Sourcery Starbot ⭐ refactored YuhaoCheng/PyAnomaly#13
Sourcery Starbot ⭐ refactored YuhaoCheng/PyAnomaly#13SourceryAI wants to merge 1 commit intoYuhaoCheng:masterfrom
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| result_image = None | ||
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| return result_image | ||
| return None |
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Function make_result refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| dy = list() | ||
| dy = [] | ||
| for i in range(video_length): | ||
| temp_dx, temp_dy = image_gradient(x[:,i,:,:,:]) | ||
| # dx.append(temp_dx.unsqueeze_(1)) | ||
| dx.append(temp_dx) | ||
| dy.append(temp_dy) | ||
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Function frame_gradient refactored with the following changes:
- Replace list() with [] (
list-literal)
| def vis_optical_flow(batch_optical, output_format, output_size, normalize): | ||
| temp = batch_optical.detach().cpu().permute(0,2,3,1).numpy() | ||
| temp_list = list() | ||
| temp_list = [] |
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Function vis_optical_flow refactored with the following changes:
- Replace list() with [] (
list-literal)
| """ | ||
| """ | ||
| Use the detecron2 | ||
| """ | ||
| image_list = list() | ||
| batch_size = batch_image.size(0) | ||
| images = torch.chunk(batch_image, batch_size, dim=0) | ||
| for image in images: | ||
| image_list.append({"image":image.squeeze_(0).mul(255).byte()[[2,0,1],:,:]}) | ||
| outputs = det_model(image_list) | ||
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| bboxs = [] | ||
| frame_objects = OrderedDict() | ||
| max_objects = 0 | ||
| min_objects = 1000 | ||
| for frame_id, out in enumerate(outputs): | ||
| temp = out['instances'].pred_boxes.tensor.detach() | ||
| temp.requires_grad = False | ||
| frame_objects[frame_id] = temp.size(0) | ||
| if frame_objects[frame_id] > max_objects: | ||
| max_objects = frame_objects[frame_id] | ||
| if frame_objects[frame_id] < min_objects: | ||
| min_objects = frame_objects[frame_id] | ||
| bboxs.append(temp) | ||
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| return bboxs | ||
| batch_size = batch_image.size(0) | ||
| images = torch.chunk(batch_image, batch_size, dim=0) | ||
| image_list = [ | ||
| {"image": image.squeeze_(0).mul(255).byte()[[2, 0, 1], :, :]} | ||
| for image in images | ||
| ] | ||
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| outputs = det_model(image_list) | ||
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| bboxs = [] | ||
| frame_objects = OrderedDict() | ||
| max_objects = 0 | ||
| min_objects = 1000 | ||
| for frame_id, out in enumerate(outputs): | ||
| temp = out['instances'].pred_boxes.tensor.detach() | ||
| temp.requires_grad = False | ||
| frame_objects[frame_id] = temp.size(0) | ||
| if frame_objects[frame_id] > max_objects: | ||
| max_objects = frame_objects[frame_id] | ||
| if frame_objects[frame_id] < min_objects: | ||
| min_objects = frame_objects[frame_id] | ||
| bboxs.append(temp) | ||
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| return bboxs |
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Function get_batch_dets refactored with the following changes:
- Replace list() with [] (
list-literal) - Convert for loop into list comprehension (
list-comprehension)
| msg = f'Step: [{current_step}/{max_step}]\t' \ | ||
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| return f'Step: [{current_step}/{max_step}]\t' \ | ||
| f'Type: {model_type}\t' \ | ||
| f'Time: {batch_time.val:.2f}s ({batch_time.avg:.2f}s)\t' \ | ||
| f'Speed: {speed:.1f} samples/s\t' \ | ||
| f'Data: {data_time.val:.2f}s ({data_time.avg:.2f}s)\t' + loss_string | ||
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| return msg |
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Function make_info_message refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| def _build_multi_optimizers(self, model_list): | ||
| param_groups = list() | ||
| param_groups = [] |
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Function OptimizerAPI._build_multi_optimizers refactored with the following changes:
- Replace list() with [] (
list-literal)
| model_combination = [] | ||
| for temp in include_parts: | ||
| model_combination.append(model[temp]) | ||
| model_combination = [model[temp] for temp in include_parts] |
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Function OptimizerAPI.__call__ refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension)
| indices = np.random.choice(num_samples, num_clusters, replace=False) | ||
| initial_state = X[indices] | ||
| return initial_state | ||
| return X[indices] |
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Function initialize refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| if distance == 'euclidean': | ||
| pairwise_distance_function = pairwise_distance | ||
| elif distance == 'cosine': | ||
| if distance == 'cosine': | ||
| pairwise_distance_function = pairwise_cosine |
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Function kmeans refactored with the following changes:
- Simplify conditional into switch-like form (
switch) - Replace assignment with augmented assignment (
aug-assign)
| last_epoch: int = -1, | ||
| ): | ||
| if not list(milestones) == sorted(milestones): | ||
| if list(milestones) != sorted(milestones): |
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Function WarmupMultiStepLR.__init__ refactored with the following changes:
- Simplify logical expression using De Morgan identities (
de-morgan)
| """ | ||
| dataset = self.factory() | ||
| return dataset | ||
| return self.factory() |
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Function DataAPI._build_dataset refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| sampler = DistTrainSampler(_data_len) | ||
| return DistTrainSampler(_data_len) | ||
| else: | ||
| sampler = TrainSampler(_data_len, self.seed) | ||
| return sampler | ||
| return TrainSampler(_data_len, self.seed) |
