Fix 6 Flake8 issues (#6541)
* F541 * F821 * F841 * E741 * E302 * E722 * Apply suggestions from code review * Update general.py * Update datasets.py * Update export.py * Update plots.py * Update plots.py Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/6542/head
Jirka Borovec
GitHub
parent
e1a6a0b100
commit
cba4303d32
14
export.py
14
export.py
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@ -244,7 +244,7 @@ def export_saved_model(model, im, file, dynamic,
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tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
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im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow
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y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
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outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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keras_model = keras.Model(inputs=inputs, outputs=outputs)
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@ -407,16 +407,17 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
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tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs')) # TensorFlow exports
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file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
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# Checks
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imgsz *= 2 if len(imgsz) == 1 else 1 # expand
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opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12
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# Load PyTorch model
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device = select_device(device)
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assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
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model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
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nc, names = model.nc, model.names # number of classes, class names
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# Checks
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imgsz *= 2 if len(imgsz) == 1 else 1 # expand
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opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12
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assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'
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# Input
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gs = int(max(model.stride)) # grid size (max stride)
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imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
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@ -438,7 +439,8 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
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for _ in range(2):
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y = model(im) # dry runs
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LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
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shape = tuple(y[0].shape) # model output shape
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LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
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# Exports
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f = [''] * 10 # exported filenames
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@ -427,13 +427,13 @@ def run(weights=ROOT / 'yolov5s.pt', # weights path
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# PyTorch model
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im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
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model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
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y = model(im) # inference
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_ = model(im) # inference
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model.info()
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# TensorFlow model
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im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
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tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
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y = tf_model.predict(im) # inference
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_ = tf_model.predict(im) # inference
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# Keras model
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im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
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@ -30,10 +30,6 @@ ignore =
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E731 # Do not assign a lambda expression, use a def
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F405 # name may be undefined, or defined from star imports: module
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E402 # module level import not at top of file
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F841 # local variable name is assigned to but never used
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E741 # do not use variables named ‘l’, ‘O’, or ‘I’
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F821 # undefined name name
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E722 # do not use bare except, specify exception instead
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F401 # module imported but unused
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W504 # line break after binary operator
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E127 # continuation line over-indented for visual indent
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@ -41,8 +37,6 @@ ignore =
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E231 # missing whitespace after ‘,’, ‘;’, or ‘:’
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E501 # line too long
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F403 # ‘from module import *’ used; unable to detect undefined names
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E302 # expected 2 blank lines, found 0
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F541 # f-string without any placeholders
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[isort]
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@ -59,7 +59,7 @@ def exif_size(img):
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s = (s[1], s[0])
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elif rotation == 8: # rotation 90
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s = (s[1], s[0])
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except:
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except Exception:
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pass
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return s
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@ -420,7 +420,7 @@ class LoadImagesAndLabels(Dataset):
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cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict
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assert cache['version'] == self.cache_version # same version
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assert cache['hash'] == get_hash(self.label_files + self.img_files) # same hash
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except:
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except Exception:
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cache, exists = self.cache_labels(cache_path, prefix), False # cache
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# Display cache
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@ -514,13 +514,13 @@ class LoadImagesAndLabels(Dataset):
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with Pool(NUM_THREADS) as pool:
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pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))),
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desc=desc, total=len(self.img_files))
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for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
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for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
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nm += nm_f
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nf += nf_f
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ne += ne_f
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nc += nc_f
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if im_file:
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x[im_file] = [l, shape, segments]
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x[im_file] = [lb, shape, segments]
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if msg:
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msgs.append(msg)
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pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt"
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@ -627,8 +627,8 @@ class LoadImagesAndLabels(Dataset):
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@staticmethod
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def collate_fn(batch):
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img, label, path, shapes = zip(*batch) # transposed
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for i, l in enumerate(label):
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l[:, 0] = i # add target image index for build_targets()
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for i, lb in enumerate(label):
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lb[:, 0] = i # add target image index for build_targets()
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return torch.stack(img, 0), torch.cat(label, 0), path, shapes
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@staticmethod
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@ -645,15 +645,15 @@ class LoadImagesAndLabels(Dataset):
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if random.random() < 0.5:
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im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', align_corners=False)[
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0].type(img[i].type())
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l = label[i]
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lb = label[i]
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else:
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im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
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l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
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lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
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img4.append(im)
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label4.append(l)
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label4.append(lb)
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for i, l in enumerate(label4):
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l[:, 0] = i # add target image index for build_targets()
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for i, lb in enumerate(label4):
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lb[:, 0] = i # add target image index for build_targets()
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return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
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@ -743,6 +743,7 @@ def load_mosaic9(self, index):
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s = self.img_size
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indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
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random.shuffle(indices)
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hp, wp = -1, -1 # height, width previous
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for i, index in enumerate(indices):
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# Load image
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img, _, (h, w) = load_image(self, index)
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@ -906,30 +907,30 @@ def verify_image_label(args):
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if os.path.isfile(lb_file):
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nf = 1 # label found
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with open(lb_file) as f:
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l = [x.split() for x in f.read().strip().splitlines() if len(x)]
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if any([len(x) > 8 for x in l]): # is segment
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classes = np.array([x[0] for x in l], dtype=np.float32)
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segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...)
