv layers.
             _in_chns, _out_chns = _out_chns, params["n_features"]
-            _dilation = 2 ** idx
+            _dilation = 2**idx
             for _ in range(params["repeat"]):
                 blocks.append(
                     HighResBlock(

monai/networks/nets/regunet.py

@@ -92,7 +92,7 @@ def __init__(
             raise AssertionError
         self.encode_kernel_sizes: List[int] = encode_kernel_sizes
 
-        self.num_channels = [self.num_channel_initial * (2 ** d) for d in range(self.depth + 1)]
+        self.num_channels = [self.num_channel_initial * (2**d) for d in range(self.depth + 1)]
         self.min_extract_level = min(self.extract_levels)
 
         # init layers
@@ -310,14 +310,14 @@ def __init__(
         encode_kernel_sizes: Union[int, List[int]] = 3,
     ):
         for size in image_size:
-            if size % (2 ** depth) != 0:
+            if size % (2**depth) != 0:
                 raise ValueError(
                     f"given depth {depth}, "
                     f"all input spatial dimension must be divisible by {2 ** depth}, "
                     f"got input of size {image_size}"
                 )
         self.image_size = image_size
-        self.decode_size = [size // (2 ** depth) for size in image_size]
+        self.decode_size = [size // (2**depth) for size in image_size]
         super().__init__(
             spatial_dims=spatial_dims,
             in_channels=in_channels,

monai/networks/nets/segresnet.py

@@ -102,7 +102,7 @@ def _make_down_layers(self):
         down_layers = nn.ModuleList()
         blocks_down, spatial_dims, filters, norm = (self.blocks_down, self.spatial_dims, self.init_filters, self.norm)
         for i in range(len(blocks_down)):
-            layer_in_channels = filters * 2 ** i
+            layer_in_channels = filters * 2**i
             pre_conv = (
                 get_conv_layer(spatial_dims, layer_in_channels // 2, layer_in_channels, stride=2)
                 if i > 0
@@ -299,12 +299,12 @@ def _get_vae_loss(self, net_input: torch.Tensor, vae_input: torch.Tensor):
         if self.vae_estimate_std:
             z_sigma = self.vae_fc2(x_vae)
             z_sigma = F.softplus(z_sigma)
-            vae_reg_loss = 0.5 * torch.mean(z_mean ** 2 + z_sigma ** 2 - torch.log(1e-8 + z_sigma ** 2) - 1)
+            vae_reg_loss = 0.5 * torch.mean(z_mean**2 + z_sigma**2 - torch.log(1e-8 + z_sigma**2) - 1)
 
             x_vae = z_mean + z_sigma * z_mean_rand
         else:
             z_sigma = self.vae_default_std
-            vae_reg_loss = torch.mean(z_mean ** 2)
+            vae_reg_loss = torch.mean(z_mean**2)
 
             x_vae = z_mean + z_sigma * z_mean_rand
 

monai/networks/utils.py

@@ -270,7 +270,7 @@ def pixelshuffle(
     dim, factor = spatial_dims, scale_factor
     input_size = list(x.size())
     batch_size, channels = input_size[:2]
-    scale_divisor = factor ** dim
+    scale_divisor = factor**dim
 
     if channels % scale_divisor != 0:
         raise ValueError(

monai/transforms/intensity/array.py

@@ -182,9 +182,9 @@ def _add_noise(self, img: NdarrayOrTensor, mean: float, std: float):
         if isinstance(img, torch.Tensor):
             n1 = torch.tensor(self._noise1, device=img.device)
             n2 = torch.tensor(self._noise2, device=img.device)
-            return torch.sqrt((img + n1) ** 2 + n2 ** 2)
+            return torch.sqrt((img + n1) ** 2 + n2**2)
 
-        return np.sqrt((img + self._noise1) ** 2 + self._noise2 ** 2)
+        return np.sqrt((img + self._noise1) ** 2 + self._noise2**2)
 
     def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
         """

monai/transforms/smooth_field/array.py

@@ -232,7 +232,7 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen
         # everything below here is to be computed using the destination type (numpy, tensor, etc.)
 
         img = (img - img_min) / (img_rng + 1e-10)  # rescale to unit values
-        img = img ** rfield  # contrast is changed by raising image data to a power, in this case the field
+        img = img**rfield  # contrast is changed by raising image data to a power, in this case the field
 
         out = (img * img_rng) + img_min  # rescale back to the original image value range
 

monai/transforms/utils_create_transform_ims.py

@@ -244,7 +244,8 @@ def update_docstring(code_path, transform_name):
     contents.insert(image_line + 1, "    :alt: example of " + transform_name + "\n")
 
     # check that we've only added two lines
-    assert len(contents) == len(contents_orig) + 2
+    if len(contents) != len(contents_orig) + 2:
+        raise AssertionError
 
     # write the updated doc to overwrite the original
     with open(code_path, "w") as f:
@@ -382,7 +383,7 @@ def get_images(data, is_label=False):
         # we might need to panel the images. this happens if a transform produces e.g. 4 output images.
         # In this case, we create a 2-by-2 grid from them. Output will be a list containing n_orthog_views,
         # each element being either the image (if num_samples is 1) or the panelled image.
-        nrows = int(np.floor(num_samples ** 0.5))
+        nrows = int(np.floor(num_samples**0.5))
         for view in range(num_orthog_views):
             result = np.asarray([d[view] for d in data])
             nindex, height, width = result.shape

tests/test_lmdbdataset.py

@@ -57,7 +57,7 @@
         SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
     ],
     (128, 128, 128),
-    {"pickle_protocol": 2, "lmdb_kwargs": {"map_size": 100 * 1024 ** 2}},
+    {"pickle_protocol": 2, "lmdb_kwargs": {"map_size": 100 * 1024**2}},
 ]
 
 TEST_CASE_6 = [
@@ -66,7 +66,7 @@
         SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
     ],
     (128, 128, 128),
-    {"db_name": "testdb", "lmdb_kwargs": {"map_size": 100 * 1024 ** 2}},
+    {"db_name": "testdb", "lmdb_kwargs": {"map_size": 100 * 1024**2}},
 ]
 
 TEST_CASE_7 = [
@@ -75,7 +75,7 @@
         SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
     ],
     (128, 128, 128),
-    {"db_name": "testdb", "lmdb_kwargs": {"map_size": 2 * 1024 ** 2}},
+    {"db_name": "testdb", "lmdb_kwargs": {"map_size": 2 * 1024**2}},
 ]
 
 

tests/test_tile_on_grid.py

@@ -107,7 +107,7 @@ def make_image(
 
     tiles = np.stack(tiles_list, axis=0)  # type: ignore
 
-    if (filter_mode == "min" or filter_mode == "max") and len(tiles) > tile_count ** 2:
+    if (filter_mode == "min" or filter_mode == "max") and len(tiles) > tile_count**2:
         tiles = tiles[np.argsort(tiles.sum(axis=(1, 2, 3)))]
 
     return imlarge, tiles

tests/test_tile_on_grid_dict.py

@@ -116,7 +116,7 @@ def make_image(
 
     tiles = np.stack(tiles_list, axis=0)  # type: ignore
 
-    if (filter_mode == "min" or filter_mode == "max") and len(tiles) > tile_count ** 2:
+    if (filter_mode == "min" or filter_mode == "max") and len(tiles) > tile_count**2:
         tiles = tiles[np.argsort(tiles.sum(axis=(1, 2, 3)))]
 
     return imlarge, tiles