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Function DataAPI._build_sampler refactored with the following changes:
- Lift return into if (
lift-return-into-if)
| dataloader_dict = self.build() | ||
| return dataloader_dict | ||
| return self.build() |
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Function DataAPI.__call__ refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| self.phase = 'train' | ||
| else: | ||
| self.phase = 'val' | ||
| self.phase = 'train' if self.is_training else 'val' |
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Function AbstractDatasetFactory.__init__ refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
| clip_list = list() | ||
| clip_list_original = list() | ||
| clip_list = [] | ||
| clip_list_original = [] |
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Function VideoLoader.read refactored with the following changes:
- Replace list() with [] (
list-literal)
| if optimal_results < results: | ||
| optimal_results = results | ||
| optimal_results = max(optimal_results, results) |
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Function precision_recall_auc refactored with the following changes:
- Replace comparison with min/max call (
min-max-identity)
| if optimal_results > results: | ||
| optimal_results = results | ||
| optimal_results = min(optimal_results, results) |
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Function compute_eer refactored with the following changes:
- Replace comparison with min/max call (
min-max-identity)
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Function compute_auc_psnr refactored with the following changes:
- Replace comparison with min/max call (
min-max-identity)
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| fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=pos_label) | ||
| auc = metrics.auc(fpr, tpr) | ||
| results = RecordResult(fpr, tpr, thresholds, auc, dataset, sub_loss_file, sigma) | ||
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| return results | ||
| return RecordResult(fpr, tpr, thresholds, auc, dataset, sub_loss_file, sigma) |
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Function compute_auc_score.get_results refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| args_name = list() | ||
| args_value = list() | ||
| args_name = [] | ||
| args_value = [] | ||
| for config in loss_cfg: | ||
| args_name.append(config[0]) | ||
| args_value.append(config[1]) | ||
| loss_args_template = namedtuple('LossArgs', args_name) | ||
| loss_args = loss_args_template._make(args_value) | ||
| return loss_args | ||
| return loss_args_template._make(args_value) |
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Function get_loss_args refactored with the following changes:
- Replace list() with [] (
list-literal) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
| x = torch.mean(torch.pow(torch.abs(gen_frames - gt_frames), self.l_num)) | ||
| # x = torch.mean(torch.abs(gen_frames - gt_frames)**self.l_num) | ||
| return x | ||
| return torch.mean(torch.pow(torch.abs(gen_frames - gt_frames), self.l_num)) |
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Function IntensityLoss.forward refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| loss = self.t1(outputs, labels) | ||
| return loss | ||
| return self.t1(outputs, labels) |
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Function AMCDiscriminateLoss.forward refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| loss = self.t1(fake_outputs, fake) | ||
| return loss | ||
| return self.t1(fake_outputs, fake) |
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Function AMCGenerateLoss.forward refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| if target_is_real: | ||
| target_tensor = self.real_label | ||
| else: | ||
| target_tensor = self.fake_label | ||
| target_tensor = self.real_label if target_is_real else self.fake_label |
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Function GANLoss.get_target_tensor refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
| if target_is_real: | ||
| loss = -prediction.mean() | ||
| else: | ||
| loss = prediction.mean() | ||
| loss = -prediction.mean() if target_is_real else prediction.mean() |
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Function GANLoss.__call__ refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
| self.netFeat = torch.nn.Sequential() | ||
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| elif intLevel == 2: | ||
| else: |
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Function LiteFlowNet.__init__.Subpixel.__init__ refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if)
| self.netFeat = torch.nn.Sequential() | ||
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| elif intLevel < 5: | ||
| else: |
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Function LiteFlowNet.__init__.Regularization.__init__ refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if)
| layers = [] | ||
| layers.append(block(self.inplanes, planes, stride, downsample)) | ||
| layers = [block(self.inplanes, planes, stride, downsample)] | ||
| self.inplanes = planes * block.expansion | ||
| for i in range(1, blocks): | ||
| for _ in range(1, blocks): |
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Function PoseResNet._make_layer refactored with the following changes:
- Merge append into list declaration (
merge-list-append) - Replace unused for index with underscore (
for-index-underscore)
| x_sigmod = F.sigmoid(x) | ||
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| return x_sigmod | ||
| return F.sigmoid(x) |
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Function AMCDiscriminiator.forward refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| # for each sequence, we need to clear the internal_state | ||
| self.internal_state = list() | ||
| self.internal_state = [] |
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Function SingleStampConvLSTM.__init__ refactored with the following changes:
- Replace list() with [] (
list-literal)
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