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l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
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l = np.array(l, dtype=np.float32)
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nl = len(l)
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lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
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if any([len(x) > 8 for x in lb]): # is segment
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classes = np.array([x[0] for x in lb], dtype=np.float32)
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segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
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lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
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lb = np.array(lb, dtype=np.float32)
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nl = len(lb)
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if nl:
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assert l.shape[1] == 5, f'labels require 5 columns, {l.shape[1]} columns detected'
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assert (l >= 0).all(), f'negative label values {l[l < 0]}'
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assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}'
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_, i = np.unique(l, axis=0, return_index=True)
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assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
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assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
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assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
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_, i = np.unique(lb, axis=0, return_index=True)
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if len(i) < nl: # duplicate row check
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l = l[i] # remove duplicates
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lb = lb[i] # remove duplicates
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if segments:
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segments = segments[i]
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msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
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else:
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ne = 1 # label empty
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l = np.zeros((0, 5), dtype=np.float32)
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lb = np.zeros((0, 5), dtype=np.float32)
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else:
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nm = 1 # label missing
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l = np.zeros((0, 5), dtype=np.float32)
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return im_file, l, shape, segments, nm, nf, ne, nc, msg
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lb = np.zeros((0, 5), dtype=np.float32)
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return im_file, lb, shape, segments, nm, nf, ne, nc, msg
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except Exception as e:
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nc = 1
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msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'
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@ -62,12 +62,12 @@ def attempt_download(file, repo='ultralytics/yolov5'): # from utils.downloads i
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response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api
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assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...]
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tag = response['tag_name'] # i.e. 'v1.0'
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except: # fallback plan
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except Exception: # fallback plan
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assets = ['yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt',
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'yolov5n6.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
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try:
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tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
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except:
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except Exception:
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tag = 'v6.0' # current release
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if name in assets:
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@ -295,7 +295,7 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
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for r in requirements:
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try:
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pkg.require(r)
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except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
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except Exception: # DistributionNotFound or VersionConflict if requirements not met
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s = f"{prefix} {r} not found and is required by YOLOv5"
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if install:
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LOGGER.info(f"{s}, attempting auto-update...")
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@ -699,16 +699,16 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
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output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
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for xi, x in enumerate(prediction): # image index, image inference
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# Apply constraints
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# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
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x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
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x = x[xc[xi]] # confidence
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# Cat apriori labels if autolabelling
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if labels and len(labels[xi]):
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l = labels[xi]
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v = torch.zeros((len(l), nc + 5), device=x.device)
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v[:, :4] = l[:, 1:5] # box
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lb = labels[xi]
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v = torch.zeros((len(lb), nc + 5), device=x.device)
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v[:, :4] = lb[:, 1:5] # box
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v[:, 4] = 1.0 # conf
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v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls
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v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
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x = torch.cat((x, v), 0)
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# If none remain process next image
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@ -783,7 +783,8 @@ def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_op
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def print_mutation(results, hyp, save_dir, bucket):
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evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml'
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evolve_csv = save_dir / 'evolve.csv'
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evolve_yaml = save_dir / 'hyp_evolve.yaml'
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keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
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'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps]
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keys = tuple(x.strip() for x in keys)
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@ -288,7 +288,7 @@ class WandbLogger():
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model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
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assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
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modeldir = model_artifact.download()
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epochs_trained = model_artifact.metadata.get('epochs_trained')
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# epochs_trained = model_artifact.metadata.get('epochs_trained')
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total_epochs = model_artifact.metadata.get('total_epochs')
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is_finished = total_epochs is None
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assert not is_finished, 'training is finished, can only resume incomplete runs.'
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@ -239,6 +239,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=
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return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
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return iou # IoU
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def box_iou(box1, box2):
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# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
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"""
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@ -54,7 +54,7 @@ def check_pil_font(font=FONT, size=10):
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font = font if font.exists() else (CONFIG_DIR / font.name)
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try:
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return ImageFont.truetype(str(font) if font.exists() else font.name, size)
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except Exception as e: # download if missing
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except Exception: # download if missing
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check_font(font)
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try:
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return ImageFont.truetype(str(font), size)
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@ -340,7 +340,7 @@ def plot_labels(labels, names=(), save_dir=Path('')):
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matplotlib.use('svg') # faster
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ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
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y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
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# [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195
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[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195
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ax[0].set_ylabel('instances')
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if 0 < len(names) < 30:
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ax[0].set_xticks(range(len(names)))
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@ -49,7 +49,7 @@ def git_describe(path=Path(__file__).parent): # path must be a directory
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s = f'git -C {path} describe --tags --long --always'
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try:
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return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
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except subprocess.CalledProcessError as e:
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except subprocess.CalledProcessError:
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return '' # not a git repository
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@ -59,7 +59,7 @@ def device_count():
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try:
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cmd = 'nvidia-smi -L | wc -l'
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return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
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except Exception as e:
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except Exception:
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return 0
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@ -124,7 +124,7 @@ def profile(input, ops, n=10, device=None):
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tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
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try:
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flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
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except:
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except Exception:
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flops = 0
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try:
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@ -135,7 +135,7 @@ def profile(input, ops, n=10, device=None):
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try:
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_ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
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t[2] = time_sync()
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except Exception as e: # no backward method
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except Exception: # no backward method
|
||||
# print(e) # for debug
|
||||
t[2] = float('nan')
|
||||
tf += (t[1] - t[0]) * 1000 / n # ms per op forward
|
||||
|
|
|
|||
Loading…
Reference in New Issue